Podcasts about Gartner

A structural transfer of liability and risk is reshaping industry engagement models, with outcome-based contracting increasing across service agreements. This shift is being driven by buyer demands for accountability in technology solutions, notably in artificial intelligence deployments, and is illustrated by recent unpublished but credible reports that OpenAI is quietly allowing select large enterprise customers to pay only when AI tasks are successfully completed. Supporting research from Gartner and CIO Dive highlights a growing disparity: while 19% of service buyers seek outcome-based payment models, only 13% of agreements from sellers currently accommodate them. The core development spotlighted is the disconnect between expectations for measurable AI-driven business outcomes and the lack of empirical evidence that such technologies are delivering on those promises at the organizational level. A large-scale survey by the National Bureau of Economic Research, encompassing nearly 6,000 senior executives across four countries, found that over 89% reported no observable improvement in employment or labor productivity from AI investments during the past three years, despite substantial organizational changes and budget reallocations. Additionally, research by Thomson Reuters found that 91% of 1,800 professionals reported their organizations were not realizing expected AI value, identifying a gap in demonstrable returns even while the technology is being deployed. Supporting data from Techaisle reveals partner capability thins dramatically as customers progress into advanced AI adoption stages. Most channel providers retain capacity only for basic "estate" work, with capability dropping to near zero for the most advanced client needs. Forrester has also identified persistent barriers to reliable measurement, such as fragmented and inconsistent data baselines, as well as dependencies on customer-side decisions. These factors magnify contract risk and reinforce the liability shift toward providers, who become responsible for defining, measuring, and underwriting outcomes without always possessing necessary levers or data. The operational implication for MSPs and IT service providers is heightened exposure to contractual and financial risk when agreeing to outcome-based terms, especially without mechanisms to price or control every relevant input. Providers are often unable to flow contract risk upstream to vendors or technology manufacturers, as their own agreements typically exclude outcomes. The episode concludes that early engagement and proactive definition of acceptable, controllable outcomes is essential, as outcome-based demands are likely to appear pre-baked in future client agreements, shifting bargaining power away from providers unprepared to quantify their exposure. Using data from their own worst-performing months, documenting client dependencies as contract conditions, and piloting outcome-based lines in otherwise standard agreements are outlined as practical tactics to mitigate downside risk before broader market adoption. 00:00 Four Numbers, One Cause 03:45 What You Ask For When You Can't Tell 06:45 Nobody Underneath You 10:20 Why Do We Care? Supported by: Guardz ScalePad

The episode centers on a structural shift driven by the falling cost of AI-assisted insight extraction and its impact on how buyers assess technology providers. Referencing companies such as OpenAI and Google, as well as research from the AI Revenue Institute and Gartner, Dave Sobel highlights how lowered model prices enable automated systems to rapidly analyze vendor documentation and shape procurement decisions, fundamentally changing the basis of competition from persuasion to transparent, retrievable data. A recent AI Revenue Institute study, as cited by Dave Sobel, found that over half of surveyed decision-makers had removed a vendor from consideration after an AI assistant highlighted a documented shortcoming. Simultaneously, OpenAI and Google have reduced their top-tier AI model pricing, with OpenAI dropping costs by more than 20% and Google offering a temporary 50% cut before reverting. Analysis from TD Cowen and Business Insider shows that such price cuts have driven up both usage and revenue, with OpenAI's low-cost models experiencing a 14-fold usage increase post-reduction. These developments are reinforced by Gartner's identification of the “inference paradox,” where greater AI capabilities and lower per-query costs actually raise overall spend due to increased volume and complexity of tasks. Supporting data includes Google's reported 50x annual increase in tokens processed and a Deloitte case of a healthcare provider with unplanned AI costs rising as much as 3x in a year. Alongside this, Pew Research identifies that a third of new web content on commercial sites is machine-generated, leading platforms like LinkedIn to introduce AI-detection and downranking measures. For MSPs and IT leaders, the implications are direct. Automated buyer research now prioritizes concrete, extractable data over marketing language; any absence or non-disclosure—especially around pricing—can result in removal from consideration without notice. Publishing specific, measurable facts (service boundaries, pricing logic, response times with dates) increasingly determines whether a provider is surfaced or omitted by AI agents assembling comparative analyses. Failure to clearly define offerings and exclusions results in unfavorable inferences or comparisons, increasing operational risk and transfer of accountability away from the provider. 00:00 The Buyers Brought a Machine 03:45 Cheaper Made It Bigger 06:49 Your Website Is a Deposition 10:06 Why Do We Care? Supported by: Proofpoint HaloPSA

In this episode, we kick things off by examining a major breakthrough in the often-painful journey from AI demo to live operations. Third-party logistics provider Kenco has successfully ⁠deployed six supply chain AI agents into production with DeepFabric⁠ in just three months, a stark contrast to broader industry trends where Gartner expects more than forty percent of agentic AI projects to be canceled by the end of twenty twenty-seven. The partnership is now scaling aggressively, with twenty agents planned across North America over the next twelve months and audit-spend reductions of forty-five percent already achieved.Next, we explore the critical role of border security in cross-border supply chain integrity. Canada Border Services Agency officers ⁠seized approximately three hundred eighty-five kilograms of suspected cocaine from a commercial truck⁠ at Blue Water Bridge in Point Edward, Ontario on August thirteenth. The driver was arrested and charged with importing a controlled substance and possession for trafficking, underscoring the ongoing enforcement challenge at busy commercial border crossings.Finally, we discuss the ocean freight sector bracing for a massive wave of new capacity that could severely pressure container rates. The ⁠global containership orderbook has reached an orderbook-to-fleet ratio of roughly thirty-eight point seven percent⁠, meaning vessels on order represent nearly two-fifths of the existing fleet's capacity. With fleet growth running at about four point two percent against expected container-trade growth of only three to four percent, industry analysts warn that vessel supply is likely to outpace demand, potentially releasing significant downward pressure on freight rates once current geopolitical disruptions normalize. ⁠Follow the FreightWaves Today Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Fair warning: Dee Kei opens this one by saying he doesn't know if he's going to release it. Congratulations — you found it.This is a looser, more speculative episode than usual, and that's exactly the point. Dee brings a theory he's been sitting with: that the rise of AI tools and plugin saturation is quietly driving a resurgence in the value of analog gear — not because analog is objectively better, but because it represents something that AI fundamentally can't replicate. Humanness. Randomness. The chaos of a real circuit doing something slightly unpredictable.Lu, who has personally moved over $157,000 in analog gear sales and spent years tracking sold listings on eBay and Reverb, brings the market perspective. They get into which pieces actually hold their value (vintage preamps and compressors from the right era), which ones don't (anything mass-produced and recently released), and the old idea that buying the right gear is just another savings account you can twist the knobs on while you wait.From there the conversation opens up into some bigger territory. They talk about the current bubble of vintage mixer restoration and servicing companies popping up, what vibe coding is doing to the plugin marketplace, and how AI tools are being deployed very differently in live sound versus the studio — including a breakdown of the L-Acoustics source intelligence system and how it can achieve up to 40dB of feedback reduction in real time on a vocal channel. The episode wraps with Dee going full speculative: the Gartner hype cycle and where AI actually lands when the bubble pops, what nuclear fusion does to the cost of running AI at scale, and the two technology shifts he thinks will fundamentally change everything about how we make and experience music.It's the kind of episode that happens when two engineers with too much on their mind decide to just talk. The industry context makes it more interesting than it sounds.SUBSCRIBE TO OUR PATREON FOR EXCLUSIVE CONTENT!⁠SUBSCRIBE TO YOUTUBE⁠Join the ‘Mixing Music Podcast' Discord!HIRE DEE KEIHIRE LU⁠HIRE JAMES⁠Find Dee Kei and Lu on Social Media:Instagram: @DeeKeiMixes @MasteredbyLu @JamesParrishMixesTwitter: @DeeKeiMixes @MasteredbyLuThe Mixing Music Podcast is sponsored by ⁠Izotope⁠, ⁠Antares (Auto Tune)⁠, Sweetwater, ⁠Plugin Boutique⁠, ⁠Lauten Audio⁠, ⁠Filepass⁠, & ⁠Canva⁠The Mixing Music Podcast is a video and audio series on the art of music production and post-production. Dee Kei, Lu, and James are professionals in the Los Angeles music industry having worked with names like Odetari, 6arelyhuman, Trey Songz, Keyshia Cole, Benny the Butcher, carolesdaughter, Crying City, Daphne Loves Derby, Natalie Jane, charlieonnafriday, bludnymph, Lay Bankz, Rico Nasty, Ayesha Erotica, ATEEZ, Dizzy Wright, Kanye West, Blackway, The Game, Dylan Espeseth, Tara Yummy, Asteria, Kets4eki, Shaquille O'Neal, Republic Records, Interscope Records, Arista Records, Position Music, Capital Records, Mercury Records, Universal Music Group, apg, Hive Music, Sony Music, and many others.This podcast is meant to be used for educational purposes only. This show is filmed and recorded at Dee Kei's private studio in North Hollywood, California. If you would like to sponsor the show, please email us at ⁠deekeimixes@gmail.com⁠.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Gavin McMahon: The Arrow of Coherence That Unifies Your Brand Storytelling Business of Story Podcast | Host: Park Howell Most brand stories fragment the moment they leave the marketing department. Gavin McMahon has a name for what stops that — and it may be the most important concept in business storytelling you haven't heard yet. Gavin McMahon is a recovering mechanical engineer, founder of a 25-year leadership and storytelling consultancy, and author of Story Business: Why Stories Rule the World and How They Can Reinvent Your Business. He joins Park Howell on the Business of Story podcast to introduce the Arrow of Coherence — the cause-and-effect narrative logic that connects every layer of an organization, from the C-suite to the front line, into a single governing story world. The problem Gavin diagnoses is one every business leader will recognize: the synonymous mess. Most companies tangle purpose, vision, mission, and strategy into one undifferentiated blob of corporate language. The result? Thousands of employees making daily decisions — small and large, value-creating and value-destroying — without a coherent narrative to guide them. In a pre-AI world, that fragmentation was a slow burn. Today, ungoverned AI scales that fragmented story at machine speed. The Arrow of Coherence is Gavin's answer. Borrowed conceptually from Nassim Nicholas Taleb's idea of an arrow of relationship, Gavin defines the Arrow of Coherence as the cause-and-effect logic that must shine through a brand — the same governing logic that makes the world of IKEA, Apple, or Patagonia feel coherent whether you encounter it in product design, customer service, or a thirty-year-old marketing campaign. When that arrow is present, every decision in the organization points the same direction. When it's absent, you have drift. In this conversation, Park Howell and Gavin McMahon explore why brand storytelling is not a marketing function but an act of world building — and why the best brands govern their stories through something closer to a show Bible than a brand guideline. They discuss why the CEO is the most underutilized storyteller in any company, how to build a coalition of the willing to embed story culturally rather than hierarchically, and why AI will amplify your narrative entropy unless a governing Brand Brain is installed first. Gavin also shares the two things he wishes he had known in the 1990s when he was leading digital transformation at Gartner — lessons about human decision-making, the stories people tell themselves, and why the truth is never driven by facts alone. What you will learn in this episode: What the Arrow of Coherence is and how to apply it to unify your brand story across every department and channel. Why most organizations suffer from a synonymous mess of purpose, vision, mission, and strategy — and the single narrative move that untangles it. How show Bibles differ from brand guidelines and why that distinction determines whether your story survives scale. Why your CEO is your most underutilized brand storyteller. How to use AI as a collaborator without surrendering your story to it. The three practical steps you can take today to start building your own Arrow of Coherence. Gavin McMahon is the author of Story Business: Why Stories Rule the World and How They Can Reinvent Your Business. Learn more at storybusiness.com. Park Howell is founder of The Business of Story, creator of the Story Cycle System™, and author of Brand Bewitchery and The Narrative Gym. Full episode show notes, transcript, and resources at businessofstory.com.

Buyers have more information than ever, but that does not mean they have more confidence.In this episode, John sits down with Zachary Gropper, Founder and CEO of Insight Revenue, to talk about Challenger, platform sales, AI research, buyer validation, and why old sales motions are breaking under modern buying pressure. Zachary brings a practical lens from his years around CEB, Gartner, Challenger, Pavilion, and the teams now trying to sell more complex platform solutions in a market full of stalled deals.If you are in sales, leadership, enablement, or go-to-market strategy, this conversation gives you a clearer way to think about insight, trust, buyer confidence, change management, and what sales reps still need to do when buyers already show up with AI-generated research.Want to build a stronger sales motion before buyers get even harder to move? Visit Visit www.jbarrows.com and learn how you can Make It Happen.What You'll LearnWhy simply sending methodology books does not change sales behaviorHow AI can make reps sound smart while weakening real credibilityWhy buyers now need validation and trust before they commitHow platform sales require change management, not just product positioningWhy sellers need to build a bridge from customer strategy to the outcomeWhy asking why matters when buyers come to the call with AI researchZachary Gropper is Founder and Chief Executive Officer of Insight Revenue. With more than twenty years studying the art and science of B2B sales and marketing best practice at CEB, Gartner, Challenger, and Pavilion, he helps commercial teams bring repeatable practices, processes, and go-to-market strategies into companies of any size or industry.Connect with Zachary Gropper:Facebook: https://www.facebook.com/zgropper/LinkedIn: https://www.linkedin.com/in/zgropper/Learn more About Insight Revenue:Website: https://www.insightrevenue.com/LinkedIn: https://www.linkedin.com/company/insightrevenue/Youtube: https://www.youtube.com/channel/UCutptmXap5pi5PuPZcU1fDQJohn Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution—helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows:LinkedIn: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/ TikTok: https://www.tiktok.com/@johnmbarrows Check out John's Membership: https://go.jbarrows.com/ Join John's Newsletter: https://www.jbarrows.com/newsletter

Don’t miss the marketplace revolution! Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ In this episode of the Ultimate Partner Podcast, Vince Menzione sits down with AWS Marketplace leaders George Maroulakos and Arif Razvi to uncover the rapidly shifting ecosystem of technology procurement and partner transformation. They dive deep into the evolution of buyer experiences, the critical necessity of executive alignment, and how agentic AI is redefining software discovery and consumption. If you want to accelerate deal velocity and ensure your business isn’t left behind, this conversation outlines exactly why integrating the AWS Marketplace into your core co-sell motion is no longer optional. https://youtu.be/Avp0sIyxRU8 Key Takeaways Embracing the AWS Marketplace should be viewed as a natural extension of your co-sell motion, not an interrupt-driven exception. Successfully leveraging the marketplace requires top-down executive sponsorship to overcome internal friction across legal, finance, and revenue operations. Partners must prepare for global expansion by ensuring local entities and currencies are wired up to meet buyers where they are. Agentic AI and natural language queries are leveling the playing field for software discovery, moving beyond traditional SEO-driven presence. SaaS pricing models are shifting toward consumption and outcome-based structures, demanding highly detailed product metadata. The speed of deal closure is drastically increased when partners are prepared to transact seamlessly through the marketplace. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AWS Marketplace, partner transformation, buyer experiences, hyperscalers, co-sell motion, revenue operations, strategic alignment, agentic AI solutions, outcome-based pricing, metadata optimization, software discovery, private offers, global expansion, deal velocity, cloud centers of excellence. Transcript Arif Razvi and George Maroulakos Audio Episode [00:00:00] George Maroulakos: From my perspective, think about marketplace as a why not as opposed to a why. [00:00:06] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:17] Arif Razvi: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. [00:00:22] Arif Razvi: And each week I sit down with leaders at the intersection of technology, [00:00:26] Vince Menzione: partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:39] Arif Razvi: It is the strategy because being in the room changes everything. [00:00:44] Arif Razvi: Let’s start. [00:00:48] Vince Menzione: I’m excited to, because we were talking about some of this earlier with Matt, but I thought this would be a great conversation. I’m gonna ask each of you to introduce yourselves, George and Arif, and your roles. ’cause you have two very unique roles. Uh. Contrasting roles, I would call it, within the AWS Marketplace Organization. [00:01:06] George Maroulakos: Yep. Happy to do so. So I think Vince just didn’t wanna say my last name, so that’s why I’m gonna introduce myself. Mayor. Very, very good. So Mayor Laki, George Meki, pleasure to meet. Hey, I can say that, uh, all of those that I haven’t met before, lots of familiar faces as well here. Uh, so it’s really great to see. [00:01:21] George Maroulakos: Uh, I’m part of the AWS Marketplace team and our center of excellence. That focuses on buyer experiences. So like why is a buyer guy here in this partner session? And I think, you know, I’m gonna help to try to bring some perspective around what our buyers see as the value for using marketplace and. [00:01:40] George Maroulakos: Working with all of these great partners that are here in the room and, uh, you know, the experiences that we see in helping to drive, you know, the blood to all organs. I’m using that a lot, Alison, to copyright it. It’s, it was really good. I love that, that analogy. But, um, for, but for both our partners as well as for our customers. [00:01:59] Vince Menzione: That’s awesome. [00:01:59] Arif Razvi: Hey everybody. I’m Arif Roski. I’m based here in New York City, although I’m a Celtics fan. Um, uh, go, go. I, I still supported the Knicks through the championship and I’m very happy for them. So excited to be here. Excited to be among, uh, other channel and alliance leaders. I’ve spent my entire 25 plus career in channels and alliances. [00:02:20] Arif Razvi: The last seven years, uh, on AWS marketplace where I lead, uh, a couple of functions. One is I support all of Matt Ian’s feature launches. So from a go-to market perspective, uh, I support his launches. I also focus on international expansion of marketplace. So, uh, Matt alluded to this earlier. I’ll mention some, I’ll talk about some stuff in a little bit. [00:02:39] Arif Razvi: And then, uh, we do some deep engagements with some of our most strategic partners to help them accelerate and unblock their marketplace business. By embedding a subject matter expert from my team into their organization to really help drive, uh, adoption of marketplace. [00:02:55] Vince Menzione: So important. Both of your roles. [00:02:56] Vince Menzione: By the way, this is standout roles. I, you know, I. I don’t want to get into trouble here. I talk about other hyperscalers, but I said this with Matt on stage earlier. You guys have been at the forefront of driving marketplace in such a strong way, having somebody who’s customer focused, right? And that lens is so important. [00:03:15] Vince Menzione: I don’t feel, I feel like that’s missed so many times. And then you also have. You know, quite a bit of an important role here in terms of what you’re doing in embedding resources. ’cause a lot of times people get lost in the process and it seems to be the, the common currents. So, um, really great both sides of the same equation here, right? [00:03:33] Vince Menzione: Buyer led, partner built, uh, let’s start George here. Um. Let’s talk about how buying has changed. Right. We go back to the early days of marketplace. It felt like it was a listing at first and it started to become important. Organizations like Work Span started to come to fruition and started to drive, at least in my part of the world. [00:03:53] Vince Menzione: And then we started talking a lot more about Marketplace. And of course the, the cus customer commits really started driving things in a different direction. You’ve got the customer, so talk about what your journey’s been like. [00:04:05] George Maroulakos: So I’ve been with Marketplace nine and a half years, which is pretty long time since the beginning of it, and watching the evolution of, of where things have come and where they started. [00:04:15] George Maroulakos: And I remember, uh, I was covering the, the northeast region when I very first started and working with, uh, you know, different enterprise accounts and financial services and, and, and healthcare and life sciences, and talking about this. This creation that occurred called, called AWS Marketplace, and even folks within AWS didn’t really know what that meant or how to talk about it or sell it. [00:04:38] George Maroulakos: So there was this creation of a infield based business development function to help supplement the account teams and the value and the importance of marketplace as a part of a customer’s AWS journey. And when I first started the, the, the highest, uh, or most frequently subscribed to products that existed were. [00:04:57] George Maroulakos: Uh, Amazon machine images a amis and it’s very specifically open source amis. So think of Bantu or Cintas as the predominant things that were being subscribed out of marketplace. So, um, by definition, not really revenue generating. You’re, these are not the a hundred million dollar transactions or the billion dollar club that we talk about today. [00:05:19] George Maroulakos: It was very much helping. The builders go again, go back 10 years, uh, who are just getting started with the cloud and how could they find partner solutions that they trusted or that they felt were secure and helping them to start to build out and migrate their workloads into, into AWS. Um, so I existed before the. [00:05:39] George Maroulakos: Very first private offer was transacted. Nice. Um, there’s actually a, a gentleman towards the back, uh, who helped with one of our very first private offers. I see you back there, Joe. Nice, nice. Um, so we, we worked on, uh, uh, uh, really big transaction with, uh, with, at the time AppDynamics, uh, and, and helping to get. [00:05:58] George Maroulakos: You know, a customer really up and running with their implementation that, so watching over the 10 years from, you know, the builder mentality of, of getting started with the cloud to really embracing all kinds of partner based, um, solutions that go along with AWS. It’s really been, uh, you know, a, a rocket ship you referenced earlier, moving to the top right quadrant, if we want to use the, the Gartner analogy, and I think we’re at another. [00:06:24] George Maroulakos: Inflection point because with what’s going on with Ag agentic solutions, the way that our customers are finding and discovering different partner solutions is better than ever I would think. And I think it gives much more of a equal opportunity in playing field for all partners of all shapes and sizes because you’re not driving your presence based on SEO or whether or not you have the best Google search results. [00:06:53] George Maroulakos: With AgTech and how you differentiate your solutions, you’re gonna be able to really get yourself in front of more customers more quickly. And as Matt talked about earlier, the, the, the really big penetration that we’ve seen thus far is real, is on the discovery piece, the, the research area, the pre-purchase activity, so validate what’s available. [00:07:13] George Maroulakos: So I think marketplace has really evolved over the 10 year, nine and a half years-ish that I’ve been there. And I think it’s gonna continue to change here in the coming months ahead. Yeah. [00:07:23] Vince Menzione: Any predictions? [00:07:25] George Maroulakos: Yeah, I, I think, you know, the, I, the, the importance of marketplaces is only going to continue to grow. [00:07:32] George Maroulakos: Uh, and I think we’re going to see, you know, even more innovation and expectation from our customers on being able to find the right solutions and being able to procure and deploy them as quickly as possible. [00:07:44] Vince Menzione: Nice. Arif, from your side. Partner transformation. So, you know, embedding resources with the partners. [00:07:51] Vince Menzione: Right. So you basically help kickstart a lot of the, the efforts, right? George? George is looking at it from the customer side, and then you’re kick-starting it on the partner side. [00:08:00] Arif Razvi: Yeah. Either kickstarting it, uh, on the initial stages with a strategic partner. Yeah. Or accelerating their adoption of something that’s already there. [00:08:07] Arif Razvi: Right. So what we’re seeing, what I’m seeing a lot of more recently is partners who say, I didn’t know you had capabilities for us to sell into Korea or Japan, or we just, we just launched India. Yep. Matt alluded to this easier, uh, earlier that we’re making it easier for partners to expand globally. And now partners are realizing, wait a minute, I can match my. [00:08:26] Arif Razvi: Uh, my business processes on marketplace, so revenue recognition, tax collection, locally, local invoices, and now they’re starting to set up multiple entities outside the US to meet those buyers, uh, where they are. [00:08:41] Vince Menzione: Nice. And you mentioned having these resources. How do you determine who gets resources and who? [00:08:47] Arif Razvi: Well, there’s, uh, staging and there’s, uh, there’s qualification mechanisms we use. We work with the PDMs for the partners. So any PDM, uh, any pd DM managed partner can be nominated for, uh, what, what we call a compensation. [00:08:59] Vince Menzione: So you’re overlaying that organization sense. [00:09:00] Arif Razvi: We’re overlaying the PDs. We’re not replacing, we’re only doing time bound sprints to unlock very specific activities. [00:09:05] Arif Razvi: Very [00:09:06] Vince Menzione: cool. So it’s not a poll. Yeah. Cool. You’re not, you’re not there forever. [00:09:08] Arif Razvi: No. [00:09:08] Vince Menzione: You’re there to get the job done. We [00:09:09] Arif Razvi: did that before. Uh, yeah, we’re, we’re doing short sprints now. [00:09:12] Vince Menzione: Yep. Let’s talk about how the buyers are using marketplace. You’ve done some really in, we talked about this earlier with Matt, but we’ve talked about some of the innovative things you’ve done. [00:09:21] Vince Menzione: You know, being, being able to embed into a website and in the storefronts. All these things seem to be unique to AWS. Talk to us about how the buyers are using the marketplace today. And [00:09:31] George Maroulakos: yeah, I think, you know, Matt talked about earlier the. Pre the, the preconception around why customers use marketplaces for the financial incentives, the, the financial benefits that go along with it, whether it’s direct incentives that are associated with the individual product that’s out there, or the Association of Marketplace to our private pricing agreements and the ability for. [00:09:55] George Maroulakos: Transactions that occur in marketplace to count towards those commits. Um, I’m gonna amplify a, a particular point that Matt made, uh, in his talk that, you know, we have many more customers who do not have PPAs with AWS than those that do. And the volume of transactions that take place in marketplace. [00:10:11] George Maroulakos: Dwarf, uh, in volume from those that, uh, are occur from our PPA customers. So yes, there are very large transactions that occur and there is Ben financial benefit that goes along with it. But there’s many other value points that our customers find from discovery. From ease of use, from consolidated billing, from post-purchase, um, you know, management and governance that goes along with it, and reconciliation that’s available, that’s, that exists. [00:10:36] George Maroulakos: Um, you mentioned storefronts. I’m really excited. That was one of the most exciting launches. That’s why I reminded him when he was talking about it, uh, around, you know, what that means. And it’s both a presence for, um, our partners and being able to create their own storefronts on behalf of, of customers or within their own property, but also for our buyers directly too. [00:10:54] George Maroulakos: Take that next generation of something that was previously, you know, our private marketplace and curate a catalog that is very specific and intentional for the things that they’re interested to innovate with and the partners that they’re interested in using. And so I think meeting our customers where they’re at. [00:11:10] George Maroulakos: And being, you know, marketplace anywhere and everywhere is I think really important for our customers. And then the other aspect, you know, in terms of how our customers use marketplace, there’s more than one persona at our customer that we need to be, be, be aligning with. Right. Interesting. We typically talk about procurement and the value points that procurement find in marketplace, but just as important as, as the technologists. [00:11:32] George Maroulakos: It’s the Cloud centers of Excellence. It’s the innovators who are looking for those business applications or those infrastructure solutions that exist and making sure that we have the right things from the right partners available to them. So we see value all over the place with our customers and how they inter interface with [00:11:47] Vince Menzione: more. [00:11:47] Vince Menzione: You brought up something really interesting, insightful, is the fact that all the different personas in the organization that are touching the marketplace, right? [00:11:54] George Maroulakos: And it’s at different points of the journey, right? Yeah. So while there may be early discovery and research and experimentation going on from, you know, the technologists or, or, or the, or the, the, the business application owners, um, as it progresses through the, the, the. [00:12:09] George Maroulakos: The opportunity lifecycle procurement and sourcing and legal, and the shared services then become a more important part of, of making sure that we get those opportunities closed and launched. [00:12:18] Vince Menzione: Yeah, and that was one of the things we were talking about earlier is the fact that how, how, uh, it, it, the work arduous it could be. [00:12:25] Vince Menzione: To go through that whole process at a customer side. Right? ’cause they have to. [00:12:29] George Maroulakos: It is, and and you know, I think when Cloud first got started, it was quite scary for procurement, right? It became another version of Shadow it. And we were seeing things that were getting purchased on P cards because they could quickly stand up infrastructure versus waiting for the IT team to do it. [00:12:44] George Maroulakos: And so now you introduce all of these other things that. Procurement kept near and dear to their heart, and here comes another wave of, is this truly positive disruption? Is it going to affect what’s, what I’m doing affect really my goals and objectives of supporting the company. And as you, you know, you continue to express the value of marketplace, they start to see, hey, this is actually very complimentary and helped me can get better control and governance in a way that I. [00:13:11] George Maroulakos: Perhaps didn’t have before with what was going on inside the cloud. [00:13:15] Vince Menzione: So this is a question for both of you actually, but I was thinking about these partners in the room and what did they get wrong? Like what are they doing? Like what are the things we always talk about, things that they’re doing right? [00:13:26] Vince Menzione: We’re having the happy talk about all the great success that’s been going on, but what is your advice to partners that maybe are not getting it right? Like what, what, what are the things you see that. The easy stumbling blocks that could be fixed. [00:13:38] Arif Razvi: Yeah. Well, probably the easiest is, uh, waiting to talk about marketplace at the last minute and not making it part of the full commercial journey [00:13:45] Vince Menzione: Yeah. [00:13:45] Arif Razvi: That the sellers will go through. Right. So, um, but that includes being able to have those internal conversations from the executive level, getting that executive sponsorship from marketplace upfront. That filters down through the rest of the organization. So you’ve gotta get rev ops teams, finance, legal, you know, the marketplace terms, the, the standard, uh, contract that goes into your listing has to be approved by legal. [00:14:08] Arif Razvi: Uh, and then you get down to the sales teams, making sure that you’re not penalizing sales teams for doing transactions on marketplace, which will create friction, then meeting buyers where they are, right? So local entities, local currency, um, having all that wired up. Uh, I met with a, a partner yesterday at Summit who is literally wiring up 10 new regions, 10 new entities in 10 new locations on marketplace in advance of what they know is a pipeline that’s going to be building into those regions. [00:14:37] Arif Razvi: So they’re getting ready and not waiting for the last minute for marketplace. [00:14:40] Vince Menzione: I love it. And the internal, go ahead. [00:14:43] George Maroulakos: Yeah, I, I echo definitely what Arif was saying and I think the, the other aspect of that is that our customers shouldn’t feel like it’s more painful. To use marketplace versus they would any other way. [00:14:54] George Maroulakos: And so whether that shows up in pricing, it shows up in awareness of what marketplace is and isn’t, or what it can or cannot do. Um, whether it’s introducing it naturally or it’s this, oh, by the way, on the end, like the, the more. Integrated marketplace is as a part of your co-sell motion, whether you’re doing it directly on your own or through a reseller or with AWS or all of the above. [00:15:17] George Maroulakos: Um, it’s, it should be a natural extension and a value point that you’re bringing to your respective customers, not some. Interrupt driven or, or exception based activity that goes along with selling your, your, your, your solution. [00:15:31] Vince Menzione: Why do you think customer, some customers or some partners are held back or what, what, what is, is it a mindset? [00:15:38] Vince Menzione: I mean, I talk about the principles, you know that, but like, is it mindset? What is it? Is it, [00:15:42] Arif Razvi: it’s, it’s internal. Friction. Yeah. A a a lot of what I see, especially in these, in, in embedded engagements is the amount of, you know, I have a lot of empathy for this room, right? Because you are the ones that have to go internally and navigate all of these stakeholders to be able to convince them that marketplace is the route to market. [00:16:01] Arif Razvi: It’s the preferred route to market, and it’s the way that a AWS wants to co-sell. With its partners. And that’s a hard thing to do if you don’t speak the same language that the tax legal, rev ops, finance, accounting, because everything changes. When you start doing transactions on marketplace, your revenue is booked differently, right? [00:16:20] Arif Razvi: It’s booked as a w from AWS and not from the, you know, from the, so things change and having that conversation is often challenging. Uh, we are working on some tools that will help make that conversation easier. Um, and we’ve already written blogs, and again, there are mechanisms that your partner managers have internally that they can request support and guidance. [00:16:43] Arif Razvi: Uh, and we’re happy to provide that. [00:16:45] Vince Menzione: What needs to change so that marketplace becomes a true go to market engine. [00:16:49] Arif Razvi: Top down, top down, top down where, where I’ve seen the most success. From a partner is where they’ve gotten strategic alignment at the executive level. That marketplace is the way we are going to, uh, sell globally. [00:17:02] Arif Razvi: Right? Then that starts to filter down, as I mentioned earlier, into the different teams and yes, it is a journey and you may start with the US or if you’re EMEA based. Partner, you may start with just amea, but eventually you will start to expand your, you’ll want to expand your business. Um, and marketplace is a great place to do that because we have all of those mechanisms globally to help you scale without adding incremental resources to handle the tax or the compliance or the invoicing and collection and all that stuff. [00:17:30] George Maroulakos: Yeah. I’ll add to that because I’m gonna steal a second thing you said, Allison, about centering around a customer. I, I loved a lot of Allison. Come on now. I’ll be your hype guy. I absolutely, but, but, but the but demand will drive supply. And I think to what you were talking about, Arif, when are customers, when. [00:17:46] George Maroulakos: Our customers come to you about wanting to use marketplace. Yeah. How are you ready to adapt to that? How are you ready to respond to it? And if it becomes a disjointed, not centered around a customer, bespoke based, independently based interaction with the customer, everybody loses. Versus if you’re ready to embrace what that means and how to make that as good of an experience, if not better, versus how they might have traditionally procured your solution and deployed it. [00:18:13] George Maroulakos: Um, now we have a much better together story. So I think understanding that customers more and more are going to use marketplaces, particularly AWS marketplace, and want to make use of partner solutions that embrace marketplace as a part of their way to procure and deploy. You have a much better chance of being successful with them. [00:18:33] Vince Menzione: You know, it made me think about this. Is there a seminal event? Like I think about, I think back to COVID changing buying behavior, right? I’m, we’ll use AWS, an example, three boxes show up in my house every day, right? That didn’t happen before. We used to go to the store. Is there a seminal event we’re waiting to happen? [00:18:50] Vince Menzione: I, I know that the millennial buyer, we talked about this earlier with Matt, is the new buying persona. Over 50% of buyers are millennial and they’re used to doing comfortable with phones and trust Is all there. Is there something else we’re missing or what do, what do you think’s gonna happen? [00:19:03] George Maroulakos: I really think it’s what in front of us right now. [00:19:05] George Maroulakos: Yeah. Vince, I think what. Agen solutions, what Agen SaaS offers, what the power of information and research that’s now directly available to customers versus maybe indirectly available through consultancies or deeper research. Um, the velocity of what our customers are going to be able to do and with partners that they may not have even heard of right before. [00:19:32] George Maroulakos: You know, they started their journey. I, I think this is a present day. Inflection point. Yeah. Um, going back, you know, over the past several years, the advent of supporting private offers I think was a pretty big milestone for marketplace. It allowed for our partners to be able to. Work through customized terms and conditions and commercials that were important for a particular opportunity in a customer. [00:19:54] George Maroulakos: But today, the here and now I think becomes the next inflection point for the success of marketplace. [00:19:59] Arif Razvi: That would [00:19:59] Vince Menzione: Go ahead. [00:20:00] Arif Razvi: I was just gonna add on top of that, but partners need to be ready for that. Right? And if they’re not ready to accelerate a deal and get it closed in days versus stalling it for weeks to negotiate. [00:20:11] Arif Razvi: Yes. Like they’re not gonna stand. Customers are not gonna stand for that. So partners need to be ready to move quickly. If you think about all the innovations that Matt is delivering for Marketplace and Partner Central. They’re about accelerating co-sell. They’re about accelerating deal velocity. They’re about, we know f from, from the Forrester studies and others that we presented, that we’ve made public that the deal value goes up when you’re dealing with marketplace and co-selling with AWS. [00:20:36] Arif Razvi: So how can we just accelerate that? Yeah. Partners need to be ready for that and move quickly. [00:20:40] Vince Menzione: And you use partners in sort of a, you know, plural sense, but I think about those organizations as so many multifaceted. We talked about finance, we talked about all of different functions in the organization. [00:20:51] Vince Menzione: What are you doing to help that? I mean, we talked about you’re doing a lot of readiness work, but I do feel like there’s a lot of evangelism still to be done internally with those organizations. How do you think about [00:21:02] Arif Razvi: that? Yeah, we, um, obviously events like this are fantastic mechanisms to at least get the conversation started and get your head thinking about what you need to think about. [00:21:10] Arif Razvi: But we have other activities. We have, uh, rev Ops squads, right? So revenue operations teams, and we bring them together, uh, around the world op, uh, ops squad, which is operational teams, so deal desks and others that help them, uh, understand the capabilities that marketplace can bring to accelerate deal velocity. [00:21:28] Arif Razvi: And then we have, like, obviously other events like the, the, uh, marketplace Seller Conference in September where we bring marketplace sellers together and give them, you know. Guidance. And so there are ways to get this information beyond just like getting somebody from my team, of which there are very few as you, you know, as we start to, to consolidate down. [00:21:47] Arif Razvi: But um, but they are available and there’s other mechanisms and we’re happy to share those out. [00:21:50] Vince Menzione: Nice, nice. Well coming here doing this and we’ll make a podcast episode out of this as well. Can you hear. I hear us all, uh, because I do think it’s important to get in front of, especially the Chief Finance Officer and operations and all those different departmental heads who aren’t really embedded into, like, they don’t come to these events. [00:22:07] Arif Razvi: Yeah. And they also don’t log into Partner Central. That’s right. So how do they get this information right? Yeah. So we have to one to one it with them. Yeah. But that doesn’t scale when you think about what’s gonna happen now with this next wave of GSIs and sis and, and others coming onto Marketplace who hadn’t been there. [00:22:22] Arif Razvi: Yes, they’re gonna need the same guidance. Yeah. So we have to start thinking about what we’re building is AI tooling to help with those conversations. [00:22:28] George Maroulakos: Well, and, and you know, if, when I think about it from, from our buyer’s perspective, even just yesterday we held a, a couple of round table discussions with some procurement leaders that were, we’re here for the summit and, you know, we do a lot of enablement and, and awareness. [00:22:42] George Maroulakos: With alliance leaders at our partners, and that’s certainly a, the tip of the spear of getting the conversation started, but it’s not enough. And one of the things that we hear from our customers is. Well, you may have a great partnership with particular partner A, but the individual experience that I had with that sales rep doesn’t match that. [00:23:02] George Maroulakos: And so yeah, the evangelism and the awareness, yes, and the understanding of marketplace has to go beyond just the alliance team. You guys are the amplifier to it. But the folks that not only are in the back office and all the, the, the operational teams, but on the front lines and field sales need to also understand and embrace, not understand all the features and capabilities of marketplace. [00:23:25] George Maroulakos: AWS needs to handle that, but understand how marketplace fits into the co-sell strategy in what’s going on for that particular customer is very, very important to make sure our customers get the right experience. [00:23:37] Vince Menzione: Well, I think Arif, you mentioned this earlier about compensation models. And that’s a, that’s a factor too. [00:23:42] Vince Menzione: ’cause people, people, um, they’re fearful of change. They’re fear, fearful of compensation changing. Especially, especially sellers. I, um, again, that’s a coaching area. [00:23:54] Arif Razvi: Yeah, it’s a coaching area. Um, it, it, it is, um. It’s probably one of the easier ones to solve. Um, and, and, and, you know, we have these conversations with partners about net, uh, cost neutral or uh, seller neutrality and things like that. [00:24:06] Arif Razvi: Once you show them the economics of how AWS can increase their deal value, those economics sort of, they go away. The problems go away, but they, you have to get in front of those. Uh, you know, senior leaders, the CROs and the CFOs, to have that conversation to then go, okay, I get what’s going on here. I get why I’m paying. [00:24:23] Arif Razvi: The listing fee is, is about funding all of these marketing activities that we do for our partners, right? [00:24:29] George Maroulakos: Well, and then you use the word neutrality. The other end of that neutrality problem or, or, or challenge is the pricing that goes along with the opportunities that are made available to customers. [00:24:38] George Maroulakos: And so to one of your earlier questions on points of friction or what, what stops it from taking off further? When, when partners and our customers have a disconnect on. What they’re expecting to pay when they use marketplace, or if there is a unnatural cost that goes along with procuring that solution through marketplace. [00:24:57] George Maroulakos: That also tends to bring a, a pretty bad experience, not only for that opportunity in front of them, but for respective opportunities that may, may be in play thereafter. So thinking about not, you know, having an a, an unnatural experience for our customers relative to pricing as well as compensation and, and everything else is, is also very important. [00:25:17] Vince Menzione: So we’ve got a few minutes left, and we haven’t really touched on ag agentic AI and agen AI solutions. A little bit different than the SaaS solutions per se. How, how are these solutions brought and sold differently than SaaS? [00:25:30] Arif Razvi: Uh, well, as Matt said, the SaaS apocalypse is not real. [00:25:34] Vince Menzione: Yeah. [00:25:34] Arif Razvi: Um, he doesn’t, I’m glad to hear that, nor nor do I. [00:25:37] Arif Razvi: Um, I, I think what, you know, where, where SaaS has been very user seat based, you’re moving now to a world that’s gonna be more consumption based or outcome-based pricing. And so that’s really changing the dynamic and I think partners need to think about what does an outcome-based, uh, pricing model look for My particular. [00:25:55] Arif Razvi: Um, product, uh, Zendesk is a good example of, Matt published a blog, uh, earlier this week, I think it was, where we, we referenced Zendesk pricing on closed tickets or, or resolutions to tickets, right? For, for outcome-based pricing. So I think we need to think about on the pricing side, how to reprice, how to think about pricing. [00:26:13] Arif Razvi: But on the discovery side, thinking about what your, what your listing looks like, it’s no longer about marketing copy. It’s about, it’s almost, I was talking yesterday about being an API contract. ’cause the agent needs to have all the details that a person doesn’t necessarily need to have in order to make a recommendation that your product is the best fit based on the technology that’s underlying that, where it’s gonna fit in the infrastructure. [00:26:37] Vince Menzione: So three and a half minutes left lightning round. Um, but seriously, what, what if I’m in the room or even any, any partner that’s listening today, what do I need to change in the next 30 days? [00:26:50] George Maroulakos: From my perspective, think about marketplace as a why not as opposed to a why. And if you change your mindset around marketplace can be better together in helping to accelerate and expand your opportunities with our mutual customers. [00:27:03] George Maroulakos: You’ll get a whole lot more value out of it. You’ll get away from the fud in the system or some of the traditional roadblocks that you either have been or could be encountering when you think about marketplace, uh, together with your, with your solution. So think about the why not think about the value that the, the, the total solution can bring to our mutual customers and integrate it much more naturally into your total sales motion. [00:27:26] Vince Menzione: Nice. [00:27:27] Arif Razvi: I would say maybe three things. One, um, don’t just sell globally. Operate locally. Think about how your buyers wanna buy and then meet them there, right? As a partner, even if you’re doing CPPO transactions, um, you know, meet them in the location. Number two, top down, go get that executive alignment so the problems start to disappear, or at least are easier to manage when you’ve got that executive alignment. [00:27:51] Arif Razvi: And the third was, uh, don’t wait till the last minute to introduce marketplace. Work with your sales teams to make sure it’s part of the early conversation versus no procurement leader wants to know at the end of the day, oh wait, it’s coming. I gotta deal with this marketplace thing. They don’t wanna deal with it on the buyer side. [00:28:06] Vince Menzione: No, absolutely. Alright, we’ve got time for like one question in the back. Is that Eric? Yeah, that’s [00:28:12] Guest: me. [00:28:13] Vince Menzione: Hey. [00:28:14] Guest: Hi, Eric Rosenstein, um, with Cornerstone Strategy XAWS. So George, you talked about, uh, tools around that customers can use to research for self discovery. So what guidance, uh, first of all, like what capabilities are these tools gonna bring and what guidance would you give an industry specific ISV? [00:28:36] Guest: So someone that’s focused on law enforcement or private equity. That solution may be Angen solution, or it may be something more SaaS related. What guidance would you give an ISV to think about how to best leverage those tools? Is it metadata? Is it like. The outcome that your solution’s gonna drive, how would you tell ’em to best utilize those coming tools? [00:28:58] George Maroulakos: Yeah, excellent question and, and I think this is gonna, going to continue to emerge in the days, literally in weeks, weeks ahead. But recently, I, I’d say over the last six months, the advent of agent mode I think has been a game changer and the ability for our customers to use marketplace directly to research. [00:29:18] George Maroulakos: Efficiently what’s in our catalog. Prior to that, we had a category based. Old school based, search based category, click through way, which became very buried beyond the first page for any solution that was out there. Now with natural language query and the ability to propose, what am I specifically looking for, it gives partners that were on the first page or the last page in equal opportunity to be surfaced. [00:29:48] George Maroulakos: So then for a partner being able to do many of the things you just enumerated. Better metadata, better keywords, better description and differentiation for what your solution offers allows for the Ag agent search, whether it’s natively within AWS or outside of AWS through Claude Chat, GBT Crock Pick your, your, your agent of choice. [00:30:10] George Maroulakos: To be able to identify and know that your solution’s available. [00:30:13] Arif Razvi: And from a partner side to your question, I would say, uh, rich metadata, uh, both so that agent mode can find it. Yeah. Use cases, problems, it solves, you know, uh, technical specifications, pricing where you can, right. So that agents can really understand the solution, uh, that the buyer is, um, is looking for. [00:30:33] Vince Menzione: GEO is. [00:30:37] Guest: Perspective should be like as detailed as saying, so this agent is gonna act on these various data sources. [00:30:44] Arif Razvi: Yes. [00:30:45] Guest: Bringing it all together to drive that outcome that you want the agent [00:30:48] Arif Razvi: drive, if you can include what it needs to connect to in order to deliver that outcome as part of the listing. [00:30:53] Arif Razvi: Absolutely. ’cause the agent will want to know. For sure. [00:30:57] Guest: Thanks. [00:30:58] Vince Menzione: Alright, we are at time and this was a great session. [00:31:01] Arif Razvi: Thank you Vince. [00:31:01] Vince Menzione: Great to see you, George. George and I grew up, uh, five miles away from each other and went to the same college. I love it. Great to, great to spend some [00:31:09] Arif Razvi: time with you, George. [00:31:11] Vince Menzione: Thanks for listening to the Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode, and if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. [00:31:38] Vince Menzione: Until next time, keep showing up in the rooms that matter because being in the room changes everything.

Are you an overachieving director, VP, or enterprise tech leader who has spent decades climbing the corporate ladder, yet feels an unshakeable urge to stop postponing the personal legacy projects that matter most?   In this transparent guest conversation, host Billy Keels sits down with Rupinder Mangat—a veteran technology executive with over 30 years leading enterprise sales, business strategy, and global initiatives at industry titans like Gartner, IBM, and Microsoft. Rupinder shares her remarkable trajectory as a British expat transitioning into corporate America, revealing the exact moment a near-miss subway injury forced her to stop doubting her value, sign her voluntary retirement agreement, and embrace true schedule sovereignty. Discover how to transfer top-tier corporate assets like "influencing without authority" into independent assets, overcome the emotional friction of surrendering your company badge, and build a vibrant second-act venture that serves underserved professional communities entirely on your own terms.  

The autonomous business era is no longer a future-state vision. It's already reshaping how supply chains operate. In this episode of Gartner ThinkCast, you'll hear from Gartner Senior Director Analyst Lindsay Azim as she delivers a preview of the 2026 Gartner Supply Chain Symposium/Xpo Opening Keynote. Drawing on both Gartner insights and real-world examples, she explores why success isn't about implementing AI first, fastest, or even best. It's about preparing supply chains for a future where people and machines work together to drive agility, resilience and growth.   You'll learn: Why Gartner believes the autonomous business era is already underway  How leaders should balance short-term AI value with long-term transformation  The difference between an automation mindset and an autonomous mindset  What autonomous-ready operations and workforces look like in practice   Dig deeper: Register to attend a Gartner Supply Chain conference Download the Gartner Supply Chain Top 25 See why Gartner is the world authority on AI   Timestamps: (00:00) Intro (01:31) A new business era begins (05:18) Defining autonomous business (07:10) What autonomous supply chains look like (09:35) The rise of machine customers (12:22) Success isn't about AI first (17:25) Exploitation versus exploration

How will AI reshape supply chain teams, technology investments, and the future workforce? In this episode of Supply Chain Now, Scott W. Luton is joined by Mike Griswold, Vice President Analyst at Gartner, for another conversation in the Supply Chain Today and Tomorrow series. Mike shares his perspective on AI strategy, supply chain technology, inventory management, and the decisions leaders must consider as business expectations continue to change. The conversation explores why organizations should be careful about replacing entry-level roles with AI, how supply chain teams can balance technology with human expertise, and what future leaders need to learn about data, decision-making, and operational excellence. Listeners will learn how to think differently about AI adoption, why high-tech companies offer valuable lessons for supply chain teams, and what skills may define supply chain leadership in the years ahead.  Jump into the conversation: (00:00) Introduction (08:36) Why more data does not always create better decisions (09:48) The future of just-in-time inventory strategies (11:31) Building alignment across business, supply chain, and AI strategy (15:36) Supply chain technologies worth investing in (18:31) Mike's supply chain hot take on AI and workforce changes (24:44) What supply chain can learn from high-tech companies (28:10) The moments that shape supply chain transformation (31:31) What future supply chain leaders will study in 2050 (33:34) How supply chain education programs need to evolve   Additional Links & Resources: Connect with Mike Griswold: https://www.linkedin.com/in/mike-griswold-6a68922/ Learn more about Gartner: https://www.gartner.com/ Gartner Announces 2026 Rankings of the Global Supply Chain Top 25: https://www.gartner.com/en/newsroom/press-releases/2026-06-17-gartner-announces-2026-rankings-of-the-global-supply-chain-top-25 Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt WEBINAR- SAP AI Inside the Supply Chain: From Silo to Orchestration: https://bit.ly/4bvpz6K WEBINAR- Operational AI in the Supply Chain: How context empowers agents and humans to operate side by side: https://bit.ly/4x7Vd2Z WEBINAR- You Can't Manage What You Can't See: Using Visibility, KPIs, and AI to Optimize Logistics Operations: https://bit.ly/4ql6iem This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/lightning-round-returns-9-key-questions-1624 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The dominant structural shift explored is the erosion of document-based differentiation for MSPs and IT service providers, driven by advances in generative AI, regulatory mandates, and automation of AI detection and content creation processes. Regulatory requirements such as the EU AI Act are compelling vendors like Anthropic and Google to introduce invisible watermarks on machine-generated content, while vendors including OpenAI have yet to standardize this practice. At the same time, third-party entities such as BlazeHive are automating the production and humanization of AI-generated output, raising concerns about the long-term viability of artifacts as proof of human oversight or competency. Evidence cited includes Anthropic's implementation of invisible watermarks on content produced by its Claude model, fulfilling regulatory obligations and planning to release detection tools to third parties. The durability of these watermarks is limited: "light editing probably won't strip the mark, but a complete rewrite... will" according to Anthropic's own guidance. Market analysis by Ramp shows a ceiling on enterprise spend for premium AI models like Anthropic's Fable 5, with adoption of high-end models remaining restricted in practice, and cost pressures pushing organizations towards locally-run, unmetered models such as Alibaba's recent release. Additional developments reinforce the structural gap in process and talent. Channel Dive and Information Week report that IT providers face increasing difficulty deploying the AI tools they sell, not because the tools are unavailable, but due to a lack of engineering skill and process clarity. Gartner's research, as reported by Information Week, identifies that failures in deploying AI agents stem from breakdowns in business process definition, not deficiencies in the technology. These trends illustrate that service providers' core asset is not tooling but an explicit, transparent process with clear review and accountability—something that automation and documentation alone cannot supply. For MSPs and IT service providers, these trends create risks around vendor substitution, diminished artifact value, and increased client scrutiny. The implication is a need to codify review standards and accountability practices for deliverables, as automated AI output can no longer serve as a market differentiator, and clients now have both the suspicion and means to probe the origins of documents. Differentiation will shift toward the ability to transparently describe, defend, and consistently execute meaningful human review and oversight—not merely the ability to generate professional-looking outputs. Providers who cannot articulate and document their review process may find themselves commoditized or excluded from competitive evaluations. 00:00 The Mark Arrives Everywhere  03:11 A Test That Can't Come Back No 06:38 Nobody Can Answer With the File 09:24 Why Do We Care?    Supported by:  OpenText Guardz 

Get the free Core Drives in the Wild guide, behavioral design applied to real products: professorgame.com/WildCD Episode Summary Rob breaks down Gartner's prediction that 40% of large warehouse operations will adopt gamification by 2028, showing why the definition attached to that number (points, badges, leaderboards, and rewards) is the setup for the White Hat to Black Hat drift rather than a win for the field. He covers the frontline cases already on record, from Disney's 2011 "electronic whip" laundry leaderboard to Amazon's FC Games and Tae Wan Kim's 2026 Carnegie Mellon paper on gamifying meaningful work, then walks through Aperam's safety training project with The Octalysis Group as the counter-example built on Core Drives 2, 3, and 5. Listeners learn how to spot extractive gamification and how to test a system by asking what would be left if the leaderboard disappeared tomorrow. About the Host Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia. Key Takeaways Gartner predicts 40% of large warehouse operations will adopt gamification by 2028, and defines it as points, badges, leaderboards, and rewards, which is the most extrinsic and most drift-prone stack available for work that runs on a multi-year horizon. The White Hat to Black Hat drift is dangerous on a warehouse floor in a way it is not in a consumer app, because a frontline worker cannot uninstall the job. Instead of churn, the cost shows up as overwork, injury, or people pushed out of their living. Amazon's FC Games offered warehouse workers virtual pets while injury rates ran above average, and workers publicly compared the program to the Black Mirror episode "Fifteen Million Merits." Carnegie Mellon business ethics professor Tae Wan Kim's 2026 paper "When Work Becomes a Game" found that gamifying meaningful work can shift a worker's reason for doing it from the purpose of the job to the points, a textbook over-justification effect measured on people at work. Aperam, one of the world's largest stainless steel producers, ran its 2017 safety training project with The Octalysis Group on Core Drive 3 (Empowerment of Creativity and Feedback) and Core Drive 5 (Social Influence and Relatedness), giving workers a voice in safety improvements and turning safety into a group quest instead of a ranking. The Aperam results were strong adoption, workers requesting early downloads of safety material, and a significant reduction in workplace accidents, because the target metric was fewer injuries rather than time spent in the training. Topics Covered 0:00 — The electronic whip and a 40% prediction 2:29 — What Gartner's definition actually contains 3:27 — White Hat to Black Hat drift, and who pays 5:06 — Amazon FC Games and virtual pets 6:08 — Tae Wan Kim on gamifying meaningful work 7:28 — Over-justification and Gartner's own warning 9:15 — Gamification can do this job well 10:06 — Aperam's safety training with Octalysis Group 12:45 — Black Hat as an on-ramp, not the engine 13:18 — Fewer accidents, not more engagement 14:23 — Questions to ask before you buy 17:21 — Where you want to sit in 2028 Mentioned in This Episode Gartner: 40% of large warehouse operations will adopt gamification tools by 2028 Federica Stufano, Gartner analyst behind the warehouse gamification prediction Tae Wan Kim, Carnegie Mellon business ethics professor, 2026 paper "When Work Becomes a Game" Amazon FC Games, the warehouse gamification program with virtual pets and arcade-style mini-games Disney's 2011 hotel laundry leaderboard, known among workers as the "electronic whip" Aperam, one of the world's largest stainless steel producers, and its safety training gamification project The Octalysis Group Core Drives in the Wild, the free Professor Game guide Free Resources and Get in Touch Core Drives in the Wild: Professor Game Free Guide Get Daily Value on Your Email Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question

How is the consulting industry changing as technology, AI, and new buyer expectations reshape the market? In this episode of Supply Chain Now, Scott W. Luton speaks with Amber Salley, Founder and Managing Director of the Amber Salley Advisory Group, about the changing consulting landscape and what supply chain leaders should consider when hiring outside expertise. With experience as a practitioner at IBM, consultant at Booz & Company and Accenture, Gartner analyst, and vendor executive, Amber shares her perspective on why traditional consulting models are under pressure, how AI is accelerating existing changes, and why specialization matters more than ever. Listeners will learn why companies are moving toward smaller technology investments, faster results, and decision-focused advisory support. Amber also discusses how consulting firms must adapt, the future of outcomes-based pricing, and why organizations should evaluate partners based on their ability to deliver measurable value quickly. Jump into the conversation: (00:00) Intro (02:19) Meet Amber Salley (03:14) How Amber Salley's career shaped her consulting perspective (08:47) Why specialized supply chain expertise matters (16:16) What is changing in the consulting model? (23:16) Why buyers are moving toward faster technology wins (28:39) How consulting firms must rethink software partnerships (33:08) Can outcomes-based pricing become the future of consulting? (36:36) What should SMB operators ask before hiring consultants? (43:12) Why companies should avoid layering AI onto outdated foundations (49:53) Where to connect with Amber Salley Additional Links & Resources: Connect with Amber Salley: https://www.linkedin.com/in/ambersalley/ Learn more about Amber Salley Advisory Group: https://www.salleyadvisory.com/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt WEBINAR- SAP AI Inside the Supply Chain: From Silo to Orchestration: https://bit.ly/4bvpz6K   This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/how-choose-right-supply-chain-consultant-1623 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode: How a virus-infected Prince of Persia floppy disk on a Commodore 64 sparked a lifelong obsession with cybersecurity (03:03 - 06:45) From NYU to Yale cryptography PhD to Goldman Sachs to Gilt Groupe, and the near-miss that changed everything (03:03 - 06:45) What SecurityScorecard actually does and why the pen and paper questionnaire era had to end (06:55 - 08:18) What it looked like in the early days, including an IKEA furniture test for business partnerships (08:26 - 12:01) Why SecurityScorecard now scores every company in the world, not just twelve million organizations (12:01 - 12:29) The Gilt Groupe credit card near-miss, what the first 24 hours looked like, and why fear was the first reaction (12:50 - 17:30) What it takes to create an entire market category from scratch and why the job to be done never changes (17:48 - 20:20) The difference between the CISO version and CEO version of Alex, and what Satya Nadella said about zooming out (20:49 - 22:36) Which version of Alex people would rather have a beer with and why any job besides CEO is more fun (22:43 - 23:45) How North Korea used a fake hedge fund to try to recruit SecurityScorecard developers (24:06 - 25:47) Why the world is not becoming safer and the critical difference between robustness and resilience (25:59 - 26:42) The True Confessions keynote: why openly admitting breaches makes the whole ecosystem stronger (27:13 - 29:22) The Jaguar Land Rover breach and what it took to double a UK company's security budget overnight (27:13 - 29:22) The single most dangerous thing a board member has ever said in a meeting about cybersecurity (29:45 - 31:09) What one thing a non-technical CEO could do this week to make their CISO's life better (31:25 - 32:12) The Lifestyle Polygraph: restaurant health scores, PowerPoint ban, The Inner Game of Tennis, chess, false advertising, and podcast advice (33:07 - 41:32)   Timestamp Highlights: (03:03) Prince of Persia, a floppy disk, and the origin of a cybersecurity career (06:07) The realization that changed everything: you can do everything right and still lose (08:26) The IKEA furniture test for business partnerships (12:50) The Gilt Groupe near-miss and what the first 24 hours looked like (17:48) What it takes to create a market category from scratch (20:49) CISO vs CEO: zooming in vs zooming out (22:43) Which version of Alex would you rather have a beer with? (24:06) North Korea's fake hedge fund operation (25:59) Robustness vs resilience: why the mindset has to change (29:45) The most dangerous thing a board member has ever said (31:25) One thing every non-technical CEO should do this week (36:48) The Inner Game of Tennis and the infinite game (40:00) Chess, false advertising, and meeting his future wife   Resources & Links: SecurityScorecard — securityscorecard.com The Perfect Scorecard by Aleksandr Yampolskiy ThreatLocker — Presenting sponsor of No Password Required DerScanner — Episode sponsor Cyber Florida — The Mother Ship

Margin pressure driven by AI adoption and automation is fundamentally altering the economic model for IT service delivery and software. Trend Micro's disclosure that operating margins fell from 19% to 15% while cloud and AI token costs nearly doubled, despite strong AI security product sales, highlights how AI-related expenses grow in step with usage. This shift breaks from the historical software margin structure, where scaling incurred negligible incremental costs, and signals a new landscape in which AI service operation continuously consumes resources. A significant development underscoring this trend is the $2 billion capital raise by Thrive Holdings at a $12 billion valuation, backed by SoftBank and OpenAI. Thrive's business model centers on acquiring professional service firms—across IT and accounting—then reorganizing their operations around AI to reduce labor costs while maintaining service levels. According to Dave Sobel, this is not speculative, but reflects direct, substantial financial bets on the ability to remove a portion of service labor without customer disruption, with over 70 acquired service companies already undergoing this transition. Additional evidence comes from channel segment data and shifts in partner economics. The Techaisle Global Channel Partner Survey found service providers under $10 million in revenue project 8.4% growth, while those above $500 million expect 16.8%. AI-related cloud spending continues to climb, with Gartner projecting $42 billion primarily moving from training to ongoing inference operations. The resulting cost structure affects everyone, from increased hardware component prices—such as memory for GPUs—and service desk automation tool adoption, to the fact that most organizations now monitor AI spend as a named line item but struggle to forecast it reliably. Only 11% of organizations can predict their AI bills, down from 15% the prior year. For MSPs and IT leaders, these developments indicate rising operational complexity and increasing pricing competition. Automation drives down service delivery costs, but savings will quickly pass to clients as competitors implement similar solutions. Providers must quantify and communicate their impact on client outcomes, translating delivered value into client financial terms rather than relying solely on traditional metrics like licenses or labor hours. Failing to do so exposes providers to rapid commoditization and margin erosion, as clients grow more able to audit, benchmark, and bid out both cost savings and revenue enablement. 00:00 Two Billion Against Your Labor  04:10 Software Got a Cost of Goods 06:56 Get On Their Income Statement 10:29 Why Do We Care?  Supported by:  ScalePad  Proofpoint

AI isn't just changing individual jobs, it's changing the shape of the org chart itself. In this episode, Neal Woolrich, Director in Gartner's Human Resources Advisory practice, joins Shahin to unpack whether the traditional "pyramid" structure is giving way to a "diamond" with fewer entry-level roles, and what that means for the next generation of managers. Neal cuts through the noise around AI-driven layoffs (much of it, he argues, is "AI washing") and shares Gartner's projection that AI becomes a net job creator by 2030. He also walks through real governance models from Lloyd's Bank, ServiceNow and Red Hat, and tackles the thorny question of what happens when AI agents start appearing on the org chart alongside humans. Guest Introduction Neal Woolrich is a Director in Gartner's Human Resources Advisory practice, based in Melbourne, where he advises organisations on culture, employee experience and organisational design. He spent 13 years as a broadcast journalist with the Australian Broadcasting Corporation (ABC) before moving into HR consulting. Key Topics Why the traditional "pyramid" org structure (many entry-level roles, fewer managers, fewer executives) is being challenged by a "diamond" shape with a shrinking entry pointThe shift from specialist workers to AI-complemented generalists, and what it means for how organisations build skills over timeWhy Neal believes many AI-attributed layoffs are actually "AI washing," with financial or market pressures the real driverGartner's projection that AI becomes a net job creator by 2030, and what the next four years of labour market disruption might look likeNew roles emerging directly from AI adoption, including AI governance and AI ethics functionsThree real-world governance models: Lloyd's Bank's "control tower" approach, ServiceNow's AI council with supporting pods, and Red Hat's three-tier framework based on degree of AI impactThe emerging practice of placing AI agents on the org chart, and the open questions around ownership and accountability when things go wrongDifferences in AI adoption pace between the US, UK and Australia, and why Neal doesn't think Australia's "behind" position is cause for panicHow organisations can communicate AI rollouts to reduce the roughly 40% of the workforce that reacts negatively to major change Resources & Links People Mentioned Rick Beato – YouTube music educator and producer Neal cited as someone whose commentary on the shift from big studios to home recording has shaped his thinking about the AI "bubble" debate Companies & Tools Gartner – Research and advisory firm where Neal works; source of the org-structure and net-job-creator research discussed throughoutRamp – Fintech company referenced for its hiring trends report, which found companies adopting AI kept similar junior hiring rates to before AILloyd's Bank – UK bank cited for its "control tower" approach to AI governanceServiceNow – HR technology company cited for its two-tier AI council and pod governance modelRed Hat – Open-source company cited for its three-tier framework grouping roles by degree of AI impactIBM – Red Hat's parent company, referenced regarding a recent stock drop tied to AI strategy commentaryModerna – Referenced for merging its IT and HR functions in recognition that AI is a human experience as much as a technology one Contact & Credits Host: Shahin Hoda Guest: Neal Woolrich Produced by:  Shahin Hoda and Alexander Hipwell Edited by: Alexander Hipwell & Dave Somido Music by: Breakmaster Cylinder Podcast Co-ordinator: Jonah Igsie APAC's B2B Growth Podcast is presented by xGrowth

According to Gartner, worldwide AI spending is forecasted to increase 44% by the end of 2026. Companies are investing in AI, and they are investing heavily. But knowing where and how to invest isn’t easy, especially with what feels like a million different AI tools out there and a million more different ways to build your own. So how do you figure out what to build, what to buy, and which investments will help you move the needle for your business? Riley Rogers: Hi, and welcome to the Win/Win Podcast. I’m your host, Riley Rogers. Join us as we dive into changing trends in the workplace and how to navigate them successfully. Here to discuss this topic is Cody Sims, head of commercial brand at Cox Communications. Cody, thank you so much for joining us today. Super excited to hear your thoughts on this one. Before we dive into what is quite a loaded topic, could you tell us a little bit about yourself, your background, and your role? Cody Sims: Yeah. So, hi, I’m Cody. I’m the head of commercial brand for Cox Communications, and it’s kind of crazy how I came into this role. So I actually started my career when I was 15 and was installing phone systems for my dad’s phone company. And after that, I had actually had two parts of what I thought was what I wanted to go into, and that was either musical theater or physics, because those were two things I really had a passion about. And when I got into college and had musical theater as my major and physics as my fallback, I realized that both of them left a part out of what I really enjoy. And so I ended up actually landing in marketing because it’s both analytical and creative, and that has served me really well over the years. So, across Cox, I have done all kinds of things from product management to market development to pricing to competitive analysis, and now in the brand world. It’s given me kind of a 360 view of the entire business from a marketing lens. I would say that I’m pretty much a transformation leader. I really enjoy breaking things and building them up new again. So, AI is happening right at the right time for me. RR: I love that story, and I love that it’s taking you to a place that especially now is getting more and more technical, more and more analytical. I’m very excited to get into that transformation leader side of things. But before we do, can you paint a little bit of a picture of your sales environment? CS: Yeah. So when I first came to Cox, it was very similar to most of what you would call a CLEC, or a competitive local exchange carrier, which is primarily internet service, voice services, and obviously because it was Cox, some cable TV services that were the triple threat. That was kind of what in the early 2000s was kind of the way that they went to market. But over time, the team at Cox realized that in order to stay competitive, they had to add to the portfolio to make sure that they were providing value to their customers, and I’m sure many would understand that and have gone through similar transformations. And so we had acquired several different other companies that added to our portfolio, and developed some of our own products, and over time that turned into a lot of products. But it’s not just 70 products. It’s 70 products, it’s nine customer segments that we have from a segmentation perspective. It’s six distinct buyer personas, industry verticals, what’s serviceable at that address. So you take all of these different components and it’s almost like three-dimensional chess for the salesperson. The way that I like to think about it is that the sellers, what they really need and what their challenge is, is that they’re not looking for specs. They’re looking for what are the business outcomes that my customer is trying to achieve, and then what do I have from my portfolio that will help them to achieve those results? So it’s no longer a world where they can memorize everything and know every product in and out, and be the technical expert. They really do have to have tools and systems that help them to have the right knowledge at the right moment for the right person in the right place. RR: Yeah, there comes a point when the human brain just can’t contain the context and the expertise that you need. So when you can’t ask for expertise, what you can do is provide, to your point, that just-in-time support. And one of the things that you alluded to was that that’s where you kind of started some of that AI investment as a way to bridge that gap. You’ve given us a little bit of a taste, but what kind of motivated that early initiative? CS: Well, I would say that, over time what we discovered was that we couldn’t keep track of all of our marketing materials, collateral, all of the pieces of information in just files, formats, and putting it online into a here’s-an-accessible-library. Because the library just becomes bigger and larger and more difficult to manage. But I would say that we didn’t set out to do AI. We didn’t sit down and say, “Oh, hey, AI looks cool. Let’s make sure we’re doing it.” We needed to transform our go-to-market strategy so that we were more nimble, we were more competitive, and that we could deliver the kind of experience that our customers were asking for. And so AI was the mechanism that would help us to get there. But what really triggered this whole thing was what I mentioned earlier, was our segmentation. When we sat down and said, “Let’s rebuild the way that we look at our audience segments,” and we did that based off of what is the value to Cox of each of these customer profiles, and then what is the technology sophistication of that client, of that business. And that intersection allowed us to create our nine different segments that we were working on. And so when we did the math, when we looked at all of that information and all of the things that we needed to be able to provide to those segments, we realized this was quickly going to turn into something that was far beyond any marketer’s ability to do. But what we knew is that the Gartner information we were tracking said that personalization was having much higher returns on the way that people respond to information. And not only that, but if you do personalization and you get it wrong, if I call you and I say, instead of, “Hey, Riley,” and I say, “Hey, Jonah,” you’re like, “Hmm, nice try.” So personalization is really important to being successful, but getting it right is even more important. So we realized that we needed to have some radical partnership between our marketing, AI, and sales teams, that we needed to make sure that this was not just an IT project, that we were going to go and pull a bunch of requirements together and everybody would be like, “Oh, hey, here’s this new tool. Everybody figure out how to use it.” And it wasn’t necessarily about optimization. It was about transformation, the way that we go to market, the way we think about our customers, and the way we show up. So I would say that AI definitely was part of the solution set, but we had to look ourselves in the mirror and say, “It’s time for us to actually think about this in a completely different way.” RR: That distinction comes through very well, and I think is very important because oftentimes when you’re in kind of the scramble to be keeping up with the market, keeping up with your competitors, there is this urge to just tack on AI because we have to. But when it’s not strategic and it’s not built into the things that you’re actually doing, to your point, it’s we put together some specs, good luck using it. But instead, now it’s something that’s really built into the way that you work. I would love to hear a little bit more about that specific use case, especially given the fact that a lot of teams are running into that question of how do we use AI and can we just build what we need ourselves? Given that you’ve done the math, answered the question, I’d love to hear how it worked and kind of where you landed. CS: It’s very easy to fall into the trap of, “Hey, everybody, here’s AI. We put it on your computers, now go use it.” And so then everybody starts using AI to try to figure out, how does this help me in the job that I already do, in the role that I already do, in the processes that I already do? And so then it really limits the impact that it can have on the business and the performance because either, A, you have a handful of people who are really smart, and they go crazy with it, and they create their own thing, or you have a bunch of people who are looking at it going, “Okay, came up with some ideas, but I still have to do my work.” What you end up with is there’s no standard. There’s no flag running up the hill to say, “Everybody follow me. Let’s go do it this way.” So, it required both the yes, we had to make sure that the teams were bought into using AI, but we also had to have a standardized way of approaching how we deploy AI. And that brought us to the question of do we buy or do we build? And because there were so many different parts of what kinds of functionality we needed, it wasn’t the same answer for every one of those needs. So in some cases, we have a tool, we have a partner, they already have AI integrated into their platform, let’s go see how we can use that. In other cases, and I’ll give you an example, in the case of content generation, that is where we started with our AI journey about a year ago. We sat down and started interviewing and reviewing all of the different providers who can do content generation. Every one of them had a different approach to content development, content generation, which were all very good, and they attempt to make sure that they are covering as much of the marketplace as possible. And so sometimes when you buy that, you end up with features maybe that you don’t need, and you also have to still go through the process of integrating those platforms into your security posture. So us being a connectivity provider for governments, for major corporations, enterprise carrier grade, we have a very, very strict and strong security policy, which means that when we bring new vendors on, it takes a lot of time and a lot of effort and a lot of back and forth. And so what we found in certain cases, it was actually better for us and more beneficial for us to build the actual platforms that we needed for that particular use case. But like I said before, in other situations, we found that there was a partner who we had who already had AI integrated into their platform, and so they were already part of our security posture. They were already inside of our ecosystem. So the question of build versus buy really had to do with time, had to do with return, and it had to do with the security measures that we had to put in place. RR: Thinking about in addition to those factors, when you’re evaluating these things that you outlined, time, potential cost, security, how are you kind of doing that ROI math to say one is going to be better than the other? CS: There’s several different parts of that. And like I mentioned, we wanted to make sure that we were following our AI strategy foundation that said, we don’t want to introduce more and more vulnerable access points. And so it’s important for us to make sure that we are all coming together with everyone across the Cox leadership team according to who are the vendors that we feel the safest with, that we can go set up and make sure that we are pulling together the best of the breeds. The assessment, like I mentioned before, is what is the value that we’re returning to the business in terms of revenue generation, new customers, cost savings in terms of not necessarily just reducing people’s time, but redeploying people to doing other important tasks. And then what are the things that we are doing that help us to keep the system all working together? So, revenue generation, cost deferment, and then keeping a cohesive connection between all of the different platforms. So some of the things that we looked at from our comparing vendors versus doing DIY, is there a maintenance tail that goes in this? So if we build it, what does that look like in 18 months? How much more people do we have to have to support it? Governance and observability, do we have the permissions, the versioning, the audit trail, all of the parts for discovering what is needed and then able to see it and observe it as we go? Interoperability, as I mentioned before, really important between different platforms that we have, that those APIs and MCPs all work together. And then whose roadmap is this? Is this our roadmap? Is this the IT roadmap? Is this the vendor’s roadmap? If we know where we need to go, is there anything that’s getting in our way of being able to get there? And then of course, obviously the speed to value against the cost of being wrong. RR: And so hearing you outline this very comprehensive list of considerations, you can start to understand why it starts to feel complicated and really hard to tackle. To your point, it’s been a year of figuring it out since you started developing that very first use case. I’d like to go into a little bit of detail about the evaluation piece and deciding what vendors you felt safe with, that you were excited to partner with and continue to either use or build upon as you’re developing your AI strategy in alignment with your business transformation. One of those that you landed on was using Highspot’s MCP server to support some of the workflows you wanted to spin up. How did you make that decision and why did that feel like the way to go? CS: Well, as I had mentioned before, as we had gone through our history of, here’s a library of a whole bunch of stuff and everybody’s trying to find the right item, and it just was such a headache to make sure that we were always getting the right information to the right customers at the right time. And not only that, but we had no real clear feedback about how it was performing. And so at that time, which I believe was in the 2015 to 2017 timeframe, is when we had first started our relationship with Highspot to help us better catalog the library, make it more searchable and usable and referenceable for the sellers to be able to share information and track the information, make sure that it was the most relevant and recent, and then help us to understand what’s working and not working. So all of that was already in place before we even started the AI conversation. And so as we were doing our work around our go-to-market roadmap, we started with content because it was probably the easiest place for us to use AI to generate content, and that looked like a two-layered approach. We had what we called a knowledge base, which is formally putting into AI rules that can be read by AI around all of our standards for brand, for legal, for segment definition, for product information, for pricing and promotion information, industries, verticals. All of that was put at this knowledge base foundation layer. And then we built the content generation engine on top of that, where each of the agents within that tool would go find what it is that the marketer was asking to do, compare it against all the information in the knowledge base, the brand standards, all of those good things, and then produce the content piece that the marketer was asking for using that foundation layer. However, once we got that moving and going, we realized that that level of personalization for marketing could be even more valuable and even more specific when used by a seller. But in order for that to work properly, the seller had to have access to a large range of information all at the same time, including any of the buying signals or online signals that we had through some of our lead generation partners, any of our information that we have within our own systems, like when was the last time they called into billing or when was the last time that they had an outage or what is their general sentiment that the customer has right now. And then all of the information about their current services, their current products, all the things that are going on in their world. But then once we have all of that information, we have propensity to buy, propensity to churn, propensity all these modeling, now we need to be able to talk to them and provide a recommendation to the seller that says, “Here’s what we recommend you use, what you should say, how you should set it up.” And all of that was inside of Highspot. And so we realized again, we could look at this and say, “Are we going to go buy a new platform? Are we going to use a platform we already have or are we going to go build something new?” And obviously when we looked at the Highspot platform, the MCP servers, and the way that it was laid out and set up already, we knew that that was the right path to go. So what we had started with was the content engine, then we went into a sales enablement engine, and as part of that sales enablement engine, the only way for it to work properly was for us to bring in the Highspot MCP service. RR: And how has that been working so far for your sellers? As you’ve rolled this out, how has it been used? Any anecdotal feedback you’ve heard? CS: It’s pretty funny because we have done multiple rollouts of sales enablement platforms over the years, and as anyone who’s ever tried to roll out new sales items and new sales tools will say, it takes time, it takes consistency, messaging over and over. But in this particular case, when we went out and did our roadshow with all of the sellers and sat down and showed them how the new tool worked, there were so many positive responses, and the adoption was much faster than most of our previous releases of other types of products. And I think that the reason why is because it was bringing together all of those pieces of information that I mentioned before and bringing in the Highspot information that they were already very familiar with. And in our world, we call it the sales asset manager, SAM. And so they were very familiar with SAM and then this new tool with the AI capabilities built into it. Now it’s specifically just telling them, “Here’s what you should do. Here’s the way to lay it out, and here’s all the content to talk to the customer about in what order.” And it took a lot of the burden off of them to research, go find a piece, start to build a story in their head, try to build a deck, and then think about what are they going to share with them in what order. So it’s been a huge benefit to the sellers. They’ve loved it. RR: Yeah, that’s such a strong signal when adoption doesn’t feel like a push and more of a grab. Curious if there are any other AI or agentic connectors that you’re pairing with Highspot in another AI application that you think would be interesting to share? CS: We have basically six different programs or parts of our roadmap, and we’re calling them AI modules, and then they work together in different components for different functions that need to be done. So as I mentioned, we have the knowledge base that is the base. Then we have the content creation tool, which we call CAMI. So it’s Content Automation Marketing Intelligence, and that has everything that is needed to produce and create new pieces of content, and then those content pieces are either generated in emails or things like that. A lot of them actually are put into the Highspot tool. And then we have what we call SAMI, which is the Sales Automation Marketing Intelligence, and that is the tool that integrates directly with Highspot to make the recommendations to the seller based off of all of the other information, the 360 view of the customer. We also have what’s called Livia, which is the Lead Validation and Enrichment. The tool uses all of these multiple different access points and different vendors to pull information about that particular contact to validate that it’s accurate, so that by the time it gets to the seller and they’re going to go do a pitch, they have a lot more confidence that who they’re talking to, the business, and it’s at the right address, and prevents them from wasting time. And then, of course, Highspot is such a critical part of how that story all comes together because it’s capturing all the content that’s being created by CAMI, and then the AI that comes from Highspot is infusing into the SAMI tool that the sellers are using. It’s an interesting thing because somebody might say, “Well, you’re not really using Highspot, you’re using SAMI.” And the reality is, well, yes, I am using Highspot because Highspot is feeding all of that into the SAMI tool. There’s a whole bunch of other stuff we add into that for flavoring, all of the information about the customer so that the seller has a 360 view, but that just sets it up. The what do you do next is what’s coming out of Highspot. The next phase of this that we’re going to is a fully agentic approach to our marketing and sales engine. And what that means is that today, most of the work that’s being done is a marketer who is saying, “Here’s what I need to go get done. I’m going to use AI to help me go do it.” We’re going to flip that script, and we’re going to say, the agents that we create are going to do the work, and the marketers are going to instruct the agents on how to do that work properly and watch it and govern it. That will then accelerate for the sellers as well. RR: We’ve heard a little bit about what’s been built in the last year, but it’s, again, to your point, crazy that that’s one year of building, thinking, strategizing, and it’s come to this point. When you look across all of that, what has changed for your sellers and for the business? CS: Well, I would say the first thing is, is that sellers are now able to focus on what they’re really good at. What I mean by that is their confidence is shifted to focus on outcomes and value. They are now able to build trust and provide value, which is honestly what all of our customers, especially our business owners and decision makers are looking for. And then for the marketers, it’s no longer about building a queue, trying to figure out what is the message that’s going to hit the most people with the most response. This idea of efficiency for media or efficiency for marketing materials. It’s like, what is the one message I can send to a million people and have the most response? Well, now you actually can flip that on its ear and say, “I’m going to personalize it at scale.” So that is super exciting. And then the last thing that I would say is that consistency became structural. The same knowledge base, the same rules across every surface, making sure that our content is clean, correct, built on the same policies and rules, but is personalized. Doing those two things at the same time is very tricky, and being able to do it with AI is the only way we could get there. RR: Curious if you’ve seen any sort of measurable returns. CS: Our lead accuracy, like I mentioned before, moving from that 13 to 18% all the way up to the 95th percentile. We have campaign speed to market of improvement of 55%, meaning the amount of time that it takes us to get to market is cut in half. The marketing content teams are 40% more productive, which means they’ve been able to redeploy their time for 40% of the time that they spend at work on other projects, which is amazing. Our conversion rates are up, our driving net new revenue is up, and we have seen material improvement in click-through rates and conversion rates when we are more specific and personalized to the audience. So Gartner was right. Yay. So that’s been really good. And I would say that part of the reason why I think that, at least for part of what we did, doing it as a build ourselves was wise, is because we learned so much by going through the process of just banging our shins on the corners and running into cabinet doors that were open, and we’re just like, “Oh, wow, that was, I did not see that.” So it’s been a huge learning process, a very, very intense learning process, but we’ve all had a really good sense of humor and amusement and just, we are having a ton of fun. RR: And I think that’s one of the more encouraging things to hear. Is that nobody starts perfect, and you just have to build your way up to good. And once you get there, you start to see again, like those measurable improvements. But it is a process. So I guess the message there is stick with it. Which I think kind of feeds into that last question I have for you, which is for anybody who is running into this question, hitting their shins on all of these problems, how would you recommend they approach the question of building, buying, blending some things together when they’re thinking about their AI investments? CS: Well, I would say the first thing is you have to look in the mirror and be real with yourself and say, “Is my processes and workflows working? If I blew up my entire go-to-market, I blew up all my processes, what would it look like?” And don’t start with a tool. Start from a place of what would serve me best. The other part of it that I would say that Highspot did really well is because of the MCP product, I was able to look at it as how am I using this from a plumbing perspective, not just a judgment perspective. And what that means is that it worked well with the strategy and the AI strict rules that we had built for ourselves. Highspot, kudos to Highspot, built a platform that is trusted and that works well with all of the other components that we had flying around, whether it was Salesforce or AWS or even our Accenture development team being able to use the components and pieces to connect to the whole ecosystem. Then the other thing I would say is that even though we’ve been doing this for a year, a year is like eons in AI’s time. It was every other week there was something that changed, something new, something shifted. So you have to go into it with this idea of you have to prepare yourself that this is how I set it up now, but I might have to change it tomorrow, and just be okay with that. So my answer for build or buy, my answer is both. Build the things that make sense for you and where you have the resources and when it’s the right fit. But definitely buy when you are in a partnership or when you have someone that you already know that you can trust. RR: Very pragmatic. That’s kind of the only way to do it. One thing I’ll say, I know I am walking away inspired, and I can imagine our audience is going to as well. So Cody, thank you for the time. I really, really appreciate it. It’s been so wonderful to hear a little bit more about what you’re building. CS: No, I love it. And the reason why this is great for me is that it forces me to think back on this journey that we’ve been on for the last year and really consider what is it that has brought us to where we are, what are the things we’ve learned, and then, maybe how are my bruises doing? RR: Well, thank you for the time again. And to our audience, thank you for listening to this episode of the Win/Win Podcast. Be sure to tune in next time for more insights on how you can maximize go-to-market success with Highspot.

KI im Vertrieb: Vom Verwalten zurück ins Verkaufen. In den meisten Vertrieben verbringen Verkäufer nur 10 bis 20 Prozent ihrer Zeit beim Kunden – der Rest ist Verwaltung. Christopher Funk zeigt, wie KI das Verhältnis umdreht: aus 300 aktiven Verkaufsstunden werden 1.200. Die Verfünffachung deiner Vertriebsschlagkraft, ohne einen einzigen neuen Verkäufer. McKinsey beziffert das KI-Automatisierungspotenzial für Deutschland auf 486 Milliarden Dollar bis 2030 – das größte in Europa, 80 Prozent davon über KI-Agenten. 59 Prozent der heutigen Arbeitsstunden sind mit existierender Technologie automatisierbar. Die Frage ist nicht mehr, ob das kommt – sondern ob du zu den Mutigen gehörst, die es nutzen. Du erfährst, wie du mit KI Zielkunden findest und priorisierst, Calls vorbereitest, Gespräche dokumentierst, Angebote aus Wissensbasis und Transkript erstellst, deine Pipeline ehrlicher machst und auf Datenbasis coachst. Plus: die Agenten-Stufe mit Angebots-, Nachfass- und Vorqualifizierungsagent – und warum Gartner erwartet, dass bis 2028 rund 90 Prozent der B2B-Einkäufe über KI-Agenten laufen.

De Casablanca aux Ardennes, Intelcia a fait de la France un marché clé de son développement. Le géant marocain de l'externalisation y emploie aujourd'hui près de 3 500 personnes et mise sur l'intelligence artificielle pour accompagner, plutôt que remplacer, ses salariés. Avec 800 millions d'euros de chiffre d'affaires et une présence dans une vingtaine de pays, Intelcia est aujourd'hui l'un des leaders mondiaux de l'outsourcing, l'externalisation de services comme la relation client ou la gestion d'infrastructures informatiques. Après dix ans sous le contrôle du groupe français Altice, l'entreprise est revenue entre les mains de ses dirigeants historiques marocains. « Aujourd'hui, 100 % de l'entreprise est détenue par ses managers », affirme Karim Bernoussi, le directeur général. Créée à Casablanca par des ingénieurs marocains, Intelcia s'adressait d'abord au marché français depuis ses centres d'appels installés au Maroc. Mais pour poursuivre sa croissance, le groupe décide en 2011 de s'implanter directement en France. « On s'est dit qu'il fallait être proche des clients français, explique Karim Bernoussi, on ne pouvait pas faire que de l'offshore, on ne pouvait pas produire pour ses clients qu'au Maroc ou en Afrique subsaharienne, il fallait être présent sur le marché. En 2011, on a eu l'opportunité de faire l'acquisition d'un acteur qui avait à peu près 1 000 collaborateurs sur quatre sites en France. Et ça nous a permis d'un coup, en 2011, d'avoir une vraie présence en France. » Aujourd'hui, Intelcia emploie près de 3 500 personnes dans l'Hexagone. Le groupe travaille aussi bien pour des administrations, comme France Travail, que pour de grandes entreprises privées. Il est notamment le premier employeur du département des Ardennes. « L'avenir se fait avec un combo IA et humain » L'an dernier, Intelcia a toutefois été visée par des accusations sur les conditions de travail de certains salariés. Une question écrite à l'Assemblée nationale évoquait notamment « des humiliations quotidiennes, des convocations publiques » et « une précarité organisée ». Des critiques rejetées par Karim Bernoussi : « On est dans un secteur dans lequel, si vous ne traitez pas vos salariés avec transparence, si vous ne mettez pas en place un climat de confiance, vous ne pouvez pas performer puisque notre première richesse, ce sont nos employés. Nous avons un regard très important sur la manière dont nos salariés sont employés, dont ils sont traités et sur la façon dont on les fait grandir. » L'autre défi du secteur est désormais l'essor de l'intelligence artificielle, qui transforme rapidement le fonctionnement des centres d'appels. Pour Karim Bernoussi, cette évolution ne remet pas en cause les emplois en France. « L'avenir se fait avec un combo IA et humain. Aujourd'hui, l'IA va permettre à nos agents de travailler de manière beaucoup plus efficace. Lorsque vous demandez un nouveau code pour une carte de crédit, vous n'avez pas besoin d'un humain pour vous le donner. En revanche, tout contact avec un client est une opportunité pour les marques de faire passer un message ou de réaliser des ventes. Et ça, le meilleur moyen de le faire, c'est avec l'humain. » Selon une étude du cabinet américain Gartner, près d'un tiers des responsables de services clients ont déjà engagé ou prévoient des réductions d'effectifs liées à l'intelligence artificielle d'ici l'année prochaine. L'Organisation internationale du travail estime toutefois que la transformation des tâches est plus probable que la disparition complète des emplois. À lire aussiCes entreprises africaines qui investissent en France: Creativo El Matador, le pari d'un transformateur nigérian [2/5]

Neste episódio do Vamos de Vendas, Gustavo Pagotto recebe Ricardo Gottschalk, cofundador e CRO da Conta Simples, para uma conversa sobre o futuro das vendas em um mundo cada vez mais impulsionado por inteligência artificial, automação e modelos Product-Led Growth (PLG).Ao longo do episódio, Ricardo compartilha a trajetória da Conta Simples, desde um modelo totalmente self-service até a construção de uma operação comercial estruturada para atender empresas maiores. Ele explica por que, mesmo em negócios altamente digitais, existe um momento em que a atuação humana se torna essencial para acelerar adoção, aumentar conversão e gerar mais valor para os clientes.

According to research by Gartner, by 2029, sales organizations with AI-driven enablement functions will achieve 40% faster sales stage velocity than those using more traditional approaches. But getting there means building the kind of workflows that tell you what actually works, which is only possible when you can see inside the conversations that selling actually happens in. If you can’t, you might just find yourself scaling an AI-powered program that’s completely misaligned with how your sellers sell and how your buyers buy. So how do you understand what your teams need, and then build an innovative, mature, AI-powered enablement program to match? Riley Rogers: Welcome to the Win/Win Podcast. I’m your host, Riley Rogers. Join us as we dive into changing trends in the workplace and how to navigate them successfully. Here to discuss this topic is Jill Sawyer, Director of Sales Enablement at RL Datix. Thank you so much for joining us today, Jill. I’m really excited to have you here. I think we’ve got a very fun conversation ahead. Could you give us a little bit of an overview of who you are, what your background is, and your work today? Jill Sawyer: Yeah, absolutely. First, thanks so much for having me. I’m super excited to be here. Currently, I sit as the Director of Sales Enablement for North America at RL Datix. I’ve been here for a little over seven and a half years. JS: My background is a bit non-traditional for sales enablement. I started more on the marketing and product marketing side. I moved into the enablement role a few years ago, a little bit over two and a half years ago, but I was really stepping into a function that needed to be built from the ground up. That meant figuring out not just what sellers needed to know, but how to help them actually use it in moments that matter. I’ve also recently gone back to school. I’m currently pursuing my Master’s of Jurisprudence in Health Law at Loyola University Chicago, which has been really valuable in this role. Our customers operate in a highly regulated healthcare environment, so understanding more about compliance, patient safety, risk, and legal frameworks helps me think differently about what our sellers need to understand and how we support them. RR: And you wear so many hats through it all. I’m really excited to see how all of this comes into play in your work today. But before we jump into what you’re working on now, could you give us a little bit of an overview of your sales environment? JS: Yeah. So the way I usually describe RL Datix is that we sit at an intersection of healthcare safety, risk compliance, and operational reliability. Our customers are health systems and hospitals that are trying to solve problems with very real stakes. They’re trying to either prevent harm, respond appropriately when something does go wrong, or maintain regulatory readiness. So it makes our sales environment really complex because those problems rarely belong to one department or one singular buyer. The buying committee can be really broad, and each stakeholder is looking at the problem through a different lens. So for our sellers, the challenge is being able to zoom in and zoom out. They need to understand specific workflows, not only within our product, but within the landscape. But they also need to connect that workflow to the larger operational and strategic priorities of our customers. This is where enablement for us becomes really important. I’m helping translate complex healthcare problems into clear, relevant conversations about value and outcomes and how RL Datix helps raise the standard of care. RR: So a lot for sellers to juggle, and a lot for you to help them juggle, especially because you built the enablement function largely from scratch. While doing so, you were learning what sales enablement as a concept is, what the function should look like, and how you should tailor it to your specific environment. So how did coming in without a more traditional enablement background change the way you thought about what to build, and was that kind of an advantage? JS: Yeah, so I think coming in without a traditional enablement background shaped our function in a really positive way. Since I’ve come from marketing and product marketing, I was always really thinking about our audience, message, positioning, and customer value. But when I moved over into enablement, I quickly realized that having the right message or right content was only part of the equation. I needed to help our field know how to use it in real customer conversations. I really came in thinking about what my sales reps actually need to be more confident, more consistent, and ultimately more effective. Of course, sellers needed content, but they also needed guidance, learning, reinforcement. Our leaders needed coaching and better visibility into what was working and what wasn’t. So in some ways, I approached enablement like building a product. My sellers were the users, the sales process was our workflow, and it was my job to understand their friction points and build them something that made it easier to have stronger customer conversations. RR: I love that. It’s enablement, but make it product marketing. You listed out some things that were important for sellers and things that were important for your leaders, and all of this you had to build from scratch. So as you were looking across what needed to happen, what your first few check boxes on the to-do list were, a key first step was assessing what you had and what you needed. How had RL Datix been supporting its sellers, and where did you start to see those first few gaps? JS: Yeah. When I first stepped into the role, we had a lot of strong subject matter expertise and a ton of people supporting the field in a variety of different ways. The issue wasn’t that our support structure didn’t exist, it was that it wasn’t really connected or, quite frankly, easy to scale. I had a lot of content, but it lived in a variety of different places. We had trainings, but much of it was event-based or dependent on live sessions and then going back and watching re-recordings. I needed to make sure my sellers were hearing the same message and had access to the same guidance on how we wanted our customers to hear about RL Datix and our offerings. And I needed visibility, not only for enablement, but for collaborative teams like product marketing and product, into what was actually being used, what was resonating with our customers, and where we still needed to support the field. So my final assessment really shaped the foundation that we built. It was truly creating what I call that connected enablement environment that could support sellers in the flow of their day-to-day work. RR: Yeah, and I think a recurring theme through all of this is that there were levers to pull, content to create, training to centralize, all of these things, but that wasn’t the end goal. The end goal was that when a seller hits the field, when a seller is on a Zoom call, they’re saying the right things, and they’re not just reading off bullets, they’re tailoring it because they know it and they know it well. So you’re looking at what you need to do. You have your content, you’re beginning to get it centralized and organized, your training is in place, you have data on what reps are using, you’re building. But you still can’t see inside the conversations that sellers are having to understand whether all of that work is, for lack of a better word, working. So what challenges did that lack of visibility create, and why did you start thinking that maybe a conversational intelligence tool was that next step forward? JS: Yeah. So when we brought Highspot in, we truly started with a baseline foundation. Before I could do anything more advanced, I needed to, step one, make it easier for sellers to just find what they needed and understand how to use it. It may sound really basic, but it was a huge step forward because it helped us connect content to the way sellers were actually selling. It also gave us a huge level of visibility. I could start to see what reps were using and what they were not using, to identify those true gaps. But the missing piece was actually our customer conversations. I could easily know if a rep opened a deck or completed a course, but I still didn’t know how that translated when they were in front of a customer. Were they asking strong discovery questions? Were they connecting the message to the customer’s priorities? How were we handling objections? What were we hearing? Without that level of visibility, there was still a lot of guesswork. Enablement, product marketing, product, and sales leaders were all trying to support the field, but a lot of times we were relying on anecdotal feedback or even secondhand information. Conversational intelligence really helped close that gap, and that foundation is really what made the next phase possible. Once we had the content, the training, and guidance all in one spot, I could start to think about how to layer other pieces in, like coaching, conversational intelligence, and eventually deal-level insights on top of it. RR: When you first had that line of sight into the field and what they were actually coming up against, was there anything that you didn’t expect that came through? Things that sellers were either saying that you were like, “Oh my gosh, have you been saying this for the last umpteen months?” Or that you had no idea they were running into that was a problem. You were like, “Oh, we should’ve been helping with this,” or, “We could be enabling differently.” JS: Yeah. I think for us, what it really helped open up was just the level of content that I needed. We had really great high-level intro, top-of-funnel materials, and then I would jump very quickly into technical documents. So I needed to be able to, like you said earlier, customize, and that was a gap that became pretty quickly consistent, that I needed to bring a middle-of-the-road option to help ultimately fast-track opportunities to close. It’s something that we’re still actively working through today. RR: Once you had that line of sight, not only into real-world conversations, but the analytics into usage that we’re talking about, what difference did that make for you and other cross-functional teams? We have this example of middle-of-the-funnel content, realizing this is a necessity and maybe we aren’t serving it. But were there any other moments that made it very clear this was helpful? JS: Yeah. It just gives me, and our whole company honestly, a much better shared understanding of what actually is happening in the field. From an enablement standpoint, it helps us see where reps were confident and where they needed support. Were they asking discovery questions? Were they connecting the message to customer priorities? It helped us figure out if there were gaps and ultimately skills I needed to help upskill. For our leaders, it gives them more specific coaching moments. So instead of coaching from memory or only from a pipeline update, they have the ability to coach from real customer interactions without having to sit in live on 50 calls a week. It also helped open up a level of visibility across cross-functional teams. Product marketing can come in and review whether messaging is landing, whether the decking is being used. They also get a great depth of understanding around customer questions, objections, and needs, more directly. So I’m not relying only on select feedback or the loudest feedback in the room. I use real customer conversation to keep improving our full enablement environment. RR: And as you alluded to, you’re beginning to use Deal Agent to capture deal-level insights and start to act on them. So how do you see it adding value to the environment you’ve already built and the environment you’ve already got your sellers comfortable using? JS: Yeah, Deal Agent is something we’re really excited about. We just launched it and had a full all-sales meeting with our Highspot customer success team, and it’s really that next layer of a foundation we’ve already built. We started with getting content and training all in one place. Now we’re layering in conversational intelligence, but Deal Agent is going to take that a step further for us by connecting the conversations and insights we’re gathering back to the opportunity level. This is really important for us because our sellers and our leaders don’t just need to know what happened on a call, they need to understand what that ultimately means for the deal, for them to close business. Are they talking to the right stakeholders? Are priorities clear? Do we have risks we need to address? For our sales reps, I think the value is that it can reduce some of the manual work and help them stay focused on the right actions. For leaders, it gives clear visibility into deal health and where they may need to coach. For the rest of the organization, it gives us another level of insight, so I can look to see what patterns are building across opportunities. The AI agents built into it are amazing too, because they’re helping eliminate some admin burden by building Digital Rooms faster, with the content needed at that moment for that deal. RR: And I think what’s kind of interesting is that this is a vision you see, and you’re like, “I see so much value in this. I see what the potential is.” But it’s so new, and it’s such a new way of working for literally everybody. You’re not trying to roll out a program you’ve seen elsewhere, you’re on the cutting edge. So how have you been tackling things like the rollout, the behavior change, getting folks accustomed to this very new way of working? JS: Yeah. So for us, it was big. We didn’t really have any tools like this in place already. The biggest thing we’ve learned is that you cannot roll it out as just another tool. Sellers already have a lot of tools, and if something feels disconnected from their day-to-day workflow, adoption is going to be really difficult. So we tried to anchor the rollout in practical use cases. How does it help a rep prepare for a meeting? Does it help them follow up faster, remember what the customer actually said? For our sales leaders, we’re focusing now on that next phase of coaching and visibility, helping them find a better way to understand what’s happening in their field and coach their teams appropriately. But in all honesty, there is a bit of change management. It’s a different way of working for some reps. Some reps were super excited and dove in headfirst, and others really needed to see the value a few times before it clicked and became muscle memory. So I try to keep that adoption path clear and practical. I’m not asking people to change everything overnight. We’re helping them build small habits: review call summaries, look at action items, use that deal-level information to pressure test next steps, use Digital Rooms or Pitches to send content and see what customers are engaging with. The key for us was making it feel useful before making it feel mandatory. Once people saw that it could save them time and improve their follow-up, it became less intimidating, and after a certain period of time, I was actually able to pull down any old resources they may have had a habit of going back to. RR: And since you’ve gone live with both Highspot and Deal Agent, what have you heard from the field on how they’re responding to it? JS: Yeah, so anecdotally we’ve heard that being able to get those next action items and the call takeaway was a really big piece. Being able to more easily share back the call recording also saved some steps from how they used to do it before. Deal Agent is still relatively new for us, but I think where we’re honing in is the ability to slice data in different ways, by our sales forecasting, by our sales stages, and then really pairing that with a lot of the intelligence and reporting we already have in Salesforce. That gives us and our leadership a much deeper understanding of what’s truly happening and where leaders need to step in. RR: So to your point of don’t boil the ocean, there are a handful of use cases that your sellers like, that you like, that your leaders like, that are slowly building the muscle. And over time, things might change, new use cases might emerge, but as of right now, there are a few recurring things that it sounds like people really are finding useful. Super cool, I love that. And you touched on this a little bit, but I’d love to hear a little bit more about how this is helping you solve for some of the guesswork you were feeling. We talked about building without knowing what was being used by the field and what was landing. So now that you have that combination of conversation, deal data, and usage data, how does that change what you and other teams are building and providing to your reps? JS: Yeah. It really helped us move from a state of assumption to being evidence-backed. Before, I had a lot of smart people supporting the field, but we would build materials based on individual feedback or something needed for one deal, and assume it was scalable across all deals. It’s not necessarily a wrong way to do it, but it’s an incomplete way to do it. Now that I’ve got conversations and deal data all in one spot, I can start to pull out patterns. Where can I look to see what customers are actually asking? Where can I look to see what customers are actually engaging with? Is it the same consistent objection that keeps coming up, and are we answering it in the right way? For my team, it means we’re much more targeted. Maybe the answer is a new training. Maybe it’s a better talk track or coaching around certain areas of the sales process. It could be working with product marketing to create a new Sales Play, or a clearer way to help position our value. The biggest change is that we’re actively, every day, listening, learning, and adjusting based on what’s actually happening out in the field. RR: I love that. And I think that middle-of-the-funnel example is such a good example of how you can stitch all that information together and recognize that there’s a component of your strategy you can really pull on that might be particularly valuable. So I love that we have that, not only the high level of what it’s doing, but how it comes into play in the actual work happening. Okay, so looking back, a year ago now, a few months ago now with Deal Agent, from where you started to where you are now, what’s the biggest impact you’ve achieved, or the proudest moment that sticks with you? JS: I think the thing I’m most proud of is that we built something literally from the ground up, and it changed how enablement shows up for our business. The biggest impact has been moving from a fragmented environment to a connected foundation that can be structured, measured, and embedded into how our go-to-market teams work. I can actually see that in a shift in our numbers. When we first brought Highspot on board, it was specifically a sales-only tool. I didn’t have any other broader teams in the tooling, and now we’ve doubled our Highspot user base. I’ve expanded from supporting just sales to supporting the broader go-to-market team. Training has been one of our largest wins. We’ve moved from one-off live training to structured onboarding paths and ongoing learning. We’re actually in the midst of a current go-to-market training refresh, taking everyone through our RL Datix 360, as we call it, and I went from maybe a 4% completion rate in the early days to now seeing nearly 100% completion. Last time I looked, we were at around 97 or 98%. That’s a huge shift in the consistency and accountability I have with my field. For content, 91% of our internal audiences are viewing our content in Highspot, and I’ve got close to 500 opportunities actively engaging with our content. So it’s telling me this isn’t just a repository where things sit. It’s becoming embedded in their daily workflows, in how they prepare, sell, and engage our customers. There’s also a time-savings piece. It’s a little harder to quantify, but I feel it every day. I’m still working toward maturing how we connect this directly to revenue outcomes like win rate and pipeline movement, but now I’ve got a solid foundation, a high level of adoption, and the visibility to keep building toward that. RR: Jill, the volume of work that happened in one year, when we talk about building the foundation, that’s the year. But you took 365 days, built out the foundation, drove the behavior change, and now you’re already in the midst of that next phase of evolution and getting people on board. So bravo, I don’t know how you did that. Hope you’re sleeping. I’d love to get a little bit of perspective on how you did it, for other leaders who are in that phase of building a strategy from the ground up. How would you recommend they get started, especially in an AI-first world? JS: Yeah. We went live with the product really, really fast. I had a great team behind me, and we worked hard every single day to get things built. But my advice would be actually not to start with AI. It’s probably going to be a little controversial, but I think you should start with the problem you’re trying to solve. It can be really tempting to jump straight into the newest tool or capability, but if your foundation isn’t there, AI can just make the mess faster. I would start by understanding where the friction is for your sellers and go-to-market teams. Can they find what they need? Do they understand the message in training? Do they know what good looks like? Are managers coaching consistently? Do you have visibility into what’s happening in customer conversations? Can you tell what’s helping deals move forward or stall? Once you understand those gaps, you can be more intentional about where AI fits. Can it help a rep prepare faster, summarize conversations, surface deal risk, build up Digital Rooms? It should really be tied to a true, real workflow and a business need. This is when AI becomes much more powerful, because it’s got the right context. It’s not just another tool. It’s a way to scale insight, reduce manual work, and help the team make better decisions faster. So for an enablement leader starting from scratch, I’d say: listen first, build your foundation, solve the real frictions in your sales force, and then use AI to scale what’s already working. RR: I really liked the question framework you threw out there. Because I think especially when people are starting to build, you have this insane arena of potential work to be done. And when you can start to ask the right questions that build toward the right purpose, you can get to the exciting work down the line. Well, Jill, thank you so much for chatting with us today. It’s been really wonderful to hear a little bit about what you’re building, how you’re thinking about building, and I’m so curious to see what’s next down the line. We’ll have to stay in touch. JS: Yeah. Thanks so much for having me. This was great. RR: To our audience, thank you for listening to this episode of the Win/Win Podcast. Be sure to tune in next time for more insights on how you can maximize go-to-market success with Highspot.

Most CDOs talk about data products. Very few approach them the way a product manager would starting with customer need, validating demand, and building only what people will actually use.

By 2030, AI models may be everywhere. Competitive advantage won't be. In this episode of Gartner ThinkCast, Distinguished Vice President Analyst and Chief of Research Rita Sallam explores what will separate the true leaders as AI becomes increasingly commoditized. Drawing from Gartner's vision for the AI-native enterprise, she outlines the critical shifts data and analytics leaders need to make now, from building trusted data and context foundations to rethinking workforce design, governance, and the role of AI agents in decision-making. You'll learn: Why trusted data will become the primary sources of AI differentiation How AI agents will reshape data and analytics workflows and operating models What AI-native organizations are doing differently to generate value Why "questioneers" may become the workforce advantage of the future   Dig deeper: Register to watch the full webinar Enhance your data, analytics and AI strategy See why Gartner is the world authority on AI   Timestamps:  (00:00) Intro (01:40) A New AI Advantage (03:25) Beyond Models and Infrastructure (07:57) Three Shifts for D&A Leaders (12:08) Human-Agent Teams (16:45) Analytics Gets Automated (20:42) Hiring for the AI Era (22:40) Making AI Pay Off

The annual release of a major industry index always sparks intense debate, but the true value lies in the core strategies driving the world's top performers. In this episode of Supply Chain Now, Scott W. Luton is joined by regular co-host Mike Griswold, Vice President Analyst at Gartner, and special guest Laura Rainier, Senior Director Analyst at Gartner, to break down the defining macro trends from the Gartner Supply Chain Top 25 for 2026. The panel explores how leading organizations navigate global volatility by moving past tech experimentation to deliver true enterprise value. They dive into the realities of the autonomous workforce era, detailing how top-tier supply chains prioritize human-machine collaboration and systems thinking over simple digital literacy. Highlighting standout examples like Unilever's shared forecasting and Schneider Electric's disciplined process optimization, the discussion illustrates what it takes to build a resilient, network-centric operation. Laura Rainier also addresses critics directly, explaining why qualitative community opinion remains the ultimate "secret sauce" for capturing true excellence, while Mike Griswold wraps up by challenging leaders to leverage the supply chain as a primary engine for strategic growth.    Jump into the conversation: (00:00) Introduction (00:44) Gartner's 2026 Top 25 preview (02:23) Meet Gartner analyst Laura Rainier (04:46) Laura's future supply chain research (07:28) Three supply chain macro trends (11:26) Network-centric strategies and orchestration (15:46) Top 25 companies leading change (21:41) Fix processes before deploying AI (24:30) What defines a Supply Chain Master (26:49) Why leaders invest in people (27:34) How Supply Chain Masters stay ahead (29:07) New and returning Top 25 companies (31:09) How companies earn their return (34:03) Why Schneider Electric ranks first (40:34) Walmart's rise and retail momentum (42:41) Why community opinion matters (47:38) 2027 ESG methodology changes (48:25) Gartner events and upcoming summits   Additional Links & Resources: Connect with Mike Griswold: https://www.linkedin.com/in/mike-griswold-6a68922/ Connect with Laura Rainier: https://www.linkedin.com/in/laura-rainier-9087857/ Learn more about Gartner: https://www.gartner.com/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- From Volume to Resilience: How Automotive Supply Chains Are Adapting to a New Market Reality: https://bit.ly/4f6SUGA WEBINAR- The Automotive Industry's Next Digital Breakthrough: https://bit.ly/4vhUwT4 WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt WEBINAR- SAP AI Inside the Supply Chain: From Silo to Orchestration: https://bit.ly/4bvpz6K   This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/macro-trends-micro-moves-inside-gartner-supply-chain-top-25-1618 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Nearly $580 billion in trailing twelve-month capital expenditure across Amazon, Microsoft, Google, Meta, Oracle, Tesla, and SpaceX. Full year 2026 approaching $900 billion. CSI's 2027 estimate: $1.5 trillion. CSI breaks down the Q2 hyperscaler CapEx numbers and makes the case for why the semiconductor bull market remains intact despite a volatile few weeks for chip stocks.AWS, Azure, Google Cloud, and Oracle are all reporting multi-year backlogs. Amazon just raised its 2026 CapEx guide to $220 billion, partly driven by rising memory prices — sending more capital directly to the semiconductor supply chain. Gartner revised data center spending up to $820 billion for 2026, a sixty-three percent year-over-year increase. The recent pullback looks like a leverage unwind, not a fundamental shift.For in-depth research and the Semiconductor Insider membership, visit chipstockinvestor.com. Use fiscal.ai/csi for 15% off any paid plan.Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.CSI owns shares of Meta, Alphabet, Oracle, and Amazon.

Neste episódio do Vamos de Vendas, Gustavo Pagotto recebe Fabio Duran, cofundador e CEO da 8D Hubify, para uma conversa sobre Revenue Operations, integração entre marketing, vendas e pós-vendas, e os perigos de seguir fórmulas prontas vendidas por gurus do mercado.Ao longo do episódio, Fabio compartilha sua trajetória do Direito ao marketing digital e explica como descobriu o conceito de RevOps ao perceber que vender mais não significava necessariamente crescer de forma sustentável. Ele mostra por que empresas precisam abandonar a visão de departamentos isolados e começar a operar com foco em receita, eficiência e previsibilidade.Outro destaque do episódio é a crítica à cultura dos "gurus de vendas" e à crença de que ferramentas de IA resolvem problemas estruturais sozinhas.

0:00 Welcome and intro1:00 Origin story: a 2002 AI research spinoff that became Markup AI3:27 Why Matt says "content is the next code"4:16 Real client results: productivity, quality, and the previously unimaginable6:13 Cloud infrastructure and how Markup AI works across multiple LLMs7:29 How Markup AI proves impact with standards and risk scoring9:32 The agent to agent economy and machine readable brand11:12 A quick detour on the metaverse and multiverse content11:48 Matt's life outside the CEO seat12:32 Teaching media literacy in the age of AI13:57 Gartner's guardian agents and why AI has to check AI15:02 Deterministic trust scores explained15:51 Who checks the checker: verifying Markup AI's own agents16:17 Selling safety, confidence, and revenue through search visibility17:10 The shift from traditional search to AI powered discovery18:24 Where paid search fits in an AI first world19:55 Predicting search one to two years out21:37 Tackling the AI slop problem22:55 AI influencers worth following24:10 What a great 2026 looks like for Markup AI24:48 Where to find Matt and Markup AI

In this episode, we discuss the Federal Reserve's latest interest rate decision, Microsoft's quarterly results and what they might reveal about the evolving AI investment landscape, the Hugging Face autonomous AI security incident, IBM's sale of its blockchain patent portfolio, and its broader focus on quantum computing. For the chart of the week, we cover Gartner's updated forecast, highlighting continued acceleration in global AI infrastructure and enterprise IT spending.    Remember to Stay Current!    To learn more, visit us on the web at https://www.morgancreekcap.com/morgan-creek-digital/. To speak to a team member or sign up for additional content, please email mcdigital@morgancreekcap.com    Legal Disclaimer    This podcast is for informational purposes only and should not be construed as investment advice or a solicitation for the sale of any security, advisory, or other service. Investments related to the themes and ideas discussed may be owned by funds managed by the host and podcast guests. Any conflicts mentioned by the host are subject to change. Listeners should consult their personal financial advisors before making any investment decisions.  

Your AI bill just stopped behaving like a software bill. For twenty years, IT leaders got very good at counting seats: buy a hundred, pay for a hundred. Then AI swapped the seat for a meter, and the number stopped holding still.This episode follows the burn from three vantage points: a financial analyst rationing a $250-a-month token budget he tore through in two days; the tech executive who watched enterprise AI bills climb 7x, 10x, 20x; and the IT leader at a 300-person company who refused to solve it with a usage dashboard. Along the way: Meta's leaked internal token leaderboard, Uber blowing its entire annual AI budget by April, and the uncomfortable question of who profits when everyone's told to use more.In this episode:Why token-based pricing breaks the budgeting playbook IT has relied on for two decadesWhat happens to the people using the tool when the meter starts running — and why rationing has a hidden costWhy measuring usage is the wrong scoreboard, and who benefits when you keep score anywayThe mid-market move that beats policing: measure centrally, push the judgment to managers, and get clear on what you're optimizing forFeaturing Brian Elliott, CEO of Work Forward; Daryl Dore, Senior Director of IT & Information Security at Higher Logic; and Benjamin, a financial analyst who spoke with us on condition of anonymity.Support our sponsor:This episode is brought to you by Sophos MDR. Running Microsoft security tools and drowning in alerts? Sophos MDR's 24/7 experts investigate and stop the real threats. >>> Learn more at: https://www.sophos.com/en-us/solutions/use-cases/microsoft#ITLeadership  #AICostManagement  #SaaSManagement  #FinOps  #EnterpriseAI  #TokenBurn  #ITAMShow Notes & ResourcesReferenced in this episodeMeta's internal AI token leaderboard (Fortune) — 85,000 employees ranked by token consumption; shut down days after it leaked.Uber burns its 2026 AI budget in four months (Forbes; TechCrunch) — adoption jumps 32% to 84% in a month; spend later capped.Jensen Huang on token consumption as a productivity signal (Tom's Hardware).Gartner: worldwide AI spending forecast to grow 47% in 2026 (Gartner).Zylo 2026 SaaS Management Index — the scale of wasted SaaS spend (Zylo).Brian Elliott's newsletter, Work Forward.Guest: Daryl Dore — Higher Logic.This episode's sponsor: Sophos MDR, in partnership with Softchoice — 24/7 managed detection and response for Microsoft environments. https://www.sophos.com/en-us/solutions/use-cases/microsoft The Catalyst by Softchoice is the podcast dedicated to exploring the intersection of humans and technology. 

Gartner predicts that more than 40% of CMOs who push for bigger budgets this year will lose influence with the C-suite—because their asks never connect to what the business runs on. Here's the thing: your CFO's language has only four words—pipeline, risk, retention, and cost-to-acquire—and 'reach' isn't one of them. In this episode, I show you how to translate your work into those four numbers, which budget lines to cut before someone cuts them for you, and why one traceable story beats 12 metrics in any budget meeting. Read the full article: https://spinsucks.com/communication/defend-marketing-budget Take the free PESO Model® Diagnostic: https://spinsucks.com/self-peso-diagnostic/  PESO Model® Certification: https://spinsucks.com/peso-model-certification/

Optics is no longer a supporting accessory in the data center network. As AI infrastructure advances from 400G and 800G toward 1.6-terabit connectivity, optical components are consuming a larger share of network cost, power and operational risk. In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent speaks with Bill Gartner, Senior Vice President and General Manager of Cisco's Optical Systems and Optics business, about how AI is changing the strategic role of optics. Gartner explains that optics represented roughly 10% of a network port's bill of materials at 10G. At 400G and above, the optics can cost more than the switch port itself. Reliability has also become critical: A single unstable link can force GPUs operating in parallel to stop, return to a checkpoint and restart. According to data Cisco has seen from hyperscale customers, link flaps can reduce GPU infrastructure efficiency by as much as 40%. The conversation maps the AI network across three distinct tiers: Scale-up: Connections within the rack, carrying approximately 500 times the bandwidth of a traditional WAN environment. Scale-out: Connections between racks, commonly using 400G and 800G pluggable optics. Scale-across: Coherent optical connections between data centers as AI clusters expand beyond the power limits of a single facility. Gartner also discusses Cisco's 1.6T roadmap, routed optical networking, coherent pluggable optics and the emerging debate around co-packaged and near-packaged optics. These architectures promise lower power consumption and greater density, but introduce new questions involving interoperability, replacement and operational resilience. Looking ahead, Gartner emphasizes that optics is not constraining AI network growth. It is enabling clusters to scale across racks, campuses and geographically distributed data centers, while the coming inference wave shifts the industry's focus toward cost and power efficiency.

"Did someone drop the ball?" is the wrong question when an escalation slips through the cracks. The right question: "Did we design a system where the ball could be seen at all?" @StacieBairdHX on how automating the routine tickets erodes the instinct to spot the non-routine ones — HX Podcast Episode 2 out now.   Key data points: Klarna cut "hundreds" of support roles for AI, saw quality decline, began rehiring1 CEO admitted "prioritizing cost over experience"1 Salesforce cut support from ~9,000 to ~5,0002 CEO said AI handles about half of cases, meaning humans still do the other half2 Gartner projects 50% of companies that cut support for AI will rehire by 20273 Visier analysis of 2.4M workers: highest boomerang rate since 2018 (Visier, 2026)4 Footnotes Orgvue Survey, 2025 ↩ ↩2 ↩3 ↩4 Forrester Research, Predictions 2026: The Future of Work ↩ ↩2 ↩3 Robert Half Survey, 2026 (via CNBC) ↩ ↩2 Careerminds Survey of 600 HR Professionals, 2026 ↩ ↩2 Harvard Business Review, Executive Survey, 2025 ↩ Challenger, Gray & Christmas, Inc. AI-Attributed Layoffs, 2026 ↩ Stacie For more episodes, visit StacieBaird.com.

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According to Gartner, at least 30% of generative AI projects will be abandoned after proof of concept due to escalating costs, poor data quality, or unclear business value. There are a lot of factors working against them, and too often failure happens when teams are right in the middle of building. They’ve spent significant time, money, IT capacity on something that may very well have been doomed from the start. So how do you know if something is actually worth building with AI or if you should just focus on buying instead? Riley Rogers: Hi, and welcome to the Win/Win Podcast. I’m your host, Riley Rogers. Join us as we dive into changing trends in the workplace and how to navigate them successfully. Here to discuss this topic is Keith Weaver, Senior Director of Global IT and Enterprise Applications at Highspot. Thanks so much for joining us today, Keith. I’d love if you could just kick us off by telling us a little bit about yourself, your background, and your role. Keith Weaver: I have been leading our global IT team at Highspot. I’ve been in IT for, I think, getting close to 20 years now, just overseeing all kinds of different systems and specialties within those systems. Our IT team at Highspot is IT operations, making sure everyone has devices, equipment, all of that globally. We’re now in seven countries. As well as system engineering, making sure everyone has the right tools, that they’re optimized for the business. And then we have specific teams in IT that specialize in financial systems and HR systems. In the world of AI, it’s a whole new world and it’s a lot of fun. RR: So 20 years in IT, you’ve kind of run the gamut. But it sounds like the last two and a half years have crammed almost 20 years’ worth of change into a very short window. When we were planning for this episode, you mentioned to me that you read four or five newsletters every morning just to keep up with the pace of innovation. Can you walk us through why you’re spending so much time learning and how IT has had to evolve to keep up with this pace of development? KW: What I’ve seen is mostly it’s the same things that we’ve done, except it’s done at a much faster pace. It is kind of an unheard of pace that AI is moving. Every day, the reason I read five different newsletters is because there are so many new things getting released, whether it’s a new model or a new tool, and then there are things within the tools that are getting released. Trying to understand all those things, the new protocols that are getting released, the new ways people are building or executing on AI, or the new things people are learning, is just really critical. And all of that is at a speed that we’re just not accustomed to. So I think that the objective of what an IT team does to continue to keep the business unlocked, able to do their best work with the technology and tools while making it secure and scalable, those things are the same. It’s just the speed now that it’s running, and the speed from the business. The business is moving so fast, and their expectation is that IT continues to deliver at that pace. I’ll give you some examples. I get probably, me personally, not my team, but me personally, I probably get 20 to 50 requests for new connections, new skills, new tools every week. That’s in Slack, in Jira. It’s just nonstop, and that’s just unheard of before AI. It just didn’t happen. It would be like, “Oh, we have this big initiative, and we’re gonna do this thing.” And now it’s just like, “Can I have this? Can I have that?” And all good things, it’s just how do we do that, and how do we keep up? RR: So when you have 20 to 50 requests hitting your desk every week, it can probably start to feel a little overwhelming. And I wonder if the folks who are sending these requests your way maybe don’t know the considerations behind every single one of these ideas. I’d love if you could give us a view of what you’re seeing across the industry as both technical and non-technical teams are all noodling on the idea of how to build with AI, how to buy AI tools, how to bring them all together and create a combination that actually works. What are you seeing? KW: Well, I think if you think back a year to a year and a half ago, mostly people were buying tools. Building was… It’s so funny ’cause we’re talking about a year ago or a year and a half ago, but building was much harder. And really with Claude, the Opus class models that came out, it really started unlocking the ability for everyone to start building. And so since then, the conversation shifted from, “Yeah, we can go buy this tool,” to “We can build this internally.” And there’s a lot of excitement to build because building’s fun. The pendulum just swings often on these things, and so we’re kind of in that wave where everyone’s super excited about building something. And I think we’ll end up in a place that is more normalized, where we’re building some, we’re buying some. We’re doing a hybrid approach. I think that is where we’ll end up. It’s just, it has swung really far to, “Look, we can just go build that thing.” RR: So it sounds like we’re kind of in the early phase of a cycle that you’ve seen time and time again, where surrounded by enthusiasm, but maybe we don’t have much direction. And like you said, building is fun, so I imagine you probably hear a lot of, “Hey, look what I built,” or, “Why can’t we just build this?” So when something like that lands in your inbox, what’s the first thing that goes through your mind? KW: If it’s, “Look what I built,” there’s lots of questions, like how is it being deployed? What’s the next step with this? Who’s gonna own it? How is this actually out in the world, and how are we gonna make sure that it’s as scalable and secure? If it’s a conversation, I would say this is more so where we hear, “We want to build this thing,” or, “We can just go build this thing. Why would we buy this?” I think the thing that I would ask is, what is the long-term path for this? Because deploying is just the first step. It’s the iteration. It’s the test. It’s continuing to deploy it. It’s maintaining it. It’s supporting it. It’s the full life cycle of any software project. What resources are gonna do that? How are we going to maintain this and support this long term? Those are usually the questions that I start asking. RR: And you’re quite equipped to speak to the build side of the equation because you’ve done it. You’ve tackled the planning, the deployment, the maintenance, the ongoing support, the full life cycle, like you said, of a software project. And you’ve done it with AI. I’d love if you could walk us through your first test case. Tell us a little bit about what you were thinking going in, and what you learned along the way about what works, what doesn’t, and what was surprisingly difficult. KW: Yeah. So we built an IT AI agent and an HR agent, and we did that with a cross-collaborative team. I think we started it last fall and then deployed it in February or March this year. And I would say the main objective there was not that these are the tools that are gonna change the way we function. The main objective was a learning. It was an R&D investment. So we knew that we had to, as an IT team, be able to help the business deploy these agents, and we found some use cases that we could. The IT one we can own in-house. The HR one, we already collaborate with them very closely on a number of things. So it felt like a good first step for us to dip our toes, learn a lot about how to build these, what works, what doesn’t. And these agents are more than just chatbots. They really were taking actions in tools like Jira and Workday, Highspot itself. And we built them using the Workato platform, and what we learned through it is it was actually really exciting and really easy to get to a place where this mostly works, and this is pretty, it feels like this could be really powerful. That was, like, the first three weeks. The rest of the time was, “Oh, gosh, how are we going to get this thing so that it doesn’t give somebody some really poor answer from a security standpoint or an HR answer that needs to be rooted in truth?” And then how can we make sure that it repeatedly does these actions no matter what people prompt, that it’s really controlled? I often described it to my team like it was training a four-year-old. I have four kids and they’re all older now, but it was like I was back in those toddler stages. Like, “Why’d you decide to do that? That was a bad decision. Stop.” And trying to train it and get it to that point, it just took a massive amount of time. And since we’ve launched it, it’s a lot of resources to read the conversation, understand what was asked, understand what it did and where it went wrong, then refining it, then retesting it, then redeploying it. It’s a cycle. It’s this continual cycle, and that takes a lot more resources and time than I originally thought it would. RR: Ballpark, what did that investment look like? How long did it take you to build? How many queries did you have to manually validate before you trusted the thing? And how much are you still investing today to keep it alive? KW: In the beginning, you just start giving it manual prompts and testing it to see what it does. Then you produce your test cases, and you manually go through those test cases, and every time you make some major changes, you go through those test cases, make sure that it’s still hitting. Because just changing one little thing, for example, if you take a tool and change the prompt or the description of that tool, you actually have to do regression testing on all the things that someone could prompt, because just changing that one little description, the agentic reasoning could choose to use that tool or not use that tool at the wrong places. So it’s really important to do this regression testing. Then we started going through how do we automate some of this testing, and we were able to do some of that. But I think the biggest takeaway is with software, traditional software, you do regression testing, but if you change this bit of code, really that’s what you have to worry about, anything within that bit of code, not the whole stack, not the whole thing. This was, I think, a really big takeaway. We would make a small change, and something unrelated, completely unrelated, would start acting weird. And then you have model changes. You’re like, “Oh, I want to move to the latest model.” Well, that latest model now, the reasoning’s different, how it picks up tools might be different, and so that also has been a challenge. I think over time, especially as the tools evolve, the automated testing will get easier. We can have Claude, for example, go through and give it a bunch of questions, evaluate the responses. But you’ve got to build that too. That’s all something you’ve got to build in order to maintain this thing. RR: Now, you said this project was conceived last fall, launched, not necessarily finalized, ’cause it’s never really finalized, but launched publicly in the February March timeframe. So now we’re almost a year out from initial conception, couple months out from launch. Given your point about things changing so much in the last year and a half, now knowing what the landscape looks like, would you have built differently or would you have built at all? KW: It’s a great question. I think if I go back to my objective that I needed to learn and I needed my team to learn how to deploy these things and the challenges of it, I wouldn’t change what we’ve done, and they’re still deployed in production right now, although we are not spending a massive amount of time making them better. Would I change what I’ve done? No, but that’s because of where my objective was. My objective wasn’t to massively change the HR process or the IT process. It was to learn. And I think that given the right investment, we could move them further along. However, I do not believe that building something that every business has to do, IT and HR stuff, that… all those questions that we get, both of those teams get, are the same across most organizations. Most of the things employees need to do in these functions are the same across all organizations. So I don’t know that I would double down and want to go build the agentic functionality behind IT and HR. What I would want to do is look at our specific processes, things that are unique about our business, not only where we have the information, but what tools we use, and then maybe specific processes of how we do something, and figure out how do I build that piece and tie it into something that’s delivered. And there are so many, just talking about the IT space, there are so many systems right now that have the agentic functionality and give you the ability to tie in all your different tools. So I would probably try to leverage those rather than just building everything from the ground up. RR: As an aside, I will say I have used both the IT and the HR agents. They’re quite cool. The HR one talked me through my vision insurance at 8:00 PM on a Saturday, so thank you for that. Very helpful. So you mentioned that this was something that you and the team invested in as a learning, so you could understand what the process of building an agentic workflow looked like, an agentic tool looked like. So having now seen what it looks like, what must be invested in order to do this successfully, how do you draw the line between what you can build, what you should build, and what you’re better off just buying? KW: I think you should build the things that are unique to your business. You could think about this as your moat, but I think it goes beyond that. It’s where your processes or the way you handle something is very unique. In those cases, it may make sense to build something from scratch. But if it’s not unique to your business, like go back to the HR and IT, most of it’s just not unique. And so in most cases, when you don’t have something unique, then it likely makes sense to buy it, because you get the power of the organization that has built that tool behind it, pushing that tool forward much faster than you’re ever going to push it forward, ’cause we’re just not gonna hire that many engineers to do it. And so if there’s an IT tool out there that is bringing all that agentic functionality to the forefront, then why would I not leverage that, as long as I believe in the company, they have a track record to deliver, and they have the ability to build on top of it for some unique IT process that I have or some deterministic termination workflow that I want to execute. If I can do that, then I would say let me build what’s unique and tie it into something where the base or the product is being delivered. RR: Outside of that uniqueness component, if you’re a GTM leader trying to make that call, what other questions would you be thinking about and asking yourself? KW: I think I would just go back to that uniqueness test. Is this unique to my business? Is this a unique sales motion, a unique thing that we do here because of what we’re selling or how we’re selling that I can’t expect a vendor to build this or be able to deliver it? Or is it roughly the same thing everyone else is doing, the same problem everyone else is trying to solve? There are unique components, ’cause there are always unique components to this, but those unique components can be built alongside or included within that tool. And here’s the wonderful thing, if you just take AI or Claude, for example, we’re using Claude internally, you have a tool that has all the MCP capabilities like Highspot, like Salesforce, and you have some unique process with some maybe even proprietary tool that you want to do. Well, the great thing is you can bring that all into Claude. You can use Workato or build a skill within Claude and use that, whatever you’re building for that unique process, that unique system, and tie it in with those other tools that already are giving you a lot of functionality, and now you’ve expanded what you already had, and you’re spending your time in that uniqueness layer. What’s unique to us? That, to me, there’s value in that, to build there, because you’re not gonna get another organization to spend the time, money, or energy building on that. RR: So it sounds like it kind of comes down to a basic cost benefit call. Is the upfront investment and ongoing maintenance worth what that unique build will actually deliver? KW: When you’re doing the cost analysis, you can’t look at just what it costs to build. You have to look at the full life cycle. An analogy for you is, if you go back, maybe 20 years when SaaS really became a big thing, you had Salesforce kind of leading the charge. In all of the sales cycles, they would say, “You no longer need servers, and you no longer need admins.” The business manages this themselves, and everyone was super excited. Oh, this is gonna, the ROI’s gonna be so great on this. The business is gonna own this. We can move really quickly because we can change workflows and do this stuff and own it all internally. We don’t need an IT team to manage servers, et cetera. And everyone got really excited about this, and I remember all those years ago being like, “Guys, I don’t know if that’s really gonna be true.” So now you fast-forward, and it hasn’t been 20 years. For the last 15 years, we’ve been actually hiring system admins like crazy. We have Salesforce admins, NetSuite admins, Workday admins, Jira admins, and we are spending millions of dollars on these admins to maintain these systems that were supposed to have a great ROI. And I haven’t seen any studies on it, but I bet if we could compare on-prem with old software that we used to buy and put on-prem to SaaS, we would say that it was a lot cheaper to do it. And we’re not gonna go back. We’re not gonna do that any differently anymore, but I think we have to use that same type of lens. The ROI is not just the build. It is how are we gonna maintain this and keep it long term. RR: Yeah, and that’s the pendulum swing you mentioned earlier. Excitement, then reality hits, then we’re back to the next shiny thing. For those in that stage of enthusiasm that maybe don’t know what they’re getting themselves into, what do you wish they understood before kicking off a build or approaching IT with an idea? KW: I think the full cost. That you can’t do the math by just looking at what it costs to build, or that I can get a couple of my key people to go build this thing in Claude, but it’s how are we going to scale this in the organization? How are we going to deploy this thing? How are we going to maintain it? How are we going to support it? Who’s gonna take the support questions at midnight when they come in? And then how are we gonna iterate to keep ahead and make sure that it can do everything that we need it to do and stays ahead of where the business is going? RR: And when you’re having those conversations, at what point should IT actually be in the room? And what happens when you’re not? KW: I think that IT should be brought in the room as soon as a team is saying, “We can build this instead of buying it.” And really, I think that IT is not there, or hopefully we’re not there to say no. We’re there to help shed a light on what it’s actually going to take long term so that we make the best decisions. Because ultimately, I think we understand the technology and we understand the challenges of deploying stuff like this and then maintaining it long term. That’s what we do. And so if we’re in the room earlier, we can at least shine a light on it and give kind of that picture of reality so that we’re making the best decisions and there’s no surprises. Because the worst thing is that decision to build is made, they start building it, they realize they need IT’s help, and IT has a roadmap. We don’t have the resources or the ability to step in and solve this problem. Now you’ve wasted money. RR: Yeah. I saw a really interesting stat from some original research by Influ2. They surveyed marketing and sales leaders that are in the market for technology, and of those leaders, only 10% of them saw the IT perspective as valuable during the evaluation. 38% of them, however, then came back and said our biggest blocker, the number one blocker, in fact, was IT. So that disconnect is very clear, and it’s causing very clear consequences down the line. KW: I think what I would say is the best IT teams don’t want to be blockers. We don’t get excited blocking what the business is doing. We get excited leveraging the technology and moving it forward as fast as possible and seeing the business successful. So that’s what we want to do. It’s really hard when decisions have been made that back us into a corner and we’re like, “There’s no way to scale this, support this, continue this.” And then we’re seen as a blocker, but if we just had the conversation earlier, we could actually mitigate that much earlier. RR: So it sounds like the theme here is that your IT team wants to be a partner, not a gatekeeper or a blocker. And so knowing that, bring them along for the ride. Give them a seat at the table. Your programs, your investments will be better for it. If you had to boil this all down to one tip for marketing, sales, or enablement leaders on partnering with IT as they bring AI into their tech stack, what would it be? KW: I think if you can bring the IT team in and have a forward-looking view over the next year, over the next two years. It’s really hard to look two years out with AI and how fast it’s moving, but over the next year maybe, this is where we want to get. This is what we want to automate. This is what we need to fix. This is what we need to deliver in order to continue to execute on our objectives. If IT is on the same page there, we can be a proactive resource and help to get them there. We read all these things every day, there might be something that I’m seeing in the market or happening that I can bring to the table. Or at very least, when you come to me in six months and say, “Okay, I pulled the trigger on this thing,” we had that conversation. I knew it was coming. I knew the direction we were going, and directionally I was aligned with you, and so now I’m supporting that initiative rather than it being a surprise with no resources. So let us help with the roadmap. I think if we could do that across the whole business with IT, we would win together. RR: The one pretty clear takeaway, I think, whether you build, whether you buy, or whether you land somewhere in between, bring your IT team into the conversation, and you’re set up either way. Keith, last question for you. Just curious, what would you say to someone who looks at this build, buy, blend conversation and asks, “Why don’t I just build Highspot myself?” KW: When you build something as big and as complex as Highspot, there’s a lot of learnings that happen, a lot of iterations, a lot of changes to get to where you are. And trying to build that from scratch is very difficult. And I think the next thing that you may hear is, “Well, we can cobble this together.” I would just say you’re going to maintain that thing that Highspot has a full engineering team to make that as wonderful and as best as it can be. And how many engineers can you staff to do that and keep the velocity of it so that whatever you’re building is gonna move faster and be better than what Highspot’s building? And then what’s that gonna cost you? Going back to that build versus buy, when you start looking at how many engineers it takes not only to build it, but then to maintain it, support it, deploy it, I think the ROI doesn’t pencil out. RR: The moment you throw out the phrase cobble together, I think you’ve kind of already answered your own question. Keith, thanks for the window into a world most of us in go-to-market rarely see. This was a lot of fun, and I’m really excited to share the news. IT is there to help, and when you give them a seat at the table, your AI investments will be better for it. KW: Yeah. Thank you so much. I appreciate the time. RR: To our audience, thanks for listening to this episode of the Win/Win Podcast. Tune in next time for more insights on how to maximize go-to-market success with Highspot.

A Delaware state court ruling rewrote the rules on corporate liability and CDOs are directly in the crosshairs. This is our most-played episode ever, and if you haven't heard it, now's your moment.

AI isn't failing. AI literacy is. Nearly half of CEOs say their AI investments are breaking even at best. In this episode of ThinkCast, Gartner Distinguished Vice President Analyst Mandi Bishop explains why the biggest barrier to AI value isn't the technology — it's people's ability to work effectively with it. Learn how critical thinking, prompt discipline and AI literacy can improve adoption, reduce risk and unlock better business outcomes.   You'll learn: Why AI literacy — not more AI tools — is the foundation for AI success How prompt discipline can improve output quality and reduce AI costs The critical thinking skills employees need to work effectively alongside AI Practical steps leaders can take to drive sustained AI adoption and value   Dig deeper: Become a client to learn more about AI literacy Attend a Gartner CIO Conference near you See why Gartner is the world authority on AI  

For thirty years, healthcare has met every new technology with the same four words, we've seen this before. Most of the time the doubt loses. The internet was called a fad, and by 2009 a majority of US adults were looking up health information online, up from a quarter in 2000. So the reflex is earned. This episode asks the harder question, whether being right about the internet tells you anything at all about AI. Chris Boyer and Reed Smith open with why the reflex is so strong. The 1999 objections to the web read almost word for word like the 2024 objections to AI. Patients won't trust it. It makes mistakes. It can't replace the clinician. Pew found 60% of US adults uncomfortable with a provider relying on AI for their own care, which rhymes with the discomfort that greeted the web. There is even a formal model for the pattern, the Gartner Hype Cycle, though its own critics point out the curve has no data behind it. In the second segment they find where the analogy breaks. The web reached a majority of adults over roughly a decade. ChatGPT reached 100 million users in about two months, the fastest ramp UBS had seen in twenty years. The web moved information you could still judge by its source. A large language model makes the answer in the moment, with no source to check, in the same confident tone whether it's right or wrong. Chris and Reed hand you one question to run in a meeting, does the thing that made the old case turn out fine actually show up here, and one way to sort your AI uses by how much a wrong answer costs. Then the conversation turns to two people who have hosted the room where healthcare argues about this since 1996. Kathy Divis and Mike Schneider of Greystone.Net mark the 30th anniversary of HCIC. They trace digital from a small side function to something so central that a keynote once argued digital marketing was dead, meaning it had stopped needing its own name. Mike makes the point plainly, the technology changes and the questions stay the same, from the internet to CRM to AI. And they share what surfaced across this year's conference proposals, a return to trust and authority as the theme running through the whole organization. In this episode, Chris, Reed and the guests cover: Why we've seen this before is both earned pattern recognition and a way to stop thinking Where the AI story tracks the internet and where it breaks, on speed and on the difference between moving information and making it The one question that separates a real analogy from a comforting one How 30 years of HCIC read the same skepticism across the internet, CRM and AI What it means when a technology stops needing its own name, and why invisible was the goal for the web and the risk for AI Why trust and authority came back as the theme in this year's proposals If someone in your next meeting says we've seen this before, this episode is about the follow-up question that tells you whether they just said something wise or talked the room out of thinking. touchpoint.health Mentions from the Show: Pew Research Center, The Social Life of Health Information, 2009 (25% of adults online for health info in 2000, 61% by 2009): https://www.pewresearch.org/internet/2009/06/11/the-social-life-of-health-information/ Pew Research Center, 60% of Americans Would Be Uncomfortable With a Provider Relying on AI in Their Own Health Care, 2023: https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/ Gartner, Hype Cycle Research Methodology: https://www.gartner.com/en/research/methodologies/gartner-hype-cycle Reuters, ChatGPT sets record for fastest-growing user base, 2023 (100M users in about two months): https://finance.yahoo.com/news/1-chatgpt-sets-record-fastest-051929384.html BMJ Open, 2026 chatbot health-information audit (nearly half of answers problematic). CONFIRM primary link and figures before publish. Medical Hallucination in Foundation Models, medRxiv, 2025 (overconfidence and poor calibration): https://www.medrxiv.org/content/10.1101/2025.02.28.25323115v1.full HCIC (Healthcare Interactive Conference), 30th anniversary, produced by Greystone: https://www.hcic.net/ Live  Kathy Divis, president, Greystone. https://www.linkedin.com/in/kathyldivis/   Mike Schneider, Greystone. https://www.linkedin.com/in/michaelschneider-greystone/  Reed Smith on LinkedIn: https://www.linkedin.com/in/reedtsmith/ Chris Boyer on LinkedIn: https://www.linkedin.com/in/chrisboyer/ Chris Boyer website: http://www.christopherboyer.com/ Chris Boyer on BlueSky: https://bsky.app/profile/chrisboyer.bsky.social Reed Smith on BlueSky: https://bsky.app/profile/reedsmith.bsky.social Learn more about your ad choices. Visit megaphone.fm/adchoices

What You'll Learn:00:00 Introduction02:13 Why Marketing Owns Zero Board KPIs08:54 There Is No Killer Marketing Campaign15:41 Why Your First 10 Customers Matter More24:16 The Biggest GTM Mistake Founders Make33:48 How AI Is Changing Customer Discovery43:11 Demand Generation Beats Chasing Leads53:02 Why Value Wins Over Price1:02:47 The New Rules of B2B Go-to-Market1:11:26 Rapid Fire with Alon WaksRethink your B2B SaaS GTM strategy from pipeline to persona SpotDraft's CMO Alon Waks shares the framework behind $100M raised and 450+ customers.Ask a B2B founder about their killer marketing campaign and most will have an answer ready. Alon Waks will tell you that answer is wrong. There is no killer campaign. There is no one channel. And most of what founders think of as marketing is stuck in one-to-many land when it should be one-to-few.That is where this conversation starts, and it does not slow down.Alon is the CMO at SpotDraft, the AI-powered contract lifecycle management platform backed by Vertex, Qualcomm, Prosus, and P&G Invest, now serving customers across the US, UK, and APAC. In this episode with Prime Venture Partners, he opens up the playbook he actually runs day to day:- How he defines persona versus segment (and why founders keep starting with the wrong one)- What changes as a company moves from 0 to 1 to 10 to 100- Why he thinks paid marketing has become a race to the bottom that most founders are still throwing money at.He also opens up his playbook for B2B influencer marketing, an angle most CMOs will not talk about publicly. He explains why validation has quietly shifted away from Gartner and toward peer-to-peer conversations on Reddit, LinkedIn, and community forums. And for the Indian founders trying to crack the US, he lays out exactly which cities matter, what not to do on outbound, and when to make your first local hire.Toward the end, Alon gets uncomfortably honest about what marketing actually owns on the board KPI list (spoiler: almost nothing) and why that changes everything about how you should measure your team.If you are a founder, a marketer, or a CMO building a B2B SaaS GTM strategy from scratch, this is the one to bookmark.Connect:Alon Waks on LinkedIn: https://www.linkedin.com/in/alonwaks/Spotdraft: https://www.spotdraft.com/Prime Venture Partners: https://www.primevp.in/Jerome Manuel on LinkedIn: https://www.linkedin.com/in/jeromermanuel/About Prime Venture Partners:Prime Venture Partners is an early-stage venture capital firm partnering with exceptional founders building category-defining companies from India.#GoToMarket #B2BMarketing

Our guest on this week's episode is Curtis Spencer, CEO of Bloodhound Tracking Device and also CEO of ISM Worldwide. We have been reporting for a couple of years now about the ongoing problem of cargo theft in our supply chains. But now we find that criminals are using artificial intelligence to make their fraudulent schemes even more believable than ever. Our guest talks with Group Editorial Director David Maloney about the problems of cargo theft and what shippers and carriers can do to secure their loads. The big story for supply chains this week continues to be the ongoing disruptions caused by the war with Iran and the closure once again of the Strait of Hormuz.  It seems that after nearly five months of back-and-forth airstrikes, things are not getting any better, with fuel prices on the rise once more. Those higher costs makes it more expensive for shippers to buy truckload, less-than-truckload, and parcel freight services. Senior News Editor Ben Ames has the breakdown of how higher prices are impacting costs for shippers using each of those modes.This week new research from advisory firm Gartner points to the growing demand for flexible supply chain network planning. The company found that 72% of the 151 supply chain leaders they surveyed have had to revisit final approvals for network investment decisions at least once—a process that contributes to delays and can drive up costs and decrease service levels in their supply chain operations. More than half also said they revisited those decisions three or more times, leading to lower satisfaction in the final outcome. Senior Editor Victoria Kickham share how the report breaks down the differences between two types of operational challenges: turbulence and disruption.Articles and resources mentioned in this episode:Bloodhound Tracking DeviceOil price shocks ripple through trucking shipping marketsResearch highlights the need for flexible supply chain network planningOcean freight early peak season pushes container rates higherVisit DC VelocityVisit Supply Chain XchangeSend feedback about this podcast to podcast@agilebme.com

We discussed a few things including: 1. Jonathan's career journey   2. Cranium AI   3. AI ecosystem and timeline 4. AI trends, opps and challenges   5. Outlook for 2026 Jonathan is CEO of Cranium AI, Inc. The company spun out of the KPMG Studio and came out of stealth mode in April 2023. Jonathan is a former Partner at KPMG, cyber security industry leader, and visionary.   Prior to KPMG, he led Prevalent to become a Gartner and Forrester industry leader in 3rd party risk management before its sale to Insight Venture Partners in late 2016. At KPMG he led third party security globally, AI Security services, and built Cranium in stealth.   He has been quoted in a number of publications and routinely speaks to groups of clients regarding trends in IT, information security, and compliance.   Jonathan received his MBA from The Pennsylvania State University, is a Certified Information Systems Security Professional (CISSP), and Certified Third Party Risk Professional (CTPRP). #podcast #AFewThingsPodcast

Every hype cycle has a sales guy. Crypto had them. AI agents have them now, and most of what's being sold as an "agent" is old automation with a new label. It's called agent washing, and Gartner projects 40%+ of agentic AI projects will be cancelled by 2027.So how do you tell the real thing from a fresh coat of paint? And even if it is real, do you actually need a complex agent for what you're looking to achieve?Host Erick Espinosa sits down with Mariano Jurich, Senior Product Leader at Making Sense, who helps mid-market and PE-backed companies separate real AI value from plausible noise, and who tells clients, more often than you'd expect, that the boring workflow automation is the better buy.Inside this episode:The four traits that define a real AI agentThe "if-then-do" test that exposes a washed agent in one sentenceWhy "agentic" is overkill for most problems companies bring himWhy agentic AI breaks when you treat it like a cloud migrationWhat to put in writing before you signFind out more about Mariano Jurich here.Learn more about Making Sense here.Reach out to today's host, Erick Espinosa - erick@sociable.coGet the latest on tech news - https://sociable.co/ Leave an iTunes review  - https://rb.gy/ampk26Follow us on your favourite podcast platform - https://link.chtbl.com/rN3x4ecY

Executive Summary AI strategic thinking for women executives is no longer optional — it's the dividing line between leaders who get exposed and those who get elevated. Executive coach Sabrina Braham and author Barry O'Reilly reveal how to build your judgment system, accelerate decisions, and think strategically at the highest level. Quick Takeaways Barry O'Reilly's defining insight: "AI is not going to replace leaders — it's going to expose them." Most executives have never documented their judgment system — and AI makes this gap impossible to hide. Decision velocity + decision advantage are the two metrics separating leaders who accelerate from those who stall. Misty Schaefer, VP at American Airlines, uses AI voice notes and scenario planning for orders-of-magnitude better decisions. The biggest missed opportunity in AI leadership is not learning together — yet the fastest learners do exactly that. There's a line on the back of Barry O'Reilly's new book that every woman executive needs to sit with: "AI is not going to replace leaders. It's going to expose them." AI strategic thinking for women executives is no longer a competitive edge — it's fast becoming the baseline expectation at the director, VP, and C-suite level. I'm Sabrina Braham, executive leadership coach (MA, MFT, PCC) with 30+ years of experience, and host of the Women's Leadership Success Podcast — top 1.5% globally with almost 900,000 downloads. My clients include leaders at Stanford University, Ernst & Young, and Autodesk. And what I see in my coaching practice right now is a clear divide emerging: women executives who are building AI-enhanced judgment, and those who are still relying on invisible, inarticulate intuition they've never made explicit. AI is about to make that gap impossible to hide. This is Part 2 of my conversation with Barry O'Reilly — author of Artificial Organizations: Build Better Judgment, Speed, and Results with Machine and Human Intelligence, keynote speaker at Gartner's CFO Conference, and one of the most sought-after AI leadership advisors in the world. In Part 1, we covered how women leaders can use AI to build personal career confidence and grow into bigger roles. Here, we go deeper — into the strategic leadership capabilities that will define who rises at the executive level in 2026 and beyond. New research from Chief and The Harris Poll (2026) confirms that 85% of senior women leaders are now active players in their organization's AI strategy — and 68% are focused on using AI to amplify human talent, not replace it. The leaders pulling ahead are those who've moved beyond productivity tools into something more fundamental: AI-enhanced judgment systems. The Uncomfortable Truth: AI Will Expose Leaders Without a Judgment System When Barry walks into executive rooms around the world, he asks a deceptively simple question: "Show me your system for making this decision." The silence that follows is telling. "Often, a lot of the time, they just don't have a system," he explains. "They've never systematically written down all the steps they're going to go through to make an actual decision." For most leaders, the judgment system — the internal algorithm for weighing options, prioritizing inputs, and reaching decisions — has never been made explicit. It works. But it has four critical limitations: It's not visible — others can't observe or learn from it. It's not repeatable — it can't be consistently applied by or handed off to others. It's not challengeable — if it's in your head, no one can push back on its blind spots. It's not improvable — you can't deliberately tune what you can't see. In my 30+ years of executive coaching, this is one of the most consistent patterns I see in women leaders who are passed over for promotion despite exceptional performance: their judgment is excellent — but it's invisible. They can't teach it, transfer it, or demonstrate it in the way boards and senior leaders need to see. AI strategic thinking for women executives forces this reckoning, and the leaders who embrace it rather than resist it will define the next decade of leadership. Here's what my Leading Before You're Ready playbook addresses directly: building the judgment, presence, and strategic clarity that precedes you into every room — before you hold the formal title. AI doesn't change that mission. It accelerates it. What Is a Judgment System — and Do You Have One? A judgment system is the explicit process and criteria you use to make leadership decisions: the information you seek first, the variables you weigh, the sequence you follow, and the principles that guide your final choice. For most leaders, this system exists — but it lives entirely in their heads, accumulated through years of experience and pattern recognition. It works. But it has critical limitations that AI strategic thinking for women executives can directly address. The Judgment System Exercise Barry Uses With Executive Teams Here's the exercise Barry runs at the opening of his executive engagements — and one I now recommend for every leader I coach: Name a key decision coming up for your team. Be specific: a resource allocation decision, a product investment, a market expansion choice. Draw out your judgment system for making that decision. What information do you look for first? What criteria matter most? What's your sequence of evaluation? What would make you say yes — or no? Share your judgment system with your peers. This is where the transformation happens. A VP of Marketing and a VP of Finance, both making the same type of decision, suddenly realize they weight customer behavior and market size differently — and neither has ever made that explicit in shared decision-making. Identify the gaps. Where is your information current and reliable? Where is it outdated, incomplete, or missing entirely? What would better information change? "Suddenly you have groups of people sitting around going, 'I've never done this before,'" Barry explains. "And they're right. Most executive teams have never made their collective judgment infrastructure explicit." From a coaching perspective, I want to highlight what this exercise does beyond improving decision quality: when you can articulate how you make decisions — not just what decisions you make — you demonstrate the executive presence that earns trust and bigger responsibilities. This is exactly what I teach in the Leading Before You're Ready playbook. Judgment Infrastructure: The Data Ecosystem That Feeds Your Decisions Once leaders map their judgment systems, Barry introduces a second connected concept: judgment infrastructure — the data ecosystem that feeds your decision-making algorithm. Barry asks leaders: "Across your company, how are you getting the information you need to run your judgment system?" The answers reveal how fragile most leadership data environments actually are. "I have a spreadsheet I get once a month." "Our customer records are three months out of date." "I usually get that information from a conversation with someone else, so it depends on who I ask." The principle is clear: the output of any system is determined by the quality of its inputs. Poor-quality, delayed, or incomplete information fed into even an excellent judgment system produces poor decisions. For women executives in tech leading AI-adjacent organizations, understanding how to design and govern your information ecosystem — not just receive reports — is a defining capability for 2026. Decision Velocity + Decision Advantage: The Two Metrics That Define Executive Performance Barry's work with high-performing organizations has surfaced two metrics that consistently predict which leaders — and companies — pull ahead: Decision velocity: How fast can you move from problem identification to a well-reasoned decision? Not impulsive speed — calibrated speed supported by quality process. Decision advantage: How current, accurate, and comprehensive is the information available to you when you need to decide? Real-time — or three months stale? "That is how companies, startups, businesses power ahead," Barry explains. "They learn faster, they make decisions at a high velocity, and the information to support those decisions is to hand in real time."   AI dramatically accelerates both metrics. A meeting co-pilot that captures and synthesizes every conversation means you're never walking into a decision without full context. Barry describes his own routine: "I run a process either the day before, the morning of, or even five minutes before I meet someone. It runs all the last conversations we've had against my judgment system for the meeting I'm about to have." He shows up to every conversation with full context, clear priorities, and the calm that comes from genuine preparation. That calm is not incidental. As I always tell the executives I coach: your cognitive and emotional state in high-stakes situations is a performance variable. Anything that reduces cognitive load — like AI capturing your meeting context — directly improves your effectiveness in those moments. AI strategic thinking for women executives isn't just a professional tool. It's a confidence tool. Case Study: How Misty Schaefer Uses AI to Lead American Airlines Strategically The most vivid example Barry shared of AI strategic thinking in action involves Misty Schaefer Sern, VP of Consumer Technology at American Airlines — responsible for AA.com, the mobile app, and every digital touch point the world's largest airline uses to serve customers. Misty's natural trait: she has her best ideas on the move — walking the terminal, talking to ground staff, observing what's happening at the gate. The traditional problem: those ideas used to evaporate between the inspiration and the desk. ...

ROI supersedes AI. That's the blunt verdict from Steve Lucas, Chairman and CEO of Boomi, who has spent 30 years at the top of enterprise software. With OpenAI burning $3 billion a month and Gartner projecting that up to 40% of enterprise AI projects will be abandoned by 2027, the blank-check era for AI spending is over — and the reckoning is coming faster than most investors realize. Motley Fool analyst Rachel Warren sits down with Steve to unpack what Wall Street is missing: why the next wave of AI winners won't be the flashy model makers, how to spot the difference between a real AI strategy and expensive spin, and the single metric that separates transformative technology from hype. Host: Rachel Warren Guest: Steve Lucas Producers: Bart Shannon, Lauren Budabin Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

A structural shift is occurring as employees and customers increasingly bypass sanctioned IT systems in favor of faster, unsanctioned "shadow" tools that offer comparable or "good enough" functionality with less friction. This shift is highlighted through evidence from Gartner, SparkToro, Microsoft, and reports from Altran Digital Business, which collectively show sanctioned internal and customer-facing systems losing relevance as users opt for alternative solutions that optimize convenience and efficiency over formal governance. The most consequential development referenced is Microsoft's move to replace premium OpenAI and Anthropic models in core applications like Excel and Outlook with lower-cost in-house models, as reported by Bloomberg and Channel Insider. Microsoft claims these new models offer similar accuracy with increased efficiency, reflecting a broader market trend toward solutions that meet minimal functional thresholds at drastically reduced costs. This mirrors broader enterprise behavior, where cost and sufficiency now outweigh premium features, driving a reconsideration of value in AI provisioning. Supporting developments include a Gartner survey showing consumers are about three times more likely to use general AI tools like ChatGPT than corporate chatbots, and a report from Altran Digital Business revealing that over half of employees rely on personal devices or unauthorized tools for work, with nearly a third ceasing to report IT problems entirely. Clickstream data shows that more than two-thirds of Google searches end without a click as users accept AI summary answers, bypassing source links altogether. Vendors such as N-Able and Okta are responding with new products aimed at identifying and gating shadow tool usage, but these approaches often add operational friction without actually closing governance gaps, as Kaseya data indicates most SaaS accounts remain unmanaged despite existing controls. For MSPs and IT leaders, the key implication is that additional controls and "lockdown" measures are likely to increase friction without effectively steering users back to sanctioned processes. Current market tools that focus on visibility and gating of shadow IT may exacerbate the problem by making official workflows less attractive. The practical recommendation is to map where users have already abandoned sanctioned paths and focus on improving those official workflows until they are easily usable and competitive with shadow alternatives. The effectiveness of service delivery should be measured not by control metrics, but by whether users actively choose sanctioned systems for their work.   00:00 The quiet walkout  03:49 Even Microsoft picked good-enough 06:22 Why more control backfires 09:00 Why Do We Care?  Supported by:  Pax8   

Many companies cut talent in pursuit of AI value. Now they're discovering what was lost in the process. In this episode, we explore a shift taking place in organizations' AI journeys: after an initial rush to pursue productivity gains and cost savings, many leaders are rediscovering that human expertise is essential to realizing AI's full value. Gartner Senior Executive Partner Denise McCurdy joins us to share what she's hearing directly from CIOs as they navigate mounting pressure to deliver AI outcomes, implement agentic AI and prepare for new operating models — all while preserving the people, knowledge and relationships that drive lasting transformation.   You'll learn: Why productivity gains alone rarely create lasting AI value The difference between "blue money" and "green money" outcomes How organizations are rehiring talent they initially replaced Why CIOs must deepen collaboration with CFOs and business leaders   Dig deeper: Download the 1H 2026 Gartner CIO Report Attend a Gartner CIO Conference near you See why Gartner is the world authority on AI  

AI for sales is everywhere right now, but most of it is noise. This episode is about what actually works: AI sales coaching that lives inside a rep's daily workflow instead of sitting unused in a Google Drive.After 18 months of building, Dave, Luigi and Regan officially launch CoachPilot on this episode of Revenue Leaders. It's the AI coaching layer that sits on top of your CRM and coaches every rep on every deal, live.The three of them have spent decades in sales, built hundreds of playbooks, and onboarded over 200 SDRs for companies like Stripe. They kept hitting the same wall: even the best playbook fails because it never lives in the rep's daily workflow. CoachPilot is their answer.What you'll learn:→ Why the number one problem in every sales org is adherence to the sales process, not talent→ The playbook problem: why even great playbooks end up sitting unused→ What a day in the life with CoachPilot looks like: morning briefings, live call coaching, post-call CRM updates→ The live coaching sidebar that prompts you on objections, pacing, and talk ratio mid-call→ Why they built their own call recorder instead of integrating with existing tools→ The four IP layers: forecast score, coaching score, deal score, and methodology score→ Gartner's 2.8x revenue stat: why action-based AI paired with humans beats AI alone→ Why AI can't replace sellers in complex, multi-stakeholder deals→ The 50% unapproved AI usage problem inside sales teams and why governance matters→ Who CoachPilot is NOT for: the honest ICP conversation most founders avoidWhether you're a founder, sales leader, or rep, this is a rare inside look at why three sales veterans killed their own consulting business model to build a product.⭐ Learn more about CoachPilot: https://coachpilot.comFollow us:https://www.instagram.com/davidfastuca/https://www.linkedin.com/in/luigiprestinenzi/https://www.linkedin.com/in/reganbarker/https://www.linkedin.com/in/davidfastuca/

The episode identifies a structural decoupling of software value from licensing units, driven by the rise of agentic AI platforms that automate tasks previously executed by human users within applications. This shift is evidenced by vendors realigning away from per-seat software economics toward service and outcome-based models. Companies such as Microsoft, Amazon, and OpenAI are redirecting resources into consulting and certification initiatives, responding to changing customer usage patterns and eroding profitability of traditional license models. According to Gartner, agentic AI could impact 20% of enterprise SaaS spend by 2030, redefining how businesses allocate budgets for software and services. A notable development illustrating this shift is Notion's decision to discontinue its Notion Mail application, not for lack of adoption, but because automated AI agents had largely replaced the need for a human-operated inbox. Microsoft has committed $2.5 billion and hired 6,000 consultants to embed AI solutions directly within client environments, bypassing traditional software seat sales. OpenAI has announced a global partner program aiming for 300,000 certified consultants within a year, while Amazon is embedding similar models into its offerings. Financial disclosures reveal that OpenAI's cost structure remains unsustainable under typical software unit economics, spending $1.60 for every $1 earned as of the most recent annual report. These developments reinforce the displacement of the per-seat licensing model. Gartner's cited mechanism is arbitrage, where agentic AI completes cross-system tasks without users actively working within apps, detaching business value from app usage. Traditional consulting's move away from hourly billing, as reported by the Wall Street Journal, echoes the software industry's realignment, emphasizing fixed-fee and outcome-based pricing over labor hours. The combination of end-client optimization efforts, vendor migration to services, and changes in consulting economics demonstrates a market-wide move toward operational accountability over software resale. For MSPs and IT providers, these changes pose direct challenges to legacy revenue assumptions and operational models. Per-user or license-based pricing faces mounting contract risk as agentic agents reduce seat counts. Service providers will be evaluated on their ability to manage this transition—internally and for their clients—by documenting workflow changes, auditing tool stacks, and adapting to new consumption and outcome-based vendor models. Early adoption of these practices within one's own business is becoming a credibility benchmark, as prospective clients scrutinize whether providers have successfully navigated the same seat retirement and cost reallocation they are expected to deliver. 00:00 Software Giants Go Human  04:26 Agents Don't Buy Seats  06:58 Squeezed From Both Ends 10;12 Why Do We Care?  Supported by: Guardz Pax8 

Dr. John Gartner returns to The Daily Beast Podcast to mark Donald Trump's 80th birthday with a deeply provocative conversation about aging, cognitive decline, power, and the presidency. Speaking with Joanna Coles, Gartner argues that the public is witnessing something far more serious than normal aging, laying out his assessment of Trump's behavior, speech patterns, late-night social media activity, health concerns, and decision-making as global crises escalate. The discussion explores dementia, malignant narcissism, stress, sleep deprivation, executive power, and why millions of Americans remain drawn to Trump despite mounting concerns from critics. From White House medical visits and cognitive testing to the psychology of leadership and the dangers of unchecked authority, this is an intense and unsettling examination of one of the most consequential figures in modern politics. Visit https://ffrf.us/BEAST or text "BEAST" to 511511 to join or learn more. #ad If you're ever injured in an accident, you can check out Morgan & Morgan. You can start your claim in just a click without having to leave your couch: https://ForThePeople.com/DAILYBEAST #ad Learn more about your ad choices. Visit podcastchoices.com/adchoices