Podcasts about enterprise ai

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

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

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's guest is Brad Mallard, Chief Technology Officer at Version 1. In a world driven by rapid technological change, AI is not just about the technology itself; it's about people, transformation, and real-world application. In today's podcast, Brad shares how Version 1 is harnessing artificial intelligence to redefine business processes and educational experiences.Topics include:0:00 Driving AI transformation across regulated industries worldwide at Version 2:39 Leading AI transformation through internal adoption and public sector innovation5:12 How they prioritise AI adoption through people, education and change management7:42 Emphasising AI governance, measurement and business value realisation11:30 Addressing rising AI costs through token optimisation and model efficiency15:25 How Version 1 enables AI agents across enterprise workflows and functions19:06 Plans for continued AI-driven growth, job creation and investment in co-creation spaces20:39 How Version 1's co-creation studios enable AI-powered, rapid prototype development with clients23:09 Co-creating AI solutions with clients focused on early ROI validation25:10 Delivering AI-for-good solutions improving education and productivity globally

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/

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.

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

AI-agents worden steeds krachtiger en autonomer, maar organisaties worstelen met de beveiliging ervan. Filip Verloy (CTO EMEA & APJ bij Rubrik) legt uit waarom 83% van de organisaties geen overzicht heeft over hun AI-agents en hoe je deze autonome systemen veilig naar productie kunt brengen.In deze aflevering bespreken we de unieke uitdagingen van AI-agent security. Traditionele guardrails schieten tekort omdat AI-agents probabilistisch en onvoorspelbaar zijn. Verloy legt SAGE uit (Semantic AI Governance Engine), een small language model dat de intentie van agents analyseert en real-time kan ingrijpen wanneer agents buiten hun governance framework opereren.Je leert over prompt injection attacks, shadow AI-risico's, de verschillen tussen platform-native guardrails en external governance, en hoe Agent Rewind essentieel kan zijn als laatste verdedigingslinie. Een must-watch voor iedereen die met AI-agents werkt of deze wil implementeren.Key takeaways:• 83% van organisaties heeft geen volledig overzicht van hun AI-agents• Lokale agent guardrails zijn onvoldoende door de probabilistische aard van LLMs• SAGE gebruikt een small language model om agent-intenties te beoordelen• Runtime-blocking voorkomt dat agents destructieve acties uitvoeren• Agent Rewind maakt datarecovery mogelijk na agent-fouten• Platform integraties met Copilot Studio, AWS Bedrock en coding assistants• MCP-protocol mist security features ("de S in MCP staat voor security")Chapters:0:11 - Introductie AI-agent security1:16 - Van pilot naar productie2:09 - Shadow AI en agent-overzicht5:23 - Guardrails en governance9:10 - SAGE: Semantic AI Governance Engine28:43 - Agent Rewind en data recovery31:24 - Marktpositie en toekomst#AIAgents #AIGovernance #CyberSecurity #Rubrik #AISafety #MachineLearning #EnterpriseAI #DataSecurity #AICompliance

This episode was brought to you by RayonRetail design teams use Rayon to create store layouts, documentation, standards, and presentations in one collaborative platform. By combining design tools and AI in a single workspace, Rayon helps teams move faster from concept to execution while maintaining consistency across locations. If you're looking to design better retail spaces and streamline your workflow, visit rayon.design and sign up for free todayWhen Ricardo Larroude first joined OFFBounds, Larroudé was producing just 300 pairs of shoes a day. Today, the company manufactures more than 2,000 pairs daily, employs 700 people, and has become one of the fastest-growing vertically integrated footwear brands in the market. In this conversation, Ricardo shares how tariffs, rapid growth, and operational complexity pushed him to rethink how he runs the business and why he decided to personally dive into AI instead of delegating it to his technology team.The result was more than automation. Ricardo built an AI-powered operating system that connects data, teams, and decision-making across the company. From improving website conversion rates to eliminating process bottlenecks and redefining how leaders should approach technology, this episode explores why the future belongs to executives who are willing to learn, experiment, and build. If you're a retail, commerce, or business leader trying to understand what AI actually means beyond the headlines, this conversation offers a practical look at what happens when a CEO gets hands-on.

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.

Send us Fan MailGuest: Ivan Lee, Founder & CEO of DatasaurWe're looking at what happens when AI changes the market faster than the old SaaS playbook can keep up.Ivan Lee, founder and CEO of Datasaur, joins SaaS Backwards to share how his company navigated one of the most dramatic shifts in enterprise AI. Datasaur started as a data annotation platform before ChatGPT changed customer priorities, paused AI roadmaps, and forced the company to rethink its product, GTM strategy, and business model.Ivan explains why out-of-the-box tools like ChatGPT Enterprise and Microsoft Copilot can be useful starting points, but often hit a ceiling for regulated enterprises that need private AI trained on their own data, workflows, and processes.He also shares how Datasaur moved from a traditional SaaS model toward end-to-end AI solutions, what founders can learn from disrupted marketing channels, and why the future of SaaS may depend less on selling software access and more on solving the customer's actual job to be done.Key Takeaways:Why enterprise AI often breaks down when it lacks access to private data and internal workflowsHow ChatGPT disrupted Datasaur's original AI roadmap and customer baseWhy old SaaS GTM channels stopped working in a crowded AI marketHow Datasaur rebuilt around private, secure AI for regulated industriesWhat SaaS founders should measure when marketing “best practices” stop producing results---Stalled pipeline? Lost deals? Diagnose your GTM gaps with a free, actionable checkup. 

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

Join Angel Cisneros, Founder and CEO of Saptiva AI, for a deep dive into the structural mechanics of building tech ecosystems that endure. In 2007, two years before WhatsApp launched, Angel co-founded Quiubas Mobile, converting a lean, bootstrapped messaging social network into the dominant underlying telecommunications backbone for all of Latin America. WhatsApp itself became his first Silicon Valley client, relying completely on the layer he built. Now, following Quiubas' high-profile acquisition by Twilio, Angel is executing the exact same playbook for the artificial intelligence era. In this episode, we explore why raw silicon and generic LLM models are commodities, and why the ultimate moat belongs to the orchestration and control plane.

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.

Enterprise AI initiatives treat design as a finishing step. Carsten Wierwille, Chief Product & Design Officer at HTEC, argues that this is a strategic mistake, and one that explains why so many AI investments produce tools that work technically but fail to change how people actually work. In this episode, Wierwille examines why enterprises keep building AI because they can rather than because they understand the problem, how the shift to AI-assisted ideation has moved the bottleneck from creation to review, and why the answer is not faster shipping but sharper design clarity at the start. The conversation covers the financial advisor as a model for AI force-multiplication, why the MVP framework breaks down for genuinely novel AI experiences, how design now extends to defining the evaluation criteria for AI output, and what Wierwille calls cognitive design, the practice of thinking about how users will perceive, decide, and trust before anyone writes a line of code. This episode is sponsored by HTEC. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

In today's Cloud Wars Minute, I look at how Microsoft and EY are combining engineering and consulting expertise to scale AI transformation. Highlights 00:10 — Microsoft and EY have announced a substantial enhancement to their existing partnership, committing to invest over a billion dollars over five years into a new initiative aimed at helping organizations to scale AI. 00:40 — So, what's the plan for it? Well, it will combine Microsoft's forward-deployed engineers, or FDEs, with the expertise of EY industry professionals to accelerate AI adoption. Their approach focuses on, and I quote, “change management delivery models powered by Microsoft's FDE AI-native hypervelocity engineering approach.” 01:05 — Now, this initiative will see teams of EY business consultants and Microsoft engineers co-develop AI solutions tailored to address clients' highest-value business opportunities. This collaboration is steering companies toward the goal of becoming frontier firms. It's a term coined by Microsoft that I've used many times, that's gaining traction across the industry. 01:29 — Frontier firms are organizations that integrate agentic AI with the human workforce at scale and continuously optimize AI initiatives while adapting their company culture to thrive in this new future. Now, an interesting aspect of this collaboration is that EY, already closely aligned with Microsoft in terms of partners, was in fact the client zero for the initiative. 01:56 — This means they [the EY team] have an insider's understanding of what works well, how to adapt to Microsoft products, and how to effectively share this knowledge with potential customers and clients. Visit Cloud Wars for more.

Join Steven Walchek, Co-Founder and CEO of Liminal, for a deep dive into the "adoption paradox" facing the modern enterprise. Despite billions in AI investment, most organizations remain trapped in perpetual pilots. A serial entrepreneur with over $1.1B in exit value and a former CINO at FIS, Steven argues that the failure isn't technical—it's strategic. In this episode, we explore why forcing standardization kills impact and how the industry is shifting toward "Secure AI Enablement" that learns from actual user behavior to autonomously deploy capabilities where they matter most.

What happens when AI agents start acting less like chatbots and more like coworkers? In this episode, Dan and Chris sit down with Craig McLuckie, CEO of Stacklok to explore MCP, Kubernetes, ToolHive, enterprise AI, and the emerging infrastructure powering AI-native applications. From identity management to agent orchestration and system architecture, this conversation dives into how organizations may soon manage entire fleets of AI agents working behind the scenes.Featuring:Craig McLuckie – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:StacklokToolhiveSponsors:Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

Aaron Levie, co-founder and CEO of Box, returns to the MAD Podcast with the clearest read in tech on what AI is actually doing inside the world's largest enterprises right now - not the hype version, the real one. After hundreds of Fortune 500 CIO conversations this year, Aaron explains why we're still in "day one" of the agent era, why one badly written agent run can now cost $1,000 in compute, and why progress at the AI labs is paradoxically slowing enterprise deployment. We get into the token cost shock now reshaping IT budgets, why coding agents have reached escape velocity while the rest of knowledge work hasn't, the rise of headless software and what replaces per-seat pricing, the emergence of the forward-deployed engineer as the hottest job in tech, why Aaron thinks the AI doomers are wrong about jobs, and where startups can still win as the labs move up the stack. (00:00) Intro(01:18) Silicon Valley engineering vs. everyone else(05:35) Are enterprise CIOs actually bullish on AI?(08:51) Tokenmaxxing & why your AI bill is about to explode(11:34) The myth of falling token costs and AI spend escaping IT budgets(17:37) The $5B startup hiding in AI compute(18:14) The mosaic of models inside every enterprise(21:28) Why coding works and the rest of knowledge work doesn't(25:53) The Bob and Sally problem: access control breaks agents(30:31) Will enterprise AI really take 10 years to roll out?(32:24) The capability overhang: why faster models slow diffusion(34:23) Data is the bottleneck (it always was)(39:02) The rise of internal forward-deployed engineers(41:23) Why the AI doomers are wrong about jobs(43:43) Headless software is inevitable(46:14) What replaces per-seat pricing(47:37) How Box itself is going headless(49:42) How the org chart actually evolves(1:00:33) Future-proofing yourself as an enterprise employee(1:06:40) Are we all just going to work for OpenAI and Anthropic?(1:07:11) Where startups can still win as the labs move up

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance. Key Moments: Why 80% of AI Projects Never Reach Production (02:13): Shub traces the root cause of stalled AI programs to a missing system for moving from demo to deployment. Most teams have no repeatable path to production. Shub's Nine-Step Framework for Building AI Products (06:00): Most AI projects start with a cool model instead of a painful problem. Shub walks through the three phases of his framework: discovery, execution, and excellence. The Case Against "Fix Your Data First" (12:41): Conventional wisdom says clean your data before building AI. Shub challenges that, arguing modern LLMs offer far more flexibility with imperfect data. Four Mindset Shifts for Scaling Enterprise AI (16:35): Shub outlines the four shifts separating organizations that scale AI from those that stall, from measuring AI performance differently to embedding trust from day one. Inside Shub's AI Trust Lab at USC (23:54): Major foundation models are already being benchmarked on trust and safety. Shub explains the lab's mission to build a standardized evaluation framework for AI model governance. Why Enterprise AI Governance Needs Multiple Disciplines (28:36): AI models can be sycophantic, manipulative, or lack candor. Shub argues that building trustworthy AI demands an interdisciplinary approach. Key Quotes: “I think the fundamental problem that organizations are facing today… is not that they have a lack of experimentation in the demo aspect. The challenge is they don't know how to take those demos to production, and that is where I saw the gap.” - Shub Agarwal “I do think data is the fuel for AI… But I think today organizations are crippled by this ‘fix your data, and then we'll build AI', and they never build AI. They never build use cases that are adding value.” - Shub Agarwal “There's no FICO scores for models, so I decided to create one. I built this lab… bringing the computer scientists, the researchers, the applied AI researchers, the policy, and the communication people together to think of what is trust, define it, and ultimately measure and evaluate it.” - Shub Agarwal Mentions USC AI Trust Hub Successful AI Product Creation: A Nine-Step Framework by Shub Agarwal Four Steps to Epiphany: Successful Strategies for Products That Win by Steve Blank Masters of Scale podcast with Reid Hoffman Guest Bios  Shub Agarwal is an associate professor of professional practice at the University of Southern California, an industry executive, and an advisor to start-ups and academic institutions. He holds an MBA from the University of California, Los Angeles (UCLA), and an MS from Carnegie Mellon University (CMU). He is the author of two books: Solve Catch-22 of Product Management and Successful AI Product Creation: A 9-Step Framework. He has made significant contributions to the fields of artificial intelligence and machine learning, holding several U.S. and global patents for his work, and is also a published author of several technical research papers. With around two decades of extensive experience in product management and leadership, his journey has been marked by a relentless pursuit of leveraging AI technologies to create impactful products that redefine industry standards. His industry experience includes leadership roles at Amazon, Silicon Valley start-ups, and other Fortune 50 firms. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical definition and requirements for navigating Enterprise AI. You’ll learn how to distinguish between consumer-grade tools and the strict standards required in regulated industries. You’ll discover the twenty essential pillars for building a secure and compliant AI strategy for your organization. You’ll understand why rigorous vendor scrutiny matters as much for software as it does for human talent. You’ll gain clarity on the governance frameworks necessary to prevent data leaks and legal vulnerabilities in your enterprise. 00:00 – Introduction 03:15 – Defining Enterprise AI vs. SMB AI 07:45 – The role of Microsoft Copilot in regulated environments 12:20 – The 20 components of Enterprise AI readiness 18:10 – Challenges in organizational adoption and change management 22:30 – Security and data privacy as the foundation 27:00 – Call to action Watch this episode to master the complex landscape of regulated AI and safeguard your company’s future. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-enterprise-ai-101.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, we are talking about Enterprise AI 101. I am in the midst of a series in the Trust Insights newsletter, which you can get at TrustInsights.ai/newsletter. Part one was last week on seven different aspects of enterprise AI. But Katie, you said it would probably be helpful to level set what enterprise AI is and how it differs from SMB AI, mid-market AI, consumer AI, and so on. Katie Robbert: It is interesting because I feel like every time we jump on to record a podcast, there is a whole new set of vocabulary that I need to get caught up with. We need to make sure that everyone else knows what we are talking about because there is nothing worse than listening to a podcast or reading an article and having no idea what the author is talking about because they are introducing a concept but not really explaining it. I wanted to take this episode to talk about what enterprise AI is. Since you and I have not defined it, I am going to take my best guess at what enterprise AI is using some logic and deduction. I could be wrong, and that is why I think it is worth covering. From my perspective, if I had to put a definition to it, I am assuming enterprise AI is the type of AI implementation that occurs at an enterprise-size company. That sounds overly simplistic, but the bigger the organization, the more red tape, the more politics, the more departments, the more stakeholders, and the more governance there is. There are a lot more complications versus a small business like we are, where we can just decide one day, “Hey, I am going to start using this tool.” There are no real hurdles to go through. Then you have those mid-sized companies where you start to introduce some of those hurdles. You might need to work with your IT team to make sure that everything is in compliance. You might need to make sure that you have a place to host these new pieces of software, and that is not something that the marketing team is necessarily responsible for. Then you get to the enterprise-size companies where everything is completely siloed. Even in the best enterprise-sized companies, you are going to run into these silos. Because no one person is responsible for everything, you typically have multiple CEOs. Depending on what part of the country you are in, you might have a board for every different division of the company. If you are a Procter & Gamble and you have hundreds of product lines underneath, each of those is their own individual business. Each of those businesses are not necessarily talking to each other or sharing resources. That is my logical guess at what enterprise AI is. Christopher S. Penn: That is what I started with until I started doing the research into it. I realized that is not what it is. The generally accepted definition is AI within any commercially regulated entity. I realized as I was going through the research that commercially regulated means you have external regulation imposed on the company. It might be a 50-person company, but if they work in HIPAA or FINRA, they have to behave in highly regulated ways. Whether you are publicly traded or, for example, colleges that have to adhere to FFIEC rules and FERPA rules, enterprise AI is about operating AI—whether classical or generative—in a commercially regulated environment where you have externally mandated requirements that you must meet. Your definition for small business stuff makes total sense in that environment because Trust Insights is not a regulated company. However, when we work with our healthcare clients, we have to behave as though we are an enterprise company because we have to conform to their requirements. Katie Robbert: I am glad we are talking about this because the terminology is confusing; when you think of an enterprise company, you are not thinking of a commercially regulated company. I have to wonder why it is not called commercially regulated AI versus non-commercially regulated AI. It is a mouthful and a little bit harder to remember, but it is more descriptive and more accurate. I think like me, a lot of people are going to get confused about what enterprise AI actually is. Christopher S. Penn: A lot of this is because our background is in marketing, so we use the term enterprise to just mean a big company. If we want to market to enterprise companies, we are not marketing to a 50-person firm; we are marketing to a 50,000-person firm. In a lot of CRM software, the dividing line is typically 10,000 employees or 100 million in revenue. This is especially relevant because you see a lot of AI companies like Anthropic and OpenAI in a fight with Microsoft to try and gain a foothold into those enterprises. Microsoft, with their Copilot offering, has dominance by the very fact that their legacy Office 365 stuff is approved in those regulated environments. Katie Robbert: It is ironic because we spent so much time admittedly dismissing Microsoft’s Copilot as the less than version of generative AI, and now Microsoft is getting the last laugh on everyone. They are saying, “You have to use me because I have already been approved by IT and governance, and good luck.” You are stuck with whatever I decide to give you. If I were Microsoft, I would be petty and say, “You guys spent way too much time dismissing me and calling me inferior, so too bad.” Christopher S. Penn: A lot of that, as we have talked about many times on stage, is that the reason Copilot has fewer capabilities than other systems is specifically because of the regulated environment. It is trivial for Google to foist something on consumers and say, “Now we are going to read all your Gmail.” That does not fly in a regulated industry. Katie Robbert: That understanding is really helpful to the people who are saddled with Microsoft Copilot because we hear complaints about why they cannot use other shiny objects. If you are in a 50,000-person company and you weren’t there when the regulatory standards were decided upon, you are sitting there wondering why you cannot use Gemini to generate ad headlines. Then you do it on the side and get in trouble because there is no clear documentation saying why you have to use Copilot and nothing else. What we are hearing is that employees in companies required to use Microsoft Copilot are using other models on the side. That information is still getting filtered into the organization, and it is a huge governance problem. Christopher S. Penn: Completely. In enterprise AI, there are 20 different components to being ready. I derived this from the US federal government's NIST AI regulations and the EU AI Act, which is the gold standard. Katie Robbert: I want to see if you can get all 20. Christopher S. Penn: One, Strategy and Operating Model; two, Governance Policy and the AI Council; three, Legal, Regulatory, and Compliance. Katie Robbert: Are you reading this off a screen? Christopher S. Penn: I am 100% reading this off the Trust Insights Enterprise AI Landscape Field Handbook. Katie Robbert: Fine, continue. Christopher S. Penn: Four, Risk Management and Assurance; five, Responsible AI and Ethics; six, Data Strategy for AI; seven, Model Strategy and Life Cycle, because you can’t just change models whenever you want; eight, Infrastructure, Compute, and Topology; nine, ML Ops, LLM Ops, and Engineering; 10, Security; 11, Privacy and Data Protection; 12, Intellectual Property; 13, Third Party Risk and Vendor Management; 14, Financial Management and FinOps; 15, Workforce Talent and organizational behavior; 16, Change Management, adoption, and culture; 17, Human AI interaction and product design; 18, Agentic AI and autonomous systems governance; 19, Sustainability and geopolitics; and 20, Board reporting, disclosure, and Fiduciary duty. Katie Robbert: I just heard a whole lot of new job opportunities listed. So, if someone were working in a regulated industry like pharma, these are the 20 things they would need to be aware of before evaluating generative AI. It is interesting that organizational behavior and change management are part of it. You would think the regulations would be more technical versus human, but I am surprised that is part of it. Christopher S. Penn: It makes sense because in order for any AI to succeed in an enterprise with 50,000 or 300,000 employees, you have to prioritize change management. Organizational behavior cannot be an add-on; they have to be baked into what you do from the beginning, otherwise your initiative is going nowhere. Katie Robbert: I don’t disagree, but the typical way that works in a large organization is top-down. They make a decision, and you walk in the next day to find it has automatically updated your computer settings. Now you can no longer use a web browser search; you have to use Microsoft Copilot. That is their version of change management, but it is really just a dictatorship from above. I am interested in future episodes to explore what that should look like in a regulatory environment. Christopher S. Penn: We have known for two years that adoption is the hardest part. Deployment is easy compared to adoption. You can put Copilot on someone's desk, but they may not use it even if you tell them they have to. It comes back to how you get them to see the benefits. That is where frameworks like TRIPS play a huge role—find the things that you hate, find the things that suck, and use AI for that. Get that one thing off your plate. Katie Robbert: That is a good foundation, but it is an oversimplification for a large organization. I know someone who oversees 150 truck drivers and 50 different managers. The layers are so deep. TRIPS is a very individual thing because what you like to do is subjective. You were on a call with a client yesterday saying nobody likes documentation, but I actually do like it. My scoring would look different than yours. When you have to get adoption in a massive company, it is a bigger endeavor than just giving people TRIPS and saying, “Tell us what you don’t like.” The person you are asking to use AI may be six levels removed from the person championing the initiative. Christopher S. Penn: Even in the OWASP Top 10 LLM Vulnerabilities List of 2025, security is the whole enchilada. Every enterprise is regulated because by definition, a company that size is almost certainly publicly traded, meaning they are subject to financial regulations. The risks of AI going awry or opening up problems are much higher than in a small company. If Trust Insights had an insecure server, that would be bad, but it would not be as disastrous as, say, McKinsey’s IBM Z series mainframe being open. Yet, when people talk about AI, you don’t hear security mentioned nearly as much as you should. Katie Robbert: It is true. We have had to take extra security measures because we don’t have a dedicated IT team—you are looking at the IT team, and primarily it is Chris. We don’t have any wiggle room to set things up haphazardly. We have to do it right from the start. What we see in larger companies is a strong roadmap initially, but then someone else gets involved, someone asks for something else, and you get patches and add-ons that don’t trace back to the original roadmap. By the end, you are wondering what the original goal was. The bigger the organization gets, the harder it is to maintain control. It becomes a snowball effect. Christopher S. Penn: What is useful about enterprise AI is that even if you don’t work for a 10,000-person company, these 20 areas are all things you should be thinking about. Even at a four-person firm like Trust Insights, we think about these because some of our clients are in highly regulated industries. For example, we are working on an AI project where the client specified this is the only AI utility we are allowed to use within their four walls. Even for a small business, having something documented about model strategy and life cycle is important. As of the day we are recording this, Google Gemini 3.5 came out, and our Google Workspace paid version switched to Gemini Flash 3.5. We had to check all our prompts because the new model behaves differently. Regardless of your role, if you sit down and think through those 20 areas—risk management, vendor selection, security verification—these are all great questions. Katie Robbert: There is a good starting place for this. You can find our downloads at TrustInsights.ai/StrategicToolkit. There is also a free version at TrustInsights.ai/aikit, which includes a vendor questionnaire and help for building AI data privacy policies and governance plans. We have already templated these things out. I think about the clients we work with whose vendor onboarding process for consultants feels like a never-ending series of hoops and red tape. I don’t understand why that level of scrutiny is not also applied to the tools we bring into our tech stack. We are renting space in those tools and freely giving them our data. Those companies now have our data and will use it for their own benefit. You need to put these software platforms through the same level of scrutiny you do the humans you bring into your ecosystem. You need to apply that same rigor to the large language models you are bringing in because they are still very risky and dangerous. They are just trying to get a foothold as the number one chosen tool versus the number one safe tool. Christopher S. Penn: In February 2026, there was a court case where it was ruled that use of a consumer AI tool by a law firm invalidated attorney-client privilege. The judge ruled that this is no longer privileged information. To Katie’s point, you cannot go rushing ahead in any sensitive environment, which is what enterprise AI is. You have to be doing your homework. If you have thoughts on how you approach enterprise AI, pop on by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,700 marketers are asking and answering questions every day. Wherever you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/tipodcast. Thanks for tuning in; we will talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Our services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as a CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you are a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

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.

Enterprise AI is entering a new phase, one defined less by experimentation and more by measurable business value. In this summit panel episode of Technovation, Steven Norton speaks with Richard Jardim of CIBC, Sears Merritt of MassMutual, and Shekar Pannala of The Hartford about what it really takes to scale AI responsibly inside large, highly regulated enterprises. The discussion explores how leading organizations are rethinking software development, governance, operating models, and financial discipline as AI capabilities rapidly evolve. Key topics include: Moving from AI pilots to measurable P&L impact Reducing feature lead times through AI-enabled development Modernizing legacy systems with GenAI Building governance-by-design operating models Managing the shift to consumption-based AI economics

SUMMARY: The biggest enterprise AI question may no longer beWhich model is smartest? Instead, which organization can most effectively operationalize, govern, and economically scale AI agents across the business?'SHOW: 1030SHOW TRANSCRIPT: The Enterprise AI Show #1030 TranscriptSHOW VIDEO: https://youtu.be/acOBfRI0P3USHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:Opening Thesis - Was the first wave of AI adoption artificially cheap? - The industry may be transitioning from subsidized growth to usage-based economics. Key Topics 1. Evidence AI Was Subsidized Massive CAPEX vs low end-user pricing Generous enterprise bundles Frontier model access for $20/month 2. The Hidden Economics of AI Agents - Agents consume exponentially more inference Tool orchestration, retries, memory, verification 3. Why Frontier Labs Are Shifting Focus From benchmark supremacy to orchestration Governance, memory, connectors, MCP, workflows 4. Forecasting AI Pricing 12 Months:  Commodity inference gets cheaper - Frontier reasoning remains premium 24 Months:  AI billing resembles AWS-style infrastructure billing Runtime, memory, latency and orchestration become billable 36 Months: Outcome-based pricing emerges AI spending shifts from IT budgets to labor budgets Final Takeaways  Commodity AI becomes utility-priced Frontier reasoning becomes premium Agents reshape enterprise economicsKey Conclusions1. AI probably was subsidizedThe economics strongly suggest adoption-first pricing.2. The subsidy era may be endingPremium tiers and metered pricing are emerging.3. AI agents fundamentally alter economicsUsage scales exponentially with autonomy.4. Commodity AI and frontier reasoning are separatingOne becomes cheap.One becomes premium.5. The real battle is moving upward in the stackThe future moat may be:orchestrationgovernanceworkflowsenterprise contextoperational toolingFinal Closing Thought“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?'”FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Google dropped like 197 new AI features this week.

The reason enterprise AI programmes stall is not the technology — it is the sequence in which decisions are made before and after the pilot succeeds. In this episode, Ronny Fehling, Chief AI Transformation Officer at HTEC, examines why AI initiatives lose momentum at the production threshold and what organisational conditions determine whether they make it through. The discussion covers production slices, decision gates with kill-switch authority, use case discipline, and why top-down AI mandates tend to reproduce the same failure modes regardless of budget. This episode is sponsored by HTEC. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

Want better AI results? The answer isn't to prompt better. (At least, not anymore.) As AI has changed drastically, so too must your company's strategy and implementation plan. Section's Bobby Isaacson joins Everyday AI to lay out the roadmap: helping guide organizations from running like hamsters on the AI treadmill to actually redefining workflows toward agentic automation. How Smart Teams Stopped Prompting AI and Started Automating Workflows -- An Everyday AI chat with Jordan Wilson and Section's Bobby IsaacsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:AI Success Metrics in Modern OrganizationsShift from Prompting to Workflow AutomationAdopting Autonomous Agents in EnterprisesImportance of Context Engineering with AITop-Down Leadership for AI TransformationBuilding AI Manifestos vs. AI PoliciesTraining Challenges: Chatbots to Agents EraClosing Skill Gaps: Elite vs. General AI UsersOvercoming Change Management in AI AdoptionFostering Curiosity and Experimentation in AI TeamsTimestamps:00:00 Defining AI success metrics05:19 The shift to context engineering08:16 Leadership setting AI adoption example11:58 CEO commitment to AI integration14:17 Setting clear AI goals19:06 Discussing automation and its challenges20:48 Enterprise AI usage challenges26:00 Leveraging AI for learning27:45 Closing and contact informationKeywords: AI workflow automation, autonomous agents, automating workflows, AI agents, enterprise AI transformation, custom GPTs, prompting AI, context engineering, AI proficiency, AI training, leadership in AI adoption, change management, AI strategy, employee enablement, AI integration, top-down AI approach, organization-wide AI, business process automation, AI-powered briefs, sales automation with AI, agent-based automation, workflow optimization, context sharing, repeatable AI workflows, AI change management strategy, AI use cases, AI in finance, ROI calculator with AI, personalized AI training, skill gap in AI, scaling AI solutions, technology adoption, digital transformation, AI challenges in enterprises, curiosity-driven learning, creative freedom in AI, AI enablement, AI policy vs manifesto, fostering innovation with AI, learning and development AI, AI champions, process innovationSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

The spray-and-pray approach to AI investing is dead. Join us as we discuss what it takes to stand out in a saturated market as we move beyond the hype to identify real moats and what's next for investors in this space. This episode was taped before a live audience at Web Summit Vancouver. We meet: Andy McLoughlin is a seed-stage investor at Uncork Capital focused on B2B software, developer tools, and applied AI — and before VC, he co-founded Huddle, an enterprise collaboration platform that was acquired in 2016. George Mathew is a self-described "deep operator turned venture capitalist" at Insight Partners, with 20+ years building companies including as CEO of Kespry and President & COO of Alteryx, which he scaled through its IPO. Emily Fontaine leads IBM's $500 million Enterprise AI fund and quantum investing strategy — she's spent 15 years at IBM, previously serving as Executive Advocate to IBM's Chairman and CEO, and as AI Federal Leader for IBM Consulting. Credits:This episode of SHIFT was produced by Jennifer Strong with help from Emma Cillekens. It was mixed by Garret Lang, with original music from him and Jacob Gorski. Art by Meg Marco.

Will your software soon be a living organism with its own immune system?Animesh Koratana, founder of PlayerZero, started his software career long before he founded the company. Growing up in Atlanta, he spent his childhood inside his father's software business, watching engineers sitting through the unglamorous work of QA and keeping systems alive after launch. He saw early that writing software was only half the problem. Maintaining it was the real battle.Years later at Stanford, he witnessed the birth of GPT-2 and Codex, the very foundation of GitHub Copilot. While much of the world focused on how AI would help engineers write software faster, he became obsessed with a different question: What happens when companies are flooded with AI-generated code that no single engineer fully understands?With PlayerZero, Animesh is building toward what he calls self-healing software: systems that behave less like static machines and more like living organisms with their own immune systems.At the center of that vision are “Context Graphs” which captures the "institutional memory" of a company: the deep knowledge held by a senior engineer who has spent years understanding how complex software breaks, the failure modes it develops, and the decisions behind fixing it.If you are building software today and wondering how reliability, debugging, and ownership will work when machines write most of the code, this episode is for you.0:00 — Trailer0:45 — Building Self-Healing Code2:03 — First Exposure to LLMs Through GPT-23:45 — What Is PlayerZero?5:42 — Institutional Memory of a Senior Engineer7:10 — How Context Is Built10:06 — The Viral “Context Graph” Piece16:24 — The Outcome PlayerZero Delivers19:59 — When the Agent Tells the Human What to Do23:43 — Who Is PlayerZero Selling To?26:56 — Why Software Should Be Treated Like Biology28:54 — The PlayerZero Customer Pitch30:37 — Can Software Really Have an Immune System?35:15 — How Animesh Chose His Investors36:55 — What's Next for PlayerZero?-------------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

Boards are pushing CIOs to commit to AI strategies built on contracts written for an entirely different era of enterprise software. In this episode, John Belden, Chief of Research and Strategy at UpperEdge, breaks down the six dimensions of uncertainty CIOs now face when weighing major AI and ERP commitments, and explains why the next five years are about flexibility, not productivity. The conversation covers the case for tighter SI accountability around adaptability, the practical role of contractually-protected optionality, and the difference between performance theater and the kind of continuous learning that keeps a transformation honest. This episode is sponsored by UpperEdge. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

If you're making AI decisions, you have to understand where you're getting your intel from.

Campus hacks bring final exams to a standstill, a blockbuster study on AI in education gets pulled, and the world's biggest technology companies face government crackdowns with barely a dent to their bottom lines. Plus, Apple returns to Intel as chip wars reshape US tech! Anthropic and OpenAI IPO Chatter Canvas Breach Disrupts Schools & Colleges Nationwide The Canvas Hack Is a New Kind of Ransomware Debacle Influential study touting ChatGPT in education retracted over red flags - Ars Technica Anthropic Says It Has Eliminated Undesirable Behaviour Like Blackmail From Claude By Deeply Explaining To It Why It Was Wrong Tech is turning increasingly to religion in a quest to create ethical AI Intel's comeback story is even wilder than it seems Apple, Intel Have Reached Preliminary Chip-Making Agreement Meta challenges Ofcom in UK High Court over the Online Safety Act, which calculates levies based on global, not UK, revenue, in a case scheduled for October Meat Industry Price Fixer Sentenced to Make Money Chrome's Prompt API: A Unilateral Gamble That Is Fracturing Web Standards NHTSA says the 2026 Tesla Model Y is the first car model to pass the agency's new ADAS tests; Tesla conducted the tests and submitted the results to the NHTSA Here is Yarbo's promise to fix the robot mower that ran me over Social Media Sites Got Information from Ad Trackers on US State Health Insurance Sites Pinterest crosses $1 billion quarterly revenue as AI-powered visual search drives advertising growth that social platforms cannot match Cloudflare beat earnings, cut 1,100 jobs because AI agents do the work now, and lost a quarter of its stock price in a day Motherboard Sales 'Collapse' By More Than 25% - Slashdot The FCC Wants Your ID Before You Get a Phone Number Kids say they can beat age checks by drawing on a fake mustache FCC to allow banned drones and routers to receive critical updates until 2029 Host: Leo Laporte Guests: Berber Jin, Iain Thomson, and Paris Martineau 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: outsystems.com/twit joindeleteme.com/twit promo code TWIT bitwarden.com/twit ziprecruiter.com/twit meter.com/twit zscaler.com/security