Podcasts about enterprise ai

A Note from James:Mark Pincus is one of the true OGs of the internet. You probably know him as the founder of Zynga, the company behind FarmVille, Zynga Poker, and Words With Friends. Zynga was eventually acquired by Take-Two in a transaction valued at approximately $12.7 billion. Before Zynga, Mark started Tribe, one of the first social networks—before MySpace and Facebook. He has spent more than 25 years building, failing, and studying what gets millions of people to click, play, share, and come back. His new book, Life at the Speed of Play, inspired me to start coming up with new business ideas while we were still recording.What I really love is how Mark teaches people to copy like a master without looking like a copycat. He has a framework called “Proven–Better–New.” Start with something that has already been proven. Make it obviously better. Then isolate the new idea you want to test. It's one of the best systems I've heard for creating products people actually want.We talk about the early days of Facebook and MySpace, the failure of Tribe, the gaming industry, consumer psychology, AI coding, and how agents could eventually network and work for us while we're doing something else.I loved talking with Mark. I was still thinking about this conversation afterward—and I'm literally building businesses based on what I learned. His new book is called Life at the Speed of Play. Listen to this episode, and then read the book.Episode Description:Most founders begin with an idea and then spend months—or years—trying to prove that people want it. Mark Pincus thinks that process is backward.At Zynga, Mark's teams built “failure machines”: simple systems that allowed them to test hundreds of concepts before writing the code. They put unfinished ideas in front of real users, watched what people clicked, and refused to build anything until the demand was obvious. The objective wasn't to avoid failure. It was to make failure fast, cheap, and useful.Mark explains the framework behind that process: Proven–Better–New. First, study an existing success down to every screen, click, and design decision. Then identify one improvement that current users would immediately recognize as better. Only after that should a team add the unproven idea—the part most likely to fail.James and Mark also examine the problems facing today's consumer entrepreneurs. AI has made software easier to build, but distribution has become harder. People aren't searching for new apps, established platforms restrict organic growth, and algorithmic reach isn't the same as users actively sharing something with friends.Mark uses the failure of his early social network, Tribe, to explain why virality is not enough. Tribe grew quickly but lacked retention and trust. He ignored the communities users loved because they didn't match the business model he had already chosen. That painful mistake became the foundation for much of his later product philosophy.The conversation ends with Mark's current experiments: personal AI agents modeled after members of his family, a proposed work network built specifically for agents, an enterprise AI company called Hivemind, and the difficult decision to end a four-year passion project without abandoning the instinct behind it.This is a practical conversation about testing ideas, separating instinct from ego, learning from the past, and killing the wrong product before it consumes the right opportunity.What You'll Learn:How to build a failure machine: Test headlines, offers, videos, and fake doors before investing in a finished product.How to apply Proven–Better–New: Begin with a proven behavior, make one unmistakable improvement, and isolate the risky innovation.Why distribution is now harder than development: AI can generate a prototype quickly, but it cannot guarantee attention, trust, or adoption.Why Tribe failed despite rapid growth: Virality without retention, safety, and alignment with user behavior does not create a lasting network.How to copy without becoming a copycat: Study successful products at the pixel level, preserve what works, and innovate only where it matters.When to abandon an idea: Preserve the underlying instinct, but stop funding the particular expression of it when the evidence turns against you.How AI agents may change networking: Agents could eventually search for opportunities, exchange work, build reputations, and bring useful leads back to their users.Timestamped Chapters: [02:00] Finding the “OMFG” Moment [02:58] A Note from James [05:00] Build a Failure Machine Before Building a Product [06:25] Testing Demand With Fake Doors and Broken Links [08:08] Writing Copy That People Actually Notice [10:52] Test More Ideas in a Week Than the Industry Tests in a Year [11:53] Why Neglected Products Become Innovation Labs [13:26] How Mobile Apps Slowed Product Experimentation [15:09] Can AI Bring Rapid Testing Back? [17:08] Why Consumer Technology Feels Uninvestable [18:38] The 90/10 Rule for Investable Platforms [20:08] Why Nobody Downloads New Apps Anymore [21:20] Franchises, “Spicy New,” and Healthy Platforms [23:21] The Internet's Lost Cocktail Party [27:58] Why Tribe Failed While Facebook Won [30:26] Virality Without Trust or Retention [31:31] Ignoring What Tribe's Users Actually Wanted [33:22] Facebook, Raya, and Designing for Trust [35:03] Social Networks as Lead-Generation Engines [37:12] Facebook, Instagram, and the App Nobody Knew It Wanted [37:51] Net Promoter Scores and the Feeling of Quitting a Drug [40:25] Algorithmic Virality vs. People Sharing With Friends [42:00] Building Products That Help People Create [43:47] What Entrepreneurs Should Build With AI [44:54] The Proven–Better–New Framework [47:12] What “Obviously Better” Actually Means [48:25] Why “All New Fails” [50:23] Zynga Poker and the Power of Removing One Click [52:00] What AI Does Well—and Where Humans Still Matter [54:25] Picasso, Slack, and Copying the Past [55:11] Adding Fun to Boring Enterprise Products [57:39] The Moral Arbitrage of Killing Your Ego [57:58] How to Copy Without Looking Like a Copy [59:10] Why Old Internet Mechanics Keep Returning [01:00:16] Anonymous Social Apps With an AI Twist [01:01:17] Don't Invent a New Business—Reinvent a Big One [01:02:00] Test 20 Variants Before Building One [01:02:58] Mark's Frustrating Experiments With AI Coding [01:05:29] Creating a Personal Team of AI Agents [01:07:57] Killing a Four-Year Passion Project [01:09:29] The “Social Membrane” of the Agentic Internet [01:09:57] Building a Work Network for AI Agents [01:12:16] Hivemind and the Human Side of Enterprise AI [01:13:52] Missing Twitch—and Knowing Your Zone [01:15:06] Why the Gaming Industry Still Isn't Social Enough [01:16:30] Chess Ratings, Competition, and Mark's Daughter [01:19:19] Writing Life at the Speed of Play [01:21:18] Don't Chase Every New Technology Race [01:22:05] Final ThoughtsAdditional Resources:Mark Pincus and the BookLife at the Speed of Play — official websiteLife at the Speed of Play — HarperCollins — published June 23, 2026. Mark Pincus on X — the account Mark recommends for updates on his agent-network experiments. Mark Pincus on LinkedIn Mark's interview about open-sourcing Stem Studio Zynga, Games, and Product ExamplesZynga's company history — covers its launch as a Facebook poker project and the development of FarmVille, CityVille, and Words With Friends. Words With Friends FarmVille Take-Two and Zynga acquisition announcement — the transaction carried an enterprise value of approximately $12.7 billion. Tribe.net history — the early social network Mark analyzes as a major product failure. Raya — the private community Mark discusses as an example of building trust through curation. Grow a Garden on Roblox See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.QuestionsWhen we say "rogue AI," what do we actually mean? Is it only malicious AI, or can legitimate systems become risky too?What are the most common ways AI systems drift outside intended boundaries? Once an organization understands what rogue AI looks like, where does that loss of control typically begin, and who is responsible for preventing it?How do shadow LLMs, unsanctioned agents, and unmanaged AI workflows create risk even when no attacker is involved? If AI drift often starts with normal business activity, where do shadow AI systems fit into that picture?Why can an AI action look legitimate in isolation but still create serious business, security, or compliance risk when viewed as part of a larger sequence of actions? As these shadow systems become more embedded in everyday workflows, why is it so difficult to recognize risk in real time?How do APIs, integrations, and connected systems amplify the impact of those seemingly legitimate actions? What changes once those actions begin flowing across APIs, business applications, and interconnected systems?What kinds of unexpected outcomes worry CIOs and CISOs most today when AI systems are operating across those interconnected environments? As that connectivity expands, what are security and business leaders most concerned about?And given those concerns, what does meaningful oversight actually look like when AI systems can act at machine speed? How should organizations distinguish between the experimentation they want to encourage and the unmanaged AI behavior they need to control? One challenge is balancing governance with innovation. How do organizations avoid slowing down AI adoption while still maintaining control?We know that many organizations can detect risky AI behavior after the fact. But if they can't stop it in real time, what critical gap still remains? Even with governance programs in place, many organizations are still operating reactively. In closing, what's the key difference between detecting AI risk and actually controlling it?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/cu-craigthomas/Full AbstractIn this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.Not every AI threat starts with an attacker. Some of the most consequential AI risks organizations face today come from systems that are working exactly as designed, just not quite as intended. An agent that calls an API it was never supposed to reach. A workflow that exposes PII because nobody mapped the data path before deployment. A shadow LLM standing up in an AWS account because a developer needed to move fast and approval processes were slow. None of these require malicious intent to create serious business, security, or compliance exposure.Rogue AI is a broader category than most governance frameworks account for. It includes the unsanctioned, the unmonitored, and the unpredictable: AI systems that drift outside intended boundaries, take actions that look legitimate in isolation but create risk in sequence, and operate at machine speed in ways that make after-the-fact detection feel like a consolation prize. The gap most organizations have is not in detecting that something went wrong. It's closing the loop fast enough to matter.Meaningful AI governance requires more than policy and discovery. It requires the ability to observe AI behavior at runtime, understand what triggered each action and what it touched, and enforce boundaries before consequences compound. That closed AI control loop, from knowing what is running to seeing what it does to stopping what it should not, is the operational standard AI transformation demands. Most organizations are not there yet.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

SaaStr 863: The Enterprise AI Reality Check: From Dashboard Graveyards to 30-Day Migrations with Databricks' Co-Founder and SVP of Field Engineering Every Fortune 500 CEO has told their team that if they are not using AI, they are behind. So now every employee is token-maxing, spend is going up, and almost nobody can tell you what they are getting out of it. That is the reality Databricks sees from the front lines, serving more of the Fortune 500 than any other data and AI company on the planet. In this episode, Databricks Co-Founder and SVP of Field Engineering, Arsalan Tavakoli, sits down with SaaStr CEO and Founder, Jason Lemkin, to cut through the Twitter noise and talk about what enterprises are actually doing, what is still broken, and why the next 24 months will fundamentally change who wins and who loses in every major software category. You'll learn: Why the BI dashboard is dead and what replaces it - including how a car manufacturer just onboarded 70,000 non-technical users to query their own data in plain language with no analyst in the loop What "context" actually means for enterprise AI and why it is harder to solve than the data problem, using a framework that explains why agents fail even when the underlying data is clean Why no software monopoly survives the next 24 months, and how collapsing migration costs and low-end AI competitors are about to give every incumbent a pricing problem they cannot ignore How Databricks now completes enterprise-grade migrations in 30 days or less using LLMs to analyze, convert, and reconcile legacy systems that previously took years and cost more than the savings Why the murky middle is the most dangerous place to be in enterprise software right now, and how to know which side of the AI budget divide your product actually sits on

Enterprise AI initiatives consistently break down in document-heavy environments, not because the underlying models are inadequate, but because fragmented data silos, page-break context loss, and uncoordinated extraction tools erode the semantic layer AI needs to reason accurately. In this episode, Sumedh Chaudhary, CTO US Industry Market at IBM, breaks down why a multi-agent architecture is the operational prerequisite for AI to function reliably in regulated, document-intensive workflows. The conversation covers how governance frameworks with measurable error-rate targets distinguish pilot success from production failure, and how enterprises can structure a phased AI approach that blends automation, fit-for-purpose models, and human oversight. ​ This episode is sponsored by Arango. In this episode, we cover how enterprises can build multi-agent AI architectures to handle document-heavy workflows — and the governance frameworks that determine whether those deployments scale. To go deeper on this topic and learn how to structure landing pages for higher conversion, and how to use self-qualification systems to prioritize high-intent leads, download our free PDF report, "B2B AI Lead Generation Guide," at emerj.com/aig1

Nikesh Arora is the Chairman and CEO of Palo Alto Networks, the global cybersecurity leader. Since taking over in 2018, he has transformed the company from an $18 billion market cap business into one worth more than $225BN with more than 21,000 employees globally. Previously, Nikesh was President and COO of SoftBank, where he worked alongside Masayoshi Son and helped shape the firm's technology investment strategy.  AGENDA: 00:00 Why AI Token Prices Will Fall 90% — And Why That's Bullish for AI 07:40 The Frontier Model Problem: Breadth vs Depth in AI 11:30 Most Enterprises Are Using AI Completely Wrong 13:10 Why AI Could Cut Marketing, HR & Finance Teams in Half 16:00 AI Applications Will Have Opinions — SaaS Never Did 20:00 OpenAI, Anthropic & The Most Important Valuation Question in Tech 24:00 The Real Business Model of AI: Transaction Revenue Beats Advertising 25:10 Why Token Prices Must Collapse 28:20 Where Value Actually Accrues in AI: Models, Memory or Apps? 29:00 Why Memory Becomes the Biggest Moat in AI 32:00 Why Every Enterprise Should Be Scared Right Now 33:15 Should Governments Regulate Frontier AI Models? 37:10 Why Brian Armstrong's AI-First Playbook Doesn't Work Everywhere 40:00 The Biggest AI Mistake CEOs Are Making Today 42:00 How Nikesh Creates Darwinian Competition Inside Palo Alto 43:00 Do AI Companies Really Need Forward-Deployed Engineers? 45:00 Why Enterprise AI Products Still Aren't Ready 52:00 Systems of Record vs Systems of Intelligence: The Future of Software 54:00 Why AI Applications Will Replace Traditional SaaS Workflows 58:00 What Nikesh Learned From Google That Still Matters Today 1:04:00 From $200 and Two Suitcases to Running a $225B Company 1:10:00 Happiness, Gratitude and Why Tomorrow Matters More Than Ten Years From Now    

In this episode of UC Today, host Kristian McCann sits down with Laura Maffucci, Head of HR at G-P, to explore one of the biggest questions facing enterprise leaders today: is AI delivering on its promises, or are organizations beginning to question the return on their investments?According to G-P's latest research, a significant number of executives report that AI investments have failed to meet expectations, raising concerns about ROI, productivity gains, and long-term value. But does this signal a problem with AI itself, or with how organizations are approaching it?Laura shares a candid assessment of the current state of enterprise AI, explaining why many companies are chasing AI initiatives without clearly defining the problems they are trying to solve. Key Discussion Points:AI investment challenges: Why many executives say AI spending is failing to deliver the expected ROI.The "supervision tax": How AI can create additional work when organizations rely on poorly implemented solutions.Responsible AI adoption: Why reimagining business processes matters more than simply adding AI tools to existing workflows.Trust, governance, and data quality: How enterprises can improve AI outcomes by using verified information sources and protecting confidential data.Next Steps:Follow the latest developments in employee engagment at UC Today

Why are so many organisations still stuck in AI pilot purgatory – and what does it actually take to move from experimentation to real operational impact?Christopher Carey sits down with Alex Ayers, Sales Director at Gamma, fresh from the company's GX 2026 event. Alex shares what he's hearing from enterprise leaders across every major sector: the real barriers to AI execution aren't the technology – they're strategy, governance, workflow design, and organisational complexity.From the dangers of replacing people before redesigning processes, to why doing one pilot brilliantly beats running dozens badly, this is a frank and practical conversation for any IT or business leader trying to close the gap between AI ambition and operational reality.For more Unified Communications & Collaboration Tech News visit UC Today.

Enterprise software is changing. I sat down with Brian Landsman, CEO of AgentExchange at Salesforce, to talk about what an agent-first future actually looks like. #salesforcepartnerThis wasn't a surface-level conversation. We went deep into what's coming next.Here's what stood out:* AgentExchange evolved from a marketplace directory into a commerce and discovery layer, is rethinking how enterprises deploy software* Headless architectures could fundamentally reshape how people interact with enterprise systems since traditional UIs matter less* Agents are moving from assistants to becoming the primary interface* Workflows need to be redesigned from the ground up for an agent-first world* Success will be defined by agents executing end-to-end tasks, not just supporting humans* The gap between AI pilots and production is finally starting to closeWe also discussed how individuals can go to market faster with $50M AgentExchange Builders Initiative.Watch the full conversation and let me know what you think.#data #ai #tdx26 #salesforce #workflows #api #headless360 #agentexchange #apps #theravitshow

today we examine the 2026 landscape of artificial intelligence, specifically comparing proprietary and open-source models regarding privacy, cost, and legal compliance. Organizations must choose between proprietary APIs, hosted open-source solutions, and self-hosting to balance performance with data sovereignty requirements like HIPAA or the EU AI Act. While proprietary models currently lead in complex reasoning, open-source weights offer significant long-term cost savings and transparency for high-volume users. However, true total cost of ownership includes hidden expenses such as specialized talent, hardware infrastructure, and continuous model maintenance. Legal frameworks like the EU AI Act introduce strict obligations for high-risk systems, making explainability and governance essential for enterprise deployment. Ultimately, the transition from experimental pilots to industrialized AI factories requires mastering token economics and navigating the evolving regulatory environment.

In today's Cloud Wars Minute, I examine why the Google Cloud-EQT deal signals a major shift in how AI is being distributed at scale. Highlights 00:03 — The rapid pace at which deals are being struck and portfolios are expanding among the leaders in the race for AI dominance isn't new. Significant partnerships are being forged, and contracts are being signed all the time. However, every so often, a deal comes along that stands out not only for its scope, but also for what it indicates about the direction of travel for the industry as a whole. 00:33 — One such deal recently announced is between Google Cloud and the Swedish private equity firm EQT. Ultimately, this partnership sees EQT commit to accelerating AI adoption through Google Cloud for over 300 companies within its portfolio, and this is, of course, a big win for Google Cloud, as it gains access to hundreds of potential enterprise AI customers. 01:04 — Beyond this, those companies will not only benefit from Google Cloud's wide-ranging AI offerings, including the Gemini Enterprise agent platform, as well as its cybersecurity portfolio, but also from its vast partner network, which includes over 330,000 consultants from major firms like Deloitte and KPMG. 01:27 — For me, the biggest takeaways here are that, firstly, agentic AI is clearly going mainstream, with equity firms eager to roll it out among their entire portfolios. We're obviously well past the experimentation phase now. 01:42 — Secondly, this really presents a major opportunity for AI infrastructure companies to leverage this growing acceptance to enhance AI distribution at the portfolio level. This shift could result in AI adoption accelerating much faster than when companies go down the traditional enterprise sales route. Visit Cloud Wars for more.

Send us Fan MailJim Piazza, Chief AI Officer at Ensono, talked about how legacy mainframe systems fit into the modern AI era and explored the practical strategies large enterprises must adopt to modernize their core infrastructure. A significant number of Fortune 500 companies continue to run their most critical workloads, such as credit card transaction processing, on IBM Z and Power platforms. He categorized the path forward into two distinct buckets: operational modernization, which leverages AI to predict system faults and prevent costly outages, and business modernization, which utilizes AI services to accelerate transactions and enable real-time fraud detection. Organizations looking to modernize can choose between migrating workloads completely to the cloud, translating legacy COBOL applications into modern languages like Python, or implementing hybrid approaches that integrate existing mainframes with distributed cloud environments.Achieving success with predictive analytics and machine learning on these platforms requires a foundation of robust data engineering. Beyond software and talent constraints, Jim also highlighted the physical and economic realities of modern infrastructure. Skyrocketing power consumption from AI workloads has become the primary near-term constraint for data centers, forcing hyperscalers to invest heavily in renewable energy and advanced cooling technologies. Additionally, the lifecycle for GPU and AI hardware is shortening rapidly, driving hyperscalers toward shorter depreciation cycles. While future innovations like silicon photonics promise to materially lower cooling and energy costs, substantial CapEx savings can be realized today by optimizing software to train large models on previous-generation hardware, or by utilizing ensembles of smaller, targeted models.Positioning itself at the center of these shifting dynamics, Ensono operates as an AI-first managed services provider dedicated to modernizing large enterprise customers across both mainframe and distributed environments.

Enterprise AI buying has moved quickly, but durable adoption still depends on context, security, workflow fit, and measurable business impact. Daniel Simon, Enterprise Account Executive at Glean, joins John Kaplan and John McMahon to discuss what it takes to sell AI in complex enterprise environments, why multi-threading matters more when buyers are evaluating broad organizational change, and how strong sellers build trust by tying use cases to productivity, governance, and ROI instead of relying on product excitement alone. Daniel Simon is an Enterprise Account Executive at Glean, where he works with large enterprises on AI adoption, knowledge discovery, and productivity across complex organizations. He brings experience selling enterprise technology into multi-stakeholder buying environments. Connect with Dan: LinkedIn Resources mentioned: The Qualified Sales Leader by by John McMahon  The Go-Giver by by Bob Burg  Key takeaways from this episode:  00:00 – Introduction 02:40 – What it really takes to move from product fluency to business impact in enterprise sales. 06:35 – Why many sellers mistake a strong champion for a qualified enterprise deal. 08:47 – A look inside how AI can expose qualification gaps without replacing sales fundamentals. 18:12 – What leaders often overlook about context as the real differentiator in enterprise AI. 30:51 – Why face-to-face engagement quietly creates leverage in a crowded AI market. 42:58 – Dan Simon's perspective on why consumption-based pricing raises the bar for customer success. 57:12 – Why AI will amplify strong sales discipline and expose weak execution. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

The conversation around artificial intelligence often creates the impression that software development has already been transformed beyond recognition. Social media feeds are filled with stories about AI agents replacing teams, generating applications automatically, and eliminating the need for traditional development processes. The Enterprise AI Reality is much more nuanced. While AI has become a valuable tool inside software organizations, large enterprises are approaching adoption far differently than many public conversations suggest. The gap between experimentation and production remains significant, especially when millions of dollars, regulatory requirements, and customer trust are involved. About Samuel Otero Samuel Otero is a Software Solutions Specialist with Deloitte US and a technology consultant with nearly 14 years of experience spanning enterprise software development, government projects, commercial consulting, and large-scale digital transformation initiatives. His career began with an early Microsoft internship that shaped his approach to continuous learning and technical humility. Since then, he has worked across media, public-sector, and enterprise environments, helping organizations deliver complex software solutions while mentoring the next generation of developers. Based in Puerto Rico, Samuel is also an advocate for developer growth, career development, and practical AI adoption in modern software engineering. Links LinkedIn Enterprise AI Reality Is Different from Social Media One of the strongest observations Samuel shared was the contrast between what people see online and what happens inside large organizations. Social media often highlights extreme success stories. Teams appear to build entire products using AI agents. Individual developers showcase impressive workflows that dramatically accelerate delivery. Those examples are real. However, enterprise software operates under different constraints. Systems support financial transactions, critical business processes, compliance requirements, and large customer bases. Mistakes carry significant consequences. As a result, organizations are adopting AI incrementally rather than replacing existing development practices overnight. Enterprise AI Reality Requires Trust Before Automation Every technology faces a trust curve. Before organizations automate critical workflows, they need evidence that systems perform reliably under real-world conditions. Samuel described how enterprises often use AI first in lower-risk scenarios before allowing it to influence more critical components of a platform. Features with limited business risk become testing grounds for new approaches. This pattern mirrors previous technological shifts. Cloud adoption happened gradually. DevOps adoption happened gradually. AI adoption is following a similar trajectory. The technology may be powerful, but trust must be earned through consistent results. Enterprises don't adopt technology because it's impressive. They adopt it because it's reliable. Enterprise AI Reality Still Depends on Human Expertise One misconception surrounding AI is that generated code eliminates the need for technical understanding. In practice, the opposite may be true. The more organizations rely on AI-generated outputs, the more important validation becomes. Developers must understand architecture, business requirements, security concerns, and implementation details well enough to verify what AI produces. Samuel emphasized a simple but powerful habit: asking AI to explain exactly what it did and why it made certain decisions.   That approach transforms AI from an answer machine into a learning tool. Developers who understand generated solutions become more effective. Developers who blindly accept generated solutions create risk. Never merge AI-generated code until you can explain its behavior to another developer. Enterprise AI Reality Is Creating New Skill Gaps The rise of AI is changing how developers gain experience. Historically, growth came from solving difficult problems manually. Developers researched documentation, struggled through debugging sessions, and built mental models through repetition. AI reduces much of that friction. While this increases productivity, it also creates new challenges. Developers may complete tasks successfully without fully understanding how those tasks were accomplished. Over time, this can create a dangerous gap between perceived capability and actual expertise. Organizations must address this by emphasizing understanding rather than output alone. The future belongs to developers who combine AI acceleration with deep technical comprehension. Enterprise AI Reality May Increase Software Complexity An interesting prediction from the discussion involved software quality. As AI accelerates development, more software will be produced. More features will be released. More experiments will reach production environments. That acceleration creates opportunity. It also creates risk. Samuel suggested that many organizations are still learning where AI performs exceptionally well and where it struggles under enterprise-scale conditions. During that learning period, users may experience more bugs, patches, and corrective updates as teams discover limitations. This isn't evidence that AI has failed. It's evidence that every transformative technology goes through a maturation phase before reaching stability. Faster development cycles can produce bugs faster if organizations don't maintain engineering discipline. Enterprise AI Reality Still Comes Back to Problem Solving Perhaps the most important lesson from the entire conversation is that technology itself is rarely the source of professional value. Languages change. Frameworks change. Platforms change. AI models will change. The underlying business need remains consistent: solving problems. Samuel's closing advice focused on developing problem-solving skills rather than attaching identity to a specific technology stack. That mindset provides resilience regardless of how quickly tools evolve. Developers who can understand problems, communicate solutions, and create business value will remain relevant long after today's AI tools are replaced by tomorrow's innovations. The most durable technical skill isn't coding. It's problem-solving. Conclusion The Enterprise AI Reality is neither the dystopian future predicted by skeptics nor the fully automated paradise promised by enthusiasts. Instead, it's a period of careful experimentation, measured adoption, and ongoing learning. Organizations are discovering where AI delivers value, where human expertise remains essential, and how both can work together to build better software. The developers who succeed during this transition won't be the ones who resist AI or blindly trust it. They'll be the ones who learn how to use it responsibly while continuing to strengthen the problem-solving skills that define great engineers. Stay Connected: Join the Developreneur Community

Who is teaching the world's most powerful AI models to think?Turing is one of the largest data partners to OpenAI, Anthropic, Google, Meta, Microsoft, and Nvidia. At a $2.2 billion valuation it has become one of the most important infrastructure layers in the AGI race.Jonathan Siddharth started Turing in 2018 with a thesis that talent matching is a trillion-dollar problem. Turing reached unicorn status in 2021. Then, in 2022, as the foundation model race accelerated, OpenAI approached Turing to provide coding data for ChatGPT.Jonathan recognised that frontier AI labs faced an enormous bottleneck: high-quality training data and human intelligence at scale. Instead of remaining just a talent marketplace, he made a bet that most unicorn CEOs never make. He built a second business on top of the first and leaned back into his AI research roots.Jonathan has a clear view of what needs to happen before we get to super intelligence. The four keys to unlocking AGI: coding, reasoning, tool use, and multimodality. He believes we solve for those four, and AI can do almost anything a human can do in front of a computer. If you are excited about where the AGI race is heading this episode is for you00:00 - Trailer01:06 - What Turing does05:55 - Why OpenAI reached out to Turing8:28 - How GPT-3 became ChatGPT17:54 - How ImageNet breakthrough changed the world21:12 - The largest provider of coding data to AI labs24:34 - Four keys to super intelligence28:45 - Every human will run multiple companies in 10 years32:27 - Can agents have self-improvement loops?34:36 - The future of software engineering36:26 - Agents should create, humans should steer39:46 - Is the line between products and services companies blurring?40:42 - How an agent can handle hiring end-to-end43:36 - Every human can now write software45:22 - Will workflow SaaS disappear?47:46 - No fine-tuning vs fine-tuning camps51:49 - A case study in compute constraints57:06 - Why the world needs so much compute1:01:26 - Where Jonathan would invest today1:03:16 - Where cybersecurity is heading1:08:31 - How the world will look in 10 years-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.We all know that you can't secure what you can't see, which is why AI discovery is a first principle for AI security, but what's really required for AI discovery? It's more than just LLMs and agents. Today's episode is entitled AI Discovery isn't just AI, and joining us is Tim Ebbers, Field CTO at Wallarm. Tim and I discuss the real requirements for AI discovery, and why the connections between assets and infrastructure are part of the puzzle.QuestionsSecurity teams often say, “You can't secure what you can't see.” In the context of AI, what exactly do they need to see? What supporting infrastructure matters most when mapping AI risk, such as APIs, cloud services, Kubernetes workloads, data stores, identities, and external integrations?Where does shadow AI typically appear first inside an enterprise environment? How can it be prevented?How do relationships between assets change the risk picture? For example, why does it matter which API an agent can call or which data source a workflow can reach?What makes AI discovery harder than traditional application or cloud asset discovery? What are the similarities and differences?How should organizations prioritize what they find? Is every AI asset equally risky?What does “continuous discovery” mean in a world where AI services can be deployed, connected, or changed in minutes?Once an organization has visibility into its AI footprint, what's next? What are the biggest gaps in today's AI security programs?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/tebbers/Full AbstractMost security teams know that you can't secure what you can't see. In the context of AI, that rule turns out to be a lot harder to satisfy than it sounds.AI discovery isn't just a matter of cataloging your LLMs and agents. The real picture includes the APIs those agents call, the data sources they reach, the infrastructure they run on, and all the AI that got deployed without anyone telling security. Building that picture requires understanding relationships, not just inventories, because risk doesn't live in assets in isolation. It lives in what those assets can do together.In this episode, Tim Ebbers, Field CTO at Wallarm, examines what a complete AI control loop actually requires at the discovery stage: what needs to be visible, why the connections between assets change the risk calculation, where shadow AI tends to appear first and how it becomes unmanaged risk, and what makes AI discovery structurally different from traditional cloud or application discovery. It also looks at what organizations should do once discovery is in place, and where the biggest gaps remain in AI security programs today.If your team is building toward continuous AI governance, this is where that work starts.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Five stories today — one of the most stacked news daysof the year.SpaceX confirmed Tuesday it is acquiring Cursor —the AI coding tool developed by Anysphere — for $60billion in an all-stock deal closing Q3 2026. Cursorgenerates $2.6 billion in annualized B2B revenue andis used by Fortune 500 developers globally. SpaceX'sstock gained 16% on Tuesday, surpassing Amazon inmarket cap. Elon Musk now controls SpaceX, xAI, Grok,Tesla, X, and the world's most used AI coding tool.SpaceX has also signed $26 billion per year in computedeals with Anthropic and Google combined.NIO Day 2026 preparations just started — advisorygroup applications are open now with a June 18 deadline.Deutsche Bank expects NIO's Q2 non-GAAP net profit toreach approximately 180 million yuan — a secondconsecutive profitable quarter driven by high-marginES8 and ES9 SUV performance. NIO AGM is June 24th.Deloitte surveyed 3,235 business and IT leaders across24 countries: 74% want AI to grow revenue, only 20%have seen it happen — a 54-point gap between ambitionand reality. 80-85% of enterprises miss their AI budgetforecasts by more than 25%. Uber blew its entire 2026AI coding budget by April. One company racked up a$500 million Claude bill from forgetting to set usagelimits. The era of AI accountability has started.Yum Brands is selling Pizza Hut for $2.7 billion.Brand recognition without innovation is nostalgia witha price tag. BlackRock is laying off for the third timein 18 months as AI replaces financial services rolesfaster than anyone predicted publicly.The US confirmed it will lift Iran oil sanctions themoment the accord is formally signed. Accord signed.Sanctions lifted. Iranian oil returns. Oil drops.Inflation eases. Rate cuts return. Growth stocksre-rate upward. Watch for the signature.

Across every industry, boards are approving AI budgets. Inside many enterprises, however, the reality is the same. Pilots never scale, tools sit unused, and transformation programmes struggle to justify their investment. In this episode of the Tech Transformed podcast, host Trisha Pillay sits down with Darin Patterson, VP of Product Advocacy and Market Strategy at Make, to find out what separates the organisations genuinely operationalising AI from those still running expensive experiments.AI Adoption GapEnterprise AI investment is accelerating. What is not accelerating at the same pace is business value. Patterson is direct about why he believes that most organisations are measuring the wrong things, assigning ownership to the wrong people, and deploying tools before they have defined the problem."The AI adoption gap is real," Patterson tells Pillay, "and it starts at the top. Leaders are approving investments without a clear framework for what success looks like."For C-suite executives, this is a critical signal. AI adoption is not primarily a technology challenge; it is an organisational one. Strategy, culture, and accountability structures determine if AI initiatives produce compounding returns or accumulate as technical debt.Ownership ModelsOne of the most instructive conversations in this episode concerns who should own AI inside an enterprise. Patterson's position is that ownership must live with the people closest to the business function being transformed."Ownership models are often unclear," he says. "And unclear ownership is where AI initiatives go to die."When AI is owned exclusively by a central IT or data science function, it becomes disconnected from the operational realities of the teams it is meant to serve. When it is owned entirely by individual business units without central governance, you get fragmented tooling, inconsistent data practices, and security exposure. The hybrid model Patterson advocates centralises governance standards, security, and infrastructure while pushing execution authority down to functional leaders. This structure creates accountability at the point of value creation rather than at a remove from it.For C-level executives building or restructuring their AI operating model, the actionable question is: do the leaders of each business unit have both the mandate and the capability to own AI outcomes in their domain?Stop Starting With the ToolA pattern Patterson sees consistently across enterprises is what he calls tool-first thinking. An organisation identifies a capable AI platform, deploys it, and then attempts to work backwards to the business problem it should solve."Focus on your business process first," he advises. "The tool is never the strategy."This is especially relevant for executives evaluating vendor proposals. The quality of an AI platform matters far less than the clarity of the problem definition sitting upstream of it. Organisations that achieve sustainable AI ROI typically begin by mapping their highest-friction processes, quantifying the cost of those inefficiencies, and only then evaluating which AI capability best addresses the root cause. The discipline of process-first thinking also prevents a common failure mode by automating a broken process rather than fixing it. AI applied to a flawed workflow does not eliminate the flaw but rather accelerates it.Culture Is the MultiplierPatterson also points to a softer but critical success indicator, which is cultural adoption. If the teams closest to an AI deployment are not using it willingly and consistently, the business case will not hold, regardless of what the pilot showed.The final, and perhaps most important, dimension Patterson raises is culture. Technical capability and strategic clarity are necessary but not sufficient conditions for AI success at scale. The organisations that are genuinely ahead are those that have invested in building an AI-literate workforce, not just an AI-enabled one."Invest in people as much as you invest in AI," Patterson says. "The technology will keep improving. Your competitive advantage comes from people who know how to use it well."For C-level leaders, this means reframing AI investment as a human capability programme as much as a technology programme. Training, change management, and psychological safety around experimentation are not soft additions to an AI strategy, but they are core to its delivery.Listen to the full conversation with Darin Patterson on the Tech Transformed podcast. Connect with Darin on LinkedIn and explore Make's automation platform at make.com.TakeawaysAI adoption challengesOrganisational culture and AIOwnership models for AIMeasuring AI successOperational AI examplesChapters00:00 The AI Adoption Landscape03:01 Bridging the ROI Gap in AI05:48 Ownership and Responsibility in AI Implementation08:57 Strategic Approaches to AI11:57 Measuring Success in AI Initiatives15:00 Cultural Transformation for AI Success18:53 Real-World AI Implementation Examples24:00 Advice for C-Level Leaders on AI Investment

The drama around Anthropic's Fable 5 model clogged our collective attention spans.

For most of my career, technology felt predictable. A new software platform arrived. A new programming language appeared. A new cloud service changed how we deploy applications. Every wave of technology helped people work faster. But AI feels different. Over the last two years, I have watched professionals across industries experience something I have never seen before. People are not simply using a new tool. They are having conversations with technology. A marketer can generate campaigns. A consultant can build frameworks. A developer can create applications in hours instead of weeks. And every week, the systems become smarter. Personally, I have experienced this while building AI frameworks, experimenting with coding agents, and working with organizations trying to adopt Generative AI. Many times I have found myself staring at a screen thinking: "How did it do that?" Not because the output was perfect. But because the pace of improvement was faster than expected. This raises an important question. If AI is becoming more capable every month, how do we ensure we build systems that remain useful, trustworthy, and safe? That is exactly what we explore in today's Open Tech Talks conversation with Dr. Craig Kaplan. Episode # 190 Today's Guest: Dr. Craig A. Kaplan, Inventor of the designs and Technologies that enable safe SuperIntelligence. He is a pioneer in artificial intelligence and the inventor behind technologies designed for safe Superintelligence. For more than four decades, he has worked at the intersection of intelligent systems, ethics, and innovation, developing architectures that help AI evolve safely and remain aligned with human values. Website: SuperIntelligence YouTube: iStudios What Listeners Will Learn: How AI evolved from symbolic systems to Generative AI The difference between AI, AGI, and Superintelligence Why are many AI researchers concerned about AI safety Enterprise AI risks leaders should understand today Why AI agents are becoming the next major AI wave The rise of multi-agent and collective intelligence systems How organizations can design safer AI solutions Why AI is shifting from a tool to a digital coworker The future impact of AI on jobs and knowledge work Practical guidance for responsible AI adoption Resources: SuperIntelligence

On this episode of The Buzz, Scott Luton is joined by special co-host Dr. Muddassir Ahmed and special guest Anthony Reeves, Vice President of Global Brand & Creative at Kohler and author of Eat the Donkey: Why Great Companies Embrace Discomfort. Together, they explore the realities of AI adoption, decision-making optimization, innovation, leadership, and what separates organizations that thrive from those that struggle to keep pace. As supply chains continue to evolve in the age of AI, organizations face critical decisions about technology adoption, data quality, change management, and leadership. Scott, Muddassir, and Anthony examine why many AI initiatives fail, what companies can learn from both successes and setbacks, and why strong decision-making remains one of the most valuable competitive advantages. The conversation also explores the growing importance of human connection, brand differentiation, organizational culture, and the willingness to embrace discomfort in pursuit of long-term growth. Drawing on experiences from Amazon, Kohler, Starbucks, and other global brands, Anthony shares powerful lessons on innovation, leadership, and staying true to what makes an organization unique. Key Takeaways: AI success depends as much on adoption, change management, and leadership as it does on technology. High-quality, contextualized data remains the foundation for effective AI implementation. Organizations must learn from failed initiatives just as much as successful ones. Soft skills, emotional intelligence, and human connection will become increasingly valuable as AI handles more routine work. Strong brands remain differentiated by purpose, customer experience, and authenticity—not technology alone. Great leaders make difficult decisions early rather than delaying action until opportunities have passed. Whether you're leading a supply chain transformation, evaluating AI investments, or building a stronger organization, this episode offers practical insights from leaders who have navigated innovation at the highest levels. You'll walk away with actionable advice on decision-making, change management, leadership, and creating organizations that can thrive amid constant disruption. Additional Links & Resources: Guest LinkedIn Profile: https://www.linkedin.com/in/anthonyreeves/ Guest Instagram Handle: @anthony.j.reeves Guest Company Website: anthonyreeves.co APL Logistics: https://www.apllogistics.com/ With That Said: https://bit.ly/WTS-7JUN2026 The Corner Market: https://bit.ly/The-Corner-Market Exclusive: Starbucks scraps AI inventory tool across North America: https://reut.rs/4vuPSkR 4 Supply Chain and AI Predictions for 2026: https://bit.ly/AI-Predictions-2026 AI Strategy Takes A Data Foundation That Cleansing Can't Provide: https://bit.ly/Paul-Noble-Gartner2026-Takeaways 5 Signs Your Supply Chain Has Outgrown How It's Managed Today: https://bit.ly/5-signs-your-SC-has-outgrown-mgmt Eat the Donkey: https://www.amazon.com/dp/B0G97CHK9F When Safety Technologies Backfire and How Managers Can Prevent It: https://bit.ly/When-Safety-Tech-Backfires Upcoming Live Programming:  https://supplychainnow.com/upcoming-live-programming/ Supply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/ Connect with Anthony on LinkedIn: https://www.linkedin.com/in/anthonyreeves/ SCMDOJO: https://sensei.scmdojo.com/ Connect with Muddassir on LinkedIn: https://www.linkedin.com/in/muddassirism/ Follow Scott on LinkedIn: https://www.linkedin.com/in/scottwindonluton/ WEBINAR- Amazon Supply Chain 101: Enabling efficiency and growth for businesses everywhere–and everywhere they sell: https://bit.ly/49r8N7D WEBINAR- The Expanding Role of Supply Chain Optimization Teams in Driving Business Impact: https://bit.ly/3PHRAAf WEBINAR- AI that moves at velocity: Cut through latency with agentic workflows: https://bit.ly/4x4626t This episode was hosted by Scott Luton and Dr. Mudassir Ahmed. For additional information, please visit our dedicated show page at: https://supplychainnow.com/buzz-ai-adoption-brand-differentiation-embracing-comfort-1595 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

SUMMARY: If the cost of public AI continues to rise, because of various market shortages, should CIOs start looking at backup plans to better own their AI journeys and futures?SHOW: 1036SHOW TRANSCRIPT: The Enterprise AI Show #1036 TranscriptSHOW VIDEO: https://youtu.be/ZgkMF7G3YfoSHOW SPONSORS:OutShift by Cisco - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:Andy Weir (The Martian) on Eps. 193Systems of Record Won the SaaS Era - Clearinghouses Will Win the Agents EraHarness Engineering is where Enterprise AI becomes realTHESIS: It comes up as different control points, but CIOs are ultimately trying to figure out how to get the value from Enterprise AI while delivering a set of consistency across different teams and use-cases. Let's explore what this “Enterprise Harness” is starting to look like. Enterprise Clearinghouse Enterprise Intelligence (a.k.a. Middleware)Enterprise Catalog - Models as a Service, Agents as a ServiceEnterprise Skills or Shareable Prompt HarnessesSymantec Routing to ModelsAI Gateway ControlsFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Enterprise AI investments frequently succeed at the pilot stage and collapse at scale, not because the technology fails, but because the organizational conditions for adoption were never established. In this episode, Darko Todorovic, CTO at HTEC Group, examines why most AI ROI gaps originate in poor problem definition and inadequate change management, and outlines how senior leaders can build the baselines, KPIs, and organizational readiness needed to measure and sustain real returns. The conversation covers practical guidance on assessing technological and organizational maturity, avoiding POC-to-production pitfalls, and selecting the right AI tools for specific business contexts. This episode is sponsored by HTEC. In this episode we cover how enterprise leaders can measure and prove AI ROI after deployment. To go deeper on this topic and learn how to identify real AI trends by tracking where venture funding is flowing, and by listening to how leading CEOs describe risk and competitive strategy, download our free PDF report, "3 Ways to Discover AI Trends in Any Sector" at emerj.com/ait1

In this Cloud Wars Special report, Bob Evans speaks with Jan Gilg about how AI is reshaping enterprise software and why the next phase of innovation will depend on trust, governance, business outcomes, and clean data. Gilg explains how SAP is positioning its Autonomous Suite as a foundation for the autonomous enterprise, combining ERP, business processes, and AI agents. Trust Powers Enterprise AI The Big Themes: Autonomous Enterprise Vision: Jan Gilg said Sapphire generated strong enthusiasm because customers finally heard a clear vision for enterprise AI. Rather than focusing solely on AI models or isolated features, SAP presented an integrated strategy built around the Autonomous Suite and Business AI. While consumer AI has dramatically improved personal productivity, enterprise leaders need AI that can help make critical business decisions and automate end-to-end processes. SAP's message resonated because it connected AI directly to business execution, positioning enterprise systems as the foundation for autonomous operations rather than treating AI as a standalone technology layer. AI Economics Matter: Another major topic was the cost of AI. Gilg noted that enterprises are becoming increasingly focused on transparency, consumption, and measurable outcomes. As AI usage expands, costs can grow rapidly, creating new concerns for business leaders. Customers want detailed visibility into which agents are being used, how resources are consumed, and whether the resulting business value justifies the expense. Gilg compared this need for transparency to a detailed telephone bill. Data Quality Determines Success: The interview concluded with examples demonstrating that AI success depends heavily on modernized systems and clean data. Gilg spoke of initiatives involving retailers such as H&M, where AI can improve customer experiences, fulfillment, and revenue generation. He also referenced work with Bayer and discussed ExxonMobil's modernization journey. These examples reinforced a key point: AI delivers the greatest value when built on standardized processes, strong master data, and simplified architectures. The Big Quote: “You have to lead with value. Yes, technology is exciting, but it does nothing if the customer doesn't see the outcome." More from Jan Gilg and SAP: Follow Jan Gilg on LinkedIn or learn more about Autonomous Suite. Visit Cloud Wars for more.

Here's my brief recap of the amazing Irresistible 2026 (photos coming), and my discussion with clients about many things, including the new insane costs of AI. I just read a study that Ramp (credit card) did, discovering that the top AI users are spending $7500 per month per employee on AI. (Yikes!) That aside, the conference was spectacular and we all learned a lot. Stay tuned for a more detailed article on the Pacesetters and other major research we unveiled. In the meantime here's my update on economics and AI maturity (companies are maturing and learning about this stuff quickly), as well as my heartfelt thanks to everyone who participated. Additional Information Announcements: The Josh Bersin Institute, HR 2030, And The Global HR Excellence Certification. HR 2030: Overview and Detailed Blueprint for clients and Galileo Users AI Prices Are Going Up, Up, Up – And What This Means For Enterprise AI Chapters (00:00:00) - Irresistible Conference 2017(00:00:55) - What I Learned at the Conference on AI & the Code(00:01:52) - The role of data in AI HR(00:03:44) - The Token Economics of AI(00:08:03) - Intro to Enterprise AI and HR(00:12:29) - The Future of AI HR(00:15:42) - Happy Summer Solstice!

Consumption pricing and AI adoption are forcing revenue teams to prove value faster, with less room to hide behind contracts, pilots, or broad technical promises. Seong Park, Senior Vice President of Customer Support and Services at Cursor, joins John Kaplan and John McMahon to examine how customer success has become a consultative, technical, and commercial function in modern go-to-market. The conversation explores why post-sale execution is now central to retention, how teams need to embed into customer workflows, what finance scrutiny means for consumption models, and why the fundamentals of pain, champions, outcomes, and evidence still matter in a market moving at unusual speed. Seong Park is the Senior Vice President of Customer Support and Services at Cursor. His background spans pre-sales, customer success, and go-to-market leadership across companies including MongoDB, ThoughtSpot, and now Cursor. Connect with Seong: LinkedIn Key takeaways from this episode:  00:00 – Seong Park's perspective on how pre-sales, open source SaaS, and customer success shaped his view of enterprise go-to-market. 02:26 – Why consumption models force revenue teams to re-earn the customer's business through usage and realized value. 08:00 – The value realization test every revenue leader should care about: what happens if the solution gets unplugged. 11:04 – Why workflow depth quietly becomes a moat in enterprise accounts. 18:04 – Why the real selling often starts after the customer signs. 23:50 – A look inside where Cursor is finding technical go-to-market talent, and what it takes to build that talent into customer-facing operators. 34:38 – Why finance scrutiny quietly changes the standard of proof for AI investments. 52:00 – The three things post-sale teams need to understand before value delivery can begin. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Fred Laluyaux has spent 25 years on the same problem: enterprises are drowning in decisions no human should be making. With 50 million digitized decisions across companies like Unilever, Exxon, and Hershey, he now has the data to prove it. When operators override the machine, performance goes down. Not sometimes — in aggregate, every time. In this episode, Fred breaks down the agentic vs. deterministic tradeoff most CIOs are getting wrong, why the software stack most companies rely on today is heading for collapse, and what a company whose entire stack is just SAP and Aera tells you about where enterprise software is going. Hit play. 3 Takeaways: After 50 million digitized decisions, the data is clear: when operators override the machine, performance drops. One Aera customer runs their entire operation on SAP and Aera. Nothing in between. That's where the stack is going. Fred calls them "born in digital" decisions — they can't be made by humans because the value is gone before the meeting starts. Chapters: [03:08] Fred's Career Journey and Lessons Learned [05:17] Why Aera Was Created [05:45] The Vision for a Self-Driving Enterprise [08:28] The Decision Memory Problem in AI [10:28] The Reality of AI ROI [11:58] From Analytics to Decision Intelligence [12:56] Humans vs Fully Autonomous Systems [15:28] What It Means to Digitize Decisions [18:42] How Aera Actually Works [22:42] Trust, Governance, and the Waymo Analogy [27:51] Deterministic vs Agentic AI [29:13] The Cloud Capacity Wake-Up Call [30:15] Where Aera Fits in the Enterprise Stack [31:54] Fast ROI and the “4-4-4” Framework [32:55] Why the Software Stack Is Collapsing [36:21] Delayering Organizations and New AI Roles [39:02] Born-Digital Companies and Micro-Decisions [43:57] Explainability, Governance, and Feedback Loops About Fred:  Fred Laluyaux is Co-Founder, President, and CEO of Aera Technology, the leader in decision intelligence and creator of Aera, the first decision intelligence agent. An entrepreneur and Silicon Valley veteran, Fred brings an impressive track record building successful startups and driving technology innovation. Prior to launching Aera, Fred was the CEO of Anaplan, which he grew to a $1 billion valuation. He has held several executive positions at SAP, Business Objects, and ALG Software. As a thought leader on the future of work and host of the Decision Intelligence podcast, Fred frequently shares his vision with influencers through media interviews and speaking engagements at industry conferences. His views have been published in business and trade publications. A technology and startup advisor, Fred is an investor and active board member of several startups in the U.S. and Europe. Guest Highlights: "We're in 2026, and the reality is that our models have not changed for 100 years. We're still relying on people to decide how to forecast, how to allocate inventory, how to change a plan." "We've got enough data, I mentioned the 50 million decisions, to demonstrate that whenever the humans are touching the system and are messing with the recommendation, they actually degrade the performance." "The autonomy is not another version or better version of my planning tool or my replenishment tool. It replaces the need to have a human touch with that software, and therefore I don't need that software anymore." Get Connected: Ian Faison: https://www.linkedin.com/in/ianfaison Fred Laluyaux: https://www.linkedin.com/in/flaluyaux/ Our Sponsor:  This episode is brought to you by Aera Technology. Enterprise AI has hit its stride. Across industries, companies are moving beyond pilots and proofs of concept, and into real, enterprise-wide results: better decisions, faster execution, and meaningful bottom-line impact.  Aera's agentic decision intelligence is built to help you seize the opportunity. Aera dynamically composes decision flows using unified decision data and multi-engine orchestration to drive action at scale. It continuously senses what's happening across your enterprise, recommends and executes the best course of action within your transaction systems, and learns from every outcome to keep improving. Leading global companies are already using Aera across supply chain, inventory, logistics, and finance, delivering rapid ROI through reduced costs, lower working capital, and better customer outcomes. This is the self-driving enterprise. And it's here now. Visit AeraTechnology.com to book a demo Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most AI failures won't come from a bad model. They'll come from bad data.Shashank Saxena spent most of his career on the buying side of enterprise technology before founding VNDLY which was acquired by Workday for $510 million. He then joined Sierra as a Managing Partner before going full time as Co-founder and CEO of Pantomath, a data operations center for enterprises that are betting their future on AI agents.We discuss why data quality is becoming one of the biggest challenges in enterprise AI. An AI agent fed bad data for 12 hours doesn't go rogue. It just makes 12 hours of wrong decisions: rejecting insurance claims, issuing credit cards, or drilling in the wrong location. As more business decisions are delegated to AI systems, companies will need far greater visibility into what is happening across their data infrastructure.Shashank also shares the decisions that led to VNDLY's acquisition, the advice he'd give founders evaluating acquisition offers today, and why a Michael Jordan analogy continues to motivate him as a second-time founder.If you're building enterprise software, selling to large companies, or trying to figure out whether experience is an asset or a liability in the AI era, this episode is for you.0:00 - Trailer01:00 - How Shashank became a second-time founder07:20 - Where Pantomath sits in the data stack10:55 - How a broken Tableau report turns mission-critical with AI12:55 - Who Pantomath sells to15:35 - Solving for a problem that doesn't exist yet19:03 - How have founder expectations changed today?20:31 - Series B companies pre- and post-AI21:26 - The Michael Jordan example23:57 - How a repeat founder chooses investors25:10 - What value Snowflake adds as a strategic investor27:05 - Data is not an open category today28:34 - The astounding Databricks outcome29:08 - The reality of the $100 million ARR number31:48 - Will non-human workers 100x in the next few years?36:00 - How to protect data in motion37:26 - How comfortable are we giving full access to agents?39:47 - Where is automation fastest today?42:09 - Why entrepreneurs tend to like uncertainty43:28 - Why Shashank chose to be a founder45:48 - A customer-driven $510M acquisition48:32 - Employees vs contractors in any organization51:22 - Building from Ohio vs the Bay Area53:14 - Learnings from selling to enterprises56:31 - How Shashank raised from Tier 1 US VCs59:19 - Heads down or network as a founder?1:02:47 - First-time vs second-time founder edge in AI1:06:22 - Hiring as a repeat founder1:08:08 - How enterprise sales has changed1:10:52 - How do you sell for a problem that isn't visible today?1:12:58 - Best piece of advice1:16:27 - The only advice for a founder considering M&A1:21:06 - Position yourself to be capable of taking risks1:24:51 - What matters to an enterprise buyer?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Today, we are kicking off a new series entitled The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.Today's episode is entitled AI Security is API Security, and joining us is Tim Erlin, VP of Product Marketing at Wallarm. We discuss the foundational link between AI security and API security, digging into the role that APIs play in the dev, deployment, and operations of AI. We explore how they contribute to the risk profile of AI transformation projects, and how securing APIs is critical for successful AI transformation.QuestionsWhen people hear “AI security,” they often think first about models, prompts, or training data. Why do you argue that AI security starts with APIs?Where do you see organizations underestimating API risk as they move AI projects from pilot to production?How does the rise of AI agents change the stakes for API security compared with traditional application architectures?What are the most common API security assumptions that break down once AI systems begin taking action autonomously?Wallarm's ThreatStats research points to APIs as a major overlap point for AI vulnerabilities and exploited vulnerabilities. What does that tell us about where attackers are likely to focus?How should security leaders think differently about authentication, authorization, and API abuse when the “user” may be an AI agent rather than a human?What is one practical step teams can take today to strengthen API security before AI adoption expands further?Once you accept that AI security depends on APIs, what do organizations actually need to discover before they can protect it?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/tim-erlin/Full AbstractIn the first episode of the AI Control Loop series, Tim Erlin, VP Product at Wallarm, examines why AI security and API security are the same problem approached from different angles, and what organizations need to discover before they can protect either one.Every AI model needs data to act on. Every AI agent needs services to call. Every AI workflow needs integrations to function. The connective tissue running through all of it is APIs, which means the security posture of any AI system is inseparable from the security posture of the APIs underneath it.That link is not theoretical. APIs are already the most targeted attack surface in enterprise environments, and AI is making that problem significantly larger. Agents that act autonomously on behalf of users do not just consume APIs the way traditional applications do. They discover them, invoke them dynamically, chain them across workflows, and do all of it at a speed and scale that makes human review impractical. The authentication assumptions, rate limiting strategies, and abuse detection models that worked for human-driven API traffic were not designed for this, and the gaps are not subtle.Most organizations moving AI from pilot to production are underestimating how much of their AI risk surface is actually API risk surface. Shadow APIs that were never inventoried, overpermissioned integrations that made sense for a human user but not for an autonomous agent, authentication patterns that cannot distinguish a legitimate AI session from an abused one. Securing AI at the foundational level means answering the API question first: what APIs does the AI touch, what can it do through them, and what would an attacker be able to reach if any part of that surface were compromised.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Enterprise AI agents fail consistently in production, not because of model limitations, but because they lack a live, temporally aware context layer grounded in the actual current state of the business. In this episode, Ravi Marwaha, Chief Operating Officer & Chief Technology Product Officer at Arango, explores how treating context as infrastructure—rather than a data pipeline problem—enables agents to reason accurately, explain their decisions, and deliver measurable outcomes across customer support, semiconductor engineering, and clinical trial site selection. The discussion covers five practical frameworks for CIOs and chief data officers on building real-time, explainable context layers on top of existing enterprise systems, without ripping and replacing current infrastructure. This episode is sponsored by Arango. To learn how to improve landing page conversion and use self-qualification systems to identify high-intent leads, download Emerj's free PDF report, "B2B AI Lead Generation Guide," at emerj.com/aig2

In today's Cloud Wars Minute, I analyze how Sana is helping Workday transform from a system of record into a system of action. Highlights 0:00 — Workday has announced two new agents: Sana for IT Service Management, or ITSM, and Sana Travel Agent. To recap, Workday acquired Sana at the end of 2025, and since then, the technology has evolved into Workday's employee AI layer, what the company describes as its "front door for work." 0:42 — Sana for ITSM automates workflows for tasks like employee onboarding, off-boarding, access changes, and standard IT requests, while the Sana Travel Agent helps employees plan work trips, book travel, and manage expenses. Both agents are built directly on Workday, meaning they have the same security and governance protocols by default, and tap into the bespoke contextual company data and policy information contained within the platform. 00:57 — Cloud Wars founder Bob Evans commented on the development in the official Workday press release: "Extending agents into adjacent workflows like onboarding, travel, and expenses, where Workday already has the people and finance data and policies, is not only practical but also a transformational way to help HR and finance leaders meet and exceed their objectives." 01:25 — Workday's acquisition of Sana was a pivotal moment in the company's recent history and accelerated its push in the enterprise AI era. The deal signaled a strategic evolution beyond Workday's traditional role as a system of record for HR and finance processes. 01:44 — At the same time, that deep system of record foundation is exactly what makes Sana's autonomous AI agents such a strong fit, because the agents can operate with rich context, permissions, policy, and workflow data already embedded within the platform.     Visit Cloud Wars for more.

Join us this week for The Tech Leaders' Podcast, where Gareth sits down with Nicola Mendelsohn, Head of Global Business Group at Meta, at Meta Conversations 2026 in the historic Methodist Central Hall in the heart of Westminster. Nicola talks about the new WhatsApp for Business, the technical challenges around it, the importance of data safeguarding and the role of Meta's Chief Privacy Officer. On this episode Nicola and Gareth discuss the challenges around Enterprise AI adoption and governance, and her advice to UK businesses. Timestamps: Introduction (1:58) Meta and Enterprise Messaging (4:30) Technical Challenges (14:51) Data Safeguarding and the Chief Privacy Officer (17:52) Enterprise AI Adoption and Governance (18:50) Advice for UK Businesses (26:55) https://www.bedigitaluk.com/

Vamshi Ambati has spent more than two decades in AI, through the symbolic era, statistical era, and the neural wave we're experiencing today. A CMU PhD, founder of LatentStructure and Predera (which was acquired), now an investor at Virama Ventures, he's one of the sharper voices on what's actually happening under the hood of the AI boom.We discuss a simple question: Who wins when models become cheaper and more abundant? And try to answer this by looking at how inference spend v/s compute spend is shifting, and why inference may become the biggest infrastructure opportunity of the next decade.Vamshi explains what actually goes into the cost of a token, why AI is simultaneously getting cheaper and more expensive, and why the inference market alone could reach $1.3 trillion by 2030. If you're building in AI or someone who wants a clear mental model of where this industry is headed, this conversation is for you. 00:00 - Trailer0:45 - How an AI researcher thinks after 20 years05:53 - Where enterprise AI adoption is headed08:35 - Drawing parallels between cloud and AI11:20 - If building is cheap, what's valuable?13:37 - Can computing get cheaper?16:41 - What is inference, really?22:22 - Why coding and customer support got eaten first?26:48 - Which technologies are overvalued and undervalued?29:56 - An accidental entrepreneur's journey33:15 - Why is healthcare slow to adopt technology?38:59 - Landing Walmart as a customer42:36 - Should founders build in services if product isn't visible?43:47 - Is Palantir a product company or a services company?44:15 - How to win as a forward-deployed company46:23 - What it takes to land large enterprise customers49:20 - Building sales muscles as a technical founder-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Enterprise marketing teams struggle with AI implementation beyond basic automation. Patrick Brown, SVP of Global Marketing at Adobe, shares his perspective on scaling AI across complex B2B and B2C marketing operations. Brown discusses why AI excels at content summarization and synthesis but falls short on predictive forecasting, and outlines Adobe's three-pillar framework for integrating AI into experience delivery, measurement systems, and foundational marketing tools.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A central structural mechanism highlighted in this episode is the exposure and amplification of technical and organizational weaknesses by enterprise AI initiatives, particularly as organizations pursue rapid AI adoption without adequate investment in data and process fundamentals. The episode draws on findings from an MIT Media Lab report, which found that 95% of enterprise AI pilots had no measurable impact on profit and loss, despite $30–40 billion in investment. Michael Privat, representing the healthcare technology firm Availability, discusses the consequences for organizations that apply “thin” AI overlays on top of unaddressed legacy data infrastructure and processes. The most consequential data point centers on AI's amplifying effect. According to the MIT Media Lab report cited by Michael Privat, 74–75% of companies expect revenue growth from AI, but only 20% are realizing gains. The root cause identified is not AI itself, but foundational failures: organizations use pilots as procurement exercises rather than outcome-driven initiatives and neglect to address data consistency and process integrity. Pilot projects, in many cases, simply accelerate the visibility and scale of existing dysfunctions rather than creating new value. Further evidence is provided through discussion of operational methodologies and organizational approaches. Michael Privat details a shift from pre-AI process benchmarks, such as DORA metrics focused on predictability and velocity, toward new models that account for AI's speed and amplification risks. He points to increasing investments in engineering capacity—in particular, tripling headcount in India—while emphasizing that efficiency gains from AI only materialize where discipline, standardization, and solid engineering “plumbing” is already in place. Both the need for audit trails and rigorous governance, especially in regulated sectors like healthcare, are flagged as structural safety requirements rather than optional layers. Operationally, the implications for MSPs and IT leaders include the risk of exposing latent deficiencies when implementing AI-driven offerings, particularly when layering automation and analytics atop fragmented or inconsistent infrastructure. Key areas of impact are the need for robust governance frameworks—especially with agentic AI, where dynamic system behaviors require ongoing accountability and auditability—and the risk that AI investments made without process and data “spring cleaning” can actually accelerate failure modes. For IT service providers, the material risks are in unexamined process debt, tool misalignment, and the temptation to prioritize velocity over resilience, ultimately increasing operational and contractual exposure. Supported by:NerdioScalePad  

Enterprise marketing teams struggle with AI implementation beyond basic automation. Patrick Brown, SVP of Global Marketing at Adobe, shares his perspective on scaling AI across complex B2B and B2C marketing operations. Brown discusses why AI excels at content summarization and synthesis but falls short on predictive forecasting, and outlines Adobe's three-pillar framework for integrating AI into experience delivery, measurement systems, and foundational marketing tools.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Europe wants AI sovereignty. But can it reduce its dependence on foreign technology without sacrificing innovation, capability and competitiveness?In this episode of This Week in European Tech, Dan Bowyer and Mads Jensen of SuperSeed are joined by Matt Russell, Managing Director (Head of Secondaries) at VenCap International, to discuss Europe's growing sovereignty push, the debate around Palantir, the future of venture secondaries, enterprise AI adoption and the latest developments from Anthropic, SpaceX and OpenAI.The conversation explores why venture secondaries may be entering a new phase of growth, why some of the best-performing secondary investments are bought at premiums rather than discounts and what Europe's path to sovereign AI infrastructure could look like.Topics covered:Europe's AI and cloud sovereignty challengeThe Palantir debate and the risks of vendor lock-inWhy venture secondaries could become a much larger marketThe biggest misconceptions about secondary investingEnterprise AI adoption and the challenge of measuring ROIAnthropic, SpaceX and the next generation of AI mega-companiesOpenAI and the future of AI regulationWhether Europe can build sovereign AI infrastructureWhy AI may ultimately be a productivity and margin storyTimestamps(00:00) Introduction and the rise of venture secondaries(01:00) Why liquidity is becoming venture capital's biggest theme(05:00) Europe's sovereignty push and the Cloud & AI Development Act(12:00) Sovereign cloud, AI infrastructure and the search for European champions(18:00) The Palantir debate: dependency, lock-in and strategic control(24:00) Enterprise AI adoption, experimentation and proving ROI(31:00) Anthropic, SpaceX and the next wave of mega-cap technology companies(38:00) AI regulation, liability and the OpenAI lawsuit(42:00) Predictions: Europe's two-tier AI future(47:00) Deal of the week: defence tech, Gigaton and autonomous systems(50:00) What's next: Apple, the ECB and the SpaceX IPO(55:00) Closing remarksSubscribe to EUVC, the home of European tech, for more insights: https://www.eu.vc/subscribe

Artificial intelligence continues to dominate supply chain conversations, but why are so many organizations struggling to move beyond pilots and into enterprise-wide success? In this episode of The Buzz powered by APL Logistics, Scott Luton and Alex Pradhan explore the latest research and developments shaping the future of supply chain leadership, AI adoption, omnichannel fulfillment, workforce transformation, and decision intelligence. Special guest Arash Aghlara, Founder and CEO of FlexRule, joins the discussion to share why better decisions—not just better data—may be the key to unlocking supply chain performance. From AI pilot purgatory and omnichannel profitability to talent shortages and protein supply disruptions, this episode tackles some of the most pressing challenges facing supply chain leaders today. Scott, Alex, and Arash dive into new research from leading organizations including MIT, Accenture, GEP, and the University of Virginia Darden School of Business. Along the way, they explore the growing importance of governance, the role of decision intelligence in modern operations, and why organizations must move beyond visibility alone to create smarter, faster, and more effective business outcomes.   Key Takeaways: Why nearly three-quarters of organizations have yet to move AI initiatives beyond planning and pilot stages. How decision intelligence helps organizations bridge the gap between data, visibility, and action. Key findings from MIT's latest State of Supply Chain Omnichannel research. Why supply chain leaders must rethink workforce development amid a projected talent shortage. The critical role governance plays in scaling AI and improving operational decision-making. How organizations can identify and manage interconnected decisions across planning, logistics, inventory, and customer service. Why practical execution—not hype—is essential for successful digital transformation.   If you're navigating AI adoption, digital transformation, talent challenges, or increasing supply chain complexity, this episode delivers practical insights and actionable strategies from experienced industry leaders. You'll walk away with a deeper understanding of how decision intelligence can help organizations move beyond visibility, improve execution, and build more resilient supply chains in an increasingly complex business environment.   Additional Links & Resources: APL Logistics: https://www.apllogistics.com/ With That Said: https://bit.ly/WTS-31-May-2026 5 Signs Your Supply Chain Has Outgrown How It's Managed Today: https://bit.ly/5-signs-your-SC-has-outgrown-mgmt Supply Chain AI Is Stuck in Pilot Purgatory Because the Operating Model Is Missing: https://bit.ly/4u4dPyj GEP Webinar: https://bit.ly/10-June-2026-Webinar State of Supply Chain Omnichannel Report: https://bit.ly/3RUcHju Turning the supply chain talent shortage into strength: https://accntu.re/4vkqNZQ Protein powder shortage threatens America's biggest food craze: https://bit.ly/3RIkrFd Upcoming Live Programming:  https://supplychainnow.com/upcoming-live-programming/ Supply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/ FlexRule: https://www.flexrule.com/ The Supply Chain Whisperer on Why Practicality Still Wins: https://bit.ly/Why-Practicality-Still-Matters Connect with Arash on LinkedIn: https://www.linkedin.com/in/arashaghlara/ Connect with Alex on LinkedIn: https://www.linkedin.com/in/alejandrapradhan/ Watch and listen to more Supply Chain Now episodes: https://supplychainnow.com/brands/supply-chain-now/ Subscribe to Supply Chain Now: https://linktr.ee/Supplychainnow Check out the Supply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/ Work with Us! Download the Supply Chain Now 2026 Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- From AI Pilots to Performance: How Supply Chain Leaders Are Scaling Agentic AI: https://bit.ly/49hCqIq WEBINAR- Amazon Supply Chain 101: Enabling efficiency and growth for businesses everywhere–and everywhere they sell: https://bit.ly/49r8N7D WEBINAR- The Expanding Role of Supply Chain Optimization Teams in Driving Business Impact: https://bit.ly/3PHRAAf WEBINAR- AI that moves at velocity: Cut through latency with agentic workflows: https://bit.ly/4x4626t For everything else, start here: https://supplychainnow.com This episode was hosted by Scott Luton and Alex Pradhan, and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychiannow.com/buzz-how-decision-intelligence-transform-supply-chain-performance-1592 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

SUMMARY: When we get to the end of 2026, how will enterprise companies be measuring the success of their AI projects? And how well will their teams be sharing their AI learning curves?SHOW: 1034SHOW TRANSCRIPT: The Enterprise AI Show #1034 TranscriptSHOW VIDEO: https://youtu.be/TvIFwNN-6ckSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoOutShift - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Why AI Economics are changingHow will team collaboration evolve with Enterprise AI?Topic 1 - How do we measure AI-adoption success? Number of workloads?Financial metrics (Spend, ROI, Costs-Saved, etc.)?Speed improvements?People-level?Topic 2 Right now the AI tools are very individual-centric The machinery to share, even at the basic enterprise-level, is very difficultThe experience to share is non-deterministic, just as everyone's working style is different.Topic 3 - The motivation to share is still unknown. How do you encourage collaboration when so many companies are laying off people, or the specter of that happening is growing?What was the motivation before (team goals?) and how does that change now? People don't want to be monitored, so how does a manager have visibility?What happens when companies remove the managers (“the counters”)? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Let us know how we're doing - text us feedback or thoughts on episode contentAI is quietly becoming one of the fastest-growing sources of corporate greenhouse gas emissions — yet not a single Fortune 500 sustainability report specifically calls it out. In this episode, Paul exposes the growing gap between AI's real emissions impact and how companies are (not) accounting for it.Drawing on a Capgemini Research survey of 2,000 executives — 48% of whom say AI has already materially impacted their corporate emissions, and 42% of whom have had to revisit their climate targets because of it — Paul explains why AI emissions are stuck in a measurement blind spot: an outdated GHG Protocol that buries AI usage, and near-total opacity from AI hyperscalers on model-specific carbon data.But there's good news: Paul lays out five practical ways enterprises can reduce their AI carbon footprint today — from right-sizing AI models and shifting workloads to cleaner grid regions, to temporal load shifting and prompt engineering audits — without reducing AI usage at all.Follow Paul on LinkedIn.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter:  https://www.exponentialview.co/ — More than three years after ChatGPT's release, only 27% of executives say AI has met their ROI expectations. The history of factory electrification explains why — most companies are at the light-bulb stage, adding Copilot licenses rather than reconceptualizing their businesses around AI. In this episode I map the three stages of AI adoption, and show what it actually takes to move from chatbots to the autonomous company — the only stage where the moat becomes real. I covered: (01:40) Ford's electricity playbook: why AI adoption needs a complete rethink (03:51) The congestion problem: why AI gains stall (05:45) Chatbot to autonomous company: your three-stage roadmap (06:40) Why individual productivity gains won't build a moat — and what will (10:17) Which companies are getting AI transformation right (14:12) My 2029 AI adoption forecast — and how to stay ahead Read my essay "Why AI isn't showing up on your bottom line" on Substack: https://www.exponentialview.co/p/why-ai-isnt-showing-up-on-your-bottom-line — Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1. Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Cassiano Surek, CTO at Beyond, joins host KJ to explore how artificial intelligence is fundamentally reshaping the workforce, enterprise structure, and even how we shop. Cassiano argues that the era of hyper-specialized talent is giving way to competent generalists who can orchestrate AI tools across the full stack, and that the companies embracing this shift are already pulling ahead. The conversation spans team architecture, the flattening of corporate hierarchies, the dawn of agentic commerce, and a surprising personal project built to lighten the mental load of moms everywhere. Four Key Takeaways: 3:39 — Curiosity is the core driver of innovation. It won't always pay off, but the compounding of near-wins over time is what ultimately leads to breakthroughs. 12:36 — Corporate hierarchies are contracting dramatically. AI enables fewer, more versatile people to do more, making deep layers of management increasingly obsolete. 17:26 — The workforce is shifting from deep specialists to competent generalists, people who can work across the full solution stack using AI tooling, unlocking a new era of entrepreneurial creativity. 17:26 — Agentic commerce is already here. AI agents will soon shop on your behalf, fundamentally disrupting how merchants, brands, and consumers interact, possibly by this Christmas. Quote of the Show (12:37):"A success is made of many almost quasi successes... It's an endless journey of exploration." — Cassiano Surek Join our Anti-PR newsletter where we’re keeping a watchful and clever eye on PR trends, PR fails, and interesting news in tech so you don't have to. You're welcome. Want PR that actually matters? Get 30 minutes of expert advice in a fast-paced, zero-nonsense session from Karla Jo Helms, a veteran Crisis PR and Anti-PR Strategist who knows how to tell your story in the best possible light and get the exposure you need to disrupt your industry. Click here to book your call: https://info.jotopr.com/free-anti-pr-eval Ways to connect with Cassiano Surek:LinkedIn: http://www.linkedin.com/in/cassianosurek Company Website: http://www.bynd.com/ How to get more Disruption/Interruption: Amazon Music - https://music.amazon.com/podcasts/eccda84d-4d5b-4c52-ba54-7fd8af3cbe87/disruption-interruption Apple Podcast - https://podcasts.apple.com/us/podcast/disruption-interruption/id1581985755 Spotify - https://open.spotify.com/show/6yGSwcSp8J354awJkCmJlD YouTube: https://www.youtube.com/results?search_query=disruption+%2F+interuuptionSee omnystudio.com/listener for privacy information.

No matter your role, experience or industry, we all (mostly) waste hours a week doing the same thing: manually creating slides. 

OpenAI and Microsoft both previewed the next phase of enterprise AI, with OpenAI pushing Codex beyond developers and Microsoft focusing on lower-cost, customizable frontier models. The bigger theme is that enterprise AI is shifting from experimentation to cost-effective scale. In the headlines: Trump's AI executive order, Anthropic expands Mythos access, and SK Hynix moves to double memory chip capacity.Sign up for AI Executive Catchup: https://aiexecutivecatchup.com/Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠Outsystems - Stop wondering how AI will change your business and start building the agents that will lead it - http://outsystems.com/Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

China processes nearly 90% of the world's rare earths.Rare earths are hidden inside everything from EVs and smartphones to fighter jets, making them one of the most critical materials powering the modern economy.When China restricted rare earth exports in April 2025, the world saw the huge risk of depending on a supply chain controlled by a single country. For Bhaktha Keshavachar, however, it was validation of a bet he had made 6 years earlier.After exiting Ezetap, Bhaktha founded Chara Tech to create electric motors that don't need rare earth magnets at all. The journey was anything but easy. Six years of R&D. Investors who didn't understand the problem. Customers who weren't convinced. And a motor technology that engineers had known about for over 200 years but never successfully commercialized at scale.Today, Chara is shipping hundreds of motors, signing major customers, and finding itself at the center of a global geopolitical shift. Bhaktha explains how software became the breakthrough that made rare-earth-free motors practical and what it takes to build a deep tech company long before the market believes the problem exists.If you are interested in building deep tech for the world, this episode is for you.0:00 - Trailer01:10 - When China bans rare earth exports04:15 - How today's rare earth shortage is like 1970s oil embargo05:26 - Are rare earths really rare?07:23 - Why China has a monopoly11:43 - 3 reasons why Chara was founded15:11 - How Chara made a 200-year-old technology practical16:45 - How software protects deep tech startups18:53 - The conviction to build deep tech in 201621:52 - Why electricity is still the biggest opportunity26:59 - 4+1 technologies every country should possess28:17 - The story of 6 years in R&D33:16 - The response from early customers36:10 - How China's ban changed Chara's journey39:30 - Why Growth-stage fundraising for DeepTech is Hard44:28 - What India needs to win in deep tech52:53 - 3 things needed for a deep tech startup55:10 - Why the wealthy should invest in deep tech58:00 - Where Chara is today01:03:47 - Why Intel lost the race it was winning-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/neon-fund/X: https://x.com/TheNeonShowwConnect with Nansi on:LinkedIn: https://in.linkedin.com/in/nansi-mishraX: https://x.com/nansi_mishra-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

June is here so guess what? It's officially Hot AI Summer.

The rapid shift from seat‑based licensing to hybrid and consumption‑based AI pricing has made technology spend significantly harder for enterprises to predict and control. In this episode, Adam Mansfield, Practice Leader at UpperEdge, examines how these new pricing models create financial exposure for buyers and why clear forecasting, transparency, and leverage are increasingly difficult to secure in negotiations with major vendors, in conversation with host Marilie Fouché. He highlights the practical steps leaders must take now — from auditing current usage and identifying under‑leveraged spend to engaging vendors early and using the broader. This episode is sponsored by UpperEdge. To go deeper into vendor negotiations and learn how to assess AI providers by leadership credibility and funding signals, download our free report, "5 Ways to Select the Right AI Vendor," at emerj.com/aiv3

SUMMARY: The biggest enterprise AI question may no longer be which model is smartest? Instead, which organization can most effectively operationalize, govern, and economically scale AI agents across the business?'SHOW: 1032SHOW TRANSCRIPT: The Enterprise AI Show #1032 TranscriptSHOW VIDEO: https://youtu.be/GsK_RUnYroISHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:Opening Thesis - How will team collaboration evolve within Enterprise AI?Question: Any suggestions on how to introduce enterprise-level governance and standardisation for agentic coding? Like skills, rules, plugins, context etcKey Topics 1. This isn't a Coding-specific problem. Every team has this issue. If your processes weren't well defined and enforced before, they will be worse nowNot it's not just process standardization, but “buy-in” standardization2. Everything moves so fast, so managers don't have the answers (yet) AI value is being created bottom-up, but paid for (and mandated) top-downThe current measurements aren't useful (tokenmaxxing, all-or-nothing, etc.)3. The governance tools don't exist yet.And it's not clear that anyone wants them. They didn't want them before. How do you even define governance? What's the baby step before that, reuse and basic sharing? 4. Are we ready to invest in “Centers of Excellence” again? 5. We under-estimate the “creativity” element in human buy-in. Is success measured in improvement or replacement?How much of that did “you” do? We don't know how to measure that.We haven't lived through an AI-centric promotion cycle yet6. Bottom-up and Top-down need to find some common language and middle ground. Have they walked a mile in each other's shoes yet (or lately)?How to bring a reality to the hype vs. demands vs. learning curve?How long is an AI-centric cycle vs. a pre-AI-centric cycle? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

How do you move beyond AI experimentation and start building systems that can genuinely reason, act, and create value across an enterprise? Recorded at Adobe Summit in Las Vegas, this episode features Daniel Sheinberg, who leads cross-portfolio product initiatives for Adobe's Customer Experience Orchestration business. Daniel is at the center of Adobe's AI and agentic strategy, helping shape how some of the world's largest organizations think about the next generation of customer experiences. During our conversation, Daniel cuts through the hype surrounding agentic AI and explains what actually separates an AI assistant from an AI agent. We explore how advances in reasoning, memory, context awareness, and tool usage are enabling systems that can move beyond generating content to actively helping organizations achieve business goals. Daniel shares practical examples of how enterprises are using these capabilities to personalize customer journeys at a level that would have been impossible with traditional workflows. We also discuss the rise of AI-powered brand concierges, including how are using agentic experiences to create more meaningful customer interactions. Daniel explains why context is becoming one of the most valuable assets in enterprise AI, how businesses can prepare their data and systems for agentic workflows, and why governance, trust, and brand intelligence will play such an important role in successful deployments. If you're trying to understand where AI is heading next, what customer experience orchestration really means, and how businesses can safely deploy agentic AI at scale, this conversation offers a valuable look at both the opportunities and the challenges ahead.

What happens when AI systems stop acting like assistants and start acting like autonomous decision-makers inside your business? And if those systems are pulling information from fragmented, inconsistent, and poorly governed data environments, how much trust can organizations really place in the outcomes? In today's episode, I'm joined by Terry Dorsey for a fascinating conversation about the growing gap between AI ambition and enterprise reality. Terry brings decades of experience spanning enterprise architecture, business intelligence, operations, healthcare, utilities, manufacturing, and defense. Long before AI became the headline topic dominating every boardroom conversation, he was already working deeply in semantic modeling, natural language systems, and the architectural foundations that modern AI now depends on. At the center of our discussion is the new AI Trust Gap report from Denodo, which reveals why so many organizations are struggling to move AI projects from experimentation into reliable production environments. We explore why live data matters so much in an agentic AI world, why "more data" often creates more confusion instead of clarity, and how inconsistent business meaning across systems quietly undermines AI trust inside large organizations. Terry explains why many enterprises are still operating on architectures originally designed for historical reporting and analytics, while now expecting those same environments to support autonomous AI systems making real-time operational decisions. From semantic sprawl and duplicated business logic to governance failures and fragmented security models, we unpack the hidden technical debt that AI is now exposing at scale. The conversation also takes a deeper philosophical turn as we discuss why enterprise meaning itself may become the future control plane for AI. Terry shares why provenance, explainability, and semantic consistency are no longer optional concerns reserved for compliance teams, they are becoming foundational requirements for trustworthy AI systems capable of operating autonomously. We also discuss why governance cannot be bolted on after deployment, how logical data management helps organizations reduce duplication and maintain operational trust, and why the companies that succeed with agentic AI will not necessarily be the fastest movers, but the ones building stable and reusable architectural foundations beneath the surface. If your organization is rushing toward AI adoption while wrestling with siloed systems, disconnected data, and growing governance concerns, this episode offers a much-needed reality check. Because, as Terry explains, the future competitive advantage may have less to do with the AI model itself and far more to do with the architecture, meaning, and trust frameworks supporting it. Useful Links Terry Dorsey LinkedIn Denodo LinkedIn Denodo Website The AI Trust Gap Report — global survey of 850 executives that explores why organizations are investing heavily in AI, but many still can't fully trust the data behind it. O'Reilly's The Rise of Logical Data Management, by Christopher Gardner — explains what's necessary to enable true self-service data access and 24/7 AI-ready data. The Enterprise AI and Data Management Glossary  — glossary that helps ensure both technical and non-technical professionals can make informed decisions, optimize strategies, and align on best practices for digital transformation. The ROI of Using the Denodo Platform alongside the Modern Data Lakehouse — Drawing on interviews with numerous global enterprises and applying a comprehensive ROI methodology, this study, conducted by independent analyst Veqtor8, found that by using Denodo alongside their data lakehouse, they realized considerable benefits. Agentic AI Manifesto — a blueprint for credible autonomy at enterprise scale. Denodo's standard for the next era of trusted, autonomous enterprise AI.

Google dropped like 197 new AI features this week.