Podcasts about enterprise ai

The model is not the expensive part of your AI stack. The missing context is. That is the argument at the centre of everything Glean announced this week, and I got into it with Emrecan Dogan, Chief Product Officer at Glean, on site at Glean:GO on The Ravit Show in San Francisco. Here is why it matters beyond one vendor's launch.When AI does not know how your company works, every task starts from zero. It hunts for files. It asks for background. A person stops what they are doing and feeds it the same context again. Glean's research puts that at 6.4 hours a week per worker, which is most of a working day spent supervising a tool that was supposed to save time.That is also why the productivity numbers keep disappointing executives. 75% of workers say AI makes them faster. Only 13% say their company is performing better because of it. Individual speed is not organizational output, and no amount of model upgrade closes that on its own.So the interesting question is not which model is smartest this quarter. It is what your AI already knows about your business before you ask it anything.Emrecan and I covered where that leaves enterprise buyers, what happens to AI spend as agents start doing multi step work, and how teams keep a growing pile of AI tools from turning into sprawl.#data #ai #glean #enterpriseai #ai #artificialintelligence #cio #agenticai #theravitshow

Plus: Mastercard and Alchemy are partnering on AI shopping tools. And Nokia partners with Microsoft to offer agentic support for telecommunications companies. Danny Lewis hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of HFS GCC Advantage, Ramkumar Narayanan, EVP, Enterprise AI and Data Platforms, and Head of FIS India and the Philippines, shares how FIS is turning its GCC into a strategic engine for AI, platform innovation, and business growth. The conversation explores how a mature GCC can move beyond execution to ownership, create enterprise value, and help shape the future of work in a regulated financial services environment. Ramkumar also explains why AI transformation and workforce resilience must advance together, and how India and the Philippines centers are becoming central to FIS's long-term growth strategy.What you'll hear:How FIS serves the banking, payments, and capital markets ecosystemWhy India and the Philippines are strategic GCC centers for FISHow the organization is shifting from application-centric work to platform-centric AIWhy reusable AI foundations help reduce rework and duplicationHow governance is being embedded into code for scale and complianceWhy AI investments must begin with real business problemsHow GCCs can move from execution to enterprise outcome ownershipWhy workforce resilience must advance alongside AI transformationHow GCCs can unlock workflow intelligence to support AI redesignWhat leaders should do to stay aligned with the shifting company strategyKey takeaways:GCCs create more value when they own outcomes, not just tasksPlatform-first AI is more scalable than one-off experimentsGovernance built into code helps regulated enterprises move faster with confidenceAI transformation and workforce transformation must happen togetherGCCs hold unique workflow knowledge that can shape enterprise AIMature GCCs should align closely with the CEO and company prioritiesTo know more about HFS on Global Capability Centers (GCCs), visit us here: https://www.hfsresearch.com/global-capability-centers-gccs/ We recently launched our GCC Pulse Report, “Ownership is the next imperative for India's GCCs to become generative enterprises.” Read more here: https://www.hfsresearch.com/research/indias-gcc-pulse-index/

Why do businesses replace the AI model when the failure may have started somewhere else entirely? In this episode of Tech Talks Daily, I speak with Richard Shaw, Technology General Manager for Databricks in the UK and Ireland. Richard leads the field engineering organization that works closely with customers on data and AI problems, giving him a practical view of what happens when promising agentic AI projects meet production workloads. Richard argues that the model often receives the blame because it is the most visible part of the system. The actual fault may come from stale data, missing business context, inconsistent permissions, an unsuccessful tool call, or another point in the workflow. Replacing the model before tracing the request from start to finish can recreate the same problem in a new place. This is why lineage, end-to-end tracing, and continuous evaluation matter once an agent moves beyond a controlled pilot. We discuss what a production-readiness rehearsal should include. Richard recommends realistic data, realistic user volumes, unauthorized requests, ambiguous questions, failed tool calls, and tests of what the agent should refuse to do. Teams also need agreed standards for quality, security, cost, and auditability, along with a clear decision about which actions an agent can complete independently and where a person must review or approve the result. The conversation also looks at model choice and infrastructure cost. Richard believes the strongest test is performance on the organization's actual work rather than a benchmark leaderboard. A frontier model may suit complex reasoning, while a smaller or open-weight model may perform routine extraction or classification at a lower cost. Access policies, observability, and spend controls need to remain consistent as those model choices change. Conversational analytics creates another governance challenge. Databricks customers such as Virgin Atlantic and Repsol are using natural-language tools to make company data easier for employees to question. Richard says wider access should preserve existing permissions, ownership, definitions, and lineage. An answer becomes far more useful when the user can see where it came from and which team owns the information behind it. We also cover the boundary between historical analytical data and fast operational workloads. Richard describes how Databricks positions the lakehouse for broad enterprise context and Lakebase for immediate reads and writes, such as updating an account, placing an order, or storing agent memory, while keeping both connected to a common data and governance base. Are companies ready to trace and test the whole AI workflow, or are too many treating the model as both the hero and the culprit? Listen to the episode and share your thoughts with me.  

Supply chain performance is still being measured by outdated assumptions. Lora Cecere argues that optimizing individual functions for lower cost does not necessarily improve the business. In fact, it can throw the entire supply chain out of balance. Better performance starts with understanding what is actually feasible within an industry, focusing on the metrics that drive value, and recognizing that supply chains are complex systems that cannot be improved one function at a time. In this episode of Supply Chain Now, Scott Luton speaks with Lora Cecere, founder of Supply Chain Insights, and Wael Abdelmalek, CEO at Uthereal, about the research behind Supply Chains to Admire and how that work is being brought into Ask Lora, an AI-enabled benchmarking and advisory platform. They explore why traditional supply chain rankings and generic AI tools can reinforce conventional thinking, how verified research and industry-specific data can create better benchmarks, and why leaders need a more balanced view of growth, operating margin, inventory turns, and return on capital employed when evaluating performance. Lora and Wael also explain how Ask Lora combines 10 years of research and benchmarking data with a private large language model designed specifically for supply chain decision-making. Rather than serving only as a Q&A tool, users are applying it as a trusted peer to benchmark performance, review strategy documents, pressure-test presentations, compare organizational and technology maturity, and challenge assumptions before taking ideas to leadership.Jump into the conversation:(00:00) Introduction(01:14) Meet Lora Cecere, founder of Supply Chain Insights, and Wael Abdelmalek, CEO at Uthereal(04:36) Fearless Friday: career leaps and personal risk-taking stories(08:04) The origin of Supply Chains to Admire and why "best practices" is a myth(14:44) What supply chain leaders still get wrong about cost and performance(19:22) What is Ask Lora? Inside the private LLM built on 10 years of research(21:30) Private LLMs vs. generic AI: accuracy, guardrails, and the Ethereal partnership(28:57) How organizations are using Ask Lora: benchmarking, strategy review, and reports(43:57) Pricing tiers, governance, and integration for Ask Lora(50:33) Vision for Ask Lora's future and how to connect with Lora and WaelAdditional Links & Resources:Connect with Lora Cecere: https://www.linkedin.com/in/loracecere/Connect with Wael Abdelmalek: https://www.linkedin.com/in/wwael/ Learn more about Supply Chain Insights: https://supplychaininsights.com/ Learn more about Uthereal: https://uthereal.ai/ Check out AskLora for yourself: https://asklora.ai/Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ Learn more about Blue Yonder Cognitive Solutions: http://blueyonder.com/cognitiveWEBINAR- Operational AI in the Supply Chain: How context empowers agents and humans to operate side by side: https://bit.ly/4x7Vd2ZWEBINAR- Demand Volatility Isn't a Forecasting Problem: How One FMCG Distributor Released Capacity Without Capital: https://bit.ly/4r94YM6WEBINAR- You Can't Manage What You Can't See: Using Visibility, KPIs, and AI to Optimize Logistics Operations: https://bit.ly/4ql6iemWEBINAR- The Real ROI of AI Analytics in the Supply Chain: Better Answers When Everyone Starts Asking: https://bit.ly/3TpCX6u This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/what-have-we-learned-thus-far-asklaura-ai-1635 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.

Most AI playbooks are built for a workforce tethered to a desk, but many workers are not near a keyboard. In this episode, Beth Miles, Chief AI Officer at Advantage Solutions, breaks down how she's building an AI-ready culture across a distributed workforce of more than 60,000 teammates. She shares why AI literacy has to start with leadership, why redesigning a broken process matters more than simply automating it, and how she measures AI success well beyond adoption metrics.Key Moments:From Chief of Staff to Chief AI Officer (05:00): Beth explains how her chief of staff role gave her the cross-functional view needed to build Advantage's AI office.Hands-On Keyboards: AI Training in the C-Suite (11:00): Beth breaks down 10+ hours of executive AI training, including a CEO who sat down for a coding workshop.Inside the AI Champions Build Summit (22:00): Over 40 teammates spent five hours building AI use cases together, breaking down silos across functions.Why You Shouldn't Optimize a Bad Process (26:00): Redesigning broken processes before automating them matters more than speed.Adoption Is Step One, Not the Finish Line (31:00): Usage is only step one. Beth points to service excellence and time to complete as the metrics that matter.Key Quotes:“AI can be an accelerant, but you have to have all the mechanics and the process and the data to actually make it run really well.” - Beth Miles“It may require moving slower now to move faster later, to actually redesign and rethink work so you're not optimizing a bad process.” - Beth Miles“ To lead a change, we need to understand it.” - Beth MilesMentions:AI Transformation Requires Redesigning Work, Not Cutting RolesEast of Eden by John SteinbeckShoe Dog by Phil KnightGuest Bio:Beth Miles leads Advantage Solutions' AI strategy, enablement, and governance to drive value for the company's clients and teammates. She drives AI deployment across the enterprise, alongside the Data Office and Growth & Strategy Office. Beth joined Advantage in early 2024 and has served the last year as Chief of Staff, leading the company's AI workstreams from diagnostic through scaled execution, driving enterprise transformation initiatives, and overseeing CEO Office operations.Before Advantage, Beth spent more than a decade leading technology and corporate transformation at global software firms, including senior roles at Sage and Cerner (now Oracle Health). At Cerner, she drove the redesign of Cerner's portfolio management process for their $400m R&D investment. At Sage, she led GTM execution across new revenue streams and managed a large cloud partnership, leading cross-functional teams across governance and delivery. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

What did I say? What did I mean? Do you understand me? We ask each other those three questions constantly without noticing. Now we ask them of machines — and this episode is about what happens in the gap. Host Bobby Brill pulls together conversations with the ServiceNow linguists, research engineers, and AI governance leads working on voice, language, and chat, including a real recording of a voice agent failing in real time. Same words. Different meaning. And a machine in the middle trying to work out which one you intended. In this episode:Why a single English word carries no meaning without context, and what that does to translationHow a name that isn't in the training data breaks the first model in the chainThe three-model cascade behind every voice agent, and what gets lost at each handoffA voice agent that hears a confirmation code correctly three times and rejects it three timesWhy formality — tu or vous — is a fluency problem, not a grammar problemWho decides what's acceptable for an AI to say when the same word is fine in London and not in Chicago. Check out our past episodes on voice and voice AI: https://youtu.be/cGXCnABXKow https://youtu.be/yxoHmZj5gOk https://youtu.be/x7Ks932T18oGuests: Lyena Solomon, Director of Globalization and Accessibility Midam Kim, Machine Learning EngineerTara Bogavelli, Research EngineerKatrina Stankovic, Staff Machine Learning EngineerGabrielle Gauthier-Melançon, Staff Applied Research Scientist Louis-Philippe Morin, AI Governance Product Manager CHAPTERS 0:00 "Half ten" — same words, an hour apart 2:04 What did I say: the word "order" 3:19 The name a machine can't hear 5:23 Three models handing meaning to each other 8:19 Naming conventions 10:04 Listen: a voice agent fails 14:47 Tu or vous: talking to you correctly 16:39 Who decides what's acceptable 18:49 Teaching it the way you'd teach a kid 20:15 Why any of this matters For more information, see: ServiceNow Training and Certification: http://www.servicenow.com/services/training-and-certification.html ServiceNow Community: https://community.servicenow.com/community ServiceNow Insights Podcast: https://www.youtube.com/playlist?list=PLCOmiTb5WX3qvGq7Cp3o2KkCiplJyqQOK For general information about ServiceNow, visit: http://www.servicenow.com/ #ServiceNow #VoiceAI #AI #ConversationalAI #Localization #AIGovernanceSee omnystudio.com/listener for privacy information.

We don't want the industry to fall apart, that's why we started this company.6 months ago, everyone was talking about model deployment. Now everyone has moved on to agentic AI deployment and autonomous agent swarms. But all of this is running on an infrastructure that itself is not automated.And this underlying infrastructure has massive GPU cluster systems. If even one GPU fails the whole training workload fails. You can't have 100,000 employees managing 100,000 GPUs. But LLMs are not the right solution for managing the infrastructure.Kandan and Randolph have spent decades operating large-scale telecom and cloud infrastructure, in a world where pagers used to run lives of people managing it. With MantisGrid they have come together to build 'The' model for AI infrastructure.Watch the episode to understand why making AI infrastructure autonomous is so important, why LLMs are not the answer, and what it takes to build the systems that will keep millions of agents running.00:00 — Invisible Infrastructure Behind AI01:05 — The Pager Nightmare04:01 — The GPU Problem Nobody's Solving05:38 — The Cloud Infrastructure Story07:53 — Why Just Watching Isn't Enough09:35 — Building the Waymo of AI Infrastructure15:00 — How an Autonomous AI Infra System Works20:35 — What Happens to DevOps?22:46 — Why LLMs Shouldn't Run Production29:14 — The Path to a Billion Dollars32:06 — The Problem Hyperscalers Can't Solve37:03 — Earning Trust to Run Infrastructure39:55 — When Agents Start Managing Agents-------------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

Brian and Aaron interview Gavriel Cohen, co-founder and CEO of Nanoco and creator of the open-source agent framework NanoClaw, about securing AI agents in enterprise environments. Cohen shares how he built NanoClaw after discovering major security and safety gaps while using agents for an AI native marketing agency, and how the project grew to over 30,000 GitHub stars and over half a million downloads. They discuss why Fortune 500s, financial institutions, universities, and government groups feel urgent pressure to adopt agents but are blocked by control, privacy, and security concerns. Cohen outlines a zero-trust approach using microVM isolation, no credentials inside agent environments, a policy-enforcing gateway with granular controls, audit logs, cost attribution, and human-in-the-loop approvals at key decision points.SHOW: 1062SHOW TRANSCRIPT: The Enterprise AI Show #1062 TranscriptSHOW VIDEO: https://youtu.be/h906EEQSRs8SHOW LINKS:NanoClaw on GitHubNanoCo homepageTechCrunch: The wild six weeks for NanoClaw's creator that led to a deal with DockerThe New Stack: Gavriel Cohen found his own code inside OpenClaw, so he walked awayOpenAI: The Hugging Face incident and the road aheadSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoNordLayer - Use ENTERPRISE10 for 10% offGUEST BIO:Gavriel Cohen is co-founder and CEO of NanoCo, and creator of NanoClaw, the open-source agent harness he built as a small, auditable, secure alternative to OpenClaw. He spent a decade as a developer and team lead at Wix before building NanoClaw in a weekend, a project that has since drawn a Docker integration and an outside security review. He holds a BSc in Physics and Computer Science from Tel Aviv University.FEEDBACK?Email: show @ the enterprise ai show dot comBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Key TopicsAI's impact on customer experienceEffective AI project strategiesAI governance and securityPrompt engineering for businessHigh ROI AI initiativesChapters00:00 Introduction to Lukas Hertig and his AI journey02:09 Lukas describes himself as a strong executor in AI04:00 Understanding customer readiness and signals06:07 Success rates of AI projects and key factors08:06 Approaching AI implementation: audits and impact analysis09:59 Building AI skills and connectors for enterprise use12:07 Security, governance, and compliance in AI projects13:55 Misconceptions about AI replacing humans16:11 Future of open-source and self-hosted AI models17:58 Key questions for AI project planning19:50 Using AI to improve sales and marketing workflows22:09 Real-world examples: call recording and data privacy23:56 Dictation and unique human insights with AI25:51 Questions for expanding AI in customer service27:55 Integrating AI with existing CRM and systems30:06 Data privacy, GDPR, and European regulations32:03 Avoiding pitfalls: data leaks and governance issues34:11 How to engage with Lukas Hertig and his team35:06 Key takeaways: focus on high ROI AI projects35:51 Closing remarks and future AI opportunities The AI for Sales Podcast is brought to you by BDR.ai, Nooks.ai, and ZoomInfo—the go-to-market intelligence platform that accelerates revenue growth.Skip the forms and website hunting—Chad will connect you directly with the right person at any of these companies.

Got a question or comment? Message us here!In this episode, we explore the hidden risks of enterprise AI and the strategies organizations can use to close security gaps before attackers exploit them.#EnterpriseAI #Cybersecurity #AIGovernance #SecurityLeadershipSupport the showWatch full episodes at youtube.com/@aliascybersecurity.Listen on Apple Podcasts, Spotify and anywhere you get your podcasts.

Context has become one of the busiest categories in AI. Every CIO now wants context Engine. Every legacy data catalog and knowledge graph platform has started calling itslef a "context platform”.  At the same time reports say that 60% of CEOs say they've got $0 benefit from AI.To understand the nuances, on this episode we have Prukalpa, Founder of Atlan. Atlan started solving this problem back in 2020, long before it became the hottest category. And today it counts 20% of the Fortune 500 as customers with an 85% competitive win rate.Prukalpa explains how context for agents requires knowledge, expertise, and norms, why Gartner's “context is king” made the category hot but also created confusion in the market, and why companies now treat context as strategic IP.00:00 Context Is King03:20 From Data To Agents05:00 Scaling To 200 Customers06:37 Product Market Fit Truths11:19 Landing The First Customers18:44 Data Teams Couldn't Trust Their Data21:07 Building the Enterprise Brain26:19 Problem With Horizontal Products30:21 Why CIOs Buy Context Now34:59 Positioning in a New Category36:33 Context as a Business Asset37:20 The Shared Enterprise Brain40:50 Shaping the Category41:22 What is AI Native?43:21 Self Improving Company46:50 Team Patterns of Frontier Companies48:42 Judgment and Taste Matter More Now50:04 Atlan AI Experiments50:59 Building Investor Network55:14 Waterbridge to Peak XV to Insight59:37 Don't Rush to Find PMF01:01:57 Everyone Must Build01:06:17 Real Bottleneck to Scale Isn't Sales-------------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

For years, the localization industry has told a story about speed, cost and quality. But as AI transforms how content is created and translated, that narrative and those metrics alone are no longer enough to define the value of language expertise. In our latest Cultural Intelligence Podcast episode, Maria Schnell, Chief Language Officer at RWS, explores why this moment is unlike any other in the history of language expertise. While many see AI as a replacement for translation, Maria argues that this view fundamentally misunderstands what language professionals actually do. Translation isn't the exchange of words. It's the transfer of meaning, intent, and context from one audience to another. The real value of language expertise lies in understanding culture, nuance, and human communication; capabilities that become even more important as AI becomes more powerful. This episode challenges the industry's long-standing narrative and asks a more fundamental question: what is the true value of language expertise in an AI-driven world? And as AI takes on more of the execution, how can human expertise evolve to guide the systems that increasingly shape global communication?

Very few people in the world can say banks trust them with their core. Arun Jain is one of them. He is the man behind Intellect Design Arena, the force who helped put India on the global fintech map, and one of the most influential product minds this country has ever produced. Founders study his playbook. Banking leaders across continents run on his technology. And an entire generation of Indian entrepreneurs builds on the path he cleared decades ago. I sat down with him at Intellect's Chennai headquarters. The full conversation is now LIVE on The Ravit Show.In this conversation, he breaks down:- Why most enterprise AI stays stuck in pilots, and the operating model shift that fixes it- Business Impact AI, measured in outcomes, not demos- How Purple Fabric embeds AI into the core of regulated banking, not on top of it- The thinking behind eMACH.ai and why composable architecture is no longer optional- Why India's next decade belongs to product builders, not service providers- The leadership mistakes that changed him, and what success means to him nowThere are very few people who have seen every technology cycle in banking and stayed ahead of all of them. Arun Jain is one of them.This one is for the builders.#data #ai #purplefabric #intellectdesignarena #theravitshow

Plus: French startup Mistral AI tops $24 billion valuation in latest funding round. And Boston Scientific issues profit warning after cyberattack. Imani Moise hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

“You have to take crazy risks” Nikhil Gupta got fired at 42, made his first VC pitch in 2000, got the idea for ArmorCode while buying toilet paper during the pandemic and received his first VC cheque in 2020.Today ArmorCode has raised $81M, grown 100% year-over-year, and counts some of the world's largest enterprises as customers.Nikhil Gupta, Founder & CEO of ArmorCode, shares why he studied 7,000 security companies before starting up, why 3 early customers can be the "kiss of death" for startups, why customers don't always tell founders the truth, why developers hate security tools and why security is the ultimate bottleneck to AI's growth.This conversation is as much about enterprise security as it is about a founder's conviction and refusing to give up on a dream.-------------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

Brian, Brandon, and Aaron discuss enterprise AI adoption using the sales analogy of whether AI is a “vitamin” or a “painkiller,” arguing that successful transformation still requires a burning-platform event. Brandon suggests AI adoption resembles past digital transformations: without urgent pressure (e.g., a data center closing), organizations resist change and justify existing processes. Brian describes a compressed hype cycle from ChatGPT excitement to pilots and guardrails, followed by difficulties with data, cost-effective scaling, and making AI behave deterministically, while fear of competitors keeps efforts alive. They add a third category, “Whippets”, short-term, resume-driven initiatives led by leaders who leave others “holding the bag.” They debate examples like Sheetz' multiple VMs and argue that AI's promise is personal productivity, but note a lack of enterprise collaboration and shared-memory tools that limit organizational impact.SHOW: 1060SHOW TRANSCRIPT: The Enterprise AI Show #1060 TranscriptSHOW VIDEO: https://youtu.be/XZqomQosv1wSHOW LINKS:The Enterprise AI ShowSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoNordLayer - Use ENTERPRISE10 for 10% off.The thesis: Successful digital transformation usually has a forcing function; you migrate the data center because the real estate got sold, not because someone promised abstract savings. Deadline + shared incentive = people actually change. AI adoption mostly lacks that: no one's forcing the migration, so it defaults to "give everyone Copilot licenses and hope."Core question: If your business is healthy and there's no burning platform, how do you adopt AI in a way that's more than expensive theater, without a crisis to manufacture urgency?Discussion topics:Forcing functions vs. vibes: What are the AI-era equivalents of "the real estate got sold"? A support team that's actually understaffed, a process with a real bottleneck, a cost center leadership is already scrutinizing vs. a mandate to "use more AI."The unknown-unknowns problem: Most orgs don't know which of their workflows AI would actually help vs. where it's a novelty. How do you go find that out cheaply, without a company-wide token-burning experiment as the discovery mechanism?Bottom-up signal vs. top-down mandate: Does real usage data (who's actually using tools, for what) surface better targets than an executive committee guessing at use cases?Contrast with the failed "abstract savings" migration: What does an AI initiative look like when it's tied to a concrete, already-painful problem instead of a general efficiency narrative?The FOMO trap: Distinguishing "we don't want to miss the platform shift" (legitimate) from "we need AI headlines for the board" (theater), and how leadership can tell which one they're actually doing.Measurement: If there's no forcing function, what replaces the natural deadline/incentive alignment as the way you know it's working, or that it's time to kill it?Final ThoughtIs the right move small, cheap, bounded bets against known pain points, treating AI adoption like a search problem, not a rollout, rather than a company-wide transformation initiative looking for a reason to exist?FEEDBACK?Email: show @ the enterprise ai show dot comBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

How do you fix a $5 Trillion Industry which wastes $500 Billion on mouse clicks every year?By not considering even a $100 Billion company a success. By hiring people who are heat-seeking missiles for pain. By questioning every default. By solving hard problems, then choosing even harder ones. By moving with an urgency that surprises everyone but you.In this episode, Tanay Tandon, Co-Founder & CEO, Commure explains why US healthcare is a massive but inefficient market, which pays ~$500B in administrative “work tax,” and how language models and agentic software can automate much of that work, make healthcare feel like a Waymo, and give healthcare professionals more time to focus on actual care.Watch this episode to learn what it takes to build and win in a massive, inefficient market. 00:00 - Heat-Seeking Missiles for Pain01:30 - Speed Above Everything03:11 - The $5 Trillion Healthcare Problem03:56 - Epic Wasn't Built for AI06:13 - The $500 Billion Admin Leak07:24 - Why Healthcare Moves Slowly10:14 - Why Hospitals Are Hard to Sell To12:09 - The Real Opportunity Is Labour13:41 - Point Solution or Platform?15:16 - One Customer Pays $100 Million22:46 - More Bad Days Than Good Days25:18 - AI Changed the Healthcare Game28:40 - Incentives Matter More Than Technology30:24 - Healthcare's App Store Moment32:20 - Why $100 Billion Isn't Big Enough36:19 - Who Takes the Blame When AI Gets It Wrong?38:32 - The Waymo Moment for Healthcare39:46 - How the Best Startups Hire42:12 - Customer Obsession Beats Everything43:52 - Stop Paying the AI Experiment Tax51:10 - Insurance Companies Are Terrific Enemies55:34 - The Hidden Opportunities in Healthcare59:35 - How to Win AI Talent War-------------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

SaaStr 876: Shipping Enterprise AI Agents with the CPOs of Rubrik, Glean, and Harvey Building AI agents is one thing. Shipping them to enterprise customers who will hold you responsible for every mistake they make is something else entirely. Jason Lemkin sits down with the CPOs of three of the most ambitious enterprise AI companies - Rubrik, Glean, and Harvey - for an unfiltered conversation on what it actually takes to go agentic in production. Not demos, not roadmaps, actual working agents at scale, inside security, legal, and enterprise search. Rubrik's CPO breaks down how they're building Ruby, their agentic interface for cyber recovery - and why determinism matters when your agent is making decisions about someone's core infrastructure. When an agent makes a mistake at Rubrik, the blast radius isn't a bad email. It's a broken recovery workflow. Emrecan Dogan (Glean) shares how Glean has evolved from enterprise search to a full system of intelligence - powering agents that run on behalf of entire departments, not just individual users. Plus: why MCP alone isn't enough, and what offline-processed context gives you that runtime fetch never will. Anique Drumright (Harvey) explains how Harvey deploys legal engineers - ex-lawyers with 8 to 10 years of practice - with every enterprise customer, and why that human expertise in the deployment pod is what makes agentic workflows actually stick in law firms. The group also digs into the question nobody wants to answer: when your agent takes an action and something goes wrong, who's responsible? And what does it mean to build a product when customers are going to use it in ways you never anticipated? If you're building agents, buying agents, or trying to figure out how to monetize AI in a market that's moving faster than any product roadmap can keep up with, this one is worth your time.

What do you do after you've founded a company, sold it to Databricks, and could coast? Amandeep Khurana went back to being hands-on. In this episode of The Product Podcast, Carlos Gonzalez de Villaumbrosia (CEO at Product School) talks with Amandeep Khurana, now on Anthropic's go-to-market team, about a career built on deliberately choosing the harder path.Amandeep traces the whole arc: training as an engineer, moving to the Valley to go customer-facing at Cloudera, catching the company-building bug "by osmosis," and founding Okera in 2016, which eventually exited to Databricks. He talks about what he learned in the ups and downs of running a startup, why he later joined AWS as a founding PM (on Kiro) to run four zero-to-one initiatives instead of taking a comfortable management track, and how he thinks about the two fundamental jobs in any company: building a product and selling a product. He and Carlos dig into the tension between being a hands-on builder and being a manager, how to decide which domains are worth jumping into when you know nothing, and why he joined Anthropic to work on bringing AI safely to enterprises. It's a candid conversation about career design, first-principles decision-making, and staying close to the work.What you'll learn:- Why Amandeep repeatedly chooses the "harder path," and how first-principles thinking drives his career decisions- What founding and exiting Okera (to Databricks) actually taught him about company building- Why he went back to hands-on building instead of a pure management track- How to think about the two core jobs in any company: building the product and selling it- How to decide whether to jump into a domain you know nothing about- What running four zero-to-one initiatives inside a big company teaches you- Why he moved into GTM and enterprise AI adoption at Anthropic- How to stay hands-on as your career pulls you toward managementConnect with Amandeep Khurana:Anthropic (GTM / Enterprise); previously co-founder and CEO of Okera, founding PM for Kiro at AWSLinkedIn: https://www.linkedin.com/in/amansk/Host: Carlos Gonzalez de Villaumbrosia, CEO at Product SchoolLinkedIn: https://www.linkedin.com/in/villaumbrosia/About the Product Podcast: Product School's podcast brings you candid conversations with the founders and product leaders shaping tech.Social Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Seven years after becoming the first-ever guest on The Tech Leaders Podcast, Dr Daniel Hulme returns to share his thinking on how AI has evolved, where it's heading, and what organisations should be focusing on now. As Global Chief AI Officer at WPP and founder of Satalia, Daniel has spent nearly three decades building AI systems and exploring some of the biggest questions around intelligence, consciousness and safe machine behaviour. In this episode, we unpack why many businesses are approaching AI in the wrong way, why chasing quick wins can be a trap, and where AI could have the biggest impact, from supply chains and marketing optimisation to governance and agent testing. Daniel also shares how WPP is using AI as a creative superpower, including a model trained on its own creative expertise to generate award-worthy ideas and help teams think bigger. Timestamps:Satalia Take over by WPP (11:45) Will AI kill agencies or make the best ones stronger? (16:28)  Inside WPP: Creative, Production, Media & Enterprise (20:30) What advice would you give to entrepreneurs to put foundations in place to grow their brand using AI technology? (21: 45) AI Controls and Governance (24:10)  Multi Agent Reasoning (29:35) What will the biggest societal impact of AI be within the next 5 years? (32:47) Is AI conscious? (38:56) https://www.bedigitaluk.com/

Nathan's guest this episode is Pete Johnson, Field CTO of AI at MongoDB, and the conversation is really two conversations woven together: a history of database architecture, and a status report on the still-unsolved problem of agent memory. Pete opens with a framing device that recurs throughout — he was born in February 1970, four months before E.F. Codd's original relational-model paper that gave rise to SQL. The relational model, he explains, was built for a world where storage was the scarce resource, so normalization — splitting data across linked tables to avoid duplication — was the rational design choice. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/write-change-recall-forget-mongodb-s-pete-johnson-on-how-retrieval-drives-agent-performance/ Sponsors: Mercury: Mercury is the banking platform loved by 300,000+ entrepreneurs, with virtual cards and Spend controls for granular budgets, receipts, and low-risk AI agent purchases. Learn more and apply in minutes at https://mercury.com Granola: Granola is an AI-powered notepad that securely transcribes meetings and turns rough notes into clean, structured action items. Try it free at https://granola.ai/tcr Diffusion: Diffusion helps organizations build custom AI software factories that scale business outcomes, not just outputs. Cognitive Revolution listeners get a 25% service credit on their first engagement at https://diffusion.io/tcr Deepgram Flux TTS: Deepgram Flux TTS brings lifelike AI voices with real personalities that handle interruptions, pauses, and natural conversation. Try all the voices free through September 12 at https://deepgram.com/keep-talking Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) About the Episode (03:15) Sponsor: Mercury (04:56) SQL versus NoSQL (11:24) Enterprise database choices (Part 1) (18:30) Sponsors: Granola | Diffusion (21:27) Enterprise database choices (Part 2) (21:27) Schema flexible search (33:50) Contextualized chunking tradeoffs (Part 1) (35:12) Sponsors: Deepgram Flux TTS | Claude (37:17) Contextualized chunking tradeoffs (Part 2) (46:22) Retrieval quality thresholds (53:17) Agent memory systems (01:05:51) Enterprise AI deployment (01:16:32) Voyage acquisition strategy (01:23:38) Global AI adoption (01:30:56) Episode Outro (01:34:41) Outro PRODUCED BY: https://aipodcast.ing

I talk with Ofir Bloch, SVP of Corporate Marketing at WalkMe, about why enterprise AI adoption is harder than buying tools and handing out licenses. We get into WalkMe's State of Digital Adoption research, the gap between executive confidence and employee trust, AI sprawl, shadow AI, Gen Z's overconfidence with AI, and why real ROI comes from changing workflows instead of just tracking usage.

An AI agent can finish a task and still violate the rules that matter most. In supply chain, that gap can affect cost limits, approved suppliers, compliance requirements, safety protocols, and escalation paths. In this episode of Supply Chain Now, Scott W. Luton speaks with Vin Vashishta, CEO and AI strategist at V-Squared, about intent contracts, audit trails, workflow reorchestration, tokenomics, semantic layers, and evidence-based AI strategy. Vin explains how to evaluate AI by the value it creates, budget for recurring usage costs, work with imperfect information, and require consultants to connect every recommendation to evidence, risk, mitigation, and business-specific ROI. Jump into the conversation: (00:00) Introduction (05:38) Intent contracts and their role in AI agent management (08:38) The need for AI audit trails in supply chain (11:10) CIO considerations for managing AI demand at scale (14:21) Gaps in workflow reorchestration and value quantification (17:00) Lessons from the semantic layer meme on imperfect data (19:36) The outcomes an AI strategy should deliver for a specific business (25:03) AI training programs generating the strongest market response (26:49) Ways to follow Vin and learn more Additional Links & Resources: Connect with Vin: https://www.linkedin.com/in/vineetvashishta/ Learn more about V-Squared: https://vsquaredai.com/ Vin's LinkedIn Post about Managing AI Agents: https://bit.ly/Managing-AI-Agents The cost of intelligence: How CIOs can manage AI demand at scale: https://mck.co/3TUD3mz Vin's LinkedIn Post about the Cost of Intelligence: https://bit.ly/Vin-on-CIO-Managing-AI Vin's LinkedIn Post on The Semantic Layers: https://bit.ly/Vin-on-Semantic-Layers Vin's LinkedIn Post on AI Strategy: https://bit.ly/Vin-on-AI-Strategy-2026 Vin's Training & Certification Classes: https://datascience.vin/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- SAP AI Inside the Supply Chain: From Silo to Orchestration: https://bit.ly/4bvpz6K WEBINAR- Operational AI in the Supply Chain: How context empowers agents and humans to operate side by side: https://bit.ly/4x7Vd2Z WEBINAR- You Can't Manage What You Can't See: Using Visibility, KPIs, and AI to Optimize Logistics Operations: https://bit.ly/4ql6iem Gartner Announces 2026 Rankings of the Global Supply Chain Top 25: https://www.gartner.com/en/newsroom/press-releases/2026-06-17-gartner-announces-2026-rankings-of-the-global-supply-chain-top-25 This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/ai-agents-semantic-layers-cio-leadership-1629 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.

Interview with Dan Meacham, CISO at Legendary Entertainment Dan Meacham joined us to share a preview of his leadership panel at InfoSec World. At this CRA event in October, Dan will be discussing The Augmented Defender - What AI Actually Changes on the Front Line with Daniel Bowden, the Global CISO at Marsh. Dan dives into the unique world of securing data and assets when film production is largely handled by partners and contractors, working from systems you'll likely have limited access to and definitely can't install agents on. It's a fascinating conversation you should check out! Visit https://securityweekly.com/infosecworld2026 and save 30% on your ISW pass with code: ISW26-SWSAVINGS Black Hat Interview 1 - Google Cloud Outpacing the Adversary with AI Threat Defense - Black Hat interview with John Hultquist, Chief Analyst, Google Threat Intelligence Group at Google The cybersecurity landscape is undergoing a radical shift. AI is no longer just a productivity accelerator for developers and analysts—it has become actively weaponized by sophisticated threat actors to discover and exploit vulnerabilities at unprecedented speed. We'll discuss Google's own approach to combating today's threats and the need for security teams to transform vulnerability management with machine-speed defense. Segment Resources: https://cloud.google.com/blog/products/identity-security/introducing-google-ai-threat-defense https://services.google.com/fh/files/misc/ebookgooglecloudsecurityaithreatdefense.pdf https://services.google.com/fh/files/misc/whitepapercombatingaidriventhreatsgooglemachinespeed_defense.pdf This segment is sponsored by Google Cloud. Visit https://securityweekly.com/googlebh to learn more! Black Hat Interview 2 - Forcepoint Decoding Agentic: Securing the Data Layer AI Just Set on Fire - Black Hat interview with Ronan Murphy, Chief Data Strategy Officer of Forcepoint AI didn't ask permission — and it permanently changed what data risk looks like. Forcepoint Chief Data Strategy officer and member of the Artificial Intelligence Advisory Council in Ireland, shares insights on a clear call to action for agentic enterprises: stop locking AI down and start securing it where the risk actually lives, in the data itself. Learn why data trust is the foundation of the agentic era and how the world's leading enterprises are ending the false choice between AI innovation and data safety. Segment Resources: https://www.forcepoint.com/resources/ebooks/enterprise-guide-ai-data-security https://www.forcepoint.com/blog/insights/forcepoint-announces-ai-data-security This segment is sponsored by Forcepoint. Visit https://securityweekly.com/forcepointbh to learn more! Black Hat Interview 3 - Keyfactor From Secrets to Verified Workload Identity—at Enterprise Scale - Black Hat interview with Ellen Boehm, SVP, Strategy & AI Innovation at Keyfactor As AI agents become autonomous participants inside enterprise environments, organizations can no longer rely on static credentials and traditional identity models to establish trust. Enterprise AI is driving a shift from possession-based access to cryptographically verified identity, as AI agents, cloud-native workloads, and automated services increasingly make decisions and interact with critical systems. In this discussion, we'll discuss why organizations need to continuously establish trust, govern machine identities and cryptography, and build a resilient foundation for securing AI across increasingly dynamic environments. Segment Resources: https://www.keyfactor.com/blog/ai-agents-the-identity-problem-nobody-owns-yet/ https://www.keyfactor.com/education-center/what-is-trust-infrastructure/ https://www.keyfactor.com/resources/topic/col/products/the-trust-control-plane?pflpid=60788&pfsid=HsCXvwPWB1 This segment is sponsored by Keyfactor. Visit https://securityweekly.com/keyfactorbh to learn more! Black Hat Interview 4 - Delinea Delinea Delivers Runtime Authorization for AI Agents, Closing Access Control Gap - Black Hat interview with Frank Vukovits, Chief Security Scientist at Delinea As AI agents move from experiments to autonomous operators inside production databases, cloud consoles, and Kubernetes clusters, enterprises face a new problem: agents with legitimate credentials taking actions no one authorized. Frank breaks down why verifying access at connection time is no longer enough and what it takes to enforce policy on every agent action before it executes. He explains how runtime authorization closes the gap between hiding credentials and actually controlling what agents do once they're inside a session. This segment is sponsored by Delinea. Visit https://securityweekly.com/delineabh to learn more! Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-474

Everyone was told to adopt AI, tried it, got burned — so why do enterprise AI rollouts keep failing? Jonathan Green talks with organizational strategist Rob Lion about the real reasons big AI initiatives miss: unrealistic expectations set by the hype, skipping change management, and the employee mindset of "make my job easier, but not so easy that I'm replaceable." Key Takeaways: • Most enterprise AI failures are change-management failures, not tech failures • The "promise of the commercials" sets expectations the rollout can't meet • If you skip how you roll it out, you bypass the most common path to success • Employees quietly resist: "make my job easier — but not so easy I'm replaceable" • Teach people the why and the workflow, not just "what's an LLM" Notable Quotes: "Make my job easier — but not so easy that I'm replaceable." — Rob Lion "When someone doesn't know what direction they're heading, the rollout's already in trouble." — Rob Lion Connect with Rob Lion: LinkedIn: https://www.linkedin.com/in/robertlion Website: https://blackriverpm.com Enjoyed this? Follow The Artificial Intelligence Podcast and share it with a leader rolling out AI.Connect with Jonathan GreenThe Bestseller: ChatGPT ProfitsFree Gift: The Master Prompt for ChatGPTFree Book on Amazon: Fire Your BossPodcast Website: https://artificialintelligencepod.com/  Subscribe, Rate, and Review: https://artificialintelligencepod.com/itunesVideo Episodes: https://www.youtube.com/@ArtificialIntelligencePodcast 

Interview with Dan Meacham, CISO at Legendary Entertainment Dan Meacham joined us to share a preview of his leadership panel at InfoSec World. At this CRA event in October, Dan will be discussing The Augmented Defender - What AI Actually Changes on the Front Line with Daniel Bowden, the Global CISO at Marsh. Dan dives into the unique world of securing data and assets when film production is largely handled by partners and contractors, working from systems you'll likely have limited access to and definitely can't install agents on. It's a fascinating conversation you should check out! Visit https://securityweekly.com/infosecworld2026 and save 30% on your ISW pass with code: ISW26-SWSAVINGS Black Hat Interview 1 - Google Cloud Outpacing the Adversary with AI Threat Defense - Black Hat interview with John Hultquist, Chief Analyst, Google Threat Intelligence Group at Google The cybersecurity landscape is undergoing a radical shift. AI is no longer just a productivity accelerator for developers and analysts—it has become actively weaponized by sophisticated threat actors to discover and exploit vulnerabilities at unprecedented speed. We'll discuss Google's own approach to combating today's threats and the need for security teams to transform vulnerability management with machine-speed defense. Segment Resources: https://cloud.google.com/blog/products/identity-security/introducing-google-ai-threat-defense https://services.google.com/fh/files/misc/ebookgooglecloudsecurityaithreatdefense.pdf https://services.google.com/fh/files/misc/whitepapercombatingaidriventhreatsgooglemachinespeed_defense.pdf This segment is sponsored by Google Cloud. Visit https://securityweekly.com/googlebh to learn more! Black Hat Interview 2 - Forcepoint Decoding Agentic: Securing the Data Layer AI Just Set on Fire - Black Hat interview with Ronan Murphy, Chief Data Strategy Officer of Forcepoint AI didn't ask permission — and it permanently changed what data risk looks like. Forcepoint Chief Data Strategy officer and member of the Artificial Intelligence Advisory Council in Ireland, shares insights on a clear call to action for agentic enterprises: stop locking AI down and start securing it where the risk actually lives, in the data itself. Learn why data trust is the foundation of the agentic era and how the world's leading enterprises are ending the false choice between AI innovation and data safety. Segment Resources: https://www.forcepoint.com/resources/ebooks/enterprise-guide-ai-data-security https://www.forcepoint.com/blog/insights/forcepoint-announces-ai-data-security This segment is sponsored by Forcepoint. Visit https://securityweekly.com/forcepointbh to learn more! Black Hat Interview 3 - Keyfactor From Secrets to Verified Workload Identity—at Enterprise Scale - Black Hat interview with Ellen Boehm, SVP, Strategy & AI Innovation at Keyfactor As AI agents become autonomous participants inside enterprise environments, organizations can no longer rely on static credentials and traditional identity models to establish trust. Enterprise AI is driving a shift from possession-based access to cryptographically verified identity, as AI agents, cloud-native workloads, and automated services increasingly make decisions and interact with critical systems. In this discussion, we'll discuss why organizations need to continuously establish trust, govern machine identities and cryptography, and build a resilient foundation for securing AI across increasingly dynamic environments. Segment Resources: https://www.keyfactor.com/blog/ai-agents-the-identity-problem-nobody-owns-yet/ https://www.keyfactor.com/education-center/what-is-trust-infrastructure/ https://www.keyfactor.com/resources/topic/col/products/the-trust-control-plane?pflpid=60788&pfsid=HsCXvwPWB1 This segment is sponsored by Keyfactor. Visit https://securityweekly.com/keyfactorbh to learn more! Black Hat Interview 4 - Delinea Delinea Delivers Runtime Authorization for AI Agents, Closing Access Control Gap - Black Hat interview with Frank Vukovits, Chief Security Scientist at Delinea As AI agents move from experiments to autonomous operators inside production databases, cloud consoles, and Kubernetes clusters, enterprises face a new problem: agents with legitimate credentials taking actions no one authorized. Frank breaks down why verifying access at connection time is no longer enough and what it takes to enforce policy on every agent action before it executes. He explains how runtime authorization closes the gap between hiding credentials and actually controlling what agents do once they're inside a session. This segment is sponsored by Delinea. Visit https://securityweekly.com/delineabh to learn more! Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-474

Interview with Dan Meacham, CISO at Legendary Entertainment Dan Meacham joined us to share a preview of his leadership panel at InfoSec World. At this CRA event in October, Dan will be discussing The Augmented Defender - What AI Actually Changes on the Front Line with Daniel Bowden, the Global CISO at Marsh. Dan dives into the unique world of securing data and assets when film production is largely handled by partners and contractors, working from systems you'll likely have limited access to and definitely can't install agents on. It's a fascinating conversation you should check out! Visit https://securityweekly.com/infosecworld2026 and save 30% on your ISW pass with code: ISW26-SWSAVINGS Black Hat Interview 1 - Google Cloud Outpacing the Adversary with AI Threat Defense - Black Hat interview with John Hultquist, Chief Analyst, Google Threat Intelligence Group at Google The cybersecurity landscape is undergoing a radical shift. AI is no longer just a productivity accelerator for developers and analysts—it has become actively weaponized by sophisticated threat actors to discover and exploit vulnerabilities at unprecedented speed. We'll discuss Google's own approach to combating today's threats and the need for security teams to transform vulnerability management with machine-speed defense. Segment Resources: https://cloud.google.com/blog/products/identity-security/introducing-google-ai-threat-defense https://services.google.com/fh/files/misc/ebookgooglecloudsecurityaithreatdefense.pdf https://services.google.com/fh/files/misc/whitepapercombatingaidriventhreatsgooglemachinespeed_defense.pdf This segment is sponsored by Google Cloud. Visit https://securityweekly.com/googlebh to learn more! Black Hat Interview 2 - Forcepoint Decoding Agentic: Securing the Data Layer AI Just Set on Fire - Black Hat interview with Ronan Murphy, Chief Data Strategy Officer of Forcepoint AI didn't ask permission — and it permanently changed what data risk looks like. Forcepoint Chief Data Strategy officer and member of the Artificial Intelligence Advisory Council in Ireland, shares insights on a clear call to action for agentic enterprises: stop locking AI down and start securing it where the risk actually lives, in the data itself. Learn why data trust is the foundation of the agentic era and how the world's leading enterprises are ending the false choice between AI innovation and data safety. Segment Resources: https://www.forcepoint.com/resources/ebooks/enterprise-guide-ai-data-security https://www.forcepoint.com/blog/insights/forcepoint-announces-ai-data-security This segment is sponsored by Forcepoint. Visit https://securityweekly.com/forcepointbh to learn more! Black Hat Interview 3 - Keyfactor From Secrets to Verified Workload Identity—at Enterprise Scale - Black Hat interview with Ellen Boehm, SVP, Strategy & AI Innovation at Keyfactor As AI agents become autonomous participants inside enterprise environments, organizations can no longer rely on static credentials and traditional identity models to establish trust. Enterprise AI is driving a shift from possession-based access to cryptographically verified identity, as AI agents, cloud-native workloads, and automated services increasingly make decisions and interact with critical systems. In this discussion, we'll discuss why organizations need to continuously establish trust, govern machine identities and cryptography, and build a resilient foundation for securing AI across increasingly dynamic environments. Segment Resources: https://www.keyfactor.com/blog/ai-agents-the-identity-problem-nobody-owns-yet/ https://www.keyfactor.com/education-center/what-is-trust-infrastructure/ https://www.keyfactor.com/resources/topic/col/products/the-trust-control-plane?pflpid=60788&pfsid=HsCXvwPWB1 This segment is sponsored by Keyfactor. Visit https://securityweekly.com/keyfactorbh to learn more! Black Hat Interview 4 - Delinea Delinea Delivers Runtime Authorization for AI Agents, Closing Access Control Gap - Black Hat interview with Frank Vukovits, Chief Security Scientist at Delinea As AI agents move from experiments to autonomous operators inside production databases, cloud consoles, and Kubernetes clusters, enterprises face a new problem: agents with legitimate credentials taking actions no one authorized. Frank breaks down why verifying access at connection time is no longer enough and what it takes to enforce policy on every agent action before it executes. He explains how runtime authorization closes the gap between hiding credentials and actually controlling what agents do once they're inside a session. This segment is sponsored by Delinea. Visit https://securityweekly.com/delineabh to learn more! Show Notes: https://securityweekly.com/esw-474

Interview with Dan Meacham, CISO at Legendary Entertainment Dan Meacham joined us to share a preview of his leadership panel at InfoSec World. At this CRA event in October, Dan will be discussing The Augmented Defender - What AI Actually Changes on the Front Line with Daniel Bowden, the Global CISO at Marsh. Dan dives into the unique world of securing data and assets when film production is largely handled by partners and contractors, working from systems you'll likely have limited access to and definitely can't install agents on. It's a fascinating conversation you should check out! Visit https://securityweekly.com/infosecworld2026 and save 30% on your ISW pass with code: ISW26-SWSAVINGS Black Hat Interview 1 - Google Cloud Outpacing the Adversary with AI Threat Defense - Black Hat interview with John Hultquist, Chief Analyst, Google Threat Intelligence Group at Google The cybersecurity landscape is undergoing a radical shift. AI is no longer just a productivity accelerator for developers and analysts—it has become actively weaponized by sophisticated threat actors to discover and exploit vulnerabilities at unprecedented speed. We'll discuss Google's own approach to combating today's threats and the need for security teams to transform vulnerability management with machine-speed defense. Segment Resources: https://cloud.google.com/blog/products/identity-security/introducing-google-ai-threat-defense https://services.google.com/fh/files/misc/ebookgooglecloudsecurityaithreatdefense.pdf https://services.google.com/fh/files/misc/whitepapercombatingaidriventhreatsgooglemachinespeed_defense.pdf This segment is sponsored by Google Cloud. Visit https://securityweekly.com/googlebh to learn more! Black Hat Interview 2 - Forcepoint Decoding Agentic: Securing the Data Layer AI Just Set on Fire - Black Hat interview with Ronan Murphy, Chief Data Strategy Officer of Forcepoint AI didn't ask permission — and it permanently changed what data risk looks like. Forcepoint Chief Data Strategy officer and member of the Artificial Intelligence Advisory Council in Ireland, shares insights on a clear call to action for agentic enterprises: stop locking AI down and start securing it where the risk actually lives, in the data itself. Learn why data trust is the foundation of the agentic era and how the world's leading enterprises are ending the false choice between AI innovation and data safety. Segment Resources: https://www.forcepoint.com/resources/ebooks/enterprise-guide-ai-data-security https://www.forcepoint.com/blog/insights/forcepoint-announces-ai-data-security This segment is sponsored by Forcepoint. Visit https://securityweekly.com/forcepointbh to learn more! Black Hat Interview 3 - Keyfactor From Secrets to Verified Workload Identity—at Enterprise Scale - Black Hat interview with Ellen Boehm, SVP, Strategy & AI Innovation at Keyfactor As AI agents become autonomous participants inside enterprise environments, organizations can no longer rely on static credentials and traditional identity models to establish trust. Enterprise AI is driving a shift from possession-based access to cryptographically verified identity, as AI agents, cloud-native workloads, and automated services increasingly make decisions and interact with critical systems. In this discussion, we'll discuss why organizations need to continuously establish trust, govern machine identities and cryptography, and build a resilient foundation for securing AI across increasingly dynamic environments. Segment Resources: https://www.keyfactor.com/blog/ai-agents-the-identity-problem-nobody-owns-yet/ https://www.keyfactor.com/education-center/what-is-trust-infrastructure/ https://www.keyfactor.com/resources/topic/col/products/the-trust-control-plane?pflpid=60788&pfsid=HsCXvwPWB1 This segment is sponsored by Keyfactor. Visit https://securityweekly.com/keyfactorbh to learn more! Black Hat Interview 4 - Delinea Delinea Delivers Runtime Authorization for AI Agents, Closing Access Control Gap - Black Hat interview with Frank Vukovits, Chief Security Scientist at Delinea As AI agents move from experiments to autonomous operators inside production databases, cloud consoles, and Kubernetes clusters, enterprises face a new problem: agents with legitimate credentials taking actions no one authorized. Frank breaks down why verifying access at connection time is no longer enough and what it takes to enforce policy on every agent action before it executes. He explains how runtime authorization closes the gap between hiding credentials and actually controlling what agents do once they're inside a session. This segment is sponsored by Delinea. Visit https://securityweekly.com/delineabh to learn more! Show Notes: https://securityweekly.com/esw-474

Has enterprise AI finally reached the point where impressive demonstrations are no longer enough? In this episode, I speak with Murali Swaminathan, CTO at Freshworks, about the growing pressure on AI investments to deliver measurable business value. Murali has over 30 years of enterprise software experience, including roles at ServiceNow and CA, and now leads engineering and architecture teams at Freshworks. Murali believes the AI hype cycle is being replaced by an accountability cycle. Buyers want to understand reliability, governance, total cost of ownership, traceability, and the return generated by every deployment. They also want the ability to audit decisions, override outcomes, and use feedback to improve performance. Productivity alone provides an incomplete measure. Within service operations, companies can examine time to resolution, the volume of repetitive work automated, the number of issues completed without human intervention, and the quality of the employee's experience. Murali describes the difference between service-level agreements and experience-level agreements. Resolving a ticket within two minutes means very little if the employee's problem remains. The better question is whether AI completed the workflow and restored the person's ability to work. We also discuss why mid-market and agile enterprises provide a demanding test for AI. These companies have complex requirements but cannot absorb lengthy implementation programs, unclear pricing, or failed experiments. Murali recommends beginning with a limited process, measuring the result, establishing whether it can be repeated, and expanding only after it has proved reliable. Architecture plays an important role. Murali argues that ease of use begins beneath the interface. Configuration-led platforms can be upgraded as new capabilities arrive, while heavily customized systems can leave companies trapped on older releases. Autonomous service operations do not require removing people from every process. Murali uses the example of a printer incident. AI can read the ticket, classify the problem, route it to IT or facilities, and apply an automated fix when a trusted process exists. People retain responsibility for unusual, uncertain, or higher-risk decisions. Scaling this model requires cloud infrastructure that respects regional data residency, privacy, encryption, routing, and audit requirements. AI requests and diagnostic logs must remain within the correct geographic and regulatory boundaries. The conversation concludes with engineering skills. AI coding tools can generate software quickly, but engineers must understand architecture, usability, testing, and customer requirements. Companies also need rules determining which code can be reviewed by AI and which changes require human approval. Is your company measuring whether AI genuinely improves service operations, or is it counting deployments and calling that progress? Listen to the episode and share your thoughts with me.

In today's Cloud Wars Minute, I look at how Google Cloud is expanding the Gemini ecosystem into the automotive world through its partnership with Mahindra. Highlights 00:03 — Google Cloud is partnering with the Indian automobile manufacturer Mahindra to get Gemini Enterprise installed directly into its vehicles. That's another interesting example that we're seeing here of AI leaping out of the digital space and becoming something much more physical. 00:23 — In this new development, the automotive agent in Gemini Enterprise is being integrated into MAIA, Mahindra's Artificial Intelligence Architecture platform. Beyond being a voice assistant, Gemini can understand natural language requests and actually interact with a car and its functions. 00:41 — Sashi Sreedharan, Managing Director of Google Cloud India, explained that "by enhancing its in-vehicle assistant with an automotive agent built on Gemini Enterprise, Mahindra is creating personalized and helpful conversational interactions for navigation, search, media communication, and other vehicle controls that will help drivers navigate and explore rich features in a more natural and helpful way." 01:13 — What we're seeing here is pretty significant: AI operating at the edge, with this idea of the digital and physical working together, and the car becoming the AI endpoint. This is also a really important way for Google Cloud to expand the Gemini ecosystem beyond smartphones, PCs, and enterprise software. 01:37 — The real opportunity that I see here isn't necessarily about Gemini as an AI assistant, but where the tech is embedded; it's those all-important endpoints. Visit Cloud Wars for more.

Enterprise AI is moving beyond experimentation toward a harder question: where does it actually create measurable business value? In this episode of Technovation, Peter High speaks with Nikhil Narvekar, CIO and SVP of Global Business Services at Graphic Packaging Holding Company, about building an AI strategy around tangible outcomes. Narvekar explains how Graphic Packaging is applying AI to supply chain and logistics, reconsidering the need for a single ERP after 23 acquisitions and counting, and democratizing AI through fusion teams and employee training. Why Graphic Packaging took a cautious approach to enterprise AI How AI is helping optimize supply chain and logistics decisions Why a common data layer could change the ERP consolidation equation How fusion teams bring business and IT together around AI outcomes Why human domain knowledge remains essential as AI matures This episode is presented by ElevenLabs — Bringing technology to life. Learn more at elevenlabs.io This episode is also presented by Retool — Build internal software better, with AI. Learn more at retool.com

When brands can't figure out how LLMs are ranking them, what do they do?upGrad raised close to $800M, made year long online degrees mainstream in India when everyone said it wasn't possible, and in the process became a trusted brand in Indian education.Phalgun Kompalli is the co-founder of upGrad. Now, on his second founder journey, he is helping brands become visible to AI.In this episode, Phalgun shares why he chose to build an AI company, how Bodhium Labs helps brands influence LLMs, why lazy marketing is dead and why AI services can be more defensible than AI products.He also shares hard-won lessons from upGrad, from expanding into too many markets at once, to hiring far too fast, to discovering that a highly valuable brand can be worth nothing to a customer.00:00 Founding UpGrad02:55 Why We Bet on LLMs06:35 The GEO Moat10:18 Inside the LLM12:07 Why Services Won Early13:35 Building an AI Research Lab18:46 Why India Sells Differently22:11 From Consultant to Founder26:50 The Early Hiring Lessons26:55 International Expansion Mistakes27:47 The EdTech Boom and Bust31:12 How We Found Product-Market Fit32:59 Supply Before Demand34:58 Differentiation Is Not Enough36:29 Borrowing Trust to Scale38:01 Why Offline Still Matters40:55 Why Cambridge Lost to MICA45:33 Raising the First Big Round46:42 The Pain of Starting Again48:31 The Future of AI + Education-------------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:   / theneonshoww  LinkedIn:   / beneon  Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn:   / 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

As agentic AI rewires enterprise IT, Cisco and Nutanix are turning a three-year strategic partnership into a full-stack answer for...

In today's Cloud Wars Minute, I explain why Workday believes AI security is becoming an ecosystem problem that no single vendor can solve independently. Highlights 00:06 — Workday has become the latest organization to join the Open Secure AI Alliance. It's a well-supported initiative that aims to improve the security of AI systems through open-source tools, research, and knowledge sharing. Workday's joining this alliance, and it's in very good company alongside companies like NVIDIA, Cloudflare, Microsoft, and IBM. 00:31 — I want to read a short passage from Workday's joining announcement to give you a sense of why the company chose to join the alliance. So, from Workday: “Delivering safe, trusted enterprise AI requires multiple layers of protection across the entire application stack, from identity management and access permissions to continuous guardrails, monitoring, and governance.” 00:53 — “Our commitment to continuously build our internal security engineering programs to meet these high standards is why Workday is joining the Open Secure AI Alliance.” Enterprise AI is shifting quickly towards more open stacks and more ambitious agents. Initiatives like the Open Secure AI Alliance are incredibly important. 01:22 — Workday and the other members of the alliance recognize that AI security is quickly becoming an ecosystem problem, not just something that one vendor can solve independently. And that's why, for example, Workday specifically said that it has historically worked on security internally for its own platform, but now is sharing what it's learned with the wider community. 01:50 — As AI becomes more open, distributed, and agentic, securing models isn't going to be enough. The challenge now, and that's one that Workday and the other members of the alliance are trying to address, is securing everything around these models. Visit Cloud Wars for more.

It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level. One of the most important questions in the tech industry today is understanding why AI is not yet delivering returns that match the investment, and what separates the small number of enterprises succeeding from the many that are not. Scale AI is known for supplying the human-labeled data behind many frontier models. It now also builds AI applications and agents for large enterprises. That combination of working alongside frontier labs and inside enterprise deployments gives the company a rare view of why enterprise AI may be stalling. Emily Xue is the Head of Enterprise AI at Scale AI, and previously spent over a decade at Google. In this episode, Emily joins Kevin Ball to discuss the three layers where enterprise AI breaks down, why frontier model benchmarks miss what enterprises actually need, the data foundation problem, how the most successful companies combine internal domain expertise with outside AI specialists, and more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post The Gap Between AI Spending and AI Value appeared first on Software Engineering Daily.

In this episode, Jo Peterson, of Cleartech Research, discusses AI security challenges with Maribel Lopez. Her commentary explores how traditional security tools fall short for AI agents and what strategies enterprises can adopt to manage AI risks effectively. Topics coveredAI security challenges and solutionsMulti-layered approach to AI agent securityToken cryptographic delegation and OBO tokensExternalized policy as code and micro-segmentationContextual and data-aware guardrailsContinuous auditing and behavioral baselinesRole of Chief AI Officer in security governanceAI risk management and operational strategies Key takeawaysTraditional security tools are not designed for AI agents and their non-deterministic behavior.Implementing least privilege for AI agents involves multi-layered strategies including token delegation and policy enforcement.Externalizing authorization to decoupled policy decision points enhances security for AI workflows.Real-time observability and kill switches are critical for managing AI agent behavior.Many organizations claim to have AI governance but lack technical implementation and operational ownership.Chapters00:00 Introduction to AI security challenges02:04 Non-deterministic nature of AI and security implications04:32 Externalized policy as code and micro-segmentation07:12 Real-time observability and kill switches09:11 Effectiveness of AI governance in organizations12:13 Immediate actions for AI security in 30 days13:45 Centralized AI traffic interception and inventory14:29 Role of Chief AI Officer in security and risk16:19 The evolving role of AI leadership in organizations17:19 Talent acquisition and upskilling for AI security STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch.com/research/Follow me on LinkedIn: https://www.linkedin.com/in/maribellopez/Follow me on X: https://x.com/MaribelLopez

Today, we're joined by Raju Malhotra, Chief Product and Technology Officer at Certinia, the leading global provider of AI-powered Professional Services Automation (PSA). We talk about:The evolution of helping professional services organizations end to endImpacts of AI on SaaS and professional services companiesHow the “old way” of thinking about SaaS has changed with AIThe five elements that make an AI agent greatThe risk of “AI Agent Slop”

It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level. One of the most important questions in the tech industry today is understanding why AI is not yet delivering returns that match the investment, and what separates the small number of enterprises succeeding from the many that are not. Scale AI is known for supplying the human-labeled data behind many frontier models. It now also builds AI applications and agents for large enterprises. That combination of working alongside frontier labs and inside enterprise deployments gives the company a rare view of why enterprise AI may be stalling. Emily Xue is the Head of Enterprise AI at Scale AI, and previously spent over a decade at Google. In this episode, Emily joins Kevin Ball to discuss the three layers where enterprise AI breaks down, why frontier model benchmarks miss what enterprises actually need, the data foundation problem, how the most successful companies combine internal domain expertise with outside AI specialists, and more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post The Gap Between AI Spending and AI Value appeared first on Software Engineering Daily.

AI adoption in supply chain is moving from curiosity to business-critical execution. The companies pulling ahead are not chasing shiny tools. They are finding root causes, cleaning up messy data, and using technology to reduce wasted time across procurement, planning, inventory, and fulfillment.   In this episode of Supply Chain Now, Scott W. Luton and Karin Bursa speak with Wiley Jones, co-founder and CEO of DOSS, about the Enterprise Unleashed series and what leaders should take from the first several conversations. Wiley shares why culture, clarity, and leadership ownership are central to successful AI programs.   The conversation also covers touchless procurement, master data, decision support, workflow redesign, and why the best companies are willing to question how work gets done.   Jump into the conversation: (00:00) Intro (03:52) Favorite outdoor adventures (06:36) How the enterprise landscape is changing (09:54) From AI curiosity to AI of consequence (11:51) Solve root causes, not symptoms (15:16) Major AI developments in supply chain (20:39) Lessons from Enterprise Unleashed (21:44) Putting people and culture first (25:16) Leadership's role in transformation (28:28) Defining success and increasing decision velocity (32:07) What to do when leadership doesn't embrace AI (34:14) What the most innovative companies do differently (37:48) Challenging legacy processes and assumptions (40:56) Doss and the impact of touchless procurement (44:39) Using AI to drive growth (46:00) The future of the AI-native enterprise   Additional Links & Resources: Connect with Wiley Jones: https://www.linkedin.com/in/wileycwjones/ Learn more about Doss: https://www.doss.com/ Connect with Karin Bursa: https://www.linkedin.com/in/karinbursa/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- SAP AI Inside the Supply Chain: From Silo to Orchestration: https://bit.ly/4bvpz6K WEBINAR- Operational AI in the Supply Chain: How context empowers agents and humans to operate side by side: https://bit.ly/4x7Vd2Z WEBINAR- You Can't Manage What You Can't See: Using Visibility, KPIs, and AI to Optimize Logistics Operations: https://bit.ly/4ql6iem   This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/enterprise-unleashed-biggest-lessons-2026-1626 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.

On-device AI takes center stage as the panel explores MacPaw's work with Liquid AI, personal assistants, and workplace restrictions. The conversation also discusses the panel's experiences with AI note-taking recording consent and privacy in both business and health care. The panel includes: Eric Bolden, Chuck Joiner, Dave Gisnburg, Marty Jencius, Jeff Gamet, and Guy Serle.  This edition of MacVoices is brought to you by our Patreon supporters. Get access to the MacVoices Slack and MacVoices After Dark by joining in at Patreon.com/macvoices. Show Notes: Chapters: 00:00 MacPaw, Liquid AI, and On-Device Intelligence 01:55 AI as a Personal Assistant 02:21 Workplace Restrictions on AI Tools 03:02 Gemini and Enterprise AI 05:10 Separating Personal and Workplace AI 06:12 Setapp, AI Credits, and MacPaw's AI Preview 07:03 Why On-Device AI Matters 07:32 AI Access to Calendars, Email, and Files 09:47 AI Note-Taking and Privacy Concerns 10:31 Consent, Cloud Storage, and Personal Data 13:13 Recording Consent and State Laws 13:48 Proprietary Information, NDAs, and AI 15:00 One-Party Consent and Remote Conversations 15:34 Recording Notifications in Online Meetings 17:47 AI Recording and Consent in Healthcare 19:16 AI in Dentistry and Medical Care 19:44 Insurance and AI Medical Documentation 21:26 Hidden Cameras and Rental Privacy 23:56 Photographing the Perseid Meteor Shower with an iPhone 24:31 Self-Driving Vehicles, Jobs, and Road Safety 26:12 Black Hat, DEF CON, and Security Conferences 27:21 Closing Thoughts Links: MacPaw Partners with Liquid AI to Bring On-Device AI to Mac Users https://appleworld.today/2026/08/macpaw-partners-with-liquid-ai-to-bring-on-device-ai-to-mac-users/   Note-Taking Apps Are Violating User Privacy https://lifehacker.com/tech/ai-note-taking-apps-are-violating-user-privacy   6-in-1 Hidden Camera Detector: $30 https://www.dealnews.com/6-in-1-Hidden-Camera-Detector-for-30-free-shipping-w-Prime/21980524.html   Here's how much personal data your favorite apps are collecting about you https://appleworld.today/2026/08/heres-how-much-personal-data-your-favorite-apps-are-collecting-about-you/   The Best Apps to Use After Gmail's 'Send As' Feature Goes Away https://lifehacker.com/tech/gmail-ending-send-as-feature   Your iPhone can capture the Perseids meteor shower if you use the right tools https://appleinsider.com/inside/iphone/tips/your-iphone-can-capture-the-perseids-meteor-shower-if-you-use-the-right-tools The California Teamsters union is suing the state over self-driving trucks - Engadget https://www.engadget.com/2231699/california-teamsters-union-is-suing-the-state-over-self-driving-trucks/ Guests: Get detailed bios and contact information about for the panel on the MacVoices Live! Panel page on our web site: https://macvoices.com/macvoiceslive/macvoices-live-panel/ Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

How much of your technology stack is being held together by people swiveling between screens, copying information, and quietly compensating for systems that cannot communicate? In this episode, I speak with John Foreman, Chief Product Officer at Clio, about what he calls the "swivel chair problem." John previously served as Chief Product Officer at Mailchimp and Podium, and now helps guide product development at a company seeking to support the complete operation of a law firm. We discuss why legal professionals have moved from understandable caution around AI toward increasingly sophisticated daily use. John explains why concerns about client confidentiality, intellectual property, model training, and data access initially slowed adoption, as well as why lawyers are now helping set the pace for responsible professional AI use. Our conversation also examines why disconnected technology stacks make AI appear far less capable. People can interpret information across documents, billing platforms, case management tools, email, and court systems. An AI system cannot perform the same work unless it receives the necessary context and access. John also explains why the familiar chatbot may be the wrong interface for many jobs. Some AI tasks should happen quietly, while work involving legal filings and client records requires structured review, accountability, and human approval. With lawyers spending an average of 62% of their time on nonbillable work, the immediate opportunity could include intake, billing, timekeeping, reviews, document processing, and filing. These lessons extend well beyond legal services. Where is the swivel chair problem hiding inside your organization? Listen to the conversation and share your thoughts with me.

AI adoption has changed the standard for go-to-market execution. Seong Park, SVP of Customer Support & Services at Cursor, explains why the old model of selling certainty is giving way to curiosity, experimentation, and workflow discovery. He breaks down why the real work starts after signature, why pilots only matter if they lead to business-critical adoption, and how revenue leaders can guide customers from AI exploration to measurable impact. 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 Listen to the full episode: The Real Sale Starts After Signature | Proving Value in AI and Consumption Models with Seong Park 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

When frontier labs can build everything, when moving fast doesn't mean what it used to, and when what was impossible can now be done in days, what do you build and how do you build it?Krishna M is an AI Partner at Elevation Capital. He has seen the 0-to-1 journey twice as a founder and has also experienced what large-scale building looks like at tech giants like Meta and Cohesity.In this episode, we discuss why a founder's fearlessness and ambition will define the destiny of the company far more than technical skills. Krishna shares how Indian founders have come a long way and now build like they know no fear. We then explore how OpenAI and Anthropic have changed what it takes to build startups, and what founder traits are needed to create outlier companies in this era.Watch this episode to understand the new definition of startup speed and what to build when frontier labs can build entire product categories themselves.01:00 From 2x Founder to AI Investor03:37 Why Bet on the US-India Corridor07:07 Can Indian Founders Build Giants?10:13 What Makes Krishna Bet on a Founder?12:33 The Founder Traits That Matter in AI14:18 Why Speed Is the New GTM17:52 The New AI Founder19:13 How Krishna Builds Conviction23:26 Why Elevation Bet on Portkey28:20 How Elevation Evaluates AI Startups31:17 The AI Security Opportunity32:04 Your PMF Can Die in a Quarter32:57 Why Open Source Still Matters34:11 India's Infrastructure Advantage36:51 Where the Next AI Opportunities Are41:35 What Matters Beyond Revenue43:36 Why India + US Is a Powerful Combination44:59 What Cohesity Taught Him About Scale47:13 Selling Data to Broke Professors50:18 Why Two Weeks in SF Won't Work52:06 Fundraising Trap Founders Miss56:47 Outliers Aren't Well-Rounded58:49 "We're Wrong More Than We're Right"-------------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:   / theneonshoww  LinkedIn:   / beneon  Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn:   / 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

Venture Capital Reporter Julia Hornstein talks with TITV Host Akash Pasricha about Anthropic's JV acquiring Casper Studios. We also talk with Julia about Nvidia's potential investment in Mercor as well as Yueqi Yang about Robinhood-backed prediction market Rothera. Lastly, we get into Fortune 500 AI ROI with PwC's Dallas Dolen.Articles discussed on this episode: https://www.theinformation.com/articles/anthropics-enterprise-ai-venture-buys-consultancyhttps://www.theinformation.com/articles/nvidia-discusses-funding-ai-data-supplier-mercor-20-billion-valuationhttps://www.theinformation.com/newsletters/the-information-finance/hot-new-prediction-exchange-never-heard-designSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Anthropic's JV Acquires Casper Studios08:12 - Nvidia In Talks for $20B Mercor Round10:25 - Robinhood-Backed Prediction Exchange Rothera19:38 - SEC's First Crypto Token Rule Proposal22:53 - PwC on Fortune 500 AI Model ROI & SaaS Margins

Welcome to The Editors' Room, a new Tearsheet Podcast series where Editor-in-Chief Zack Miller and Managing Editor Sara Khairi take the conversations that usually happen behind the scenes about our biggest stories and put them on the record. There isn't a rehearsed interview or carefully choreographed panel answers. It's just two editors comparing notes, challenging each other's takes and trying to make sense of what is actually happening in financial services. Think of it as pulling up a chair after the meeting ends. It's the stuff we usually debate after the calls end: what a new product actually means, which industry trends have legs, and where the hype gets ahead of reality. Raw, conversational, and occasionally accompanied by a blooper. For our inaugural conversation, the topic was AI and, more specifically, what would you happily delegate to AI and what would you never hand over? From there, we got into the bigger question of how enterprise AI is taking shape. On that first question, Zack's line is creativity. AI can handle planning and logistics, and he uses it as a kind of editorial sparring partner, asking questions, challenging ideas, and pushing him to dig deeper. But the creative judgment stays human. Sara draws the line at decision-making. Take an expensive laptop: she'll happily let AI compare the options, but she wants to be the person who clicks buy. It turns out that tiny distinction – AI can help make the decision, but shouldn't necessarily make it – is becoming a much bigger question in financial services.

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

Could the real reason enterprise AI projects remain stuck in pilot mode be hidden inside the company's unstructured data? In this episode of Tech Talks Daily, I welcome back Oded Nagel, CEO of CTERA. We discuss why enterprise AI success depends on the condition, location, permissions, and business value of the data sitting underneath models and agents. Oded defines AI-ready data as information that is searchable, classified, and permission-aware. Many enterprises have petabytes of files distributed across offices, edge locations, legacy network-attached storage, and cloud platforms. Before introducing AI, leaders need to know what information they possess, where it resides, who can access it, and whether it remains valuable. The cost implications are significant. Copying every available file into an AI ecosystem can create expensive ingestion and storage bills. It may also reduce answer quality when stale, duplicated, irrelevant, or personal files enter the model's source material. Oded describes a customer classification project where approximately 80% of the data examined was stale or archival. The company also discovered personal content, including MP3 files, stored alongside enterprise information. Feeding such material into an AI system would consume resources without improving business results. We discuss Oded's recommendation to bring AI to governed data rather than moving data outside existing controls. Keeping intelligence close to the file system can preserve access permissions, audit logs, snapshots, and recovery mechanisms. Those protections become increasingly important when autonomous agents can read, move, modify, or delete files. Oded argues that every agent should be identifiable and its activity monitored. Businesses need to know which agent accessed which information, what action it performed, and whether the result can be reversed. Without those controls, a misunderstood instruction or malicious input could cause serious damage. The conversation also covers CTERA InsightAI, an agentic intelligence layer built into the company's data platform. Oded says it analyzes security activity and file-system metadata, allowing users to ask questions about stale data, file types, access patterns, deleted files, and ransomware impact using natural language. Rather than working through traditional dashboards and filters, users can question the data and request conclusions or recommended actions. Oded says some customers are piloting InsightAI while others already use it in production. For leaders measuring enterprise AI ROI, Oded recommends concentrating on storage costs, time savings, and speed to production. AI tools should make complex information easier to understand and reduce the time required to act. A ten-page report generated instantly provides limited value if nobody knows what decision to make from it. Does your company have enough visibility and control over its unstructured data to support production AI, or would classification uncover years of stale information and unnecessary expense? Listen to the conversation and share your thoughts with me.

In this episode, Munjal Shah, Co-founder & CEO of Hippocratic AI, discusses how agentic orchestration can coordinate teams of AI agents to achieve healthcare outcomes such as reducing readmissions, closing care gaps and improving patient follow-up. This episode is sponsored by Hippocratic AI.

Plus: Anthropic is meeting with potential investors ahead of its planned IPO. And Intel is increasing its stock offering to raise $20 billion. Danny Lewis hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.