Podcasts about enterprise ai

AI is no longer a future bet — it's a board-level mandate. But for corporate innovation leaders, the real question isn't whether to invest in AI… it's how to turn AI from experimentation theater into measurable enterprise value. Taylor Black, Director of AI & Venture Ecosystems in Microsoft's Office of the CTO, works at the intersection of AI strategy, venture ecosystems, and internal venture building. Taylor brings a rare dual perspective: enterprise AI leadership inside one of the world's largest technology companies — combined with firsthand startup-building experience. We unpack how AI takes impossible problems and makes them merely difficult, how this growth mindset of hyper abundance is paired with the enterprise rigor and the internal velocity needed to scale.

Enterprise AI is moving fast, and it’s already moved beyond large language models. The focus now is on “agentic AI”: systems that combine raw AI intelligence with business logic, workflows and governance to deliver predictable, enterprise-grade outcomes.Sean Aylmer speaks with Sanjna Parulekar, Senior Vice President of Product Marketing for Agentforce at Salesforce, about how companies overcome the “last mile” challenge of moving from pilot to production, and why in three years agents won’t feel revolutionary - they’ll just feel normal.Fear & Greed is proud to partner with Salesforce for the Agentforce World Tour Sydney. This interview is part of a series exploring how agentic AI is transforming Aussie businesses. Visit agentforce.com for more information.Find out more: https://fearandgreed.com.au/See omnystudio.com/listener for privacy information.

This episode marks the transition from The Cloudcast to The Reasoning Show, focusing more on AI and cloud topics. Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss recent trends in AI, the evolution of tech teams, and the shifting landscape of enterprise AI tools.SHOW: 1006SHOW TRANSCRIPT: The Cloudcast #1006 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Link to February 2026 News and ArticlesFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

This is Episode 71 of the Dare Real AgileIs Enterprise AI the New Fake Agile?The AI Chasm: Are We About to Repeat the Agile Industrial Complex Disaster?Ten years ago, Daniel Mezick, Mike Beedle, and Martin Fowler raised the alarm. The Agile revolution was being consumed by an industrial machine — certification factories, corporate theater, and self-appointed coaches who had never transformed anything real. Enterprises paid the price. A decade of fake agility. Wasted budgets. Broken trust.We did not learn the lesson.Today, Blockchain’s decentralized identity standard and Enterprise AI — two of the most consequential innovations of our generation — are walking the same dangerous road. A new class of impostors, packaging shallow prompts as enterprise transformation, is already at the gate.The commodification has begun.In this solo editorial, Coach AF draws the direct line between yesterday’s Agile Industrial Complex and the AI chasm forming right now — and proposes a bold, empirical path forward built on genuine AI Craftmanship.Chapters – Timestamps:00:00:00 Welcome and Intro00:06:17 The Agile Hijacking — History & Warning Signs00:14:53 Blockchain & Bitcoin — The Same Pattern Emerging00:19:35 Enterprise AI Chasm — The New Impostor Economy00:32:13 The AI Craftmanship Path — Empirical & Bold Solution00:42:53 Event DIX Invitation and ConclusionSee it clearly. Demand better. Dare Real Agile.Dare Real AI with Human Integration!Your AI Consultant Is Probably Faking ItThe Great AI Con: Why Enterprises Are Paying Millions for NothingWe Got Scammed by Fake Agile. Now AI Is Coming for Your BudgetStop Hiring AI Consultants. Most of Them Don’t Exist Yet.The AI Industrial Complex Is Here. And It’s Already Lying to YouThe AI Gold Rush Is Creating a New Class of Impostor ConsultantsEnterprise AI Is Being Hijacked.Don't just adapt: design your future!!Subscribe now. Join the Freedom Lifestyle Club and The Responsible Citizenship Movement.If you are receiving this trance-mission, you are part of the shifting onward!Don't forget to subscribe, like, and share this episode with your team.Join us on this journey to elevate your enterprise into an AI-powered, agile-first organization.Buy my Guests and me a Coffee to Warm us our Hearts Want to Watch this Episode in Video? Watch on Rumble Uncut Full Show – Subscribe Watch on YouTube – Subscribe To all innovative and High potential Minds out there. Love Intro Music and Sounds mix created by: El Muñecon / The Lounge King. More goodness from him HERE!Dare Real Agile™ Podcast for Business Agility by Agile Lounge® Part of the Next Level Agility & Open Leadership Movement ©2020-2025We DARE Because We CARE!https://agile-lounge.com/wp-content/uploads/2026/02/DRA-EP-71-The-Great-AI-Con-Why-Enterprises-Are-Paying-Millions-for-Nothing-2026-02-28.mp3Listen & Subscribe: Or subscribe with your favorite app using this address:https://agile-lounge.com/feed/podcast/ The post The Great AI Con: Why Enterprises Are Paying Millions for Nothing appeared first on Agile Lounge.

In this AI Agent & Copilot Minute, Mason Siefert outlines how Microsoft's latest enhancements to Copilot Studio — especially the new tools in the Power CAT Copilot Studio Kit — are designed to bring structure, governance, and measurable quality to enterprise-scale AI agents. Key Takeaways Rubrics refinement: The headline feature in the updated kit is the rubrics refinement tool, which addresses a growing challenge in agentic AI operations — how to consistently and accurately grade agent responses. The tool introduces a repeatable feedback loop where teams define evaluation rubrics, compare AI-generated grades with human evaluations, and then refine instructions when the two don't align. The result is a more systematic, scalable way to ensure automated assessments meet human-level standards. Governance & visibility: Beyond evaluation, the kit strengthens oversight across the AI estate. A new compliance hub automatically flags configuration risks to help teams stay ahead of governance concerns. Conversation KPIs allow organizations to track agent performance without manually reviewing transcripts, and an agent inventory provides a centralized view of custom agents and the capabilities they rely on. Together, these features bring operational clarity to expanding AI environments. Looking ahead: As agentic systems scale, structured coordination between humans and AI will be critical. Tools like the rubrics refinement workflow signal a shift from experimentation to disciplined operations, where evaluation, compliance, and performance tracking are embedded into the lifecycle of every agent. Organizations that formalize these processes now will be better positioned to manage complexity and deliver trustworthy AI outcomes at scale. Visit Cloud Wars for more.

What if AI can learn the “why” behind decision making of humans?Ashu Garg and Jaya Gupta recently wrote one of the most discussed articles on AI this year. Their idea drew public responses from Dharmesh Shah, Aaron Levie, and Arvind Jain.Enterprise software has always captured what happened. It records the order, the ticket, and the approval. But it has never captured why it happened. It does not store the reasoning, the exception, or the past decisions that shaped the outcome. Ashu argues that this missing layer is the biggest opportunity in enterprise AI right now, and that the startups that capture it will be the biggest winners in AI.In this episode, we go deeper into what context graphs really are, how they get built, why startups have an edge over incumbents, and how close we are to seeing this work in practice.00:00 – Trailer00:42 – What are context graphs?03:57 – Why agents haven't lived up to the hype?07:03 – The “why” of Decision Making10:47 – How agents will store data for context graphs13:17 – What will be possible for Digital twins?17:32 – Can context graphs reveal a company's moat?19:48 – Guardrails on Access for agents24:47 – Managing agents vs being managed by agents28:46 – Will winners be vertical or horizontal players?32:20 – The future is agent swarms35:54 – Finding PMF is what makes a great CEO39:34 – What will set apart successful enterprises of 203042:10 – Where Foundation Capital is investing44:05 – Why AI won't be winner-takes-all47:03 – Where will the context graph reside?50:56 – Will systems of record be replaced?53:22 – Human in the loop → hands-off execution55:57 – A reality check on where we are today58:24 – Where startups will win in orchestration-------------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 a text

Shomron Jacob, an AI Strategy Expert and Technology Advisor based in Silicon Valley with over a decade of experience in enterprise AI, GenAI, and machine … Read more The post Where Enterprise AI Is Breaking Down and the Strategic Bet CIOs Must Rethink appeared first on Top Entrepreneurs Podcast | Enterprise Podcast Network.

Enterprise AI experimentation is slowing, and a harder question is emerging: where is the business value? With only 10–15% of AI projects reaching sustained production, many organisations are facing a “production gap” between pilots and profitability. The Breakfast Show speaks with Guna Chellappan, General Manager Singapore at Red Hat, about why AI demos don’t automatically scale, the infrastructure and governance challenges holding companies back, and how to move AI from the playground to the profit centre.See omnystudio.com/listener for privacy information.

January and February 2026 reveal a disciplined expansion across the DACH startup ecosystem. After reviewing more than 15,000 funding announcements, we identify structural capital concentration in enterprise AI, ESG/CSRD compliance platforms, defense and dual-use technologies, industrial robotics, and milestone-driven biotech — with visible institutional participation from EIB, KfW, and major German banks. DACH venture capital in early 2026 shows selective growth-stage normalization. Capital flows favor revenue clarity, regulatory alignment, and strategic industrial positioning. Enterprise AI integrated into operational workflows, compliance SaaS driven by CSRD mandates, defense-adjacent deep tech, robotics modernization of the Mittelstand, and clinical-stage biotech programs attract institutional participation. Consumer and speculative categories remain constrained.

Recent analysis from Goldman Sachs indicates that $700 billion in AI investment during 2025 resulted in no measurable U.S. GDP growth, with most AI equipment imports negating domestic benefits and 80% of surveyed firms reporting no productivity or employment improvements. This pattern suggests that AI-related spending has primarily shifted margins from enterprise IT budgets to a small number of infrastructure vendors rather than delivering distributed value. Internal concerns are rising, with 90% of IT leaders questioning AI's return on investment, and 80% citing fragmented data as a primary challenge to measuring outcomes. Further context reveals that agentic AI initiatives face operational headwinds: Gartner expects 40% of such projects to be cancelled by 2027, and S&P Global found nearly half are abandoned before production, most often due to inadequate planning and data foundations. Margin erosion is widespread, attributed to AI implementation costs, and attempts to scale AI agents into production remain limited by inference costs and insufficient infrastructure. Despite increased adoption efforts, sustainable value delivery from AI platforms remains elusive for most organizations. Enterprise AI access is becoming increasingly concentrated. OpenAI's partnership with consulting firms such as BCG, McKinsey, Accenture, and Capgemini consolidates control of the enterprise distribution layer, narrowing competitive opportunities for smaller providers. Meanwhile, Amazon's 13-hour AWS outage, linked to the misconfiguration of an internal AI tool, underscores the liability ambiguity in agentic systems—where vendors may attribute autonomous actions to user error, complicating risk assignment. Additional updates from vendors such as Anthropic, Cloudflare, and New Relic address incremental technical capabilities, with a distinct focus on cost, operational governance, and policy enforcement. The prevailing themes for MSPs and IT leaders are increased scrutiny of AI value, heightened exposure to cost and accountability risk, and the emergence of managed service opportunities around data governance, cost instrumentation, and liability management. With enterprise market channels consolidating and risk shifting toward service providers, integrating robust contractual definitions for autonomy, incident attribution, and financial boundaries is essential to limit harm and clarify responsibility before incidents occur. Four things to know today 00:00 Goldman: $700B AI Spend Delivered Near-Zero U.S. GDP Growth in 2025 03:49 OpenAI Enlists BCG, McKinsey, Accenture to Distribute Enterprise AI Agents 06:44 Report: Amazon's Own Engineers Prefer Claude Over Its Mandated Internal Tools 08:56 AI Inference Costs Are Falling — But Governance Gaps Are Growing This is the Business of Tech.    Supported by: CometBackup  Small Biz Thoughts Community   

Enterprise AI search visibility faces a 70% misconception rate among SEO teams. Thomas Peham, CEO and co-founder of Otterly.AI, shares insights from building a product-led AI search optimization platform that serves enterprise clients through strategic partnerships. The discussion covers founder-led sales methodologies for AI startups, scalable enterprise deal frameworks before hiring sales executives, and product-first go-to-market strategies that validate market fit through direct founder engagement.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Samay Kohli spent 12 years at GreyOrange, scaling it to over $100 million in revenue and a $3 billion valuation at its peak, making it one of the world's largest warehouse robotics companies. Two years ago, he started again with Budy, this time in the US senior care industry.In this industry, decisions are emotional, sales cycles can run for years, and multiple stakeholders are involved. While the market sits at the intersection of real estate, healthcare, and hospitality, most sales still depend on manual follow-ups and scattered tools.Budy builds digital workers for sales teams: AI teammates that handle follow-ups, scheduling, and lead management across CRMs, calendars, and inboxes. Instead of adding another layer of software, Budy went zero UI-UX and focused on enabling sales teams in an industry with 99% inbound leads to manage their cold leads better.Today, Samay joins Siddhartha (Partner at Neon Fund, and a proud investor in Budy) and shares his journey from building robots to building digital teammates for a very non-traditional industry.00:00 – Trailer01:00 – What Budy is building for senior care05:15 – Real Estate × Healthcare × Hospitality06:25 – Zero UI UX technology10:09 – AI teammates not assistants12:03 – How sales teams operated before Budy12:51 – A ninety nine percent inbound industry13:45 – The real cost of senior care homes15:35 – Can a CRM alone solve this17:55 – Direct benefits of a digital worker20:49 – Two founder archetypes22:06 – Can lights out operations become real24:49 – What Samay underestimated about the market25:58 – The largest players in the industry29:07 – Treat your customer's company like your own30:52 – Entrepreneurship as a profession35:36 – Unlearnings as a second time founder37:30 – What digital workers actually are39:47 – The original promise of SaaS42:04 – The next decade of digital workers45:25 – Digital workers that read best selling books47:26 – Will Claude build CRMs49:38 – Business etiquette across the world55:18 – How a second time founder chooses investors01:01:00 – Why every team member should track the P and L01:02:14 – How Samay's view on growth evolved-------------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 a text

This conversation was recorded at GOTO Copenhagen 2025.https://gotocph.comRod Johnson - Building the future of agent frameworks at EmbabelArjen Poutsma - Practical Insights from a Spring Framework VeteranTrisha Gee - Award-winning Engineer, Author and PC MemberRESOURCESRodhttps://twitter.com/springrodhttps://github.com/johnsonrhttps://www.linkedin.com/in/johnsonrodahttps://the-composition.com/@springrodArjenhttps://bsky.app/profile/poutsma.bsky.socialhttps://fosstodon.org/@poutsmahttps://github.com/poutsmahttps://www.linkedin.com/in/arjen-poutsma-288ba6Trishahttps://bsky.app/profile/trishagee.bsky.socialhttps://twitter.com/trisha_geehttps://github.com/trishageehttps://www.linkedin.com/in/trishageehttps://trishagee.comABSTRACTAsk me anything.Read the full abstract here:https://gotocph.com/2025/sessions/3927RECOMMENDED BOOKSRod Johnson • Expert One-On-One J2Ee Design and Development • https://amzn.to/48oCxAJJohnson, Höller, Arendsen, Risbert & Sampaleanu • Professional Java Development with the Spring Framework • https://amzn.to/44J4SRbTrisha Gee & Helen Scott • Getting to Know IntelliJ IDEA • https://amzn.to/3ZBgnGcKevlin Henney & Trisha Gee • 97 Things Every Java Programmer Should Know • https://amzn.to/3kiTwJJTrisha Gee, Kathy Sierra & Bert Bates • Head First Java • https://amzn.to/3k59BJ6BlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

This is the takeaway episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we will unpack how ontology, events and culture unlock enterprise AI. We discuss why your ontologist needs to visit the fulfillment center, how to shift data teams from afterthought to proactive partner, and why the five why's matter more than your tech stack.See omnystudio.com/listener for privacy information.

Is AI really eliminating jobs, or is it redefining skills? In this episode of Technovation, Peter High speaks with Ehren Powell, Chief Digital Officer of Marathon Petroleum Corporation, about leading digital transformation at one of America's largest and most complex industrial enterprises. Powell shares how he is building a skills-first organization—decomposing roles, augmenting capabilities with AI, and reassembling work around differentiated processes. Key topics include: Why AI should be treated as a value multiplier—not a strategy How data contextualization unlocks massive sensor environments The creation of data domain ownership across the enterprise Applying edge technology and AI to improve safety and reliability Why curiosity and reinvention define the future workforce

This is the takeaway episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we will unpack how ontology, events and culture unlock enterprise AI. We discuss why your ontologist needs to visit the fulfillment center, how to shift data teams from afterthought to proactive partner, and why the five why's matter more than your tech stack.See omnystudio.com/listener for privacy information.

Juan and Tim are back for a LIVE episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we unpack how ontology, events and culture unlock enterprise AI. We discuss the gap between what your systems think is happening and what's actually happening on the ground and dive into real world examples: a trailer with expensive merchandise sat forgotten in a yard for weeks. $35M in delayed orders. The math added up, but nobody saw it coming. Why? Because we're obsessed with cleaning data and building dashboards, but nobody mapped the happy path vs. the exceptions actually happening on the ground. What is delivery when the customer isn't home? What's "lost in transit" versus "sitting in our own yard"? The solution? Send your ontologist to the fulfillment center. Build tiger teams. Shift your data teams left to act like software product teams. And most importantly: connect the five why's back to your OKRs, or you're just building features nobody needs.See omnystudio.com/listener for privacy information.

Today's guest is Nishtha Jain, AI Innovation Leader. Nishtha leads enterprise data and AI strategy in the biopharma sector, focusing on aligning advanced analytics and AI systems with real-world clinical, regulatory, and operational workflows. Nishtha joins Emerj Client Narrative & Content Strategy Lead Nick Gertsch to examine why most enterprise AI pilots fail to scale, how unrealistic expectations and poorly defined use cases undermine ROI, and what it takes to design human-centered AI systems that fit how teams actually work in regulated environments. Nishtha also breaks down practical frameworks for measuring value beyond headcount reduction, including return on employee experience and long-term capability building, along with concrete approaches to faster experimentation, customer-driven use-case prioritization, and building flexible operating models that adapt as technology and market conditions evolve. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

This episode is sponsored by tastytrade.  Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature.   Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.   While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.   We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.   If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"  

Tim Crawford and Isaac Sacolick, both former Chief Information Officers and world-class CIO advisors, join Michael Krigsman on CXOTalk episode 909 to break down why enterprise AI strategies are failing, what separates transformational CIOs from those who are drowning, and why earning your seat at the table matters more than ever in 2026.You'll discover:✅ Why Tim says both AI strategy AND IT execution are failing, and what CIOs are focused on instead of outcomes✅ The "three-legged race" framework: how CIO behavior, IT culture, and external perception must align for strategic credibility✅ Why most CIOs have only a "layperson's understanding" of their own business, and how that kills AI value✅ Tim's two swim lanes of AI success: invisible integration or robust training (there is no middle ground)✅ Why Isaac says AI is "reshaping" business but not yet "transforming" it, and the product management shift that changes everything✅ How to evaluate agentic AI: the human-in-the-loop vs. human-out-of-the-loop decision framework and why cybersecurity proves you can't wait✅ The shadow AI paradox: why the best CIOs encourage it (with guardrails) instead of shutting it down✅ The three skills every IT professional needs now: business acumen, critical thinking, and data literacy⏱️ TIMESTAMPS0:00 Cold open: "If you think you should have a seat at the table, you've failed"0:35 Why both AI strategy and IT execution are failing2:08 The productivity measurement problem with AI2:45 What CEOs and boards want from CIOs in 20264:28 Why CIOs don't truly understand their business6:54 Why organizations are stuck in AI pilot mode9:04 Tim's 2 swim lanes: invisible AI vs. training-wrapped AI11:23 Audience Q&A: Inside-out thinking vs. outside-in thinking14:34 The 3-legged race: earning your seat at the table17:09 Moving from AI efficiency to true business transformation20:03 The shift from project-oriented to product-oriented IT20:31 AI governance, CISO alignment, and data sensitivity27:15 Agentic AI: fully autonomous vs. human-in-the-loop34:46 Agentic AI strategy and the value equation (opportunity minus cost)38:46 Shadow AI: innovation source or security threat?43:00 Governance as culture, not a bolt-on46:00 The AI skills gap: business acumen, critical thinking, data skills, and curiosity49:46 Are survival-mode CIOs sabotaging their careers?52:15 What CIO greatness looks like in 2026

Every enterprise is building an AI stack, but most are doing it wrong. In this episode, Ross breaks down a tactical, use-case-driven framework for building an AI stack that actually works. If you're a marketer, operator, or executive looking to leverage AI strategically (without blowing your budget or ignoring compliance), this episode gives you the structure you need to win. Key Takeaways and Insights: 1. The Hard Truth About Enterprise AI - Most companies choose AI tools based on hype, not strategy. - Vendor pitches and social buzz are driving long-term contracts. - Locking into the wrong platform can create scaling and security nightmares. - The AI landscape changes weekly, three-year commitments require serious thought. 2. There Is No “Best” AI Tool - The right question isn't “What's best?” but “What's best for this use case?” - Different teams (marketing, engineering, finance) need different tools. - Constraints, industry, and goals should guide tool selection. - Build a stack…Don't look for a silver bullet. 3.  The 5-Layer AI Stack Framework - Layer 1: Writing & Communication Tools - Layer 2: Research & Analysis - Layer 3: Code & Technical Execution - Layer 4: Automations & Workflow Integration - Layer 5: Security & Compliance 4. Training, Ownership & Continuous Improvement - AI adoption fails without real, ongoing training. - Appoint an AI stack owner responsible for optimization and updates. - Create internal systems (e.g., Slack channels) to share prompts and workflows. - Capture institutional knowledge so it doesn't leave with one employee. 5. Start Small, But Start Strategic - Don't wait for “the perfect moment.” AI is already reshaping competition. - Experiment but build security and compliance from day one. - Budget realistically for training, tools, and maintenance. - Strategic AI adoption is a long-term competitive advantage. Resources & Tools:

After many weeks of work with corporate HR leaders, technology companies, and implementation teams I'm realizing the word that describes AI is “confusion.” Too much going on, too many unanswered questions, and no clarity about what to do. And many of you have been asked (or told) to lead the “AI Transformation” (which is the wrong phrase, as I explain) to reduce cost. Well I hope today's podcast gives you some clarity. Obviously the space is changing quickly, but there is a clear strategy emerging. I discuss the technology market, vendor strategies, and most important of all, how you as a business leader can leverage AI without going down dead ends. I hope this gives you clarity, and I urge you to read our 2026 Imperatives for Enterprise AI for more. Topics covered: Why AI adoption isn't a transformation — it's a continuous learning process How to design an architecture that avoids vendor chaos and data silos The real ROI of AI: rethinking workflows and job structures, not just automating tasks Strategies for navigating a confusing vendor landscape and building your own solutions How to build a culture of trust and change, and empower employees What to tell employees so they'll lean in to change The importance of speed, experimentation, and trusting the data over perfection. If you're in the middle of your AI strategy, please contact us. Our Systemic HR AI Blueprint will show you the way, and Galileo will help you with vendor analysis, process design, job redesign, and of course the training you need to enable your organization. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information 2026 Imperatives for Enterprise AI: The Road Ahead The Great Reinvention of Human Resources Has Begun Get Galileo, The AI Agent for Everything HR Chapters (00:00:00) - AI Confusion(00:05:19) - Self-Service HCM Software Companies(00:08:13) - Job architecture and the process of changing jobs(00:11:45) - Don't Wait for Perfection in AI Projects(00:14:43) - Will We Run Out of Jobs?(00:18:01) - Will AI Reduce Headcount?(00:19:39) - The Need for Trust in AI

This week, we discuss the future of SaaS, OpenAI vs. Anthropic strategies, and cloud capex. Plus, when will you let an AI book your flights? Watch the YouTube Live Recording of Episode 559 Runner-up Titles Do we get to eat Moon Pies? Some days it's just me and the AI We have a LinkedIn page The state of the world has not gotten better, it's just moved to Kubernetes Trained on the Corpse of Stack Overflow. We just have to get the files right It is all just files It's all an OODA loop Rinse and reply. Is Software dead? Your margin is my yacht. claude-travel.md Vegans have morals though Rundown DriftlessAF: Introducing Chainguard Factory 2.0 Is Software dead? Clouded Judgement 2.6.26 - Software Is Dead...Again...For Real this Time...Maybe? Anthropic's breakout moment: how Claude won business and shook markets Besieged The $285 Billion 'SaaSpocalypse' Is the Wrong Panic The "whole product" is more relevant than ever Cloud Earnings Microsoft Q2 earnings beat on top and bottom lines as cloud revenue tops $50 billion, but stock falls Microsoft stock plunges as Wall Street questions AI investments A day of reckoning for the AI boom Oracle says it plans to raise up to $50 billion in debt and equity this year Google Earnings Beat. Cloud Computing Momentum Builds Amid Spending Boom Amazon stock falls 10% on $200 billion spending forecast, earnings miss Amazon's $200 Billion Spending Plan Raises Stakes in A.I. Race [Follow the CAPEX: Cloud Table Stakes 2024 Retrospective](http://(https://platformonomics.com/2025/02/follow-the-capex-cloud-table-stakes-2024-retrospective/) Amazon Earnings, CapEx Concerns, Commodity AI Google's parent company raises billions of dollars in debt sale OpenAI Drama Amazon in Talks to Invest Up to $50 Billion in OpenAI The $100 Billion Megadeal Between OpenAI and Nvidia Is on Ice Sam Altman got exceptionally testy over Claude Super Bowl ads | TechCrunch OpenAI will reportedly start testing ads in ChatGPT today Relevant to your Interests Deploying Moltbot (Formerly Clawdbot) Apple tops Q1 earnings estimates on record-breaking iPhone sales Clouded Judgement 1.30.26 - Software is Dead...Again! Leaders, gainers, and unexpected winners in the Enterprise AI arms race All Enterprise software is dead The Dumbest Thing I've Seen This Week SpaceX acquires xAI in record-setting deal as Musk looks to unify AI and space ambitions AWS destiny: becoming the next Lumen CloudBees CEO: Why Migration Is a Mirage Costing You Millions Xcode 26.3 unlocks the power of agentic coding The world is trying to log off U.S. tech Anthropic's newest AI model uncovered 500 zero-day software flaws in testing DHH on OpenClaw Adam Jacob really likes AI code generation Cautionary Tales – The WOW Machine Stops (Part 2) Kyndryl Shares Halved Amid CFO Departure, Accounting Review Our $200M Series C / Oxide Presentations — Benedict Evans Matrix messaging gaining ground in government IT Hello Entire World · Entire Blog Former GitHub CEO raises record $60M dev tool seed round at $300M valuation From magic to malware: How OpenClaw's agent skills become an attack surface Nonsense What If the Sensors on Your Car Were Inspecting Potholes for the Government? Honda Found Out Superbowl Ad 404 Conferences DevOpsDay LA at SCALE23x, March 6th, Pasadena, CA Use code: DEVOP for 50% off. Devnexus 2026, March 4th to 6th, Atlanta, GA. Use this 30% off discount code from your pals at Tanzu: DN26VMWARE30. Check out the Tanzu and Spring talks and trading cards on THE LANDING PAGE. Austin Meetup, March 10th, Open Lakehouse and AI — Listener Steve Anness speaking KubeCon EU, March 23rd to 26th, 2026 - Coté will be there on a media pass. Devopsdays Atlanta 2026. April 21-22 VMware User Groups (VMUGs): Amsterdam (March 17-19, 2026) - Coté speaking. Minneapolis (April 7-9, 2026) Toronto (May 12-14, 2026) Dallas (June 9-11, 2026) Orlando (October 20-22, 2026) SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Recommendations Brandon: YouTube TV plans launch this week Matt: Send Help Steal Coté: AI, open source, talent, and more, live at cfgmgmtcamp 2026, with Andrew Clay Shafer Tapistry

Do startup valuations today make sense?Umesh Padval, an early investor in Cohere, now valued at about $7 billion shares why Cohere stood out at the time of his investment. He shares what he saw early that made him believe this was not just another AI model company.Umesh is the Founding Managing Partner, Seligman Ventures and previously at Thomvest and Bessemer Venture Partners. He brings experience from investing across multiple tech cycles, from chips to cloud to AI. Umesh talks about how deals are really done in venture capital and what he looks for when everything feels noisy and crowded in AI.He also shares why many strong companies are choosing to stay private and what has changed in the IPO market. Public markets now demand cash flow and durability, not just fast growth.Umesh talks about why open source has become a powerful sales funnel for modern AI companies. Developers become the first users, and community adoption turns into long-term enterprise revenue.After four decades in Silicon Valley and 20 years as a VC, Umesh shares what keeps him in building and investing.0:00 – How big is the scope for investing in AI startups?04:04 – Do unit economics justify large AI valuations?06:00 – Thomvest's LLM investment thesis (Cohere case study)09:18 – Are CTO roles changing in AI11:21 – Traits of the best AI founding teams13:40 – Timeline to find the best founders16:52 – Partnership with Jyoti Bansal19:07 – Where is the IPO market headed?23:40 – Salesforce–Clari acquisition25:18 – Is profitability a prerequisite to go public?26:00 – Can the India–US corridor beat US–Israel?28:53 – Umesh's investment philosophy31:08 – Open source as a sales funnel33:38 – IIT → Stanford → Startups41:45 – The only CEO with 60 direct reports43:43 – Why Jensen never does 1-on-1s?48:23 – What ultimately drives Umesh Padval?-------------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 a text

Sreedhar brings deep experience building and scaling B2B technology companies and offers a grounded, operator's perspective on enterprise AI adoption. The discussion explores where AI is truly creating leverage inside organizations, why access to AI tools is no longer a competitive advantage, and how leaders, engineers, and GTM teams must rethink workflows, skills, and execution to stay ahead.

At ITEXPO / MSP EXPO, Doug Green, Publisher of Technology Reseller News, spoke with Lyle Pratt, CEO of Vida, about the company's latest release: an expanded AI Agent Operating System designed for enterprise scale and built specifically for MSPs and channel partners. Vida provides AI-powered phone agents that integrate directly into existing UCaaS and telecom environments. With native SIP registration, Vida's agents can register back to an MSP's current UCaaS platform and appear just like any other VoIP endpoint. The new release enhances omnichannel capabilities, centralized control, observability, billing integrations, and reseller management—allowing MSPs to deploy, monitor, and monetize AI agents at scale across multiple customers. Pratt emphasized that the platform was architected from a telecom channel background. “We've designed the OS specifically for MSPs,” he said. “We make it extremely easy to roll those out to all your customers using our AI Agent OS.” Vida supports a multi-tier model—partners, resellers, enterprises, and agents—enabling white-label deployments where MSPs retain brand control and pricing authority. The platform also includes built-in billing and reporting capabilities to streamline recurring revenue operations. A key opportunity lies in redirecting call traffic that traditionally flows to third-party call centers or BPOs. Vida's AI phone agents can handle first-tier interactions at approximately 15 cents per minute, enabling MSPs to capture revenue streams that previously bypassed them. “Software is going to begin to eat into the labor market,” Pratt noted. “And that actually is great for MSPs because they sell software solutions—now they can collect those margins for themselves.” As AI continues to reshape communications infrastructure, Vida is positioning its platform as the backbone for next-generation IVRs, auto attendants, and voice-driven automation. With SOC 2 and HIPAA compliance, flexible integrations, and omnichannel automation capabilities across voice, SMS, and email, the company is aiming to simplify AI deployment for MSPs while opening new, high-margin revenue paths. Visit https://vida.io/

Enterprise AI doesn't fail because the models aren't ready. It stalls when operating models, data foundations, and decision paths can't keep up. Recorded at NRF, this AiR Podcast features Yael Kochman in conversation with Rakesh Srinivasan (The Estée Lauder Companies) and Chris Daniels (Toptal) on what it takes to move from scattered experimentation to repeatable, enterprise deployment - across brands, regions, teams, and partner ecosystems. Key takeaways - Why saturation, skepticism, and a fragmented customer journey are forcing beauty brands to rethink how innovation gets delivered - What must change inside the enterprise to scale AI: product-oriented teams, clear intake and sequencing, and strategy-led roadmaps - The real governance challenge: maintaining brand voice and trust as data flows through platforms, vendors, and generative layers - How modular, “build once, scale many” platforms enable faster deployment across brands - When partnering accelerates outcomes - and why building everything internally can slow progress - A concrete example: Jo Malone London's Scent Finder and what it shows about treating AI as a product, not a pilot Learn more about The Estée Lauder Companies and Toptal

Today's guest is Naveen Kumar, Head of AI Governance at TD Bank. With extensive experience in AI risk management and governance, he provides actionable strategies for secure AI scaling in regulated environments. Naveen joins Emerj Editorial Director Matthew DeMello to discuss foundational challenges blocking AI adoption in banking, including data leakage, prompt injection, shadow AI, and hallucinations. Naveen also shares practical takeaways, such as role-based AI guardrails for data access, safe sandboxes for experimentation, hybrid deployments to protect sensitive data, and treating AI agents as de-risked employees with human oversight for compliance and ROI. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast!

On this episode of CES Tech Talk, Zeta Global's CEO David Steinberg explores how Zeta uses big data and AI agents to cut waste, boost relevance and drive measurable revenue for large enterprises, why the company built its Athena conversational “super agent” and what their OpenAI collaboration could unlock next.

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

How Do Marketplaces Turn AI Ambition Into Scalable, Trusted Enterprise Reality? That is the question I explore in this episode with Julie Teigland, Global Vice Chair for Alliances and Ecosystems at EY, someone who sits right at the intersection of enterprise demand, technology platforms, and the ecosystems that increasingly power modern AI adoption. As organizations race to deploy AI at scale, many are discovering that the real challenge is not a lack of tools, but the complexity of choosing, integrating, governing, and standing behind those decisions with confidence. Julie explains why marketplaces are becoming a powerful mechanism for reducing friction in this process, helping enterprises move beyond experimentation toward AI solutions that are trusted, scalable, and aligned with real business outcomes. We talk about how marketplaces can collapse complexity, curate choice, and bring much needed clarity to leaders who are overwhelmed by the sheer volume of AI options available today. Julie also shares how EY approaches this challenge through its "client zero" mindset, turning the lens inward and treating EY itself as the first marketplace customer. By doing so, EY stress tests governance, security, and integration at real enterprise scale, serving tens of thousands of clients, running hundreds of thousands of servers, and processing hundreds of millions of transactions every day. That internal experience shapes how EY helps clients navigate trust, accountability, and cross-vendor integration risks, particularly as AI becomes more embedded into workflows and decision-making. We also explore how strong alliances with cloud leaders like Microsoft and SAP are shaping how AI solutions are vetted, standardized, and deployed across industries, as well as how regulation, particularly in Europe, is influencing a shift toward responsibility by design. This conversation goes beyond technology to focus on orchestration, trust, and outcomes, and why marketplaces are evolving from simple app stores into something far more strategic for enterprise AI. If you are trying to understand how ecosystems, governance, and marketplaces can help turn AI from isolated projects into sustained business value, this episode offers a thoughtful and grounded perspective.  I would love to know what resonated with you most. How do you see marketplaces shaping the future of AI adoption inside your organization? Useful LInks Connect With Julie Teigland Learn More About EY

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

Alex Gleason was one of the main architects behind Donald Trump's Truth Social. Now he focuses on the intersection of nostr, ai, and bitcoin. We explore open source ai agents, such as OpenClaw, and the wider implications of the tech on society.Alex on Nostr: https://primal.net/p/nprofile1qqsqgc0uhmxycvm5gwvn944c7yfxnnxm0nyh8tt62zhrvtd3xkj8fhggpt7fyClawstr: https://clawstr.com/Soapbox Tools: https://soapbox.pub/toolsMy bot's nostr account: https://primal.net/p/nprofile1qqsfzaahg24yf7kujwrzje8rwa7xmt359tf9zyyjeczc9dhll30k8pgmlfee2 EPISODE: 190BLOCK: 935786PRICE: 1422 sats per dollar(00:02:30) Value-for-value, no sponsors, and show philosophy(00:02:39) Alex Gleason returns to talk AI(00:03:56) From vibe coding to open-source agents with memory(00:05:24) Messaging-first UX: Signal, Nostr, WhatsApp as AI interfaces(00:06:10) Why chatbots beat traditional AI apps for mainstream users(00:07:07) Open protocols pain vs closed platforms; Bitcoin and Nostr(00:08:52) Automating social games: price tracker and agent posting on Nostr(00:10:01) AI mediators for collective action, constitutions, and nonprofits(00:11:46) Scaling governance: trust, bias, and Discord vs freedom tech(00:13:14) Bot barriers on centralized messengers and need for open chat(00:14:04) Clawstr: decentralized AI-to-AI discussions on Nostr(00:15:21) Hype vs reality in AI agents; emergent behaviors and money(00:16:26) Agentic payments: bots with Cashu wallets and earnings(00:18:40) Agents solving UX pain: relay management, keys, and UTXOs(00:20:00) Cold storage approvals with chat agents: a new wallet paradigm(00:20:22) Specialized agents, skills, and distribution challenges(00:22:34) Cost tradeoffs: pay another agent vs build skills yourself(00:24:55) Token burn lessons(00:27:44) Beyond OpenClaw: bloated stacks, Icarus, and cost-optimized agents(00:28:52) Hybrid model routing: local small models with cloud for heavy lifts(00:29:47) Agents paying humans directly: disintermediating platforms(00:30:47) Voice, screens, and form factors: AirPods, text, and brain chips(00:33:01) Apple, privacy branding, and the Siri gap(00:34:35) Enterprise AI choices: Google, Microsoft, trust, and lock-in(00:36:01) Model personalities: Gemini concerns and OpenAI "openwashing"(00:37:23) Obvious agent UX wins: flights, rides, and social media shifts(00:38:50) Local-first social: group chats, neighbors, and healthier networks(00:40:16) Antiprimal.net: standardizing stats from Primal's caching server(00:43:34) Open specs, documentation via AI, and trust tradeoffs(00:45:18) Indexes vs client-side scans: performance and verification(00:46:20) APIs, rate limits, and a market for paid Nostr data(00:47:57) Agents and DVMs: paying sats for services on demand(00:48:49) Degenerate bots: LN Markets, costs, and Polymarket curiosity(00:50:42) Truth feeds for agents: Nostr, webs of trust, and OSINT sources(00:53:51) Post-truth reality: verification, signatures, and subjectivity(00:56:04) Polymarket mechanics: on-chain prediction markets and signals(01:00:10) Trading perception vs truth; sports markets as timelines(01:01:45) The Clawstr token saga: hype, claims, and misinformation(01:07:11) Why meme coins are scams: no equity, utility myths, slow rugs(01:08:55) Pulling the rug back: swapping out, fallout, and donations(01:10:49) Aftermath: donating to OpenSats and lessons learned(01:12:14) Prediction markets vs meme coins: societal value distinction(01:15:25) Iterating beyond OpenClaw and MoltBook; experiments on Nostr(01:18:00) Do bots need Clawstr? Segregating AI content and labels(01:21:02) Reverse CAPTCHA: proving bot-ness and the honor system(01:23:38) Souls, prompts, and token costs; agents with personalities(01:27:01) Wrap-up: acceleration, optimism, and next check-in(01:28:21) Open-source models, China's incentives, and local hardware(01:30:06) The dream stack: home server agent, Nostr chat, hybrid modelsmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

AI Agents promise to revolutionize how we operate our companies, but this is much more than just recording meetings and summarizing emails. How do you build an Agent (and Superagent) architecture to re-engineer HR and what is the role of your core HCM platforms? Well this is the trillion dollar question challenging every business software provider, and it has a huge impact on your HR and overall AI strategy. In this podcast I explain this topic and describe how employee onboarding, as an example, could be entirely redesigned for speed, scale, and agility. This is a new world and for the first time in my career each of us, regardless of tech experience, will be able to redesign how our HR function works to move from “work productivity” to automation and tremendous new value creation strategies in HR. Note that this week OpenAI announced its Frontier platform to help build enterprise agents. Microsoft recently introduced Agent 365 to help build enterprise Superagents. ServiceNow offers its Enterprise AI Control Tower, and Workday has introduced the Workday Agent System of Record. The space of agent management platforms is just beginning. As you listen to this and ponder your situation I hope you consult Galileo for advice or call us. Our Systemic HR AI Blueprint is here to help you design and implement AI apps that will revolutionize HR and your business. Enterprise AI is an exciting new domain and we are here to help. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information 2026 Imperatives for Enterprise AI: The Road Ahead The Great Reinvention of Human Resources Has Begun Get Galileo, The AI Agent for Everything HR     Chapters (00:00:00) - Onboarding and AI: The Confusion in Corporate IT(00:06:42) - Agents and the role of the ERP(00:11:01) - The AI Agents: Will You Build Them?

Matt MacInnis spent 6 years as COO at Rippling and now leads as CPO. He joined Rippling in 2019, when there were only 70 people, and has led the company across multiple stages.Before that, Matt was a founder for 9 years, building Inkling after 7 years at Apple. These three chapters of his career shape this conversation. We focus on how to build and operate teams as a company scales. Matt explains how he thinks about speed versus real progress, and which parts of building a company should move fast and which should move slowly. He shares how he decided when to introduce processes at Rippling, when to keep things informal, and how to recognize when a process that once helped the company had started to slow it down.We discuss how his role changed as Rippling grew from around 70 people to 100, then to 500, and now to thousands. He explains what he paid attention to at each stage and which metrics he deliberately did not obsess over.These are practical lessons for founders, from the earliest days of a startup to the challenges of scaling a large organization.0:00 - Trailer01:11 – One thing people get wrong about building a business?04:01 – Great founders find markets that already exist06:36 – What does a “death march” mean at Apple?10:11 – How to build a good team in early-stage startup?12:33 – Learnings from Apple to Inkling18:11 – Processes to set up in startups25:20 – Humans always optimize for comfort (and why that's bad instinct)33:09 – Why success teaches you more than failure36:01 – How should processes change as company scales?42:11 – How is AI changing the software industry?54:03 – If Matt were starting up today, how would he do it?57:07 – How would Next-gen PM roles look like?01:01:51 – Matt shares about Rippling CEO Parker01:04:32 – Founder instinct vs Data01:06:06 – Over-optimizing for employee comfort01:07:27 – If building a startup feels comfortable, it's probably dead01:08:36 – One thing only CEO's should do forever01:11:15 – One piece of startup advice Matt doesn't trust-------------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 a text

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/-----At Davos 2026, the mood was unlike any previous World Economic Forum gathering. With Donald Trump arriving amid escalating geopolitical tensions and European leaders sounding alarms about sovereignty, I recorded live dispatches from the ground. In this special episode, I bring together observations from four days at the annual meeting, tracking the seismic shifts in global order alongside the practical realities of AI adoption in the enterprise.Skip to the best bits:(00:38) Day one at Davos(02:10) Three recurring themes through the week(03:55) Day three at Davos(05:12) Mark Carney's stirring speech(05:52) Why European leaders are sounding the alarm(06:51) Why technological sovereignty just became urgent(09:31) Day four at Davos(12:59) What leaders really have to say on AI adoption(14:07) The case for only using open source modelsWhere to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.