Podcasts about Pocs

Cap

Today's guest is Chandra Sekhar Chappa, Global Head, Co-Innovation - ServiceNow at Google Cloud. Founded in 2016, Google Cloud is Google's enterprise cloud computing platform, providing organizations with scalable infrastructure, data analytics, AI, machine learning, security and application development services. Google Cloud helps businesses modernize operations, accelerate innovation, and securely build, deploy and manage applications and data at scale across hybrid and multi-cloud environments.Chandra is a technology leader with over 16 years of experience across product management, cloud operations, IT service management, infrastructure, and governance, risk and compliance. He specializes in ServiceNow, hyperscaler partnerships, cloud marketplace integrations, and enterprise service management. Chandra has led large-scale technology initiatives that have generated significant revenue growth and cost savings, while helping more than 1,100 enterprise customers improve cloud governance and operational efficiency.In the episode, Chandra talks about:0:00 His journey from IT tutor to leader in ServiceNow innovation at Google2:08 The importance of mentors in his career and giving back4:31 How the Google Cloud - ServiceNow integration/partnership combines AI with workflow transformation5:26 Enabling enterprise-scale workflows, execution and governance8:01 How their AI control tower provides full agent visibility and governance10:26 How the Google Cloud - ServiceNow integration/partnership enables real-time data and AI-driven CRM gains13:29 His advice to leverage AI ecosystems, avoid POCs and build faster15:51 The need to use trusted partners, align leadership and balance decision-making17:58 How mentorship, self-belief and persistence through uncertainty leads growthTo find out more about all the great work happening at Google Cloud, check out the website cloud.google.com

Introduction Most insurers say they want to be innovative. Fewer have a systematic way to know what's worth pursuing, who's building it, and whether they should partner, invest, or simply wait. Matt Connolly has spent ten years building the answer to that problem. Connolly is the founder of Sønr, a global market intelligence platform that tracks over five million companies and helps insurers, reinsurers, and brokers make better decisions about innovation and technology. Working with fifty-plus tier-one carriers—from Travelers and Liberty Mutual to Munich Re, Allianz, and Tokio Marine—as well as brokers like Guy Carpenter and WTW, Sønr sits at the intersection of the startups changing the industry and the incumbents that need to understand them. In this conversation, Josh Hollander and Connolly dig into where innovation intent breaks down inside large carriers, the four points where value leaks out of a corporate innovation process, why POC purgatory is a symptom not the disease, and how Sønr 2.0 is bringing market intelligence to operators who've been tasked to innovate but not given the tools to do it. Guest Bio Matt Connolly is the Founder and CEO of Sønr, a global insurtech market intelligence platform used by fifty-plus tier-one insurers, reinsurers, and brokers worldwide. Founded ten years ago, Sønr tracks over five million companies and has built a proprietary data set on insurance innovation unavailable to general AI platforms. He also hosts his own podcast interviewing innovation leaders from major global carriers. Sønr now generates half its revenue from North America and recently made its first US hire. Key Topics • Where innovation intent breaks down — At the CEO level. Without clear sponsorship and direction from leadership, innovation functions become disconnected from real business priorities. Ten years of data backs this up. • The four value leaks — Not understanding trends, poor scouting discipline, year-long POCs that should be three weeks, and failing to move from POC to pilot to scale. Each is a distinct failure mode with a distinct fix. • POC purgatory — Mature innovation programs are running more POCs than ever but scaling fewer. The root cause is almost always people: wrong sponsors, wrong internal champions, or wrong startup for the actual need. Sønr's fix: a one-day workshop to build a mini business case before a three-week POC begins, with KPIs and go/no-go criteria agreed upfront. • The decentralization of innovation — Carriers that once had centralized innovation functions have spread that mandate across underwriting, claims, and distribution—but capability hasn't followed. Operators have been tasked to innovate with no networks, no tooling, and no experience. This is the gap Sønr 2.0 addresses. • Sønr 2.0 and the Emerging Trends Academy — A simple front-end into ten years of proprietary insurance innovation data, priced for operators not just innovation teams. The Emerging Trends Academy goes deeper: cross-industry groups going deep on specific trends with startups, carriers, consultants, and academics in the same room. • The data moat — Ten years of tracking every company, trend signal, and client engagement within insurance innovation. Data that Connolly notes even Anthropic or OpenAI simply can't access. That compounded intelligence sits behind both the platform and the research offering. Notable Quotes "Don't go with the startup that is the best salesperson. Do the scouting properly—where are they based, what's their culture, who are their people, does the technology align to your needs?" "POC purgatory. We're seeing mature innovation businesses doing more POCs than ever but not moving beyond them. The answer is often the people." "The data we sit on is not available to anybody else. It's compounded intelligence from ten years. Anthropic or OpenAI simply can't get to it." "If you don't get your direction right from the top, the value leak is going to be huge later on. Just start in the right place." Resources Guest: • Sønr: https://www.sonr.io • Matt Connolly on LinkedIn: https://www.linkedin.com/in/wearematt/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

Un home de profunda fe en D

GitOps ist ein DevOps-Ansatz, bei dem der Betrieb von Services als Code in Git abgelegt und versioniert wird, statt Deployments manuell über Oberflächen zusammenzuklicken. In dieser Episode erklären Mira und Andreas, was GitOps ausmacht, wie sich der deklarative Ansatz vom klassischen imperativen Vorgehen unterscheidet und wo die Abgrenzung zu Infrastructure as Code verläuft. Sie sprechen über die Vorteile – etwa Nachvollziehbarkeit, Versionskontrolle, Automatisierung und geringere Fehleranfälligkeit – ebenso wie über Herausforderungen rund um Secrets-Management und das nötige Umdenken. Außerdem ordnen sie ein, wann sich der Einsatz lohnt und wann manuelles Vorgehen sinnvoller bleibt. Den Abschluss bildet ein Hands-on-Teil mit konkreten Einstiegsschritten und Werkzeugen wie ArgoCD.   **Zusammenfassung** Was GitOps ist: Betrieb von Services als versionierter Code in Git, inklusive Konfiguration und laufender Versionen Beispiel API-Deployment: früher alles in der Pipeline, heute ein separates Repo, das den gewünschten Zustand beschreibt und von Tools wie ArgoCD mit dem Cluster abgeglichen wird Abgrenzung zu Infrastructure as Code: GitOps fokussiert die laufenden Services statt der Infrastruktur und gleicht Änderungen aktiv und kontinuierlich an Vorteile: Dokumentation, Rollback per Versionskontrolle, Automatisierung, weniger Fehler, Review-Möglichkeit und gemeinsame Verwaltung mehrerer Service-Versionen Herausforderungen: Umstieg von imperativ auf deklarativ, schwierigeres Debugging, alles muss in Git liegen, Secrets brauchen ein zusätzliches Tool Wann sinnvoll: ab MVP fast immer; bei kurzlebigen PoCs ruhig manuell oder per Pipeline Einstieg: mit neueren, einfacheren Projekten starten, ArgoCD installieren und schrittweise komplexer werden (dev/prod, mehrere Services) Fazit: kurze Einarbeitung, dann lohnt es sich – inzwischen etablierter Standard und "Deployments mit Ruhepuls" **Links** ArgoCD: https://argo-cd.readthedocs.io FluxCD: https://fluxcd.io ArgoCD Image Updater: https://argocd-image-updater.readthedocs.io Sealed Secrets: https://github.com/bitnami-labs/sealed-secrets External Secrets Operator: https://external-secrets.io Helm: https://helm.sh Kustomize: https://kustomize.io Kubernetes: https://kubernetes.io

Pocs moviments entre els socis del govern S

Pocs moments s

How to find prime contractors and pitch them for real subcontracting work is one of the most overlooked skills in federal contracting, and most small businesses get it completely wrong. In this episode of the Federal Help Center podcast, Eric Coffie sits down with Zach Golden to break down a step-by-step research method for stalking primes, profiling tribal 8(a) firms, and writing outreach that actually gets a response. If you've been chasing contracts you can't win solo, this is the workflow that opens doors. Here's what you'll learn inside this episode: Why large 8(a) primes like ANCs, tribal entities, and Native Hawaiian Organizations operate more like Amazon than Walmart and how to position yourself inside their partner network The exact research workflow Zach uses inside OpenCube IQ to pull a prime's financials, NAICS codes, contract history, and government POCs before sending a single email How to write a short capability statement email that doesn't over-explain and triggers a real reply from busy CEOs and business development leads How to use NAICS code spending data and state filters to surface the right primes when you don't yet know who's holding the contracts in your space The talking points strategy that lets you sound like an insider in conversations with primes even when you're early in your govcon journey EPISODE CHAPTERS: 0:00 - Why large 8a primes work like Amazon partners 1:25 - How to approach tribal entities with capability statements 2:50 - Sending capability statements into the network 3:30 - Researching tribal primes inside OpenCube IQ 5:00 - Reading contract history and finding government POCs 6:30 - Using NAICS code spending to find the right primes 7:45 - Filtering by state to narrow down vendor lists 8:45 - Building talking points that prove you know the game Market Intelligence gives you the federal opportunities, agency signals, recompete intel, and pursuit briefs that tell you not just what contracts exist, but which ones to chase and how to win them. Sign up for free Daily Alerts and get opportunities delivered to your inbox before the day starts.

Anthony Tanoury, senior director of distribution at Dell Technologies Distribution doesn’t get a lot of editorial love. It’s easy to treat it as the background infrastructure of the channel – the warehousing, the credit lines, the logistics layer that keeps product moving. But as anyone who’s been paying attention knows, that picture is well out of date. At Dell Technologies World in Las Vegas this week, In the Channel sat down with Anthony Tanoury, Dell’s senior director of distribution, to talk about what distribution actually looks like in 2026 – and the conversation ranged from supply chain strategy to AI-assisted deal registration to the shifting economics of the partner ecosystem. The headline number: Dell moved approximately ten thousand partners to a distribution-led buying model last year. Partners who previously purchased direct from Dell now route exclusively through distribution. The more interesting data point is what happened next – those partners are growing faster than the ones who remained on a direct model. Tanoury attributes it to the enablement depth that distributors can offer at a scale that Dell simply can’t replicate directly. On the Modern Partner Platform rollout – one of the bigger announcements at DTW this week – the conversation came down to speed. Deal registration that today takes two to three days is being redesigned, with AI-assisted automation in the pipeline to bring that down to two to three hours. The plumbing involves integrating Dell’s systems tightly with distributor platforms, streamlining the multi-system, multi-email-thread process that currently slows everything down. And when asked for the single most underutilized resource available to partners through distribution, Tanoury didn’t hesitate: the AI accelerator programs that distributors have built to help partners get started in the AI practice space. With every partner asking “where do I begin,” the answer may already be sitting in the distributor’s enablement catalogue. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor at ChannelBuzz.ca and your host for the show. We’re continuing our coverage from Dell Technologies World in Las Vegas this week, and I wanted to close the series of Dell execs with a conversation that I think will resonate with pretty much anyone who moves Dell product – which, let’s be honest, is a lot of you. Distribution is one of the topics that often gets taken for granted. It’s the plumbing, it’s the logistics, it’s the credit line. Except that’s not really what distribution is anymore, and Anthony Tanoury has about as good a vantage point as anyone to explain why. He spent 30 years in the industry on both the vendor and distributor side of the table, and he’s now Dell’s senior director of distribution, which means he’s the person responsible for making the relationship between Dell and its distributor partners actually work at scale. This week at DTW, Dell announced some significant changes to how it’s thinking about its partner ecosystem, and distribution’s right at the center of that. We talked about the evolution of distribution from warehouse and financing shop to AI enablement engine, what it actually means for partners that Dell moved 10,000 of them to distribution-led buying last year, and what the promise of deal registration in hours rather than days actually requires to make real. Let’s get right into it. My chat with Anthony Tanoury. Anthony, thanks for taking the time. I appreciate it.Anthony Tanoury: Thanks for having me. Robert Dutt: To kick things off – the definition of distribution, and the definition from distributors themselves of what they do, has changed so dramatically over the last few years, as you’ve been party to on both sides of the fence, vendor and distributor, with your background. Sitting where you are now as senior director of distribution, how do you define the core value proposition for your distribution partners today compared to the way it may have looked a few years ago if you were in the seat, or in a previous seat managing distribution? Anthony Tanoury: Yeah, I think 30 years in distribution – dating myself here. The idea of a distributor was warehousing, finance, so on. Really, the way that that’s evolved – and still evolving, because not everyone fully understands distribution and the value of distribution – but it’s really become the engine for all of us OEMs to really dive deep into the mid-market, and as lead generation for all of us. So SMB, mid-market, and then really leveraging their enablement platforms for our partners. So as an example, this week here at Dell Technologies World, we’ve launched our full AI portfolio. And really at the end of the day, it’s a platform to build off of. And our distributors, through our partners, are really enabling those partners – especially in the mid-market. The enterprise partners have hired data scientists and so on. And those mid-market and SMB partners, they need our help. And we really rely on our distributors, who have AI accelerator programs and can really take a partner through the journey of how to look at AI, how to start, and then how to implement and really get started in this space. We’ve met with multiple partners at this show and we’ve had our partner advisory boards. And that’s the number one takeaway when we’re talking to our partners: “How do I get started?” And I think Jeff Clarke and Michael Dell talked about that on stage – it’s really, we’ve got the platform to build off of, and then really rely on our distributors to go enable all of our partners out there to have those conversations, and then to build the proof, the POCs for us with their customers and take it to the next step. Robert Dutt: Let’s talk about this moment in time and managing distribution right now. Whenever I think of running a hardware vendor, running distribution, or being on the purchasing side of the solution provider right now – boy, that’s an interesting challenge – with the supply chain issue, with the pricing issue, with all of that. I guess it boils down to, from your perspective: how are you leaning on distribution differently to help you guys and your partners ultimately, especially the smaller ones, handle this issue of availability, of supply chain, of capacity, as we’ve seen the component price challenges across the industry? Anthony Tanoury: Yeah, so that’s not unique to Dell. We’re all challenged with the supply chain challenges, and it’s really about having a consistent message to our partner community, to our customers, on how – or why – to partner with Dell in these times. And our distributors have really leaned in with us right now and are getting that message out to our partners that “Dell’s got a plan. Here’s the plan.” And this is how we want you to message that and relay that to your partner community. So as an example, I did a keynote speech at one of our large partner events recently, and my talk track was based on how to navigate those supply challenges with us. I spent a lot of time on that, and had multiple partners come up afterwards, catching me outside. And the comment was, “That’s what we need to hear. That’s our challenge today, and you’re tackling that head on.” So to get back to your question from a distribution perspective – they enabled me to take that message to them, and then they’re expanding on that to their 20,000 partners in their ecosystem. Robert Dutt: As you bring up an interesting thread there – I don’t have time obviously to go through the whole keynote, but the elevator pitch, boiled-down version of it – what’s the advice to partners on tackling it from where you sit and from where Dell sits? Anthony Tanoury: Yeah, really leaning in with us and going deeper with your customers. And so that’s where you’re going to work with Dell and get priority allocation – looking long-term versus short-term, “I just need this product in the next week to get through this phase.” Now, let’s look at a long-term solution together and let’s plan two years out. Let’s plan longer in some cases, and then we’ll take it from there. Robert Dutt: And that’s something we heard also from Jeff Clarke in Q&A – that idea of build out those long-term plans, put your hand up as early as you can. Because it sounds like if you’ve got your hand up early, you’ve obviously got the best chance of getting that list fulfilled. Anthony Tanoury: Yeah, whether it’s a customer or a partner – I mean, that’s a true partnership and we’ll lean in when customers want to lean in with Dell. Robert Dutt: I wanted to touch on the changes that are coming to the partner program, specifically as it involves your interactions with distribution. The Dell portal is getting redone and the Dell program is getting redone with the modern partner platform rolling out this year. You guys are baking agentic AI into your partner platform. Meanwhile, your distributors are doing the same thing with their partner platforms. I’m curious – obviously very early in the game – but how are you and your distribution partners thinking long-term about how those various platforms interact with each other, in terms of delineating who covers what base, when it comes to serving the partner and what you may be able to do down the road as a result of having those platforms? Anthony Tanoury: Yeah, so the key is cutting down on SLAs. How do we take getting pricing out to a partner, out to a customer, from two to three days down to a matter of hours, right? And we’ve worked closely with all of our distributors over the last year or two, because our partners rely on our distributors’ platforms. And how does that integrate with ours? But the key is speed. How do we do things faster? And that is, as you stated, embedding AI into that. And so again, can’t get too far ahead, because we’re still going down this path and things sometimes get pushed out. But we’ve been working on this for a long time with them. We’ve had a lot of meetings with them here. We’ve gone deep into their platforms. They’re all rolling out new platforms as well. So making sure we’re doing it all at the same time, and together, has been key. Robert Dutt: One area I did want to double-click on there. One of the big promises of the new platform is deal-reg approval in minutes, AI-generated demand signals, those kinds of things. As Dell is accelerating its own systems, how does distribution plug into that? How does the distributor help manage and act on those AI-driven demand signals and facilitate a faster quote-to-deal-reg? Anthony Tanoury: Without getting too deep into deal-reg, there are a lot of nuances there. But yes, today where you’ve got multiple partners of record and you’ve got multiple partner IDs – simplifying that down to one or two partner IDs versus 20 today that we have – and then with deal registration, having partner of record is key in that mix, and we do have that today. But the distributors are really where it starts. So a partner comes to the distributor, says, “Hey, I need pricing on this and I want deal registration.” Today it might take the full SLA – the two to three days we just talked about – to get deal registration approved, with multiple systems flowing back and forth. In the future – and when I say future, we’re close, we’ll get there – is having that one stream go, starting from the distributor, through AI, plays into that, where it’ll do the work of looking in and making sure: here’s the partner of record. Is there a partner on record? Does the end user qualify? And without multiple people, multiple email streams going back and forth, it locks it in. And so now you’ve got an answer back in two to three hours versus two to three days. Robert Dutt: A lot of MSPs prefer to consume technology as a service, because it’s kind of in what they do – the name’s kind of on the tin – and bundle that with vendors like Microsoft or security or what have you. How are you working with distributors to make APEX and infrastructure solutions seamlessly consumable within distribution, and particularly on their marketplace? Anthony Tanoury: Yeah, so that’s a good question. So there’s APEX, right? We have Dell APEX, and our competitors have their own, but we have Dell APEX. But our distributors also have their own versions of APEX, or as-a-service models. And at the end of the day, we leverage theirs just as well as we do our own. And it depends on the customer, depends on the contract situation, but there are multiple vehicles to get an as-a-service deal done today that didn’t exist a year ago, didn’t exist two years ago, right? And then there’s – moving to another topic, and really the same topic – device as a service, right? And that was something we’ve been talking about for a few years now and hasn’t really taken off, but that’s all part of this now. Because the device at the edge is co-mingled now – especially in the new AI world – with your server infrastructure. So it could all become part of a recurring revenue stream for MSPs. Robert Dutt: And I think it makes potentially hardware more compelling to the MSP. When you’ve gotten that tie-in – I know it’s early days and it’s a way off from being fully operationalized – but what you’re talking about, and what Jeff Clarke was talking about today about basically acting as the arbiter, sort of an open orchestration layer, saying “all right, this particular bit is best handled in the infrastructure and the data center, this particular bit is best handled right here on the machine sitting by the desk side.” Anthony Tanoury: Absolutely. Robert Dutt: We’ve heard a lot this week about the focused accounts incentive, rewarding partners for selling across lines of business. And it’s kind of a cliche almost, in that vendors such as yourselves who have multiple lines of business are always looking for great ways to get partners to sell across those businesses. And certainly incentives are a classic way of doing that. How are you using distribution to train, enable, and facilitate partners making that leap across the portfolio – especially as this seems to be something that Denise Millard and the team are putting a lot of the wood behind? Anthony Tanoury: Yeah, so you mentioned the partner program – and that’s really what we leverage with the push coming from distribution. You typically focus where you can earn the most dollars. And so we’re putting the dollars on driving all lines of business for us. So today you may have a lot of infrastructure-focused partners – like MSPs, they don’t want to sell the client the edge device. But again, with AI driving from both ends now, it’s become an imperative that they don’t ignore the edge devices anymore. So really leveraging distribution both ways. We’ve got CSG partners that don’t sell storage and infrastructure, and then we’ve got partners that are trying to move in that direction. And then we’ve got other partners saying, “Hey, I’ve got to get on board too,” that are in the infrastructure space and have got to move in the other direction. And that’s where we leverage distribution – they have multiple enablement engines, all of our distributors, to enable those partners to do that. So for us – and again, to the partner program – we’ve announced some changes here at this event, with our partner advisory board meeting coming up. Partner programs, you want to keep them simple, predictable for partners, with tweaks along the way. And AI is one of those tweaks where we’ve got to pull the levers in different directions to get partners and distributors moving in that motion. So yeah, it’s an exciting time to be at Dell with this opportunity in front of us. Robert Dutt: That’s a big tweak – or more accurately, a big series, whole family, whole universe of tweaks to be made. But you don’t want to pull a whole program apart. You’ve got partners that have invested and distributors that have invested in that program. So you’ve got to make sure you do those incremental tweaks when you need them, but not blow up the whole program. Anthony Tanoury: Absolutely. Robert Dutt: You mentioned off the top the classic framing of distribution as the warehouse and the bank kind of structure. Let’s touch on the bank side of things a little bit there. In light of everything that’s going on today, in light of the infrastructure refresh opportunity that’s out there, the constraints in the marketplace – financial engineering is probably more critical than ever. Dell Financial Services is doing a lot of heavy lifting, but how do you view the role of the distributor when it comes to PO financing, terms, bridging the financing gap for complex projects, and helping partners manage this whole multiple-balls-in-the-air situation? Anthony Tanoury: You can’t look at a partner just through the lens of what they do with Dell. The business they have with Dell – partners procure from many places. We love them to only sell Dell for us, but they have other options, other solutions, other areas of the business that we’re not focused on. They procure through distribution. Distributors have huge businesses with a lot of these partners. They have financial terms through the distributors that maybe we can’t offer them through Dell – and leveraging our partner programs to deliver extended terms in this environment. With the supply shortages and lead times getting pushed out, really leveraging distribution with terms that we can’t give them today. There are multiple levels, and they have much higher credit lines with the distributors than maybe we have with them. And then going back to the as-a-service model – really leveraging distributors who have all those options in place for them today, that maybe they don’t have with us. Robert Dutt: When you’re looking at distribution, what’s the one metric you look at first to judge whether a distributor is meeting the bar – is delivering net new value to Dell? Anthony Tanoury: New partner recruitment, right? Multiple lines of business – not just focused in one area of our business, but selling across all lines of business. Then we rely on distribution. We just moved 10,000 partners last year over to distribution-led. Where those partners could procure direct from Dell in the past, now they can’t, and they buy strictly through distribution. Those are our authorized partner community – and potentially in the future, expanding that to other levels of our business and offloading them to distribution. Dell is a more channel- and distribution-friendly company than we get credit for. I think that doesn’t always get seen, and we’re moving that way. Robert Dutt: How did that process go, and any learnings from moving those 10,000 partners that may inform what you do in moving the next group, if there is a next group to be moved? Anthony Tanoury: Exactly, a lot. A lot of that is in data transfer and making sure that the distributors have the right data to target those partners and give those partners the service they need. The distributors all had to ramp up their infrastructure to support those partners – credit line facilities with those partners – because they didn’t do business with those partners before. Onboarding some of those partners as net new to distribution, who had never bought from distribution before. And then again, really letting those partners know the value of distribution. Since we’ve moved those partners over, those partners that have embraced distribution are growing faster than the partners that haven’t. It’s sometimes a lot easier to get that additional support, that additional attention from a disti, than it is to try to navigate that directly. In some cases, they can support them better than we can, and it’s proven out in the last year. Robert Dutt: What’s the single most underutilized resource that you guys have through distribution, in terms of what partners are using? Anthony Tanoury: I would say the AI accelerator programs I spoke about earlier. That’s key. Going back to the enablement piece – I just don’t think a lot of partners understand the value. They come to these events, they make the statements, “Hey, we need help here. We need to leverage distribution for that help.” Especially when you come to a Dell Technologies World, or you go to one of our competitors’ or peers’ events. Our distributors have that enablement piece for you to get started, that you need to leverage, because it’s not just a point-solution type of conversation, it’s broad. Really leveraging them to help. Robert Dutt: Along the same lines, but a little bit different – obviously we’ve touched on the idea of cross-selling, and the idea that, surprise surprise, Dell would like partners to sell more of the portfolio, better together, all that kind of stuff. For an MSP or VAR whose primary look at Dell to date has been selling end devices – laptops, desktops, et cetera – sourced through distribution, what do you see as the most likely next logical step to expand that relationship? To get thinking across lines? What are some of the common threads for the best ways to approach that? Anthony Tanoury: Yeah, that’s a tough question. Common ways to approach how to sell across lines of business – take it back to the customer level. Your customer is buying these products, and they may be buying them from somebody else or they may be buying them online, depending on the size of the organization, so on. Again, the service model – going back to it, it’s another service revenue stream that they can leverage. But I think when you look at the distributors, they have a lot of talk tracks with the partners on how to do that, and frankly do it better than we do. So that’s why we really leverage them. When we say, “Hey, we want to sell more of our client and peripheral devices,” we start with distribution. We start with the partner community, and it’s paid off. I think it’s just – really, don’t leave revenue on the table. We’ve been saying it for years and I think it’s starting to resonate, and leveraging distribution to push that message forward. And I think partners are starting to catch on. Robert Dutt: All right, great insights. Anthony, I thank you for taking the time. I’m sure it’s been a busy week for you here. Thanks for joining us. Anthony Tanoury: Thanks for having me. I appreciate it. Robert Dutt: There you have it, Anthony Tanoury from Dell Technologies. I’d like to thank Anthony for carving out some time in what I’m sure was a very busy week on the show floor here at DTW. Few things from the conversation that I thought were worth pulling out. First, the 10,000 partners that Dell moved to distribution-led buying last year – that’s not a small number, and the fact that those partners are outgrowing the ones who haven’t yet made that transition should be a data point for anyone still on the fence about how they structure their Dell relationship. Second, when Anthony named net new partner recruitment as his primary metric for judging distributor performance – not revenue, not attach rate, net new – that tells you something about where Dell thinks its distribution channel still has room to grow. And third, if you haven’t looked at the AI accelerator programs your distributor is running, that came up twice as the single most underutilized resource available to partners right now. Probably worth a phone call. I’d like to thank you as always for listening to the show. Please follow or subscribe wherever you get your podcasts – Apple Podcasts, Spotify, YouTube, most directories. Ratings and reviews are always appreciated as well. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

In this episode, Raghu Nandakumara sits down with Andrew Rubin, Founder & CEO of Illumio, for a candid conversation about the next phase of AI-driven cybersecurity risk. Just weeks after a major AI breakthrough sparked shockwaves across the security industry, Andrew shares his immediate reaction — from the sobering implications of machine-speed vulnerability discovery to a frank assessment of why the cybersecurity industry's fundamental model may already be broken. The conversation explores what actually changes in an era where vulnerabilities could be discovered and exploited faster than any human-driven operation could manage. Andrew argues that while segmentation as a concept is decades old, its role as a critical backstop has never been more urgent. If attackers begin operating at machine speed, defenders must rethink not just their tools, but their entire operating model — from how they assess risk to how quickly they can respond. Raghu and Andrew discuss: Why the cybersecurity industry has spent more every year while outcomes have gotten worse How AI creates an asymmetric threat unlike anything defenders have faced before Why patching alone won't solve the problem — and the COVID vaccine analogy that explains why The shift from prevention to resilience as the new security north star What the SolarWinds story reveals about how organizations miscalculate tail risk Why segmentation becomes one of the few reliable backstops in a model-driven world How the era of 12-month RFPs and POCs may be coming to a swift and necessary end Stay Connected with our host, Raghu on LinkedIn: https://www.linkedin.com/in/raghunandakumara/ For more information about Illumio, check out our website at illumio.com    Resources Mentioned: Hard Truths in Cybersecurity: Fear, Liability, and the Industry's Biggest Lies | RSAC 2026 Panel:  https://www.youtube.com/watch?v=88XjfZBYIw0

Alan Ashby, senior director of Americas data center presales and specialty sales at Dell. Today’s episode of In The Channel comes to you from the floor of Dell Technologies World 2026, where the expansion of the Dell AI Factory has been dominating the headlines. But what does that mean for partners who aren’t selling multi-million dollar deployments to the Fortune 500? To find out, we sat down with Alan Ashby, senior director of Americas data center presales and specialty sales at Dell. Ashby breaks down the practical realities of the AI infrastructure boom, explaining how partners can start small by deploying “AI supercomputers” like the Dell Pro Max GB10 directly to SMB desktops to unlock local, highly secure agentic AI workflows. We also dive into the economics of on-prem AI versus the public cloud, how partners can help customers escape “prototype purgatory” by narrowing their focus, and the massive opportunity remaining in traditional data center modernization—including the staggering claim that Dell’s new 18G platforms can consolidate 13 legacy servers into one. We also touch on how Dell is leveraging its Customer Solution Centers to help partners de-risk these complex deployments before the customer signs the PO. Read Full Transcript Robert Dutt: Hello and welcome to In the Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. We’re coming to you today from the floor of Dell Technologies World in Las Vegas where the expansion of the Dell AI Factory and new agentic AI capabilities have completely dominated the Day 1 headlines. But as we know, the keynote hype doesn’t always translate immediately to the loading dock. To understand how partners are supposed to actually size, architect, and sell these new AI infrastructure solutions, I sat down with Alan Ashby. He’s the senior director of Americas Data Center pre-sales and specialty sales at Dell. We dig into the economics of on-prem AI versus the public cloud, how partners can get mid-market customers started with an AI supercomputer right at their desk, and why the traditional data center refresh is still a massive and highly lucrative play for the channel. Let’s get right into it. My chat with Alan Ashby. Alan, thanks for taking the time. Appreciate it. Alan Ashby: Absolutely. Thanks for having us. Robert Dutt: Americas Data Center pre-sales and specialty sales. That’s a broad title. A lot of ground to cover there. To set the stage for MSPs, solution providers, folks listening to this, what can you tell me about what your team actually does kind of day-to-day when it comes to working with partners around infrastructure and AI solutions? Alan Ashby: Yeah, absolutely. So we’ve got a handful of folks that, you know, we’re aligned and dedicated to the partner ecosystem focused across the Americas. We have a couple of primary roles. So from a pre-sales perspective, helping support our partners from a technical enablement, understanding our product portfolio, understanding how to position the products correctly, both amongst the portfolio itself, but also kind of competitively in the marketplace. We also run what we call a technical account plan with our partners. So, you know, supporting them on their certifications, their enablement motions, etc. And then we also run what we have a program we call Heroes for our partners. So Heroes is our foundational enablement motion for partners. We run in the Americas somewhere between 15 and 30 regional face-to-face sessions every single quarter. Those we’d love to see partners participate in, try to do them all over the country. And those are deep dive sessions, you know, going through products and roadmaps and futures and how to position products, etc. And, you know, those have been an enablement motion for the last several years and been incredibly successful. Robert Dutt: All right. We’re hearing a lot this week, obviously, about the expansion of Dell AI Factory and the idea of bringing AI on-premise to the edge, closer to the enterprise itself. And from an infrastructure perspective, you’ve got PowerRack, the pitch there being you go to live customer workloads from kind of the box to deployed in six hours and change. For a partner who’s trying to sell into the mid-market or the enterprise, you know, how does that kind of speed of value fundamentally change the conversation that they’re having with their customer, whether that’s the CEO, CIO, or the business leader? Alan Ashby: Yeah, I don’t think there’s been a more exciting time for our partners with what the market’s putting out there for us. You know, when we look at, you know, you mentioned the mid-market space, I actually think there’s a massive opportunity for partners to go support those customers, especially with some of the agentic workflow processes that we announced today with some of the platforms. You know, it may not be those 100 million, 200 million dollar opportunities, but almost every single small business and medium business, you know, you start with maybe a product like the Dell Pro Max GB10, and you start there and you start building out that agentic workflows, you know, building out automated dashboards with AI assistance built into it. You know, a lot of great things that a partner could go deliver that everybody can see value in. Sometimes in that mid-market space and small business space, it’s easier to get started on some of these agentic flows because they don’t have data that’s kind of messy. They don’t have legacy debt from a data center infrastructure perspective. And then from a larger enterprise or commercial customer, you know, we have seen a number of very good successes across our partner ecosystem with delivering services and value to our customer sets collectively, you know, to help customers really try to find value through their AI journeys. Understanding and identifying key use cases or workloads that they think they can get value out of it, understanding the infrastructure, the architecture that’s designing it right. You know, early days, you know, we had a lot of times where, you know, customers and partners struggle with just, you know, how do we deploy this thing because power and cooling needs are maybe bigger than what I was expecting and, you know, managing through that challenge. So partners have a phenomenal opportunity, I think, to help provide that value to our customers collectively together. You know, every one of our partners, they bring a unique skill set and differentiators on their own to the marketplace and help support those customers to that kind of their own journeys together. Robert Dutt: What is that infrastructure pitch down to that, especially that mid-market or even SMB customer? In the past, there was interest in doing it, I think often they would end up, if they were going to do it, doing it on public cloud, because the alternative was a big old infrastructure solution that doesn’t really fit them, unless maybe a partner can bring it on and kind of do a multi-tenant kind of situation there. But where are we at in terms of having right-fit infrastructure to make that work? Alan Ashby: Yeah, I think, you know, even the stuff that we announced today on stage, you know, products we announced at GTC, I think really helped kind of build out that situation and story for a small customer to be able to scale. You think about going back to the Dell Pro Max GB10, you know, you can take that device and you can, you know, run a small business basically off that depending on the concurrent users and be able to move up from that to some of our Pro workstations all the way up to the GB300. You know, we can run a model as big as a trillion parameters, it’s kind of crazy what you can do on a desktop, you know, and that doesn’t require any unique power requirements, I can plug that into a normal outlet. And then I could scale into, you know, actual infrastructure depending on the size of what the need is. And that’s where I think there’s a lot of opportunity for partners to think through, you know, how do they help customers scale through that. And so we talked a lot today at the show around, you know, the economics of everything. And in the long term, it’s going to be very challenging economically to run things in a public cloud. Yeah, on-prem is going to be a massive opportunity. And the fact that Michael today even talked about things about running foundation models and open source models on-prem, you know, your data is fully secure, you manage it all yourself. You know, it’s a lot easier to think about how I actually, you know, pull and extract value out of those different solutions. Robert Dutt: Well, and that’s the pitch right for the desk-side agentic AI solution is the idea, I think that the number was 87% reduction in token cost and in terms of comparing the cost of acquiring, deploying, running the solution on-prem. I think the break-even was three months or something like that against running the same kind of solution in public cloud. Alan Ashby: Yeah, I think that’s where customers are challenged today is, you know, you can have a lot of different, you know, foundational models and, you know, some of the agentic tools that are out there today that are subscription-based, cloud-based. And you can run through usage real fast without getting a lot of value out of it. When you start thinking about deploying stuff on-prem, you know, you know exactly what your output per day could be, and you can scale accordingly. Robert Dutt: How does that change how a partner approaches both selling and thinking about running, maintaining that infrastructure as opposed to something that’s all outsourced to the cloud and has those significant question marks of cost attached? Alan Ashby: I think there’s a lot of stuff we’re still figuring out, to be honest. You know, I think a lot of partners are trying to understand that and every customer is going to be a little bit in a different spot in their journey. And I think, you know, that’s where some of our partner ecosystems have tremendous value to help meet them where they are and help them take that first or second step forward to try to be able to deliver overall value to the company. Robert Dutt: Do you see that kind of time to value, that reduction in overall costs being something that can get unstuck some of those classic cases of AI workloads that are getting put into prototype, into test phase, but never quite see the light of day, partially perhaps because of that economic headwind that you discover when you start trying to scale these things? Alan Ashby: I think there’s that. I also think sometimes some customers probably try to maybe bite off more than they can chew at one time. And I think when we start thinking about these AI use cases, sometimes we’ll talk with some customers and partners helping them through them. They have, you know, two, three dozen things they want to try to accomplish out of one solution or one opportunity. It’s how do we narrow that down a little bit to where we actually extract value out of that particular use case that you’re trying to drive value with. And we’ve seen some really great success with some of our partners being able to help, you know, negotiate and navigate partner customers through that journey. You know, I think it takes a skill set that’s unique, and we’re starting to see more and more of our partners, you know, invest in and put attention to building out dedicated AI practice teams, helping them understand the skill set. The market’s moving incredibly fast, unlike ever before. And so, you know, it takes somebody who has a real passionate interest and a lot of curiosity to understand how these things all work together and all the pieces fit together and how do you take advantage of everything as you go forward. Robert Dutt: How do you see the co-delivery model evolving over time as you say, things are moving fast. When it comes to deploying AI factories, I think we heard earlier that, you know, the model is sort of Dell handling deployment and management of the overall environment while partners are being asked to focus on the application, the vertical, those kinds of things. How do you see the role of the channel, I guess, especially professional services and advisory-type partners evolving? Alan Ashby: Yeah, I think that to your point, I think it’s evolving. And I think that, you know, there’s a lot of opportunities here from an educational services perspective, consulting services perspective, services for our partners, you know, very few customers, especially when you think about, you know, a traditional commercial customer, mid-market customer, know exactly what to do and what to do next. You know, they might have started a pilot out in the public cloud. And then they’re trying to figure out where to go from here. And like, there’s a lot of service opportunity for our partners there. When it comes from, you know, other deployment services, I think there’s opportunities there for our partners, you know, depending on the solutions. When you look at post-delivery of the product into the customer, I think that there’s even more opportunity for partners of how, once things are deployed and installed, what’s next? And how do you help customers really extract value out of the infrastructure they spent a lot of money on, and have pretty high expectations of the ROI and the benefits they get out of it? I think there’s a massive opportunity for partners to help those customers through that journey. I think there’s a big opportunity for partners to take a product like our GB10, GP300 products and say, how do I go show you how to build an agentic workflow on those systems that can deliver value for your customers? You know, those are all going to be partner-delivered opportunities. Robert Dutt: All right. It sounds like even though it’s relatively early in the process, we are at the point where some of those next steps are becoming clear then. Alan Ashby: Yeah, I would say so. I mean, the question is, how fast do things change? You know, and it’s one of those things like I look at the agentic opportunities, probably one of the biggest things that can bring value for our partners. We’re really looking for a partner ecosystem that has the skill sets to deliver those for customers. Robert Dutt: Speaking of things changing, moving from traditional virtualization workloads to AI is a pretty big shift in how you think about structure, infrastructure, especially around storage, IO, networking, GPUs, needless to say. How’s the pre-sales team helping partners to figure out what the right size is for these solutions, both for current state and future state, so that you’re not either over-provisioning or under-provisioning customers? Alan Ashby: That’s a great question, actually. I mean, we’ve done a lot of things internally at Dell to get better ourselves and have the right talent and resources to support the partner ecosystem. You know, we have teams that can help support partners, both from a sizing, scoping of the opportunity, all the way down to configuring and deploying that solution if the partner needs that help. We’re also trying to help up-level our partners to be able to do it on their own. It’s kind of self-service and building the tools to help them through that motion. A couple of years ago, we started launching AI workshops, the different skill sets to help up-level and help that motion for a lot of our partners. The partners that have participated in those have seen a lot more success than those that didn’t. We do those multiple times a quarter and encourage partners to participate through those motions. We have an AI workshop multiple times a quarter in North America, and we go through every step of the phase from how do you have a conversation with a customer all the way through, how do you narrow down use cases, to all the way to how do you actually develop, design, and build the systems for what you need. Robert Dutt: Along those same lines, but a little bit more customer-facing and kind of looking at the economics of it, AI projects carry a lot of financial and technical risk for CIOs. What resources are there, whether it’s proof of concept, technical validation, or specialty engineering teams that partners can tap in to kind of prove the math and de-risk a solution such as AI Factory for customers? Alan Ashby: Yeah, there’s a couple of them actually, and I encourage all partners to kind of look at the options. We have at Dell, we have what we call our Customer Solution Centers, and those Customer Solution Centers have the ability to be able to work with a pre-sales specialist, a pre-sales expert on various different solutions. We have data centers where partners can take advantage of and leverage to be able to do proof of concept for customers, proof of value with those folks, and that can vary from any size of the architecture, from small all the way up to very large, and help support them through that. Also encourage partners to reach out to their Dell teams and how do you take advantage of those CSC resources. It’s a very simple process, but work through Dell teams. Same thing would be to go spend time with us in our labs. We have a great lab up in the Hopkinton area where AI factories are manufactured and built, and love to take partners through that facility to be able to see what’s possible there. We have an AI lab down in Austin to help them through that as well. So there’s a lot of opportunities. I would say the other one is we have a lot of partners also building out their own capabilities, their own labs, and we’ve helped support them through that as well. I think that they’re providing some amazing value to their customers, being able to do their own POCs and demonstrations and whatever it might be to help support that customer throughout the process. Robert Dutt: AI obviously gets the big headlines because it’s the 2020s as it is. But customers still have traditional enterprise apps and aging infrastructure that is going to need a refresh. I guess, how does your team handle guiding partners around going after the new shiny thing, the big opportunity that’s out there versus the kind of day-to-day operational challenge of standard data center modernization and refresh? Alan Ashby: Yeah, it’s hard when they have two of these really big shiny objects out there that have a lot of potential value for customers, both with AI but also just traditional data center modernization. We’ve seen a really great success over the last year of helping customers, I would say, clean up the data center, think through what they’ve got today in there and how to modernize it and right-size everything. When you look at some of the things that we’ll announce here at the show, it’s pretty exciting, honestly. There’s some great announcements we had in the Day 1 keynote, Day 2 keynote will be just as exciting, more from an infrastructure perspective of things. I’m really excited what we’re doing just with traditional servers and we’ve seen a lot of great success by our partner ecosystem over the last several quarters with them going in and helping customers look at consolidation of those environments. Our 18G server platforms, which we’ll announce, can consolidate 13 legacy servers into one. That’s kind of crazy math when you think about that. It’s easy now to think about how do I help customers free up space and modernize things that makes it so AI is possible in their own data centers; consolidating racks in the servers is kind of a crazy concept. Then you think of how we’re looking at modernizing just traditional architecture with HCI architecture and the disaggregated architecture providing real value for customers with right-sizing, both compute capacity and storage capacity to be able to extract as much value as possible across the ecosystem of the portfolio. Robert Dutt: Along those lines, any other, I guess hidden opportunities for partners, things that maybe don’t get the big attention of the desk-side AI or PowerRack or some of those things, but still represent—sort of along the lines of the data center example you just gave—opportunities that are worth pursuing, that are worth looking at, but maybe not quite the highest profile? Alan Ashby: I mean, 100%. It’s easy to get excited with what we’re doing in AI. The market’s obviously kind of dictating a lot of that, but there’s a lot of opportunity, a lot of money to be made for our partners to be able to focus on classical data center architecture. We’ve got some great solutions. Our Dell Private Cloud is one that’s extremely exciting for partners, the opportunity to be able to help those customers through that process and think through that. I also am extremely excited with what we’re doing around the security front with our data protection portfolio, our PowerProtect product lines. Security is one that I think in the age of AI, we need to think through security differently. There’s some additional opportunities for partners to think about how do they provide those services, those extra value pieces to help make sure all of these customers are ready for what could be an AI security threat. Robert Dutt: I assume there’s a better together story to be told there between the hardware, the infrastructure, and the cyber protection. Alan Ashby: 100%. That’s one of the biggest values that we have at Dell. There’s inherent value between the products themselves being able to support each other differently, but also they have the large Dell value prop with the Dell supply chain, our security chain, how we build products. Everything provides value across the entire portfolio. Robert Dutt: What’s the single biggest misconception you see customers have around the idea of deploying on-prem AI in particular? Alan Ashby: That’s interesting. The big one I would say is where do I get started and how big do I need to get started? I think that we saw early days, a lot of customers thought initially you had to just get in line for supply on large GPU systems when you could run a lot of workloads, really interesting and exciting AI workloads on a server with a PCIe-based GPU, and now even more so with some of the other platforms with workstations or GB300, GB10. The biggest misconception is just thinking about how big I have to get started. I would encourage almost every executive, every leader of every company to start thinking differently about you probably should have an AI PC in your office and on your desk. You should have one of our, I always call it an AI supercomputer on your desk with the GB10. It’s about who’s going to be the most curious. There’s nothing that limits you from capabilities with what the models can do today. We really just need people to start using and playing and practicing and helping support the overall value to the customers and to our partners. Robert Dutt: It’s an interesting concept that a computer with a better NPU or GPU on board can unlock that curiosity towards AI and ultimately drag to infrastructure refresh down the road, I think. Alan Ashby: I think the key thing is you don’t have to be a coder. You don’t have to be a developer. Really today, anybody could be a developer. You could build your own application if you wanted to. You can build your own dashboards if you wanted to. You can run it 100% on-prem if you wanted to. You can use a coding assistant to help you manage through that. All you have to do is understand how to talk to it. How do you manage it like an individual and how do you manage it like an agent? It’s a secondary employee that helps you basically give you superpowers. Robert Dutt: If an MSP wants to get serious about the data center and AI with Dell, what’s the first step if they’re already in terms of certification, competency, that kind of thing that they should be looking at? Alan Ashby: Yeah, again, the portfolio is changing very quickly. I would say that table stakes obviously is having a good understanding of our compute platforms with what we’ve got put together with NVIDIA. That’d probably be step one. Step two would be thinking about what you can provide from a storage perspective and how you take advantage of both PowerScale and ObjectScale and all the way up through our lightning file systems, having good understanding how you can deploy that for your customers at scale. Then the other one would be how do you work closely with the Dell teams? That’s one of the things that is always encouraging for partners to think through is Dell has this incredibly large sales force that can help give them scale, give them opportunity. How do you share as a partner? How do you share your value back to the Dell teams? Make sure that they understand where you can be supportive of their customer experience. How do you work collaboratively with the Dell teams across the ecosystem? So forth. Tons of opportunity. We’re always looking for partners that have the right skill sets and the right capabilities. Our Dell teams want to bring them into customer accounts because we need their support. We need their help. Robert Dutt: Acknowledging this might be a wide range, what are some of those common threads that make for a good partner for you in terms of skill sets, areas of focus, that kind of thing? Alan Ashby: Yeah, I think it’s evolving over time. Today, I look at partners that have unique skill sets are incredibly important. Partners that have a competency across our portfolio. Table stakes of having competencies around our compute platform, our storage platforms, but then thinking even deeper, how do you have competency around some of our more isolated platforms like what we do in our unstructured storage space with PowerScale and ObjectScale and access scale that we announced today? Same thing with our data protection portfolio, our cyber resilience platforms, our SRP platforms, like partners that have deep technical specialty expertise in those areas, they’re always going to be needed and valued in our partner ecosystem. AI is one other area to differentiate a partner from, but there’s a lot of those opportunities. Even today with our Dell Private Cloud, I always tell partners that whenever you see a pivot change in our portfolio, like we did when we launched the Dell Private Cloud, this is an opportunity to differentiate yourself as a partner from other partners. To jump in early and be able to build the skill sets that our Dell team is looking for out of a partner to support their customers. Our Dell teams are always looking for those partners that can help lead the charge, especially from a technical perspective with the customers to validate the solution themselves to be able to provide that extensive value to the customer themselves. Robert Dutt: All right. Last one for me, without naming any names or with naming names, should you feel like doing so? What’s the most creative, unexpected, surprising use case for a Dell AI factory that you’ve seen a customer deploy thus far? Alan Ashby: Wow, that’s a hard one. I mean, there’s a lot of really interesting ones I’ve seen. I mean, early days, some of the ones I thought was some of the most exciting stuff that we did with Amarillo County in Texas. It’s a county that there’s a lot of languages natively spoken there and the community there needed to provide basically language services to a very large broad-based set of individuals in the community in their native tongue. And the Dell team worked closely with those folks to make that happen. All the way down there to where we got a number of partners helping small entities, both commercial and public entities, really think about how they can drive agentic workflows and some of the things that are dealing around that with dashboarding. Chat, agents, obviously is an easy one. And then helping customers through kind of how do you do code assist models. Those are probably the really big ones that we see from a use case perspective from our partners. Robert Dutt: No shortage of opportunities. Alan Ashby: Oh my gosh, it’s unbelievable how many there are today. Robert Dutt: Thank you for taking the time. Alan Ashby: Absolutely. This is great. Thank you. Robert Dutt: There you have it. Alan Ashby from Dell. I’d like to thank Alan for his time, carving out a few minutes for me amidst the chaos of day one here at DTW. My big takeaway from that conversation is that you don’t have to be deploying a multimillion dollar PowerRack system to get into the AI game with Dell right now. Between the new desktop workstations running localized agentic workflows and the massive 13 to one server consolidation plays they’re seeing in the traditional data center, there’s a very practical immediate path towards revenue here for partners in the mid market. I’d like to thank you as always for listening to the show. If you’re enjoying our coverage from Dell Technologies World, please do take a second and follow or subscribe in the podcast app of your choice. You can find us on Apple Podcasts, Spotify, YouTube, wherever you get your audio. And if you have a moment to leave a rating or review, always hugely appreciated. Until next time, I’m Robert Dutt for channelbuzz.ca and I’ll see you in the channel.

Patrick Moorhead and Daniel Newman dig into the week's biggest moves in enterprise AI: Anthropic and OpenAI launching PE-backed enterprise JVs on the same day, Anthropic filling its compute gap with SpaceX's Colossus, Cerebris filing for a $3.5 billion IPO, NVIDIA going deep on co-packaged optics with Corning, and a full IBM Think and ServiceNow recap. Plus, for The Flip, hosts debate whether Anthropic, at $1.2 trillion, is the most important company in enterprise tech. The handpicked topics for this week are: 1. Anthropic and OpenAI Launch PE-Backed Enterprise JVs on the Same Day — Both companies announced private equity joint ventures, with OpenAI backed by Bain, Brookfield, and Advent, and Anthropic partnering with Blackstone, Goldman Sachs, Apollo, and General Atlantic. Daniel's read is that this is fundamentally a distribution play, using private equity portfolio companies as a deployment channel for AI at scale. Pat sees it as the clearest admission yet that enterprise AI cannot be self-implemented at scale without specialized consulting support, and flags that mid-tier systems integrators (SIs) could get cut out of the middle. (The Decode) 2. Anthropic Signs Massive Compute Deal with SpaceX Colossus — Anthropic urgently needed compute and SpaceX had 300 megawatts and 220,000 GPUs sitting at Colossus One in Memphis without enough business to fill them. Pat's take is blunt: this move is pragmatic. Anthropic needs it, xAI has it. Daniel adds that Dario himself said they planned for 10x growth and got 80x, and this deal is the fast backfill that reality demanded. The side note both hosts flag: Anthropic is running on H100s, H200s, and B200s, which puts the whole "Anthropic only runs on Trainium and TPUs" narrative to rest. (The Decode) 3. Cerebris Files for a $3.5 Billion IPO at $26.6 Billion Valuation — This marks their second attempt at an IPO after pulling the first filing. The architecture is genuinely unique, a complete wafer with massive on-chip SRAM and interconnects built directly onto the wafer rather than copper or photonics. Pat calls it the first credible Western alternative for AI inference. Daniel's framing cuts through: you do not have to beat NVIDIA to sell right now. You just need to have availability. The more interesting headline, both hosts agree, is that Sam Altman and Greg Brockman are angel investors, which adds fuel to the ongoing OpenAI lawsuit. (The Decode) 4. NVIDIA and Corning Announce $500 Million Optical Partnership — Three new US factories, co-packaged optics for Vera Rubin, and a supply chain strategy that mirrors what NVIDIA did with Coherent. Pat's context: this is vertical integration through investment rather than acquisition. Daniel's observation is that the pace of movement toward co-packaged optics is accelerating faster than anyone expected, and his "rule of and" applies here too. Copper is not going away. Optics are being added on top because the data volumes moving across these racks are outrunning what copper alone can handle. US manufacturing in North Carolina and Texas is a strategic bonus. (The Decode) 5. IBM Think 2026: Day Zero, Sovereign Core, and the Quantum Plus AI Bet — Pat moderated on stage with CEO Arvind Krishna and calls this IBM's best showing in five years. Arvind opened with the AI divide, the gap between companies still running POCs and companies already in production, and framed where IBM sits as day zero, not because nothing has happened, but because enterprise AI deployment at scale is still so early. Daniel's biggest takeaways: watsonX Orchestrate updates, Sovereign Core going GA with policy at runtime, and the Confluent acquisition potentially being IBM's most important asset since Red Hat, given that 40% of Fortune 500 companies run on it and real-time streaming data is foundational to agentic systems. Both hosts land on quantum plus AI as IBM's next inflection moment. (The Decode) 6. ServiceNow Knowledge 2026: Enterprise SaaS 2.0 is Emerging — Daniel got there on day three of the event and noted the conference was densely packed. His observation: enterprises have not gotten the memo from Wall Street that SaaS is supposedly dead. His emerging thesis is that middleware could make a comeback for AI, with companies needing a layer that lets agents work across any infrastructure, any app, and within the rules of their specific business. Pat agrees and adds that the growth question is about mix, not survival. (The Decode) 7. The Flip: Is Anthropic at $1.2 Trillion the Most Important Company in Enterprise Tech? — Daniel took the affirmative citing that Claude Code is deeply entrenched in developer workflows. Anthropic went from $9 billion to $45 billion ARR in months. Every major hyperscaler is both a customer and an investor. The PE JVs are turning verticals into Anthropic engines. Dario said they planned for 10x and got 80x. Pat's counter: the enterprise trust gap is real after what Anthropic pulled on pricing and performance. Microsoft has 2 billion users across 365, Azure, and Copilot. NVIDIA is the infrastructure Anthropic runs on. And workforce replacement, which is how Anthropic extracts its terminal value, is not arriving as fast as the valuation suggests. In reality, both hosts admit their notes looked almost identical. (The Flip) 8. AMD — Lisa Su guided AI data center growth up from 60% to 80%. With OpEx growing 83%, net income up 95%, free cash flow ripping, and CPUs growing at nearly 40% without price increases, Pat reads this as unit market share gains coming soon. Daniel's framing: AMD is now a two-headed juggernaut with CPUs and GPUs for the data center. And Helios has not even started shipping yet. Both hosts take a victory lap for previously calling this one. (Bulls and Bears) 9. Palantir — Triple beat on revenue, EPS, and forward guidance. Rule of 40 at 145%. Government revenue up 84%, 47 deals over $10 million, and the largest guidance raise in the company's history. Daniel's take: Palantir is redefining the category entirely. It's not a software company in the Salesforce or ServiceNow sense. It's technology, plus ontology, plus people, deployed at the deepest layers inside governments and enterprises. Pat adds that the four deployed FTE model lets them stand up AIP POCs within a week, which is why they are winning business at this pace. (Bulls and Bears) 10. ARM — AGI processor demand doubled from $1 billion to $2 billion within 45 days. Record revenue, strong pipeline, royalty growth at 21% for the full year. The stock ripped after hours, then sold the next day when management confirmed only enough supply for $1 billion of that $2 billion demand. Pat's read: 50% CPU market share with hyperscalers at the core level is the most underdiscussed signal on the call. Daniel adds that the worry about ARM competing with its own customer base in custom silicon has been quietly swept away by the sheer volume of compute demand. (Bulls and Bears) 11. Supermicro — A board member allegedly used a hairdryer to remove labels from GPU boxes being shipped to China. Approximately 20% of their revenue has reportedly been illegally shipped to China. They beat on EPS and Q4 guide but missed Q3 revenue versus consensus. Stock still ripped 18%. Daniel's take: if you are selling picks and shovels during a gold rush and you are this messed up, he cannot imagine owning it with the overhang that is building. (Bulls and Bears) 12. Lattice Semi and Coherent — Lattice revenue up 42%, back into growth, guiding to 50% year-on-year at midpoint. The AMI acquisition at $1.65 billion doubles their serviceable market from $6 billion to $12 billion and puts them inside every AI server on the planet at the BIOS and platform firmware layer. Pat calls the timing right: core financials crushing it, time to make a move. Coherent printed 21% year-on-year growth, 55% EPS growth, margins expanding, debt coming down, entered the S&P 500, and sits at the center of the co-packaged optics trend that is accelerating. Pat's choke point note: Indium phosphide capacity is the constraint. Six-inch fabs are doubling capacity in 2026, a quarter ahead of plan, and competitors are still ramping their transitions. (Bulls and Bears) Want the full breakdown from IBM Think and ServiceNow Knowledge, and check out our on-the-ground coverage linked in the show notes. Be part of our community. Hit that subscribe button and let us know what you want us to cover next week in the comments. Intro Pat on Stage at IBM Think https://x.com/PatrickMoorhead/status/2051381046537601101?s=20 The Decode OpenAI and Anthropic Both Launch PE-Backed Enterprise Services JVs on the Same Day — The Palantir FDE Model Goes Mainstream https://www.bloomberg.com/news/articles/2026-05-04/openai-finalizes-10-billion-joint-venture-with-pe-firms-to-deploy-ai https://techcrunch.com/2026/05/04/anthropic-and-openai-are-both-launching-joint-ventures-for-enterprise-ai-services/ https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push Anthropic and SpaceX Sign Massive Compute Deal — Full 300MW / 220,000 GPU Colossus 1 Memphis Data Center Plus Exploration of Multi-Gigawatt Orbital AI Compute https://www.cnbc.com/2026/05/06/anthropic-spacex-data-center-capacity.html https://www.bloomberg.com/news/articles/2026-05-06/anthropic-inks-computing-deal-with-spacex-to-meet-ai-demand https://www.tomshardware.com/tech-industry/artificial-intelligence/musks-spacex-has-rented-out-access-to-its-supercomputers-220-000-nvidia-gpus-and-300-megawatts-of-ai-compute-power-to-rival-anthropic Cerebras Files for $3.5B IPO at $26.6B Valuation — The First Major AI Chip IPO of 2026 https://www.cnbc.com/2026/05/04/cerebras-ipo-ai-chipmaker.html https://theaiinsider.tech/2026/05/06/cerebras-systems-eyes-3-5b-in-largest-tech-ipo-of-2026-on-strength-of-ai-chip-demand/ https://www.briefs.co/news/ai-chipmaker-cerebras-just-filed-for-a-3-5-billion-ipo/ NVIDIA and Corning Announce Game-Changing Optical Partnership — $500M Investment, 3 New U.S. Factories, and Co-Packaged Optics for Vera Rubin and Beyond https://www.corning.com/worldwide/en/about-us/news-events/news-releases/2026/05/nvidia-and-corning-announce-long-term-partnership-to-strengthen-us-manufacturing-for-ai-infrastructure.html https://www.cnbc.com/2026/05/06/nvidia-corning-optical-factories-nc-texas-ai.html https://www.wsj.com/tech/nvidia-corning-form-partnership-to-expand-fiber-optic-manufacturing-17f525de https://kfgo.com/2026/05/06/corning-partners-with-nvidia-to-expand-us-fiber-optic-output-for-ai-growth/ IBM Think 2026 Boston — Watsonx Orchestrate Next-Gen, Confluent Real-Time Data, IBM Concert, and Sovereign Core Define IBM's Agentic Operating Model https://newsroom.ibm.com/2026-05-05-think-2026-ibm-delivers-the-blueprint-for-the-ai-operating-model-as-the-ai-divide-widens https://www.ibm.com/new/announcements/ibm-announcements-at-think-2026 https://www.instagram.com/reel/DX42DlrglOs/ ServiceNow Knowledge 2026 Las Vegas https://www.servicenow.com/events/knowledge.html https://newsroom.servicenow.com/press-releases/details/2026/Cohesity-and-ServiceNow-Deliver-Real-Time-Recovery-for-Enterprise-AI-Agents/default.aspx https://www.cnbc.com/2025/09/04/nvidia-backed-cohesity-eyes-2026-ipo-with-valuation-rivaling-17-billion-rubrik.html   The Flip: Anthropic at $1.2T Now the Most Important Company in Enterprise Tech — More Important Than NVIDIA, Microsoft, or OpenAI FOR: Dual-hyperscaler compute anchor (Amazon $33B + Google $40B = $73B) is structural — unmatched https://futurumgroup.com/insights/anthropics-gigawatt-scale-tpu-deal-with-broadcom-creates-a-structural-advantage/ Constitutional AI safety positioning wins regulated industries https://www.anthropic.com/news/anthropic-nec-japan-ai-engineering-workforce $900B valuation surpasses OpenAI ($852B) at faster revenue growth and lower burn rate https://techcrunch.com/2026/04/30/anthropic-potential-900b-valuation-round-could-happen-within-two-weeks/   AGAINST: NVIDIA still controls the substrate — every Anthropic dollar of revenue requires NVIDIA inference at some layer https://www.cnbc.com/2026/04/27/nvidia-just-hit-an-all-time-high-why-some-think-a-rally-is-just-getting-started.html Microsoft has the enterprise distribution — 365 + Azure + Copilot reach >2 billion users https://www.marketbeat.com/originals/microsofts-maia-200-the-profit-engine-ai-needs/ $900B valuation is venture marketing — the IPO will reset the number https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push   Bulls & Bears: AMD Q1 2026 — Revenue $10.3B (+38% YoY), MI300X Data Center GPU Demand Drives Stock +20% on the Print https://ir.amd.com/news-events/press-releases/detail/1284/amd-reports-first-quarter-2026-financial-results https://www.cnbc.com/2026/05/05/amd-q1-2026-earnings-report.html https://finance.yahoo.com/markets/stocks/articles/amd-q1-2026-earnings-revenue-203331768.html Palantir Q1 2026 — Revenue +85% YoY, US Commercial +133%, Rule of 40 Score Hits 145%; Largest Guidance Raise in Company History https://investors.palantir.com/files/Palantir%20-%20Q1%202026%20Business%20Update.pdf https://www.reddit.com/r/PLTR/comments/1t3t0me/palantir_reports_q1_2026_us_revenue_growth_of_104/ https://finance.yahoo.com/markets/stocks/articles/palantir-technologies-inc-q1-2026-002218719.html https://semiconalpha.substack.com/p/palantir-q1-2026-rewriting-the-rule Arm Holdings Q4 FY2026 — Record $1.49B Quarter, Full-Year Revenue Crosses $4.92B, $2B AGI CPU Pipeline; Stock +16% After Hours https://finance.yahoo.com/markets/stocks/articles/arm-q4-earnings-call-highlights-225942093.html https://www.stocktitan.net/sec-filings/ARM/6-k-arm-holdings-plc-uk-current-report-foreign-issuer-7e9ca9ac7dda.html https://semiconalpha.substack.com/p/arm-q4-fy2026-record-quarter-2-billion Super Micro Computer Q3 FY2026 — Revenue $10.2B (+123% YoY), Strong Q4 Guide; Stock +18% AH on First Earnings Call Since Co-Founder Indictment Drama https://www.cnbc.com/2026/05/05/super-micro-smci-q3-earnings-report-2026.html https://www.stocktitan.net/sec-filings/SMCI/8-k-super-micro-computer-inc-reports-material-event-e70b2f8b3cb7.html https://www.instagram.com/reel/DX42DlrglOs/ Lattice Semiconductor Q1 2026 — Beat-and-Raise Quarter ($170.9M, +42% YoY) Paired With $1.65B AMI Acquisition That Doubles Lattice's SAM to $12B https://www.stocktitan.net/sec-filings/LSCC/8-k-lattice-semiconductor-corp-reports-material-event-642a862b2bf9.html https://www.ami.com/resources/ami-announces-agreement-to-be-acquired-by-lattice-semiconductor/ https://www.linkedin.com/posts/patmoorhead_lattice-semiconductor-posts-beat-and-raise-activity-7457411226944425984-xA8T Coherent Q3 2026 Earnings https://www.msn.com/en-us/money/companies/coherent-cohr-tops-revenue-expectations-in-q3-as-ai-demand-accelerates-shares-decline/ar-AA22Bz24?ocid=finance-verthp-feeds  

L'Olga Angaril va ser la primera dona taxista de Sitges, ara ja jubilada repassa els seus inicis en un món d'homes. Pocs anys després es va incorporar la Sònia Bages que ja suma quinze anys portant el taxi a Sitges, i aquesta darrera setmana s'ha incorporat la Carla Delgado, propietària de llicència que ha decidit fer un canvi de rumb laboral i deixar la banca per incorporar-se al taxi. Amb elles conversem sobre com és treballar en un món altament masculinitzat on només el 10% dels treballadors són dones. L'entrada Olga, Sònia i Carla, històries de tres dones taxistes de Sitges ha aparegut primer a Radio Maricel.

Hoje, fraudes não são mais eventos isolados — elas viraram processos automáticos, digitais e orquestrados em escala, impulsionados por IA e pela perda do controle do perímetro tradicional de segurança. Como as empresas estão reagindo a essa nova realidade para proteger dados, reduzir riscos e, ao mesmo tempo, garantir uma boa experiência para o usuário?No episódio, converso com dois especialistas que estão na linha de frente desse desafio: Marcelo Queiroz, Head Product Innovation ID&F no Serasa Experian, que lidera soluções de identidade digital, prevenção à fraude e cibersegurança Pedro Ivo Lima, CEO & Co-Founder da PhishX, startup focada em transformar o comportamento humano em uma linha de defesa contra ataques digitais.Falamos sobre a importância da orquestração de múltiplos fatores de autenticação, os dilemas entre segurança versus experiência do usuário, e os riscos trazidos pela IA generativa que pode tanto proteger como facilitar ataques sofisticados.Também exploramos o conceito de identidade digital reutilizável, a mudança radical no modo como negócios vão se estruturar com agentes digitais autorizados e um olhar prático para startups — que precisam abraçar a segurança desde a concepção do produto, mesmo com recursos limitados.E por falar em oportunidade para startups: as inscrições para o Programa de Inovação Aberta da Serasa Experian estão abertas! Sua startup pode conquistar investimento de até R$ 50 milhões, mentoria com especialistas, POCs com investimento e acesso a dados e infraestrutura da maior datatech do Brasil — com escala real junto à Serasa Experian. Não deixe para depois: essa é a chance de construir o próximo padrão de confiança digital. Link nas nossas redes sociais.Se você quer entender como fraude virou software, por que o futuro da segurança digital passa por inovação constante e quais atitudes aplicar hoje no seu negócio para não ser mais uma vítima — dá o play e vem com a gente!Para conferir mais conteúdos,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠acesse nosso site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!Instagram:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠@aceventuresbr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠ACE Ventures⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠E-mail: ⁠⁠⁠⁠⁠⁠contato@goace.vc⁠⁠⁠

Podcast de ventas B2B y prospección moderna Cuando tu equipo técnico tiene que vender, pero nadie les ha enseñado cómo, se llenan la agenda de demos, POCs infinitos y oportunidades que mueren en el comité de dirección. En este episodio, hablamos con Sancho Lerena, CEO y fundador de Pandora FMS, sobre qué ha aprendido en 20 años para que técnicos y comerciales funcionen como un solo equipo. Desde una empresa “de ingenieros” con venta inbound desordenada hasta un modelo estructurado con canal, preventas dedicados y equipo comercial puro, Sancho cuenta con detalle qué ha cambiado en su organización para vender soluciones técnicas complejas en B2B. En este episodio verás: - Qué carencias suelen tener los técnicos cuando les toca vender y qué ventajas traen a la venta consultiva. - Por qué no basta con que el técnico del cliente esté enamorado del producto si nadie habla el idioma del CEO y del CFO. - Cómo separar y coordinar los roles de preventa y ventas para no quemar al equipo ni cerrar deals mal encuadrados. - Cómo plantear POCs (pruebas de concepto) con alcance limitado, expectativas claras y sin regalar un año de trabajo. - Cuándo hablar de precio y cómo usar rangos para evitar sorpresas y forzar conversaciones internas en serio. Cómo entrenar al champion técnico del cliente para que venda el proyecto dentro de su organización. El papel de la cultura y de las diferencias entre venta “latina” y modelos más asertivos de Norte de Europa o EEUU. - Por qué muchos directores comerciales vienen del mundo técnico y cómo ver la venta como una ingeniería con método y creatividad. Invitado: Sancho Lerena – CEO y fundador de Pandora FMS. https://www.linkedin.com/in/slerena https://www.linkedin.com/company/pandora-pfms/ Presentan: David Navas y Eduardo Laseca – Outbounders, formación y consultoría en ventas B2B. ......................................................................................................................................... Y si quieres mejorar tu Maquinaría de Ventas Outbound o formar a tus equipos en #modernprospecting Pues lo tienes fácil: 699 45 85 82 Más en https://outbounders.es/

Doug Houghton, director of global channels at Alkira There’s a line from this episode that’s worth leading with: “Networking is not sexy until it doesn’t work.” That’s Doug Houghton, Director of Global Channels at Alkira, and it’s a pretty concise summary of why his company exists. Alkira was founded by the team behind Viptela – the startup that essentially created the SD-WAN category before being acquired by Cisco. The lesson they carried out of that experience is that SD-WAN, for all its promise, still ran into the limits of underlying infrastructure. You ended up with disparate networks, latency constraints, and complexity that didn’t disappear – it just moved somewhere else. What they built in response is Network Infrastructure as a Service (NIaaS) – a cloud-native, consumption-based global backbone that abstracts multi-cloud connectivity into a single managed plane. The pitch to partners is concrete: consolidate 50 physical firewalls into virtualized functions, reduce total cost of ownership by 40-70%, and do it without a rip-and-replace cycle. The timing matters, and Houghton is direct about why. AI workloads – distributed large language models, agentic workflows reaching across multiple clouds simultaneously – demand a level of network elasticity that legacy infrastructure simply wasn’t designed for. Alkira’s argument is that they’re the smooth road that makes AI-driven infrastructure actually work in practice. For Canadian partners, Alkira has real resources on the ground: a solution architect based in Toronto, a dedicated channel account manager, and publicly referenceable Canadian customers including contact center provider ContactPoint 360. The Connect Partner Program, launched in March 2026, puts approximately 20 percent total margin on the table across base discount, rebates, MDF, and POC SPIFFs – with average initial deals around $500,000 USD and typical expansion of 4x in year one. Canadian partners interested in the conversation can reach the team at partners@alkira.com. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last sixteen years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. If you were around when SD-WAN was the big disruptive idea in networking – the promise of simplifying branch connectivity, cutting costs, getting smarter about traffic – you probably also remember it didn’t quite deliver everything it promised. Not because the technology was bad, but because the underlying network architecture couldn’t keep up. You still ended up with complexity. It just moved somewhere else. That problem is essentially the founding insight behind Alkira. The company was built by Amir Khan and Atif Khan, the same team behind Viptela, the startup widely credited with creating the SD-WAN category before Cisco acquired it. What they learned in that experience is that SD-WAN, without a proper global backbone, just creates a different set of headaches. So they started fresh and built what they call NIaaS – Network Infrastructure as a Service – a cloud-native, consumption-based approach that abstracts the complexity of multi-cloud connectivity into something you could stand up, as my guest today puts it, with just a username and a password. The timing is not accidental, because what AI demands from a network – elasticity, low latency, the ability to reach distributed workloads almost anywhere instantly – is exactly what legacy infrastructure wasn’t built to handle. My guest is Doug Houghton, Director of Global Channels at Alkira. Doug has been in the channel a long time, knows the technology in a way that might genuinely surprise you coming from a channel chief, and has a lot to say about what it all means as a real business opportunity for Canadian VARs and MSPs. Let’s get right into it, my chat with Doug Houghton. Doug, thanks for taking the time. I appreciate it. Doug Houghton: It’s my pleasure. Thank you for having me on today, Robert. Robert Dutt: So you were part of the team that built up the SD-WAN market at Viptela back in the day. What did you learn there that told you the next big thing was going to be NIaaS, and why now? Doug Houghton: First off, that’s a great question. I felt a bit like a passenger in a car racing a thousand miles an hour when we were doing software-defined wide-area networking. What we learned was that without organizing your cloud infrastructure properly, your cloud bill gets ridiculously large – especially if you keep your control element decoupled from your data plane in the cloud with all these workloads churning. But what we really learned, and what’s applicable to what we’re now doing at Alkira, is that SD-WAN truly did deliver on its core promise. It allows customers to influence traffic based on link quality and improve the user experience. If you’re on a phone call and it starts to get goofy, you can move over to a better-performing link in real time without dropping the call. That’s powerful. And the same with data traffic. What I hadn’t fully thought through was what happens as global companies start to adopt SD-WAN and disaggregate across locations in Southeast Asia, China, Latin America, and everywhere else. The latency back to the control element isn’t easy to contend with. So you ended up with organizations making decisions that effectively created four separate, disparate networks for latency purposes. And that was not part of the original promise. What we learned was that you need a global backbone that’s high throughput and low latency. The edge can still be SD-WAN – there are real things in SD-WAN that people still want, whether that’s WAN optimization, deduplication, caching, policy-based routing, forward error correction. All of that still has practical application, and site-to-site communications are still needed in many use cases. But Alkira was built inside the cloud first, employing the same principle of decoupling control plane from data plane for scale. By abstracting the cloud infrastructure, we were able to remediate the latency that those four geographically dispersed networks created. We’re the global backbone – that middle mile with high throughput and low latency – and then you connect these clusters of SD-WAN networks together and all of a sudden the promise of SD-WAN gets a lot more consumable. You have a singular network managed from a singular control plane and element management orchestrator, and you can still get all the benefits of SD-WAN at the local sites. Robert Dutt So in plain language, a Canadian MSP or VAR is used to selling network hardware or managing someone else’s infrastructure. How is selling, deploying, and managing NIaaS different from what they’re already doing, and what makes that distinction important? Doug Houghton: Let’s take a half step back and talk about what NIaaS actually is. It’s Network Infrastructure as a Service. What Alkira does is abstract the cloud infrastructure and build a routed overlay on top of it. We think of it as a virtualized colocation facility that connects and normalizes communications across your entire network. For managed service providers and service providers, our solution accelerates bringing their customers to cloud applications, cloud workloads, storage, and everything else the cloud promises. The way I explain it to my mom – and I’ve told this joke once already today because I’m sitting in a partner’s office right now – is this: if you went to Russia, Japan, Argentina, and San Francisco all in one day and had to transact in each place, and you could speak the native language in each one, that would be ideal. What we focused on was normalizing communications regardless of the cloud service provider, colocation provider, data centre – private or public – or whatever type of router is at the branch office. As an MSP or service provider that comes in, what we give to our customers and partners is a username and a password. That lets you come in and – for your old-school folks in the audience – essentially etch-a-sketch your network together. You can turn a couple of knobs, and it’s not that we’ve cranked the amp up to eleven, we’ve just removed all the numbers and automated everything. It just knows what you want to do. It’s a routed BGP overlay with the control plane abstracted from it, so the forwarding plane can route around things like the CrowdStrike outage, or losing an AWS region – which happens more frequently than AWS would like to admit – or any cloud service provider incident. The multi-cloud reality has accelerated adoption, but it presents a new problem: you’ve got an AWS expert on staff, but you don’t have an Azure, GCP, OCI, or Alibaba Cloud expert. Those are all different languages. When I tell my mom that we normalize the communications between all the assets in the network and make it easy to connect to all of them, she gets that. For the MSP looking to monetize something new or add another revenue stream, we offer a couple of compelling things. In the middle of our stack, we place a solution inside the cloud – sitting in a VPC, VNet, VCN, or Google VPC – right in the middle of all the cloud, SaaS, and WAN workloads. We’ve pleased a lot of customers by lowering total cost of ownership through the consolidation of network services they already have in their environment, in the form of virtualized network functions. Take a Palo Alto firewall deployment – say you have fifty Palos out there, all talking to Panorama, with a security engineer managing policy centrally. Instead of having fifty firewalls on the ground, you consolidate them. You go from the ground – five to ten milliseconds to the nearest public cloud PoP – hop onto the Alkira fabric, and terminate that traffic on a virtual port on our exchange point. In the middle of that exchange point, sitting in a VPC or VNet, you place a Palo Alto virtualized network function. You get the IP address of the Panorama server, and if you didn’t tell the security engineer anything had changed, they would not know. The form factor changes, but not how they interact with Panorama, how they build policy, or anything about how they secure the traffic. That remains exactly the same. We virtualize the instance and place it on a global high-throughput, low-latency backbone inside our exchange point. We deploy exchange points in HA pairs, anywhere from 100 Mbps to 40 Gbps. The customer or service provider consumes one, and we maintain the other on their behalf – because every thirty days we’re fixing bugs and doing maintenance. We swing production workloads to the backup, do the work on the primary, then reverse the order, all while keeping these customers up and running. Because we’re delivering this as a service, it has to always be on. One of the most important architectural decisions we made from the start was ensuring those two exchange points are always running active-active in a full mesh configuration, buttressed by hundreds of other exchange points globally distributed – all synchronized and aware of each other’s states. Robert Dutt: You’ve said that legacy networks can’t handle what AI demands, specifically in terms of elasticity. Can you unpack that a little? When an MSP’s customer starts deploying language models or agentic workflows, what is it that actually breaks? Doug Houghton: Good question, and I’ll give you an honest answer. I’ve started to fall in love with Claude – I think it’s one of the coolest things in the world. I can do all sorts of creative things with it. But Claude isn’t talking only to me. He’s a bit of a flirt – he goes to a lot of different places to get knowledgeable about various things and produce the outcomes I’ve asked for. And those other places are where you run into problems. I used to say the three biggest AI providers are GCP, AWS, and Azure. That’s still largely true. But the likes of Anthropic and other AI labs are distributing LLM workloads everywhere. Without the right network underneath that, it’s like buying the hottest car and driving it down a pothole-filled road. What we offer is a high-throughput, low-latency, elastic network. If you need to turn it up in a heartbeat, you can. We helped complete the S&P Global and IHS Markit merger network integration in about a tenth of the time they expected, because we’re natively segmented. Think about those two networks as large datasets that AI agents need to access. You have to secure the traffic, and you need it to be elastic – able to reach anywhere, instantly, to produce the outcome the agent was asked for. The ability to go anywhere on a road that’s smooth as glass, in the hottest car possible – that’s what we offer. Our network infrastructure solution is an abstraction: a forwarding plane that goes everywhere, and your imagination is really the only limitation. Speed, elasticity, and securing access – even for agentic, self-directed workflows – it’s still a critical element. And nobody – I said this earlier today, so I’ll say it again – networking is not really sexy until it doesn’t work. If I have to get in and route-peer and manually configure transit gateways, I’m going to punch myself in the face repeatedly. I just don’t want to do it. It slows everything down. I can automate it with Terraform, sure. But I want to consume it now. I want to prompt it now. I want the outcome now. Robert Dutt: You’ve launched Alkira NIA, your AI co-pilot and network infrastructure assistant, along with an MCP server last year. It’s interesting – you’re essentially putting AI on top of the infrastructure that’s enabling AI. What does NIA actually do for an MSP’s day-to-day operations? Doug Houghton: Maybe I have a limited imagination, but I still use it like a utility. NIA is great because it allows you to search through all our documentation in a more organized way. We have amazing documentation – there’s a lot of it – and when you’re looking for a specific configuration or something captured in a knowledge base, that tool is really useful. But continuing the utility theme: how do I do something? If I want to create a micro-segment to distribute to a bunch of business units, or build an isolated Layer 3 routing table and get it to various business units, and then set up billing with specific billing tags for each segment – I know how to do that because I’ve done it many times. But a new user may not. You can use the NIA agent to search the documentation, search previous implementation notes, best practices, all of that. That’s real value. But you can also ask it something like “why is the sun bright” and it won’t return the answer you expect. I’ve done that too. Robert Dutt: Let’s talk about the Connect Partner Program and the economics. You’ve got the Partner Profit Stack – tiered margins, quarterly rebates, MDF, SPIFFs, the Connect Pipeline Fund. It’s a full toolkit, and it’s stuff partners have seen before. What’s the real math? What does a Canadian MSP at the Premier tier actually walk away with on a typical deal after they’ve done the work? Doug Houghton: Usually about nineteen percentage points – maybe a little more. On the pre-sale side, when we get into a POC, our Premier partners can earn a $1,000 SPIFF. We close about 85% of our POCs, so there’s real value in that. Add in the rebates and MDF access, and the total haul is closer to 20% on each deal. Worth mentioning: we’ve been a 100% channel company since May 2022. My partner David Klubinoff, my technical counterpart – we worked together at Viptela and we started the Alkira channel together. It took a couple of weeks to convince our CEO that going 100% channel was the right call. I think he’s a believer now. We’ve driven significant revenue for the company, and our partners are our thought leaders – out in the market talking about our solution and solving customer problems. I was in Chicago yesterday doing a technical enablement session with thirty-plus SAs and SEs. We had the classic SD-WAN questions, and a lot of questions about segmentation and M&A. There’s enormous consolidation happening in insurance, healthcare, and other sectors, and the overlapping IP address problem that comes with mergers is something MSPs face all the time. We’ve entirely simplified that. You build a NAT policy right in the solution and the overlapping IP issue is resolved within an hour. In the case of S&P Global and IHS Markit, they thought their merger network integration was going to take a couple of years. The issue was largely the overlapping IP addresses – IHS couldn’t talk to the HR applications at S&P, and vice versa, plus all the other interdependencies. You need a fast way to solve the overlapping IP problem before you can even get to the real work. That’s been a core design element of our solution from the very start: take care of the small things, and people can move faster and get to market faster. Our biggest MSP – and this is a publicly referenceable customer – is CEDA, a French-based organization that provides managed network services to 95% of the world’s airlines. For them, it means being able to turn up a new customer faster, connecting on-premises assets to their control elements so they can begin actually managing that network. Speed, and the efficiencies and cost reductions that come from it – that’s what it does for all MSPs. If you’re consolidating fifty firewalls into virtualized functions, you’re making a good commission, getting MDF support, quarterly rebates, and a SPIFF when you engage us collaboratively on a POC. All of that happens at an accelerated rate. I’ve been screaming from the mountaintop about our solution for about four years. Invariably, you’d walk into a room, say “Hi, I’m Doug Houghton from Alkira,” and they’d say “Who?” That’s starting to happen a lot less, which is a genuinely nice thing. Over the last twelve to twenty-four months, the business has grown exponentially, the diversity of our partner ecosystem has increased, and partner margins have been very healthy. The tiered structure was really about celebrating partners who have invested in us. Honestly, I’m waiting for the day my boss tells me to stop incentivizing partners – because when that happens, I’ll know we’ve hit the apex. Our partners will be generating so much revenue that someone gets uncomfortable with what we’re paying out. I can’t wait for that day. Some of the more interesting things in the program came from actually listening. I went around and talked to a bunch of partners about their ideal partner programs and built from there. And one of the realizations – I thought it was significant – was what we were actually doing on the post-sale side. We white-glove every implementation right now, because it’s critically important to us. We haven’t lost a customer, and we intend to keep it that way. But that doesn’t scale forever. So the question became: why don’t we help our partners productize the post-sale work? We built a product catalog, a pricing calculator, and a new partner portal we’re about to release, with its own AI agent for searching market assets. The product catalog was a light bulb moment. We pay healthy margins on the pre-sale side at every tier of Alkira Connect. But we had never touched the post-sale side at all. We’re largely automated and NIaaS is as simple as possible to consume – a username and a password. My thirteen-year-old could configure a network, and she’s really smart. But there’s still some implementation work. You still need to build policies in Panorama. There’s still DDI work. There are still services that partners can benefit from – and all partner types, MSPs, VARs, master agents, sub-agents, service providers, now have a post-sale commission opportunity. Robert Dutt: You mentioned services – you’ve got services attach plays around modernization assessments, segmentation design, migration sprints. Starting from zero, how long does it realistically take a partner to get their first deal with those services attached through the door, and what does the ramp look like? Doug Houghton: There’s a lot in that question. Let’s take a half step back. We have virtual sales and go-to-market training – three modules – and then five or six technical training modules. We’ve got a lab-in-a-box environment, foundational and advanced technical training, and DDI training. Partners typically start there. Then we run regular in-person and virtual sessions – one partner has regular office hours with me, my SE counterpart David, or our architect Christopher Arenas, and we just invite partners to come and ask questions. Getting partners genuinely comfortable with the technology is the most important thing we do, because nobody goes out and sells anything unless they’re confident they can explain how Alkira solves their customer’s problem. That’s what I’m doing in Chicago today. Our customers tend to be fairly large. We’ve got our first Fortune 10 customer now. The more complex the network, the larger and more global the deployment – multiple countries, security vendors, firewalls, DDI providers, load balancers, service providers, colos. We sit right on top of all of that. The average sales cycle is about 190 days – a little over six months. A newly enabled partner might encounter an M&A overlapping IP use case, recognize the problem, and say “I think we can solve this with Alkira.” They go through a POC together with us, the customer commits, and that first deal closes around 190 days. A little class week: it’s actually 190 and a half. The average deal size is about $500,000 USD. We then see significant expansion: typically 4x growth in the first twelve months after the initial close, and around 8x in the second twelve months. Real incentive to stick with it. We’re loyal – if the customer doesn’t kick the partner out, we go to bat with that partner on every expansion deal. We land, then expand, with the same partner. BNSF, one of our other public references, has expanded several times to address more and more use cases. The solution gets sticky and customers are genuinely surprised by how easy it is. On the post-sale side, we come in and help with implementation, especially early on. But we’re reaching the point where more capable partners can handle it themselves. We’re building a post-sale certification for Alkira right now. In the meantime, we ride shotgun through the first couple of implementations – virtually in Slack or in person – until partners are fully up to speed. All partners have access to our Slack channel, along with our entire solutions architecture and SE staff. One partner working on a Fortune 10 engagement has a great habit of putting a subject header in Slack and starting a conversation. He’s been on services at this customer for three or four months – a significant engagement. He’s the one who originally described the network as a “spaghetti mess,” which I still chuckle about. I actually built the product catalog based on those Slack headers – pulled them together, socialized them with a group of partners, got input, and built from there. To directly answer your question: you’ve got to get through that first deal, and we’re going to ride shotgun with you through the first couple of implementations. The partner learns, gets comfortable, can monetize it, and can deliver independently from there. We have no illusions about going back to being a direct company after May 2022. It’s ride or die – 100% channel, and we enable our partners to solve their customers’ problems and support them while they do it. Because our partners have been our biggest growth engine. Robert Dutt: You’ve talked about a goal of doubling revenue through partners. What does the ecosystem look like when you get there? This sounds like it could primarily be a GSI or large integrator play, given the customer complexity you’re describing. Or do you genuinely see a path for mid-market MSPs and VARs to build a meaningful NIaaS practice? Doug Houghton: Another tough question. Yes, I do have GSIs as partners. We have a fairly robust and diverse partner ecosystem, and we see small shops rising up while larger shops are moving a bit more slowly, honestly. We’re still in that brand awareness honeymoon period – people are realizing our technology is compelling, getting themselves enabled. Some large partners we’ve recently brought on are still ramping. The biggest and most established organizations aren’t yet as capable as they will be, but we’re working diligently on that. Some of our smaller partners, on the other hand – I’m thinking of a friend of mine in Utah who is just an absolute champion. He knows our solution better than almost anyone. He closed six or seven deals in the past year, supported the implementations, did it largely on his own, because he’s curious, motivated, read all the documentation, and has been through full implementation cycles with us. He works at a ten-person shop. They just happen to have really good customers, and he knows the solution cold. So we’re at different stages with different partners in terms of maturity. The answer to your question is genuinely both. The small shop in Utah and the large national partner dedicating more resources as they see more customer problems Alkira can solve – we see wins across both. In the networking space, a six-month sales cycle is about as fast as it gets. I’m giving you a username and a password and you’re going in and connecting all of a customer’s assets together. The path exists for partners of every size. Robert Dutt: You’ve called out Canada specifically in your expansion plans, alongside the UK, EU, and the Middle East. What does that look like operationally – localized support, a Canadian channel team – or is it more of a global platform available to Canadian partners? Doug Houghton: Let’s talk personnel. We have a dedicated rep in eastern Canada, based out of New Hampshire, and a brilliant solutions architect just outside of Toronto. We’ve got a channel account manager – very capable teammate of mine, Savannah Stone – and the entire global solutions architecture staff accessible via Slack. We recently closed a very significant logo in Canada – a large insurance company – and our publicly referenceable Canadian customer is ContactPoint 360, a contact centre and BPO provider. They wanted to connect their Latin American operations back to Canada and couldn’t find an effective way to do it without us. We route them through the US West region, and the results have been excellent. We’ve also added CDW Canada as a partner, and I’ve got a value-added distributor that helps with field events. It’s not a massive footprint yet – it’s a bit of “they come first, then we build” – but there is a tremendous amount of opportunity in Canada and in Latin America that I’m genuinely excited about. Nobody’s told me no yet on spending budget, so here we go. A great story on the Canadian side: a gentleman named Chris Thelosinos, an architect and consultant who works with others in our space, is a member at a wine shop in Toronto. During the Toronto International Film Festival last year, we hosted a wine event right next to TIFF. I don’t drink alcohol, so it was entirely about the conversations for me – and I had the best time. We had significant customers come out, and the demand for simplicity, ease of implementation, and everything Alkira does well was just as strong in Canada as anywhere else. The market need is real. We talk about global backbone as a service all the time. Connecting China to San Francisco carries a distance and time tax, but it’s easy to configure. For organizations navigating geopolitical complexity around China access, or needing GPU connectivity in and out, we just abstract the Azure and AWS mainland China instances. They operate the same way as their Canadian or US equivalents. And you can consume it pay-as-you-go – stop using it, stop paying for it. That’s a compelling model for MSPs looking to grow into different regions. Robert Dutt: Last question then. For that Canadian MSP who’s listened to this and is thinking, “This sounds like a real opportunity” – what’s the one thing you’d want them to take away and act on? Doug Houghton: I’d ask them to go to partners@alkira.com and send us a note. And I will ply them with all sorts of content – videos, learnings, deal registration information, everything they need to get started in the space. Tongue in cheek, and also completely seriously: partners@alkira.com. If you’re looking to grow your business as a managed service provider – managed network, managed security, managed load balancing, managed DDI, managed connectivity – we’re a really great place to start. Because it’s never unpopular to walk into a customer and solve their problem quickly and say, “I can help you with X, Y, and Z, and I can do it in the next couple of hours – and that’s going to drive a total cost of ownership savings of 40 to 70%.” Nobody ever kicks you out of the office when you say something like that. Robert Dutt: Amazing. Doug, I appreciate you taking the time. Thank you very much. Doug Houghton: Robert, thank you for the engaging conversation. I hope your listeners get some good stuff out of it. Robert Dutt: There you have it – Doug Houghton from Alkira. I’d like to thank Doug for his time, and honestly for being one of the more entertaining guests I’ve had on in a while. “Networking is not sexy until it doesn’t work” is a line I’m going to be thinking about for a while. Thanks to you for listening as well. If this conversation sparked something – whether it’s curiosity about NIaaS, the AI infrastructure angle, or what roughly 20% total margin on a $500,000 average deal could do for your business – Doug made it easy for you to take the next step. Drop a note to partners@alkira.com. That’s the front door. And from what I heard today, they will absolutely get back to you. Here’s the thing that stuck with me most in this conversation: the argument that the AI moment isn’t just a software or services play. It’s going to force a reckoning with network infrastructure that a lot of organizations have been deferring for years. The partners who treat that reckoning as an opportunity rather than a fire drill are probably going to look very smart in about three years. If you’re finding the In The Channel podcast from ChannelBuzz.ca useful, the best thing you can do is follow or subscribe wherever you get your podcasts. We’re on Apple Podcasts, Spotify, YouTube, and most major directories. And if you’re enjoying the show, ratings and reviews are genuinely appreciated – they help other people in the Canadian channel find us. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

In Episode 3, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Daragh Kelly, Chief Data Officer at The Economist, where they discuss why most AI initiatives are still failing, why there's not much measurable progress and how insight functions have become toolmakers and decision intelligence partners, which includes;Why most AI initiatives fail because they as a solution looking for problems.The importance of aligning AI use cases to strategic goals, KPIs and measurable outcomes.Why speed rather than velocity leads to very little measurable progress.Why compelling POCs create false confidence before the real production challenges begin.The deployment gap: why robust, scalable and commercially viable AI is still hard.Why disconnected tools and poor workflow integration stall AI value realisation.The simple test for prioritisation: is this problem big enough to matter?Why the best AI use cases act as building blocks for future capability.How AI and UX together are driving true self-service insight generation.Why insight teams are evolving from answer providers to toolmakers.The growing importance of data governance, quality and observability in an AI-first world.How distributed insight creation can weaken corporate memory and knowledge curation.The skills shift toward UX, enablement, storytelling and decision intelligence.Practical build vs buy criteria in fast-moving and rapidly commoditising AI markets.Why operating models matters less than discipline, purpose and capability building.

“Just go and show our tool in the best way possible.”I have heard this sentence wayyyy too often coming from a salesperson, and the solution engineer on the receiving end just died a little bit inside.Of course you want to make a good impression when showing your tools to your customer, but more importantly, you want to start building a relationship and engage with them. For that you have to get them to a point where they open up and tell you what they *really* think—and a “no” is a good indicator that this relationship has formed.And we are happy to have a pro in this field as the guest of this episode: Max Lüpertz. Max is a solution engineer who took over as account executive and grew until he led the whole sales organization of the UK for one of the companies he worked for. Now he helps fast-growing SaaS companies close more deals by making their sales demos (and their general presales) better. He provides hands-on coaching and sets up a simple, repeatable demo process with his firm, PreSales Rockstars.In this episode of the podcast, we talk about: Solution engineers are too often treated as “demo monkeys”—pulled in before proper discovery has happened because AEs need to show pipeline progress. There is no solution without a problem: if you don't understand what the customer is trying to solve, any demo you run risks being irrelevant or overwhelming.Once a prospect has seen the functionality and shortlisted vendors, their mindset shifts entirely—from “Can it do this?” to “What happens to me personally if this goes wrong?” Oversharing is one of the most common and costly demo mistakes. Bombarding a prospect with features increases cognitive load, raises perceived risk, and dilutes the message. Max's lesson from an 18-month stalled deal: FOMO caused him to show 50 features when the customer only needed three. The extra complexity made the project feel like a burden, and the prospect concluded they weren't ready. The “shotgun” method—showing everything and hoping something lands—is an AE-driven trap. Effective demos need a curated storyline built around confirmed needs, not a feature parade.Discovery is not a one-time AE activity. SEs need to run a secondary, deeper discovery to uncover the personal risks and motivations of individual stakeholders—not just the organizational problem.How you introduce yourself sets the ceiling on your influence. Being framed as the “technical conscience” boxes you into a narrow role. Instead, position yourself as someone who knows the industry, has seen implementations succeed and fail, and will proactively surface the risks the customer doesn't yet know about—the things they don't know they don't know.SEs act as a “human API” between customers and product management—translating vague feature requests into actionable feedback and pushing back on requests that turn out to be aspirational rather than genuine buying signals. POCs are high-cost investments—often two people for two to four weeks—and should never be offered just because it's “the next step.” Success criteria must be defined upfront, and the SE should use the POC as a “gift and get”.The value conversation must anchor every interaction. If a customer can't explain why they want to model processes beyond “so that we have modeled processes,” they aren't ready to buy. Every conversation needs to come back to outcomes, not features. Max is also on LinkedIn—check out his profile here: https://www.linkedin.com/in/max-luepertz/.Please reach out to us by either sending an email to hello@whatsyourbaseline.com or signing up for our newsletter and reading articles about process and architecture on our Substack… Go and subscribe at whatsyourbaseline.substack.com.And if you like to support “the little podcast that could,” become a Patron at https://www.patreon.com/c/whatsyourbaseline. We appreciate you!

80% of enterprise AI projects never reach production. After two decades helping enterprises adopt new technology, Kashif Manzoor breaks down the five failure modes killing enterprise AI initiatives, introduces the GenAI Maturity Framework, and shares three questions every CTO should ask before approving their next AI project. Episode #: 185 In this episode, you'll learn: The 5 failure modes killing enterprise AI initiatives The GenAI Maturity Framework (6 dimensions, 6 levels) 3 questions every CTO should ask before their next AI initiative Why the gap between perceived and actual AI maturity is where POCs go to die Practical actions you can take this week   TIMESTAMPS: 0:00 - The POC graveyard (a real conversation) 1:30 - Welcome + Why this episode exists 3:30 - My journey: Oracle → Cloud → GenAI 7:00 - The 80% problem: Why enterprise AI fails 10:00 - Failure Mode 1: The Strategy Gap 12:30 - Failure Mode 2: The Architecture Gap 15:00 - Failure Mode 3: The Governance Gap 17:00 - Failure Mode 4: The Talent Gap 19:00 - Failure Mode 5: The Measurement Gap 21:00 - The GenAI Maturity Framework (6 levels explained) 24:00 - 3 Questions Every CTO Should Ask 26:30 - What's coming next 28:00 - Subscribe + Connect

DescriptionBhaskar was employee #1 at AppDynamics, which was sold to Cisco for $3.7B. He and co-founder Jyoti found a way to change how enterprise monitoring tools worked. From tracking low-level code metrics that ops teams didn't understand to monitoring what the business actually cares about.In this episode, Bhaskar breaks down how that one insight won them Netflix and Priceline as early customers, why they ran production POCs that no competitor would dare try, and how a free download called AppDynamics Lite generated over 60% of their leads—in an industry where getting started normally took weeks of professional services and six-figure contracts.Why You Should ListenWhy selling to developers is operating on hard mode.How one-day POCs became the killer enterprise sales weapon.Why freemium disrupted an industry that required weeks of professional services to get started.How they grew from $2M to $12M in revenue in just one year post launch.Keywords startup podcast, startup podcast for founders, product market fit, AppDynamics, application monitoring, enterprise SaaS, B2B sales, finding pmf, freemium strategy, Cisco acquisition, production POCChapters00:00:00 Intro00:11:33 Choosing the ICP00:20:37 Landing Netflix with Freemium00:28:44 Growing from $2M to $12M in Year Two00:30:10 The Free Download Strategy That Generated 60% of Leads00:32:04 Days from the NASDAQ Bell—Then Cisco Offered $3.7B00:41:28 The Moment of True Product Market FitSend me a message to let me know what you think!

Idén februárban hirtelen tíz éve nem látott szintre emelkedett a munkanélküliségi ráta Magyarországon. A témát Hornyák József, a Portfolio munkaerőpiaccal foglalkozó elemzője segített értelmezni. A második részben a szőlő aranyszínű sárgaság betegségével foglalkoztunk: Kovács Nóra, az Agrárszektor vezető szerkesztője arról beszélt, hogy a tavaszi munkák indulásával a veszély ismét aktuálissá vált. Főbb részek: Intro – (00:00) Munkanélküliség – (01:32) Aranyszínű sárgaság – (13:09) Tőkepiaci kitekintő – (22:14) Kép forrása: Getty ImagesSee omnystudio.com/listener for privacy information.

Nauta is building the data infrastructure layer for global supply chain, starting with mid-market shippers who manage 600+ suppliers across 40+ countries but lack a single source of truth. Co-founded by Valentina Jordan, who spent six and a half years at Rappi, Nauta targets the $200M-$2B revenue segment where companies face enterprise-level complexity without enterprise resources. In this episode of BUILDERS, Valentina shares how Nauta moved from Excel automation to building data pipes that connect 12-13 stakeholders touching a single product—and why they refuse to run POCs.Topics Discussed:Why shippers with ERP, TMS, and WMS systems still run operations in ExcelThe tribal knowledge crisis: 20-30 year operators retiring with undocumented institutional knowledgeNauta's no-POC policy and why it requires contract exit clauses insteadThe cost reduction vs. revenue generation framework that escapes pilot purgatoryBuilding familiar interfaces (Excel-like tables) over novel UX for conservative industriesThe shift from hiding AI capabilities (January 2025) to leading with them (eight months later)GTM Lessons For B2B Founders:Distinguish symptoms from root cause pain in discovery: Most enterprise buyers surface symptoms, not problems. A client reporting penalty costs isn't revealing the root issue—just downstream impact. Valentina uses the five whys methodology to drill into actual pain: "A client can tell me, hey, I'm paying X amount of dollars in penalties. That's not necessarily the root cause, it's just a symptom of the actual pain." This prevents building features that address surface-level complaints while missing the structural problem. The real issue might be data fragmentation across systems, lack of visibility into supplier performance, or decision-making bottlenecks—each requiring different solutions.Structure POC alternatives that demand mutual commitment: Nauta kills traditional POCs entirely because "it implies that they are testing us and that it's not a collaborative process." Instead, they offer contract exit clauses if expectations aren't met while requiring upfront commitment. This only works when you have proven results and can confidently deliver value. The insight: POCs create evaluator-vendor dynamics where the burden of proof sits entirely on you. Paid engagements with performance-based exits create partner dynamics where both parties invest in success. For early-stage companies without case studies, this won't work—but once you have repeatable results, test this approach.Layer revenue generation on top of cost reduction: Nauta starts every engagement with 3-4 cost reduction KPIs—penalties, reconciliation time, manual labor automation—then transitions to revenue generation through fill rate optimization and cash-on-cash improvements. "You need to go beyond just cutting costs. That way you transition from a nice to have to a must have." Supply chain has historically been viewed as a cost center; proving top-line impact changes budget conversations entirely. This matters because cost reduction has a ceiling (you can only cut so much), while revenue generation creates expanding budget headroom. Map your product capabilities to both from day one.//Sponsors:Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co//Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM  This episode reframes how leaders should approach generative AI, with insights from Justin Trombold. Instead of chasing use cases or tools, the focus is on fixing processes, incentives, and operating models. The conversation explores why many AI pilots fail, how ROI thinking can mislead, and why AI should be treated as a new way of working rather than a software upgrade. Practical examples show how small, disciplined changes can unlock productivity, innovation, and meaningful business impact without overinvesting or freezing in fear. 

Qualytics is redefining enterprise data quality by positioning it as a collaborative business function rather than an isolated data engineering problem. Founded at the start of the pandemic by Gorkem Sevinc - a former CTO and CDO who spent years managing reactive data quality firefights - Qualytics emerged from a clear practitioner pain point: writing endless custom rules to catch data issues after they'd already broken dashboards and KPIs. The company raised pre-seed and seed rounds while building with beta customers, then closed a Series A as repeatability patterns emerged in their POC process. Now, as enterprises scramble to operationalize AI initiatives, Qualytics is experiencing explosive inbound demand from organizations realizing their data foundations aren't ready for democratized data access. Topics Discussed The practitioner insight that sparked Qualytics: reactive rule-writing doesn't scale Leveraging existing CTO/CDO networks and PE portfolio connections for beta customers The evolution from free POCs to paid POCs as a mutual commitment mechanism Identifying repeatability through week-by-week POC conversion patterns Building practitioner credibility into the sales motion while hiring for enterprise sales grit The decision to hire sales and marketing leadership simultaneously post-Series A Tracking in-product engagement metrics (DQ operations frequency, anomaly detection, rule editing) as churn prevention Positioning data quality as vertical-specific business problems (premium leakage, regulatory compliance) The timing advantage: AI adoption forcing enterprises to treat data governance as mandatory infrastructure GTM Lessons For B2B Founders Talk to 100 prospects before writing code—even with deep domain expertise: After burning 18 months building a radiology second opinion product that patients didn't want (they didn't even know radiologists were doctors), Gorkem adopted a hard rule: validate with 100 conversations before building. His advantage as a former CTO who lived the data quality problem created false confidence. Practitioners often assume their pain is universal, but buyer awareness and willingness to pay are separate questions. Start with NSF I-Corps-style problem validation: show rough sketches, probe what happened when they hit the pain point, understand how it hurt them financially or operationally. Repeatability appears in micro-conversions during trials, not just closed-won rates: Gorkem didn't declare product-market fit when deals closed—he declared it when he could predict POC behavior by week. "Week two, I'm expecting this. Week three, I'm expecting this." That predictability enabled ROI calculators and internal champion enablement materials. For technical founders, this means instrumenting your trial or POC to track leading indicators: specific features activated, data volumes processed, number of team members engaged, frequency of logins. When those patterns stabilize across prospects, you have a repeatable motion. Use paid POCs as a procurement front-loading mechanism, not a revenue play: Qualytics charges nominal amounts for some POCs—not for the revenue, but to get the MSA signed and force both parties through legal/security review upfront. This eliminates the pattern where free POCs succeed technically but die in procurement. Large enterprises often refuse to pay for POCs, which Gorkem accepts—but only if they commit equivalent effort (executive time, cross-functional teams). The paid POC is a qualification tool: if they won't commit anything, they're not a real opportunity. Hire sales and marketing leadership in parallel and hold them to unified GTM metrics: Gorkem regrets hiring early sales reps before leadership and delaying marketing investment. Post-Series A, he hired both leaders simultaneously and holds them jointly accountable to pipeline generation and velocity—not siloed MQL counts or quota attainment. This structural decision forces collaboration on messaging, ICP definition, and campaign strategy from day one. For technical founders who "figured out" founder-led sales, resist the urge to replicate your motion with more SDRs. Bring in strategic leadership that can build a scalable system. Instrument product engagement as your earliest churn signal—then intervene immediately: Beyond quarterly NPS and executive QBRs, Gorkem tracks granular product usage: how many data quality operations users run, how many anomalies they discover, how actively they're editing rules. When engagement drops, he doesn't wait—he jumps into the customer's existing weekly meetings to diagnose and course-correct. For B2B founders building complex products with long time-to-value, passive health scores aren't enough. You need active usage telemetry and a low-latency intervention process. Translate technical capabilities into vertical-specific business outcomes: Gorkem doesn't pitch "data quality for data engineers." He talks about premium leakage with insurance companies and OCC/SEC data controls with banks. This reframing works because buyers recognize their problem, not a vendor category. The shift requires research: understand each vertical's regulatory environment, operational pain points, and the business metrics executives care about. When you walk in speaking their language about their P&L impact, you're not another vendor—you're someone who gets it. Time your market entry to when "nice-to-have" becomes "must-have": When Qualytics launched, some enterprises called data quality a "nice-to-have." AI adoption changed that calculus overnight. Organizations planning to let 20,000 employees interrogate data through AI interfaces suddenly realized they need robust data governance, quality controls, and cataloging first. Gorkem's timing wasn't luck—he built during the "nice-to-have" phase so he'd be ready when AI budgets made it mandatory. Technical founders should identify the external forcing function (regulation, technology shift, economic change) that will transform their solution from vitamin to painkiller. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Tots dos ho havien desitjat des de petits, ell ser Carnestoltes i ella ser Reina i aquest 2026 han fet realitat el somni d'infància. En Miku Berbegal de mà de la colla del Retiro Èrem Pocs i la Davinia Herrada amb la colla que ella mateixa lidera i que va crear pensant en ser reina algun dia, La Salsa del Xató. Ara els queda digerir tot el viscut però l'endemà de Dimecres de Cendra asseguren que s'han superat les expectatives tot i quedar-se amb un cert gust agredolç per no haver pogut gaudir de la visita a les escoles amb tots els companys de les colles degut a la pluja. Fora d'això es queden amb el moment en que van ser presentats a les seves respectives colles i amb anècdotes tendres on canalla els regalava dibuixos o els demanaven fotografies. Han gaudit del Carnaval des de la posició privilegiada que els dona el personatge i la carrossa i asseguren haver vist un Carnaval en bon estat de forma. Amb el Carnaval ja acabat ara posen la mirada en la propera cita, la Davinia amb les Eternes i en Miku amb els Socarrimats, des d'avui formen part d'aquests grups selectes de sitgetans. L'entrada Miku i Davinia i un desig d’infància compartit, ser Carnestoltes i Reina del Carnaval ha aparegut primer a Radio Maricel.

No episódio do Hipsters.Talks, PAULO SILVEIRA, CVO do Grupo Alura, conversa com KUNTUALA ZELI, diretora de vendas na Oracle, sobre como funciona toda a jornada de vendas em empresas de software, cloud e SaaS. Vamos explorar o papel do pré-vendas/arquitetos, que fazem a ponte entre as demandas do cliente e a tecnologia, e dos engenheiros, que mergulham na profundidade técnica e conduzem as POCs. Uma conversa que mostra como vendas em tecnologia deixaram de ser apenas comerciais e passaram a exigir profundo entendimento técnico, visão de negócio e colaboração entre times. além de revelar como carreiras híbridas entre tech e vendas estão se tornando cada vez mais comuns.

In this episode of the Comparative Agility Podcast, Dee Rhoda speaks with Jason Molesworth, CEO and Chief AI Strategist at Accelerated Innovation Group, about why 2026 will be a make-or-break year for organizations adopting Generative AI.They explore what it really takes to move from experimentation to impact, including the importance of strategic clarity, secure and responsible AI, data readiness, and people enablement. Jason also shares why agility, focus, and incremental value delivery are critical for scaling GenAI and avoiding the trap of endless POCs.

I'm digging into a frustrating reality many teams face: even technically superior analytics and AI products routinely lose deals—not because the KPIs or models aren't good enough, but because buyers and users can't clearly see how the product fits into their day-to-day work. Your demos and POCs may prove what's possible, but long time-to-understanding, heavy thinking burden on the user, and required behavior or process changes introduce risk—and risk kills momentum. When value feels complicated, sales don't move forward.     Adding to the challenge is that many sales efforts focus almost entirely on the fiscal buyer while overlooking the end users who actually have to adopt the product to create outcomes. This buyer–user mismatch, combined with status quo bias, often leads to indecision rather than change. To address this, I explore the idea of thinking about the sales challenge as a product problem—and I introduce the idea of achieving Flow of Work Alignment (FOWA). The goal isn't better persuasion—it's clearer value. Strong FOWA means transitioning from demonstrating capabilities to helping customers see themselves—and their workflows—represented in your demos and POCs. The result? Prospects understand your value quickly, ask deeper, contextual questions, and deals move forward.      Highlights/ Skip to: Data products must work harder to expose value clearly to avoid the dreaded “closed-lost” deal stage in your CRM (1:38) Making your data product's value instantly obvious (5:18) How the “old model” of selling based on capabilities and feature demos can lead to lost sales (7:22) What  Flow-of-Work Alignment is and how it can help you unlock deals (13:02) How to know if you have achieved FOWA or not in your product and sales process (13:58)

La presentació del còmic es va fer dissabte al Mambo davant d'un nombrós públic que esperava ansiós per conèixer la història que ha creat la colla Érem Pocs per anunciar el personatge de Sa Majestat Carnestoltes. El còmic ha estat realitzat per un dibuixant de còmic professional, Vicente Cifuentes, que ha donat vida al guió escrit per uns quants membres de la colla i que relata l'arribada a Sitges de dos éssers estranys (els capes grisos). Altra novetat és que aquest any i per primera vegada a la història del carnaval sitgetà aquesta colla durà una carrossa sostenible que funcionarà amb bateries recarregables, una idea pionera que podria quedar-se per sempre. És la segona vegada que la colla organitza el personatge central del Carnaval després de fer-ho l'any 2015 i aquest 2026 volen celebrar el cinquanta aniversari del Carnaval al carrer de Sitges després que la colla Érem Pocs sortís l'any 1976 vestida de senyals de tràfic i no ho deixés de fer mai. Ens en fa cinc cèntims la cap de colla Sílvia Peñas, filla dels creadors de la colla l'any 1976 i ara qui se n'encarrega junt al seu germà Roger. L'entrada Un còmic anuncia l’arribada del Carnestoltes dels Érem Pocs després de 50 anys amb la família Peñas al capdavant de la colla ha aparegut primer a Radio Maricel.

L'espai Joan Tarrida ha acollit la presentació del cartell i del programa del Carnaval 2026 que manté l'agenda habitual d'actes i inclou algunes novetats. Més enllà del so centralitzat i el retorn de les grades al punt de la televisió tal com va avançar en aquest mitjà el president de la Comissió del Carnaval, Carles Garcia, aquest any s'ha previst ambientació musical prèvia a la rua de La Disbauxa amb una festa entre les 16h i les 18h de diumenge a la zona del Pic Nic. D'altra banda abans de les rues de La Disbauxa i l'Extermini l'organització ha previst també l'amenització musical a càrrec de dues xarangues que es mouran pel recorregut. En aquesta línia s'ha augmentat també la il·luminació a l'inici de la rua, al gir del Passeig de la Ribera amb el carrer de la Bassa Rodona i també a la plaça Pou Vedre. Pel que fa a canvis enguany també s'ha modificat el recorregut de la Rua d'Antes que aquest 2026 no serà circular i finalitzarà a la zona dels gronxadors del Passeig de la Ribera. Tot i que l'inici oficial de la festa és dijous gras amb l'Arribo de Sa Majestat Carnestoltes a càrrec de la colla Èrem Pocs el cert és que el dissabte anterior, 7 de febrer, tindrà lloc la presentació de la Reina del Carnaval de mans de la colla La Salsa del Xató, una cita que segons la regidora s'ha convertit en l'inici oficiòs de la festa. El Carnaval 2026 comptarà amb el mateix dispositiu de seguretat dels darrers anys que conformen mil efectius entre els diferents cossos per vetllar per les més de dues mil persones que desfilaran a les rues de la tarda i el miler d'infants que ho faran en les matinals. Ho han explicat la regidora de festes Eva Martín, el president de la Comissió del Carnaval, Carles Garcia, l'autora del cartell Lídia Estany i les representants de les colles organitzadores dels personatges protagonistes, Sílvia Peñas d'Èrem Pocs i Davinia Herrada de La Salsa del Xató. L'entrada El Carnaval 2026 celebra 50 anys de rues al carrer amb un cartell de Lídia Estany i anuncia més música al carrer i nou recorregut per la Rua d’Antes ha aparegut primer a Radio Maricel.

aiOla is pioneering speech-to-data technology that transforms unstructured speech into actionable data for enterprise operations. As a serial entrepreneur on his sixth startup, Co-Founder Amir Haramaty built aiOla after witnessing firsthand how traditional AI implementations fail to deliver ROI in enterprise settings. The company has developed proprietary technology that achieves near-100% accuracy in challenging environments with heavy jargon, multiple languages, and difficult acoustics. With strategic investors including a major airline and partnerships with Nvidia, Accenture, and USG, aiOla is addressing the fundamental challenge that 95% of enterprise AI pilots fail to show value by focusing on immediate, measurable ROI through speech-based data capture. Topics Discussed: The genesis of aiOla from consulting work revealing AI's implementation gaps in traditional enterprises Solving the triple challenge of speech recognition: accuracy in jargon-heavy environments, separating signal from noise, and converting speech to structured workflow data aiOla's "jargonic" approach: creating hyper-personalized language models for specific processes without retraining Early customer acquisition through serendipitous encounters and demonstrating immediate ROI Vertical expansion strategy from food manufacturing to aviation, travel, hospitality, and retail Channel partnership strategy refined from previous startups to achieve scale The shift from convincing customers about speech technology to being pulled into diverse use cases Building the aiOla Intelligate orchestration layer to dynamically select optimal speech recognition models GTM Lessons For B2B Founders: Make CFOs your best friend, not IT departments: Amir explicitly targets CFOs rather than IT as primary buyers because "it doesn't matter how small or big you are, you still have to do more with less." While IT serves as facilitators, CFOs control budgets focused on operational efficiency and ROI. B2B founders should identify which executive truly owns the pain point and budget authority, even if IT will implement the solution. Deploy capital strategically to remove obstacles before they emerge: aiOla convinced their airline investor to provide working capital specifically to fund POCs for prospects without existing budgets. This eliminated the "we don't have pilot budget" objection before it arose. B2B founders should proactively identify and neutralize common barriers in their sales process, whether through creative deal structures, proof-of-concept funding, or implementation support. Prioritize instant ROI over long-term transformation promises: Amir explicitly avoids "digital transformation" conversations, instead selecting use cases delivering "biggest impact within shortest period of time with minimum obstacle possible." The airline baggage tracking example saved 110,000 hours immediately, creating momentum for expansion. B2B founders should resist selling comprehensive transformation and instead identify narrow use cases with quantifiable, rapid returns that create internal champions. Replicate proven use cases across customers rather than customizing: Once aiOla achieved success with specific applications like CRM data entry or pre-op inspections, they "stop, print, replicate" rather than reinventing for each customer. This approach reduced a two-hour inspection process to 34 minutes in food manufacturing, then replicated across industries. B2B founders should document successful implementations as repeatable playbooks and resist the urge to over-customize for each prospect. Channel success requires speaking the partner's economic language: When working with telcos, Amir demonstrated that his solution increased ARPU by 34% and reduced churn by 17%—the only two metrics telcos prioritize. He built predictable models showing exactly how many units each channel rep would sell by geography. B2B founders pursuing channel strategies must translate their value proposition into the specific KPIs that drive partner economics and compensation. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Podcast: Tech Transformed PodcastGuest: Manesh Tailor, EMEA Field CTO, New Relic Host: Shubhangi Dua, B2B Tech Journalist, EM360TechAI-driven development has become obsessive recently, with vibe-coding becoming more common and accelerating innovation at an unprecedented rate. This, however, is also leading to a substantial increase in costly outages. Many organisations do not fully grasp the repercussions until their customers are affected.In this episode of the Tech Transformed Podcast, EM360Tech's Podcast Producer and B2B Tech Journalist, Shubhangi Dua, spoke with Manesh Tailor, EMEA Field CTO at New Relic, about why AI-generated code, also called vibe-coding, rapid prototyping, and a focus on speed create dangerous gaps. They also talked about why full-stack observability is now crucial for operational resilience in 2026 and beyond.AI Vibe Code Prioritising Speed over StabilityAI has changed how software is built. Problems are solved faster, prototypes are created in hours, and proofs-of-concept (POC) swiftly reach production. But this speed comes with drawbacks.“These prototypes, these POCs, make it to production very readily,” Tailor explained. “Because they work—and they work very quickly.”In the past, the time needed to design and implement a solution served as a natural filter. However, the barrier has now disappeared.Tailor tells Dua: “The problem occurs, the solution is quick, and these things get out into production super, super fast. Now you've got something that wasn't necessarily designed well.”The outcome is that the new systems work but do not scale. They lack operational resilience and greatly increase the cognitive load on engineering teams.New Relic's research indicates that in EMEA alone:The annual median cost of high-impact IT outages for EMEA businesses is $102 million per yearDowntime costs EMEA businesses an average of $2 million per hourMore than a third (37%) of EMEA businesses experience high-impact outages weekly or more often.Essentially, AI-driven development heightens risks and increases blind spots. “There are unrealised problems that take longer to solve—and they occur more often,” Tailor noted. This is because many AI-generated solutions overlook operability, scaling, or long-term maintenance.Modern architectures were already complex before AI came along. Microservices, SaaS dependencies, and distributed systems scatter visibility across the stack.“We've got more solutions, more technology, more unknowns, all moving faster,” he tells Dua. “That's generated more data, more noise—and more blind spots.”Traditional...

Neo4j's Ajay Singh discusses future shifts in AI and why knowledge graphs may be the missing layer in your Gen AI strategy.Topics Include:Ajay Singh from Neo4j discusses graph intelligence platform serving 80+ Fortune 100 companies.Financial services firms use Neo4j knowledge graphs to detect fraud rings and accounts.IT companies build digital twins of infrastructure to analyze attack surfaces and vulnerabilities.Knowledge graphs provide richer context for Gen AI agents beyond what vector search offers.Gaming company achieved 10x faster insights and 92% reduction in analyst data gathering.Transportation company improved tariff code workflow from 50% abandonment to 95% completion rate.Neo4j has partnered with AWS since 2013, running on AWS infrastructure and Marketplace.Customers combine Neo4j with AWS Bedrock and SageMaker to build agentic AI applications.Neo4j evolved from late-stage AWS collaboration to early-stage joint customer solution development approach.Success requires business-first mindset over technology-first to avoid POCs that never reach production.Effective Gen AI needs semantic layers and knowledge graphs, not just throwing documents at LLMs.Future agents will tackle outcome-based objectives requiring explainability, security, and proper LLM operations.Participants:Ajay Singh – Global Vice President, Neo4jSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Corey Quinn sits down with Avery Pennarun, co-founder and CEO of Tailscale, for a deep dive into how the company is reinventing networking for the modern era. From finally making VPNs behave the way they should to tackling AI security with zero-click authentication, Avery shares candid insights on building infrastructure people actually love using, and love talking about.They get into everything: surviving 100% year-over-year growth, why running on two tailnets at once is pure chaos, and how Tailscale makes “secure by default” feel effortless. Plus, they dig into why FreeBSD firewalls needed some tough love, the uncomfortable truth behind POCs, and even the surprisingly useful trick of turning your Apple TV into an exit node.About Avery: Avery Pennarun is the co-founder and CEO of Tailscale, where he's redefining secure networking with a simple, Zero Trust approach. A veteran software engineer with experience ranging from startups to Google, he's known for turning complex systems into approachable, user-friendly tools. His contributions to projects like wvdial, bup, and sshuttle reflect his belief that great technology should be both powerful and easy to use. With a mix of technical depth and dry humor, Avery shares insights on modern networking, internet evolution, and the realities of scaling a startup.Highlights:(0:00) Introduction to Tailscale and Security(00:52) Sponsorship and Personal Experiences(02:07) Technical Deep Dive into Tail Scale(06:10) Challenges and Future of Tail Scale(22:45) Building the Tail Net's API(23:54) Connecting Cloud Providers with Tailscale(25:22) Tailscale as a Security Solution(26:44) Innovations and Future of TailscaleSponsored by: duckbillhq.com

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM 

Catching yourself rereading last year's VC emails while you're back in Silicon Valley is a pretty good way to realize how wild the last 12 months have been. Colin, Chuck, Canisius, and Todd break down how Collide AI is turning fast POCs into real production workflows, why change management is the actual moat, and how a stacked forward deployed team plus community driven distribution is setting up 2026 to be the year everything scales.Click here to watch a video of this episode.Join the conversation shaping the future of energy.Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.Apply today at collide.ioClick here to view the episode transcript. 00:00 Product market fit jokes and kickoff00:28 VC email flashback and velocity01:29 Forward deployed model and AI first mindset02:18 Sam Texas and AI coding shift04:04 What AI first actually means06:18 Not just podcast bros anymore07:00 AI breaks silos across the business08:21 Doglegs example and incentives09:57 Change management is the advantage10:18 Client story and regulatory filings win12:42 Selling outcomes not hype13:36 Building the FTE team and faster delivery16:24 AI strategy as workflow ROI first18:26 Grok as a thought partner and GPU cluster20:15 Shale revolution mindset parallel22:29 Recruiting, software DNA, and stacked team26:16 Content and community as a recruiting engine29:11 Distribution flywheel in the real world30:22 Team distribution vs product debate32:32 2026 is the scaling year34:02 Community platform finally clicking36:09 Building the community platform the hard way39:20 Scaling clients, POCs, and production41:09 Why mom and pops matter41:55 Energy demand tailwinds and macro impact44:44 One word answer for next year: scale45:20 POC to production cycle time focus47:12 Scaling tech, sales, and financing49:45 Moving at AI speed story50:14 Raising capital and building serious software52:56 Collide as the operator layer vision54:02 Gratitude and community over everythinghttps://twitter.com/collide_iohttps://www.tiktok.com/@collide.iohttps://www.facebook.com/collide.iohttps://www.instagram.com/collide.iohttps://www.youtube.com/@collide_iohttps://bsky.app/profile/digitalwildcatters.bsky.socialhttps://www.linkedin.com/company/collide-digital-wildcatters

Datawizz is pioneering continuous reinforcement learning infrastructure for AI systems that need to evolve in production, not ossify after deployment. After building and exiting RapidAPI—which served 10 million developers and had at least one team at 75% of Fortune 500 companies using and paying for the platform—Founder and CEO Iddo Gino returned to building when he noticed a pattern: nearly every AI agent pitch he reviewed as an angel investor assumed models would simultaneously get orders of magnitude better and cheaper. In a recent episode of BUILDERS, we sat down with Iddo to explore why that dual assumption breaks most AI economics, how traditional ML training approaches fail in the LLM era, and why specialized models will capture 50-60% of AI inference by 2030. Topics Discussed Why running two distinct businesses under one roof—RapidAPI's developer marketplace and enterprise API hub—ultimately capped scale despite compelling synergy narratives The "Big Short moment" reviewing AI pitches: every business model assumed simultaneous 1-2 order of magnitude improvements in accuracy and cost Why companies spending 2-3 months on fine-tuning repeatedly saw frontier models (GPT-4, Claude 3) obsolete their custom work The continuous learning flywheel: online evaluation → suspect inference queuing → human validation → daily/weekly RL batches → deployment How human evaluation companies like Scale AI shift from offline batch labeling to real-time inference correction queues Early GTM through LinkedIn DMs to founders running serious agent production volume, working backward through less mature adopters ICP discovery: qualifying on whether 20% accuracy gains or 10x cost reductions would be transformational versus incremental The integration layer approach: orchestrating the continuous learning loop across observability, evaluation, training, and inference tools Why the first $10M is about selling to believers in continuous learning, not evangelizing the category GTM Lessons For B2B Founders Recognize when distribution narratives mask structural incompatibility: RapidAPI had 10 million developers and teams at 75% of Fortune 500 paying for the platform—massive distribution that theoretically fed enterprise sales. The problem: Iddo could always find anecdotes where POC teams had used RapidAPI, creating a compelling story about grassroots adoption. The critical question he should have asked earlier: "Is self-service really the driver for why we're winning deals, or is it a nice-to-have contributor?" When two businesses have fundamentally different product roadmaps, cultures, and buying journeys, distribution overlap doesn't create a sustainable single company. Stop asking if synergies exist—ask if they're causal. Qualify on whether improvements cross phase-transition thresholds: Datawizz disqualifies prospects who acknowledge value but lack acute pain. The diagnostic questions: "If we improved model accuracy by 20%, how impactful is that?" and "If we cut your costs 10x, what does that mean?" Companies already automating human labor often respond that inference costs are rounding errors compared to savings. The ideal customers hit differently: "We need accuracy at X% to fully automate this process and remove humans from the loop. Until then, it's just AI-assisted. Getting over that line is a step-function change in how we deploy this agent." Qualify on whether your improvement crosses a threshold that changes what's possible, not just what's better. Use discovery to map market structure, not just validate hypotheses: Iddo validated that the most mature companies run specialized, fine-tuned models in production. The surprise: "The chasm between them and everybody else was a lot wider than I thought." This insight reshaped their entire strategy—the tooling gap, approaches to model development, and timeline to maturity differed dramatically across segments. Most founders use discovery to confirm their assumptions. Better founders use it to understand where different cohorts sit on the maturity curve, what bridges or blocks their progression, and which segments can buy versus which need multi-year evangelism. Target spend thresholds that indicate real commitment: Datawizz focuses on companies spending "at a minimum five to six figures a month on AI and specifically on LLM inference, using the APIs directly"—meaning they're building on top of OpenAI/Anthropic/etc., not just using ChatGPT. This filters for companies with skin in the game. Below that threshold, AI is an experiment. Above it, unit economics and quality bars matter operationally. For infrastructure plays, find the spend level that indicates your problem is a daily operational reality, not a future consideration. Structure discovery to extract insight, not close deals: Iddo's framework: "If I could run [a call where] 29 of 30 minutes could be us just asking questions and learning, that would be the perfect call in my mind." He compared it to "the dentist with the probe trying to touch everything and see where it hurts." The most valuable calls weren't those that converted to POCs—they came from people who approached the problem differently or had conflicting considerations. In hot markets with abundant budgets, founders easily collect false positives by selling when they should be learning. The discipline: exhaust your question list before explaining what you build. If they don't eventually ask "What do you do?" you're not surfacing real pain. Avoid the false-positive trap in well-funded categories: Iddo identified a specific risk in AI: "You can very easily run these calls, you think you're doing discovery, really you're doing sales, you end up getting a bunch of POCs and maybe some paying customers. So you get really good initial signs but you've never done any actual discovery. You have all the wrong indications—you're getting a lot of false positive feedback while building the completely wrong thing." When capital is abundant and your space is hot, early revenue can mask product-market misalignment. Good initial signs aren't validation if you skipped the work to understand why people bought. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Welcome to a special end-of-the-year series on Making Risk Flow as we count down the weeks to the end of 2025. Each Tuesday, we will re-release one standout episode as we build up to releasing our top fan favourite on the last Tuesday. In this episode, Juan de Castro is joined by his colleagues, Rich Lewis, Cytora's Sales Director, and Zaheer Hooda, Head of North America, for a deep dive into what makes proof-of-concept (POC) initiatives in risk digitisation succeed or fail.Drawing on firsthand experience from working with leading carriers, they break down five essential capabilities insurers need to get right when implementing digitisation initiatives, from extraction accuracy and full-spectrum intake handling to scalable deployment and human-in-the-loop exception management.They also provide a practical, inside look at how insurers structure effective proof of concept processes, including live workshops, data preparation, success metrics, and how to align POC design with measurable business outcomes.Whether you're revisiting the episode or viewing it for the first time,  this episode offers tactical guidance to ensure your technology investments deliver meaningful impact.Fan Mail: Got a challenge digitizing your intake? Share it with us, and we'll unpack solutions from our experience at Cytora.To receive a custom demo from Cytora, click here and use the code 'Making Risk Flow'.Our previous guests include: Bronek Masojada of PPL, Craig Knightly of Inigo, Andrew Horton of QBE Insurance, Simon McGinn of Allianz, Stephane Flaquet of Hiscox, Matthew Grant of InsTech, Paul Brand of Convex, Paolo Cuomo of Gallagher Re, and Thierry Daucourt of AXA.Check out the three most downloaded episodes: The Five Pillars of Data Analytics Strategy in Insurance | Craig Knightly, Inigo 20 Years as CEO of Hiscox: Personal Reflections and the Evolution of PPL | Bronek Masojada Implementing ESG in the Insurance and Underwriting Space | Simon Tighe, Chaucer, and Paul McCarney, Moody's

Episode OverviewIn this episode, Malcolm sits down with Jeremi Karnell of InvestNet to explore how the company is transforming six trillion dollars of “digital exhaust” into powerful decision-intelligence capabilities for financial advisors. Jeremi explains how predictive models, knowledge graphs, and generative AI are reshaping advisor workflows, driving measurable revenue lift, and redefining what modern data products look like in financial services. This is a rare look at an AI success story in an industry where most POCs still fail, and a blueprint for any data leader seeking real ROI.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Jeremi Karnell on LinkedIn

Dr. Joe Ghalbouni, a quantum communication PhD who moved from academia into Point72's innovation team and now runs Ghalbouni Consulting, is interviewed by Yuval Boger. They discuss how he helped a major hedge fund move from quantum curiosity to concrete education, use case discovery, and POCs, and why he believes the real bottleneck today is not hardware but algorithms and sector-aware problem mapping. The conversation explores where quantum is most promising in financial services, from optimization to quantum machine learning, and how quantum inspired methods on classical hardware are already delivering value. They also cover PQC and QKD roadmaps, what it really takes to move a quantum solution into production, and why Joe is surprisingly optimistic about seeing useful quantum advantage in specific use cases within the next few years.

“Tecnologia pela tecnologia tem que morrer. Área de tecnologia que não pensa em entregar valor pro negócio ou pro usuário final, ela tende a morrer. Você não deveria gastar dinheiro por gastar dinheiro” No décimo quinto episódio do Hipsters.Talks, PAULO SILVEIRA, CVO do Grupo Alun, conversa com ANATERRA OLIVEIRA, CTO da DASA, sobre inovação aberta, parcerias com startups e por que experiência do usuário é mais importante que tecnologia sofisticada. Uma conversa sobre o dia a dia de quem lidera tecnologia em uma das maiores empresas de saúde do Brasil. Prepare-se para um episódio cheio de conhecimento e inspiração!

"92% of all POCs today in the US are failing because they're trying to use bad data and LLMs that are standardized or generalized."   At Money 2020 I sat down with Deuna (www.deuna.com) co-founder Roberto Kafati (REKS) and their US head of GTM Chase Foster to explore the critical importance of leveraging high-quality, actionable data and intelligent systems to drive business value, especially in complex enterprise environments. The core challenge today is that while most companies possess vast amounts of data, a staggering 92% of AI pilot projects fail because they rely on data that isn't "AI-ready" that is lacking the necessary context, cleanliness, and standardization to be effectively used by large language models (LLMs). The key is transforming raw data, such as the 638 direct and indirect data points per payment transaction, into a strategically usable asset that goes beyond cost-cutting to unlock significant revenue growth across the organization.   The company's platform, Athia, is designed to solve this by acting as an agentic intelligence platform that utilizes merchant-specific data from massive commerce operations (like major airlines, movie chains, and retailers) to provide proactive, highly focused insights. Instead of forcing teams to manually analyze hundreds of performance dashboards, Athia surfaces the most critical information, alerting teams to revenue leakages and recommending direct, real-time actions, such as optimizing payment routing or detecting opportunities in developing economies. This approach allows businesses to embrace the future of "agentic commerce" by maintaining control over the customer experience and ensuring data-driven decision-making is implemented automatically and continuously across all critical functions, fostering a new era of cross-departmental collaboration between areas like payments and marketing.

Wultra provides post-quantum authentication for banks, fintechs, and governments—protecting digital identities from emerging quantum computing threats. In this episode, Peter Dvorak shares how he broke into the notoriously closed banking ecosystem by leveraging his early experience in mobile banking development. From navigating multi-stakeholder enterprise sales to positioning quantum-safe cryptography when the threat timeline remains uncertain (consensus: 2035, but could accelerate), Peter reveals the specific strategies required to sell mission-critical security infrastructure to regulated financial institutions. Topics Discussed How post-quantum cryptography runs on classical computers while protecting against quantum threats Why European banking regulation drives global authentication standards The multi-stakeholder sales process: quantum threat teams, CISOs, CTOs, and digital product owners Conference strategy and analyst relationships (Gartner, KuppingerCole) for category positioning Banking budget cycles and why June/July approaches fail Breaking the "who else is using this?" barrier with banking-specific proof points Positioning as the only post-quantum cryptography provider for digital identity in banking GTM Lessons For B2B Founders Layer future-proofing onto immediate ROI: Post-quantum cryptography doesn't require quantum computers to function—it runs on classical infrastructure while providing superior security. Peter sells banks on moving from SMS OTP to mobile app authentication (tangible, immediate benefit) while positioning quantum resistance as migration insurance: "You won't have to rip-and-replace in three years." For emerging tech, anchor value in today's operational wins, not future scenarios. Give struggling departments concrete wins: Large banks have quantum threat teams tasked with replacing every piece of software by 2030-2035. Peter gives them measurable progress: "We move you from 5% to 10% completion on authentication and digital identity." These teams need defensible projects to justify their existence. Identify which internal groups are fighting for relevance and deliver projects they can report upward. Banking references are binary gatekeepers: Every bank asks "who else is using this?" Non-banking customers (telcos, gaming, lottery) don't count—banking regulation and systems are fundamentally different. The first banking customer is the hardest barrier. Once cleared, subsequent conversations become tractable. Budget aggressively to land that first bank, even at unfavorable terms. Respect the annual budget cycle: Banks allocate resources 12 months ahead. Approaching in Q2/Q3 means budgets are locked—even free POCs fail because internal resources are committed. Peter's pipeline strategy: build relationships and maintain visibility throughout the year, then activate when budget windows open. Don't confuse market education with active pipeline. Map and sequence multi-stakeholder buys: Authentication purchases require alignment across quantum threat teams (if they exist), cybersecurity/compliance, CTO/CIO (infrastructure acceptance), and digital product owners (UX concerns affecting their KPIs). Start at director level—board executives are too removed from technical details. Research each bank's org structure before engaging, then tailor sequencing. EU regulatory leadership creates expansion vectors: European regulations like PSD2 and strong authentication requirements get replicated in Southeast Asia, MENA, and other regions. Peter benefits from solving EU compliance first, then riding regulatory diffusion. The US remains fragmented with smaller regional banks still using username/password. Founders should analyze which geographies lead regulatory adoption in their category. Maintain composure through 18+ month cycles: Peter's regret: losing his temper during negotiations cost him time. Banking doesn't buy impulsively—sales require patience through lengthy security reviews, compliance checks, and committee approvals. Incremental progress and rational positioning matter more than aggressive closing. Emotional control is operational discipline. // Sponsors:  Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Svetlana Zavelskaya, Head of Software Engineering for Data Platform and Infrastructure at Quanata, joins the show to unpack what it really takes to make the “impossible” possible in tech. From re-architecting a startup codebase to scaling innovation inside an insurance giant, she shares how her team turns complex R&D challenges into production-ready systems. This conversation dives deep into engineering discipline, AI tool adoption, and why the next wave of insurance innovation is powered by data and software.Key Takeaways• Real innovation often means balancing speed with long-term architecture decisions• AI coding tools are valuable for exploration but need governance and clear security guardrails• POCs fail when expectations aren't aligned, not because the tech doesn't work• Insurance tech is evolving fast through telematics and context-based data models• Well-structured, well-documented code is still the foundation for scalable innovationTimestamped Highlights00:33 How telematics is changing the economics of insurance and rewarding better drivers03:59 Cars as software platforms and what that means for data privacy and innovation06:02 The growing pains of re-architecting an organically built startup codebase08:38 Evaluating new AI tools and maintaining data security across teams11:08 Why most AI POCs never make it to production16:29 How Quanata's R&D work feeds into State Farm's larger technology initiatives20:40 Safe-driving challenges, behavioral change, and saving lives with dataA Thought That Stuck“If we can prevent just 1 percent of drivers in the world from using their phone behind the wheel, imagine how many lives we can save.”Pro Tips• Before starting a POC, define if it's an experiment or a potential product foundation• Let engineers explore new tools but build frameworks to govern how data and results are handledCall to ActionIf you enjoy exploring how data, AI, and engineering innovation come together to solve real-world problems, follow The Tech Trek on Apple Podcasts or Spotify and share this episode with a colleague who builds at the edge of what's possible.

In this episode of the Predictable Revenue Podcast, Collin Stewart interviews Kunick Kapadia, co-founder of Anova, as they discuss the journey of building a data analytics platform. They explore the importance of product market fit, learning from past mistakes, customer acquisition strategies, pricing strategies, and overcoming imposter syndrome. The conversation highlights the importance of honest feedback, the challenges of scaling a startup, and the significance of standing out in a crowded market. Highlights include: Validating Ideas: The Importance of Customer Feedback (03:04), Navigating Customer Development and POCs (09:54), Overcoming Imposter Syndrome in Entrepreneurship (11:23), Pricing Strategies: Finding the Right Value (14:41), Finding a Unique Go-to-Market Strategy Finding a Unique Go-to-Market Strategy (19:55), And more... Stay updated with our podcast and the latest insights on Outbound Sales and Go-to-Market Strategies!

Send us a textStart with a simple truth: when the platform breaks, your clever architecture won't save you. We dig into the AWS US‑East‑1 outage where DynamoDB's role in DNS planning for load balancers collided with a race condition, leaving empty records and stalled EC2 instances. Forget the finger‑wagging about “well‑architected” apps—this was a platform failure with limited customer escape routes. We weigh multi‑region and multi‑cloud trade‑offs with a sober look at cost, complexity, and operational burden.Security took center stage with two high‑risk stories you need to act on. First, a critical WSUS flaw enabling remote unauthenticated code execution against the very servers meant to protect fleets. If WSUS is still live, patch immediately or take it offline until you can. Then, the F5 source code theft: not a cloning threat, but a blueprint for discovering subtle bugs and crafting precise exploits. Attribution points toward Chinese state‑sponsored actors, which means targeted, quiet use rather than noisy mass exploitation. The risk isn't gone when headlines fade; it's just harder to see.We connect this to rising exploitation of vSock across hypervisors like VMware ESXi. With public PoCs and active abuse, vSock opens covert channels from host to guest, making segmentation and management plane isolation non‑negotiable. Patch aggressively, gate access through jump hosts, enforce MFA, and consider disabling vSock where viable on QEMU stacks. These are concrete steps that cut real risk.Then we turn to the elephant in the data center: AI ROI. Vendors keep shipping agentic assistants and copilots, but few can show durable returns outside a subsidized token economy. We share a pragmatic lens for measuring value—cycle time, MTTR, defect rates—while acknowledging the dot‑com‑style arc ahead: hype, correction, then durable wins that prioritize efficiency. As AI demand drives massive new builds, the physical footprint of the cloud is showing up in local power grids and skylines. Infrastructure choices now carry community and energy implications leaders can't ignore.Subscribe, share with a colleague who owns platform reliability or security, and leave a review with your biggest takeaway or question—what will you patch, segment, or measure first?Purchase Chris and Tim's book on AWS Cloud Networking: https://www.amazon.com/Certified-Advanced-Networking-Certification-certification/dp/1835080839/ Check out the Monthly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj

ISV leaders from Automation Anywhere, DataVisor, and Sumo Logic share battle-tested strategies for deploying AI agents at scale, including pricing models, proof of concepts and ROI.Topics Include:Panel brings together ISV leaders from automation, fraud detection, and security operations.Companies rethinking entire business processes rather than automating incremental portions with agents.Start with immutable data before tackling real-time changing data in production.Intent for change must come from board, CEO, and customers simultaneously.Challenge: proving agent value beyond CSAT when internal teams block deployment.Sumo Logic measures Mean Time to Resolution, aiming to cut hours to zero.DataVisor cuts fraud alert resolution from one hour down to twenty minutes.Customers demand reliability as workflows shift from deterministic to probabilistic agent decisions.Automation Anywhere spent three years making every platform component fully agent-ready.Focus on business outcomes, not chasing every new model release each week.Human oversight still critical—agents are task-oriented and prone to hallucinations and drift.Humans validate agent findings, then let agents scale actions across hundreds instances.Pricing experiments range from platform-plus-consumption to outcome-based to decision-event models.Token pricing doesn't work due to varied data modalities and complexity.Next two quarters: more POCs moving to production with productive agents deployed.Future prediction: enterprise apps becoming systems of knowledge powered by MCP protocol.Participants:Jay Bala - Senior Vice President of Product, Automation AnywhereKedar Toraskar – VP Product Partnerships, DataVisorBill Peterson - Senior Director, Product Marketing, Sumo LogicJillian D'Arcy - ISV Senior Leader, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Three years since the launch of ChatGPT, what does the landscape of Enterprise AI look like today? What's working, what's struggling and what's still unknown?SHOW: 966SHOW TRANSCRIPT: The Cloudcast #966 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:HOW ARE ENTERPRISES USING AI IN LATE 2025?5% have a clear vision of how to apply Predictive and Generative AI to a set of use-cases that drive differentiation, productivity improvements and cost reductions. They are keeping the details close to the vest.10% have allocated about 3-5% of their IT budgets to AI, typically from a C-level mandate, and have given it to Microsoft or Google. They have checked “the business is AI-enabled” and signaled to the market that they have fully embraced AI. The market is rewarding these companies at higher multiples. 85% aren't sure what use-cases to focus on, have unrealistic expectations during POCs, and are focused on the “no” areas instead of their own learning curves. Enterprises don't have great visibility into AI costs, and limited baselines of what AI should cost - pay for outcomes, pay for seats, pay for tokens, or pay for GPUs?Enterprises don't have easy access to GPUs outside of via SaaS services - makes it challenging for Private or Sovereign AI demand to be metRight now, there is no simple way for Enterprises to build AI AgentsRight now, there is no simple way for Enterprises to share AI experience / learning curve - AI is a very individualized experienceFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Madhavan Ramanujam is the world's foremost expert on pricing and monetization strategy. As managing partner at Simon-Kucher, he helped over 250 companies, including 30 unicorns, architect their pricing strategies. He's the author of the definitive book on pricing, Monetizing Innovation. Now he's back with a sequel, Scaling Innovation, which reveals how to build enduring businesses by dominating both market share and wallet share. He recently left Simon-Kucher to launch his own fund, 49 Palms, focused on helping early-stage AI companies.In this conversation, we discuss:1. The 2x2 framework that identifies your optimal pricing model2. Why AI companies can capture 25% to 50% of value created, vs. 10% to 20% for traditional SaaS products3. Why popular AI coding tools may have already doomed themselves with underpricing4. The “give-and-get” framework top negotiators use to extract maximum value from every deal5. The negotiation strategy that helped one founder 4x their deal size overnight6. How to frame POCs as “business case creation” instead of technical demos (and why this changes everything)7. Why AI companies must get monetization right from day one—not “figure it out later”8. How companies like Intercom's Fin and Sierra pioneered outcome-based pricing (charging $0.99 per AI resolution)9. The single question that reveals if your pricing is too complex—Brought to you by:Enterpret—Transform customer feedback into product growth: https://enterpret.com/lennyDX—A platform for measuring and improving developer productivity: https://getdx.com/lennyPersona—A global leader in digital identity verification: https://withpersona.com/lenny—Transcript: https://www.lennysnewsletter.com/p/pricing-and-scaling-your-ai-product-madhavan-ramanujam— My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/168109183/my-biggest-takeaways-from-this-conversation—Where to find Madhavan Ramanujam:• X: https://x.com/madhavansf• LinkedIn: https://www.linkedin.com/in/madhavansf/• Promo email for Scaling Innovation: promo@49palmsvc.com — If you're purchasing more than five copies, send a screenshot of your receipt to enter Madhavan's exclusive bundle raffle.—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Madhavan and his work(04:30) The core thesis of Scaling Innovation(09:20) Common traps founders fall into(12:06) Beautifully simple pricing(15:00) Mastering negotiations(26:51) Other strategies for effective pricing and monetization(27:35) How AI pricing is different(31:33) Handling POCs(36:25) The importance of mastering monetization(38:58) Choosing the right AI pricing model(43:13) Current trends in AI pricing(44:48) Strategizing for outcome-based models(50:23) Packaging strategies for scaling(51:37) Adapting pricing strategies over time(53:40) Key axioms for pricing success(58:00) Takeaways for founders(01:01:33) Lightning round and final thoughts—Referenced:• The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher): https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan• Cursor: https://www.cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Sierra Finn: http://www.sierrafinn.com/• Chargeflow: https://www.chargeflow.io/• GitHub: https://github.com/• Intercom: https://www.intercom.com/• Warren Buffett's quote: https://www.goodreads.com/quotes/11478913-if-you-ve-got-the-power-to-raise-prices-without-losing• Sierra: https://sierra.ai/• Clay Bavor on LinkedIn: https://www.linkedin.com/in/claybavor/• Mission: Impossible—The Final Reckoning: https://www.imdb.com/title/tt9603208/• Delphi: https://www.delphi.ai/• Dara Ladjevardian on LinkedIn: https://www.linkedin.com/in/dara-ladjevardian/• Sam Spelsberg on LinkedIn: https://www.linkedin.com/in/samuel-spelsberg/• Lennybot: https://www.lennybot.com/• Granola: https://www.granola.ai/• Simon-Kucher: https://www.simon-kucher.com/• Josh Bloom on LinkedIn: https://www.linkedin.com/in/joshuabloompricingconsulting/—Recommended books:• Monetizing Innovation: How Smart Companies Design the Product Around the Price: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867• Scaling Innovation: How Smart Companies Architect Profitable Growth: https://www.amazon.com/dp/1119633060• Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers: https://www.amazon.com/Business-Model-Generation-Visionaries-Challengers/dp/0470876417• Thinking Fast and Slow: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555/• Contagious: Why Things Catch On: https://www.amazon.com/Contagious-Things-Catch-Jonah-Berger/dp/1451686587/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com