Podcasts about enterprise ai

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 

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.

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

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.

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.

CNBC reported that OpenAI CFO Sarah Friar told employees the company plans to be public in 2027 or sooner. The timeline signals active IPO preparation, including audit readiness, internal controls, and potential S-1 planning. OpenAI's capped-profit structure and non-profit parent will require clear disclosures, along with details on its partnership with Microsoft. Investors will weigh enterprise subscriptions, API usage, and licensing against training and serving costs and concentration risk. Regulators and the SEC will scrutinize AI claims, cybersecurity controls, and model governance. Employees may see tender offers, lock-up periods, and changes to equity programs. Customers should expect tighter contracts, clearer data policies, and possible pricing adjustments as listing preparations progress.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Bloomberg reported that Anthropic surpassed a $65 billion annualized revenue run rate ahead of a planned IPO. Anthropic, founded by CEO Dario Amodei and President Daniela Amodei, sells access to Claude models via APIs, enterprise products, and cloud marketplaces. The company partners with Amazon and Google, with Amazon committing up to $4 billion and Google providing about $2 billion in financing reported in late 2023. The run rate metric can include usage-based contracts, commitments, and prepayments, and does not disclose margins or revenue concentration. Cloud providers such as AWS and Google Cloud are positioned to benefit through compute consumption and bundled services. Enterprise buyers are shifting to negotiated volume tiers, reserved capacity, and cost controls while comparing embedded AI features to direct API use. If Anthropic files an S-1, disclosures on revenue mix, cloud commitments, and gross margins will shape investor expectations.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

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.

The fact that OpenAI has quickly adopted Temporal, a rapidly expanding AI ecosystem, and has even entered into a partnership with Crystal Palace FC shows that its business strategy is to pursue community-first innovation.For years, tech enterprises competed against each other on the grounds of innovative features. However, the competition has changed recently. Now the winners are those who build communities.In the recent episode of the Tech Transformed podcast, host Christina Stathopoulos, Founder of Dare to Data, is joined by Melissa Herrera, Senior Developer Advocate at Temporal, and Les Jackson, Staff Developer Advocate, to discuss the pivotal role of the community in technology. They further explore how Temporal's open-source philosophy fosters developer engagement, the impact of community feedback on product development, and the significance of partnerships, such as with OpenAI and Crystal Palace. The conversation emphasises the importance of authentic community relationships and the future direction of Temporal, highlighting the need for continuous integration and collaboration with developers.TakeawaysCommunity is a central part of Temporal's growth.Temporal's philosophy is rooted in open-source software.In-person community interactions are invaluable for developers.Feedback from the community directly shapes product direction.OpenAI's adoption of Temporal led to significant scaling.Partnerships should focus on community engagement, not just transactions.Temporal's collaboration with Crystal Palace merges tech and sports communities.Investing in community fosters trust and collaboration.Continuous integration with developer tools is essential for success.Authentic community relationships drive technology innovation.Chapters00:00 Introduction to Tech Transformed Podcast02:45 The Importance of Community in Technology05:13 Feedback from Developers: Shaping Product Direction07:51 OpenAI's Adoption of Temporal: A Case Study10:02 Unique Partnerships: Temporal and Crystal Palace15:12 Looking Ahead: Future of Temporal and Community Engagement19:38 Final Thoughts: Investing in CommunityTemporal, Enterprise AI, Developer Communities, Developer Advocacy, Open Source, OpenAI, AI Agents, AI Infrastructure, Durable Execution, AI Orchestration, Enterprise Software, Developer Experience, AI Workflows, AI Adoption, Community Led Growth, Developer Led Growth, Temporal SDK, Software Engineering, AI Engineering, Enterprise Technology, Crystal Palace, Tech Transformed

In today's Cloud Wars Minute, I look at why Oracle is rejecting a single-model AI strategy and embracing a flexible, multi-model future. Highlights 00:03 — Oracle has announced that it's extending its partnership with Google Cloud and will be making Google's Gemini models available across its enterprise AI portfolio. This includes Oracle Fusion Cloud Applications, NetSuite, Oracle AI Agent Studio, and Oracle Cloud Infrastructure, or OCI. 00:22 — In true Oracle style, the company will embed Gemini into business applications and AI agents, allowing customers the flexibility to use Google's models within their existing workflows. At the same time, customers will have the freedom to switch between the various models offered in Oracle's suite. Now, what this is doing is giving customers more choice when they build Fusion-native agents and agentic apps. 00:53 — Oracle and Google Cloud already have a strong relationship, but the broader story here is how Oracle is positioning itself as an AI control plane that delivers outstanding infrastructure without the need to roll out a host of foundation models itself. Now, the company is really embedding itself in this area, and I think it's working out incredibly well for it. 01:17 — The company has really pushed the idea of flexibility, interoperability, and choice. Now, Oracle, from very early on, has really avoided that single-model strategy, saying that's a strategy that's aging quickly, and instead, Oracle's building a platform that can adapt as the AI landscape continues to evolve. 01:40 — I think this really ties in with the ambitions of those enterprises that want to take advantage of rapid AI developments while still controlling their data, applications, and workflows. And that's where Oracle stands out in its ability to bring together infrastructure, applications, and multiple AI models while seamlessly enabling companies to maximize the benefits of the latest AI developments. Visit Cloud Wars for more.

Bloomberg reported that OpenAI reached a $40 billion annualized revenue run rate and is preparing for an IPO. The company monetizes through ChatGPT consumer subscriptions, ChatGPT Enterprise, API usage, and co-selling via Microsoft's Azure OpenAI Service. Investors will scrutinize margins, revenue mix, and dependence on Microsoft when the S-1 is filed. OpenAI hired Sarah Friar as CFO in 2024 to build public company readiness. Competition from Anthropic, Google, and Meta is pressuring price and performance. Enterprises are negotiating multi-year agreements with data controls and service levels, while startups are building multi-model architectures to manage cost and reliability.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

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.

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.

Joel McKelvey, VP of Product Marketing at Glean, shares why the enterprises winning with AI right now aren't the ones chasing the latest model—they're the ones who got their context layer right first. He explains why AI has moved from experimentation to company-wide production almost overnight, and why the biggest mistake enterprises are making is one they'll only recognize in hindsight. Key Takeaways Why the model you're running matters far less than the context you're feeding it How to think about AI ROI across three levels: individual, team, and organization What actually breaks employee trust in AI agents Why being model-agnostic is the best future-proof decision How fear of AI disappears quickly once employees get firsthand experience of a productivity win Guest Bio: Joel McKelvey is VP of Product Marketing at Glean, where he helps organizations turn AI initiatives into practical, metrics-driven business transformation. He brings more than 30 years of experience across engineering, marketing, and strategy, with deep expertise in AI, machine learning, analytics, and data architectures. His work focuses on how enterprises move from experimentation to execution using AI agents, enterprise search, and trusted data foundations to improve productivity, decision-making, and customer and employee experiences. ---------------------------------------------------------------------------------------- About this Show: The Brave Technologist is here to shed light on the opportunities and challenges of emerging tech. To make it digestible, less scary, and more approachable for all! Join us as we embark on a mission to demystify artificial intelligence, challenge the status quo, and empower everyday people to embrace the digital revolution. Whether you're a tech enthusiast, a curious mind, or an industry professional, this podcast invites you to join the conversation and explore the future of AI together. The Brave Technologist Podcast is hosted by Luke Mulks, VP Business Operations at Brave Software—makers of the privacy-respecting Brave browser and Search engine, and now powering AI everywhere with the Brave Search API. Music by: Ari Dvorin Produced by: Sam Laliberte  

My guest this week is James Brown, an engineering lead at Schroders, and that's a useful vantage point — asset managers carry all the regulation and organisational weight of a bank, mixed with the first-to-market pressure of a startup. James starts with his own "Claude mania": months of agents running around the clock, and the wave of anxiety that hit him one morning walking to the shop without one running at home. From there, Clair — the plugin he's building to give coding agents proximal awareness of each other, using git orphan branches as a zero-infrastructure message bus; why AI behaves like oxygen in a room full of tiny fires; what "going well" actually measures inside a regulated firm; and why he thinks team sizes won't change, even when the number of teams does.There's a darker thread running under all of it. The collapse in junior hiring, the advice we no longer know how to give a 20-year-old, dark factories and evolutionary harnesses that might make the AI's ideas better than ours, and the burnout James expects to be our dominant topic for the next couple of years. If AI is working beautifully on your side projects but landing with a thud at work, James has some honest answers — including several about what he doesn't know yet.---Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinClair (James's multi-agent proximity plugin): https://github.com/JBJamesBrownJB/clairClair product docs: https://github.com/JBJamesBrownJB/clair/blob/main/docs/product.mdJames's blog: https://medium.com/@jameskinnahbrown"Milk, Eggs and Claude Mania": https://medium.com/@jameskinnahbrown/milk-eggs-and-claude-mania-49f445c5a77eSchroders: https://www.schroders.com/Claude Code: https://www.claude.com/product/claude-codeAgent Skills & progressive disclosure: https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overviewGit orphan branches (git checkout --orphan): https://git-scm.com/docs/git-checkoutMoltbook: https://www.moltbook.com/Team Topologies: https://teamtopologies.com/"Expert Panel: How Far Can We Accelerate with AI?": https://youtu.be/Bg7L4vmmSKgXT26 (the conference the panel was part of): https://www.juxt.pro/xt26/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

OCR has been around for more than 40 years. So why do the world's biggest banks still have thousands of people reading documents by hand? When Dan Maloney became CEO of Landing AI in 2024, as Andrew Ng stepped back from the day-to-day, he was returning to a problem he had first worked on at SAP back in 2001. When he looked closely at it again two decades later, he was struck by how little it had actually moved. Landing AI's mission is to make the world's documents computable. Instead of growing up from OCR and patching its limits with templates and heuristics, Landing AI came at the problem from visual AI, blending purpose-built models, an intelligent router, and agentic reasoning into a single system that reads a document the way a person does. Today that system extracts structured data from the messiest documents enterprises have, the scanned pages, the tables inside tables, the handwritten forms, at accuracy levels they can build on. Before every enterprise had an AI strategy... Before "agentic" became a boardroom word... Before the industry spent a year token maxing... There was a quieter, more stubborn problem: The world's data was trapped in documents, and no one could read it at scale. In this episode of the Future of Data & AI Podcast, Dan Maloney, CEO of Landing AI and a two-decade veteran of enterprise software and AI, joins Raja Iqbal for a grounded conversation about what it actually takes to move visual AI from an impressive demo into production. Dan is candid about where the hype outruns reality, why the model is the smallest part of the equation, and how a company earns the trust of a compliance team, not just an engineering one. What You'll Discover

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.

On today's show, we dive into the shifting macro landscape, major breakthroughs in artificial intelligence, and new solutions for fleet electrification:  Freight Market Shakeout: Ryan Farlow, Industrials Senior Analyst at RSM, discusses how regulatory enforcement, small-carrier cost pressures, and changing capital costs could trigger a capacity shakeout to rebalance freight pricing.  Enterprise AI at Scale: Adrian Smith, Co-Founder and CEO of Ripple, breaks down their record enterprise deployment with global freight forwarder JAS, detailing how bespoke AI automation transforms core workflows.  Autonomous Tech Milestones: Raquel Urtasun, Founder and CEO of Waabi, explains how the Waabi Driver achieved zero-retraining AI generalization by seamlessly transferring to the Volvo VNL Autonomous platform without new data.  Electrification Solutions: Pat White, VP of Commercial at OptiGrid, highlights how battery-integrated fast chargers enable fleets to bypass long utility delays and slash deployment timelines from years to weeks. ⁠Follow the FreightWaves Today Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Enterprise AI has moved past experimentation and now has to prove its return. Ed Sim, Founder and General Partner of boldstart ventures, ranked the No. 1 seed investor in the Business Insider Seed 100 two years running, sees hundreds of AI startup pitches a year, and writes the first check into companies enterprises buy from years later. He wrote the first check into Snyk and backed Protect AI, which Palo Alto Networks acquired for more than $700 million. In this conversation, he lays out the three waves of enterprise AI adoption, why rising token costs are pushing companies toward open-weight models and their own hardware, how agent identity and access create a new attack surface, and what separates AI vendors that survive a shakeout from the ones that do not.YOU'LL DISCOVER✅ The three waves of enterprise AI: get AI running, get agents running, and the wave happening now, where ROI and tokenomics decide what survives✅ Why Ed expects dozens of models inside a single enterprise, and the choice he frames as renting intelligence versus owning it✅ How one portfolio company packaged eight GPUs, CPUs, and a model router into an appliance, routing roughly 10% of queries to the frontier labs and claiming 70% savings per year✅ Why agents should be granted access at runtime that expires when the task ends, so a breach's blast radius stays contained to one narrow authorization✅ Cost per outcome as the yardstick: the human doing the task, the AI doing the task, and the human assisted by AI, applied first to discrete work like coding and customer support✅ A 57-step insurance claims process where the AI was correct 98% of the time and the humans 85%, a gap only visible because every step was recorded✅ The real difference between open source and open weight models, and why most of Ed's startups now build on open weight models under the hood✅ Why he argues offense is the new defense, and what the Black Hat sandbox escape means for CISOs planning autonomous defense⏱️ TIMESTAMPS0:00 Introduction0:36 Three waves and the ROI test3:06 Many models and where startups win10:32 Who owns access, context, and evaluations17:21 It's the people, not the architecture20:04 Measure the outcome, then cut the cost28:14 Buying talent and changing culture33:05 Systems of record versus headless agents36:31 Venture money pivots to robotics and chips40:11 Open weights and owning your intelligence44:44 Autonomous attacks need autonomous defense51:32 Judging vendors and earning enterprise trust

On today's show, we dive into the shifting macro landscape, major breakthroughs in artificial intelligence, and new solutions for fleet electrification:  Freight Market Shakeout: Ryan Farlow, Industrials Senior Analyst at RSM, discusses how regulatory enforcement, small-carrier cost pressures, and changing capital costs could trigger a capacity shakeout to rebalance freight pricing.  Enterprise AI at Scale: Adrian Smith, Co-Founder and CEO of Ripple, breaks down their record enterprise deployment with global freight forwarder JAS, detailing how bespoke AI automation transforms core workflows.  Autonomous Tech Milestones: Raquel Urtasun, Founder and CEO of Waabi, explains how the Waabi Driver achieved zero-retraining AI generalization by seamlessly transferring to the Volvo VNL Autonomous platform without new data.  Electrification Solutions: Pat White, VP of Commercial at OptiGrid, highlights how battery-integrated fast chargers enable fleets to bypass long utility delays and slash deployment timelines from years to weeks. Follow the FreightWaves Today Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this Risky Business sponsored interview, Catalin Cimpanu talks with Michael Leland, Field CTO at Island, about the company's seamless expansion into SASE and enterprise AI. Show notes About Michael Leland

In today's Cloud Wars AI Minute, I explain how Microsoft's AI agent momentum could transform business applications at scale. Highlights 00:00 — So we're now hearing, according to Microsoft, that it has 40 million agents deployed now in the Microsoft ecosystem. Now compare that to the 30 million seats that it actually has sold for the M365 Copilot, and that really starts to show us that AI agent creation and things of this nature are starting to take off when we're starting to see more than one-to-one ratios here. 00:33 — So this is a really big situation for Microsoft. It's actually a really good signal that custom agents and agents that are being built are starting to get more value off the backside. Now we can't necessarily completely get to this level because what you'll see is a lot of these agents are probably really simple right now. 00:54 — But with some of the new applications coming out and the focus that Microsoft also came out and announced — that they are looking at going through and producing a Super App Copilot — and this was something that Satya Nadella just talked about in the news just last week. 01:13 — So when we think about this and we start thinking about how is it that Microsoft's going to start combining all of its different Copilot assets or its AI agent assets, it really opens up a door where we might start seeing a much bigger multiplier effect happening on the number of agents created. 01:32 — Due to the fact that they're going to make it not so confusing to be able to determine where to get started and be able to help you get this all down. So this is really exciting news, and it's really a good thing for why you should start looking and understanding these agents because they're really starting to take off as business applications themselves. Visit Cloud Wars for more.

Would you trust AI at only 80% accuracy? What about for your enterprise? KPMG Chief Digital Officer Kelle Fontenot explains why AI success cannot be measured by headcount reduction alone - and how synthetic data can help companies test agents safely, protect sensitive information, and focus on the moments that matter most to employees. -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most enterprise AI pilots stall or fail before production, and the blocker is rarely the model. Nate B. Jones, an AI analyst and advisor who works with Fortune 500 companies and global banks, tells Michael Krigsman that naming an effort a pilot invites small budgets and safe goals. He explains how to pick a first project that matters to the business, why adoption is roughly 80 percent a people problem, and how to budget AI by cost per completed task rather than cost per token. Recorded live on CXOTalk with questions from the audience throughout.YOU'LL DISCOVER✅ Why calling the work a pilot produces smaller budgets, safer goals, and weaker learning✅ The two starting points Jones gives leaders: get hands-on with the tools yourself, then pick a project where success creates real business leverage✅ Why adoption follows a bell curve, and what actually moves the middle of the distribution✅ Why data flow, not the model, is the technical issue that stops initiatives most often✅ The harness (context, memory, reusable procedures, review gates) treated as company intellectual property✅ How to compare open weights against frontier models on cost per completed action, including token efficiency between models✅ Why cost per task keeps falling even as frontier work stays expensive, and how to budget against that✅ The case for one named owner per agent, and what Jones tells CIOs about shadow AI and cyber defense⏱️ TIMESTAMPS0:00 Introduction0:24 Why pilots fail and where to start3:30 Adoption is mostly a people problem8:51 Data, outcomes, and undocumented knowledge14:19 Learning from pilots and proving value17:57 The harness and AI fluency23:59 Why a culture of experimentation wins28:03 Open weights, costs, and team fluency33:25 When new model releases matter38:15 Job fear, AI costs, and accountability46:41 Agent owners, evals, and production gates51:03 Advice for CIOs and when to stopSubscribe for weekly conversations with the business and technology leaders shaping enterprise AI strategy: https://newsletter.cxotalk.comShow notes, transcript, and summary: https://www.cxotalk.com/episode/why-ai-pilots-stall-how-to-make-enterprise-ai-workEpisode 927 | Recorded Friday, July 31, 2026#CXOTalk #EnterpriseAI #AIStrategy #AIAdoption #DigitalTransformation #CIO #AIAgents #Tokenomics #AIGovernance

Natalia Konstantinova, Lead Enterprise Architect in AI at Natwest, shares why most enterprise AI projects don't fail because of the technology, they fail because organizations skip the foundations. She also explains how responsible AI and scalable AI have converged, and why you can't achieve one without the other. Key Takeaways: Why most enterprise AI projects stall before scale What software engineers consistently get wrong about AI reliability Why governance actually accelerates delivery How to make AI transformational rather than just another digital tool Guest Bio: Dr Natalia Konstantinova is Lead Enterprise Architect for AI at NatWest Group, where she leads the strategic adoption of AI across the enterprise. With a PhD in Information and Language Processing and a background spanning academia, industry, and innovation, she specialises in AI strategy, enterprise architecture, governance, and responsible AI. Natalia is passionate about helping organisations translate complex AI technologies into scalable business value, and is a recognised voice on the future of AI in highly regulated industries. ---------------------------------------------------------------------------------------- About this Show: The Brave Technologist is here to shed light on the opportunities and challenges of emerging tech. To make it digestible, less scary, and more approachable for all! Join us as we embark on a mission to demystify artificial intelligence, challenge the status quo, and empower everyday people to embrace the digital revolution. Whether you're a tech enthusiast, a curious mind, or an industry professional, this podcast invites you to join the conversation and explore the future of AI together. The Brave Technologist Podcast is hosted by Luke Mulks, VP Business Operations at Brave Software—makers of the privacy-respecting Brave browser and Search engine, and now powering AI everywhere with the Brave Search API. Music by: Ari Dvorin Produced by: Sam Laliberte  

Join us this week for The Tech Leaders Podcast, where Gareth sits down with Mike Fitzgerald, Founder of Senttr. Mike shares lessons on managing AI estates, evolving consulting practices, and the skills needed for the future workforce.On this episode, Mike and Gareth discuss the importance of governance, privacy and security when deploying hundreds or thousands of AI agents, strategies for managing enterprise AI costs and ensuring predictable licensing models, and what the future holds for AI pricing models, and the evolving MSP ecosystem.Personal journey: From Ireland to founding Senttr (04:58)The problem with Software Licensing  (12:03)Building Businesses Around Unsolved Problems (15:21)AI Governance Challenges (26:27)Cost Management of Enterprise AI (30:19)Will AI Take Our Jobs? (39:11)The Evolution of IT Consulting (45:39)Advice to a 21-Year-Old Self (50:04)https://www.bedigitaluk.com/

In this episode, Chris Nichols sits down with Walt Wear, SouthState's AI Enablement Manager, to discuss the bank's Copilot adoption journey, lessons learned from scaling AI across the organization, and what's next as the industry moves toward agentic AI. Walt shares how a community-first approach, weekly office hours, AI Central, and a crowdsourced prompt library helped drive adoption rates above Microsoft's benchmark while empowering employees to work more efficiently and expand their skill sets.   The views, information, or opinions expressed during this show are solely those of the participants involved and do not necessarily represent those of SouthState Bank and its employees. SouthState Bank, N.A. - Member FDIC

In this episode, we kick things off by examining how Schneider National is aggressively pushing rates higher in a tight capacity environment. The Green Bay-based carrier handily beat second-quarter expectations and raised its full-year outlook, declaring that ⁠the truckload market is only in the early stages of rate recovery⁠ as it captures double-digit increases on contract renewals. Next, we explore the autonomous trucking sector where Aurora Innovation is spelling out exactly what its driverless technology will cost carriers. The Pittsburgh-based developer reported a second-quarter loss of two hundred seventy million dollars and ⁠detailed the per-mile pricing behind its two distinct business models⁠, targeting different revenue ranges for its transportation-as-a-service and driver-as-a-service offerings. Finally, we cover the rapidly evolving enterprise artificial intelligence landscape as a leading CEO argues that ⁠the competitive battleground has shifted away from model quality entirely⁠. Speaking at the Supply Chain AI Symposium in Chicago, Reindeer's founder explained that large language models are becoming commoditized and that the real edge now lies in the maintenance layer—detecting when workflows drift and adjusting agents without costly engineering intervention. ⁠Follow the FreightWaves Today Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Craig Jeffery and Arjun Krishnan discuss five key limitations of enterprise AI in treasury: data quality, pattern-based reasoning without true understanding, difficulty handling novel situations, variable outputs, and limited explainability. They also explore the opportunities and solutions behind these constraints, including AI-assisted data cleanup, anomaly detection, stronger controls, human oversight, and safer agent-based system design. Enterprise AI for Treasury: A Guide to Agentic Implementation: https://amzn.to/4vTH8Fv AI: The Automation Spectrum and Examples in Treasury (Valorean Technologies) (2026): https://strategictreasurer.com/420-ai-automation-spectrum-and-examples-in-treasury/ Valorean Technologies: https://valorean.ai/ Timestamps: 00:00 Introduction 00:34 AI limitations and opportunities 02:05 Data quality shapes AI quality 05:35 AI correlates but does not understand 09:29 Arjun Krishnan's background 10:18 AI and genuinely novel situations 12:37 Why AI outputs can vary 16:29 Explainability and audit trails 19:14 The most critical limitations 20:32 Final thoughts and book 20:54 Outro ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT STRATEGIC TREASURER ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Download The Strategic Treasurer: A Partnership for Corporate Growth by Craig A. Jeffery in Kindle, or hardcover: https://amzn.to/4uqwFQq As an Amazon Associate, we earn from qualifying purchases. Strategic Treasurer is recognized as a top tier consulting firm in the area of treasury and risk management. Corporate clients, banks, and technology vendors all rely on their industry leading advisory services that are backed by a deep awareness of current needs, practices, and budgeting priorities of treasury professionals through Strategic Treasurer's annual industry surveys and decades of treasury experience. Strategic Treasurer utilizes a senior consultant model where every project is managed by senior consultants with actual practitioner experience in corporate and/or banking roles. Visit us today at http://strategictreasurer.com. Or join in the discussion at one of our leading LinkedIn groups: http://strategictreasurer.com/linkedin/

Google didn't ship its big model, but they shipped a TON of new useful AI you can use today. And Google wasn't the only company updating their features behind the scenes. Replit is bringin vibe designing, ChatGPT got a lot more useful on the web, and Meta is changing from chatbot to agent. We'll get you caught up quickly. Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today -- an Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Replit Design Suite Launches With Free MobbinChatGPT Chrome Extension Adds YouTube SummarizationChatGPT Side Chat Integrates Tabs and Highlighted TextMeta AI Rolls Out Recurring Agent TasksGoogle Gemini Generates Images in Google DocsGemini AI Summarizes Comments, Edits in DocsGoogle Gemini Spark Agent Arrives in ChromeChrome Agent Uses Saved Accounts and PasswordsGoogle Lyria 3.5 Music Model ReleasedBuzz by Block Unites Team and Agent CollaborationTimestamps:00:00 Recent AI updates and developments05:01 Creating with Replit and AI models09:42 Real-time research tracking benefits10:34 Meta AI new recurring features13:35 New features of Meta AI17:53 Google Spark integrates with Chrome22:09 Google DeepMind's new music model25:25 Buzz from Block messaging tool29:42 Building a collaborative platform31:23 AI feature updates recapKeywords: Gemini Spark, Google Chrome AI integration, Google Docs AI features, AI image generation, Gemini in Docs, ChatGPT Chrome extension, YouTube video summarization, OpenAI ChatGPT update, Codex, Vibe design, Replit design suite, Mobbin integration, AI reference library, Design export automation, Project management AI, Figma competitor, Replit creative tools, Meta AI, Muse Spark 1.1, Agentic model, Recurring AI tasks, AI scheduling, Daily briefings, AI productivity tools, Google Lyria 3.5, AI music model, Flow Music, Suno, Yudio, AI generated lyrics, Vocal delivery in AI music, Licensing in AI music, Buzz collaboration platform, Block, Square, AI agent teamwork, Slack-like AI platform, Open source collaboration, Agent governance, Cryptographic identity, Agentic browser, Automated web errands, Chrome passwords integration, Google Drive data access, Multi-agent collaboration, Research automation, Enterprise AI workflow, AI productivity boost.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Sarah Wang and Kimberly Tan are joined by Jesse Zhang and Ashwin Sreenivas, co-founders of Decagon, to discuss the evolution of enterprise AI agents, why the company increasingly relies on open-source models, and how it is helping some of the world's largest companies deploy AI in production. Decagon has become one of the fastest-growing AI companies by building agents that automate customer support, sales, and operational workflows. Jesse, Decagon's CEO, and Ashwin, its president, explain how the company is building enterprise AI at scale. They unpack why Decagon moved most of its inference to open-source models, how latency, evaluation, and fine-tuning shape production AI systems, and why enterprise AI requires far more than simply plugging into frontier models. The conversation also explores forward-deployed engineering, enterprise sales, AI's impact on jobs, and why application companies will continue to thrive alongside the foundation model labs.   Resources: Follow Jesse Zhang on X: https://x.com/thejessezhang Follow Ashwin Sreenivas on X: https://x.com/AshwinSreenivas Follow Sarah Wang on X: https://x.com/sarahdingwang Follow Kimberly Tan on X: https://x.com/kimberlywtan Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

New names: Kimi K3, Llama, Nemotron, Mistral, Cohere, Deepseek, Phi-4 – these are just a few of the fast-growing open source models from major AI providers. These systems threaten the business models and financial plans of OpenAI, Anthropic, Google, and X.ai. They perform at levels close to Frontier models and the can run up to five-times cheaper on a variety of hardware platforms. What is the disruptive impact of these open source LLMs and how does this impact your AI investments? As you'll hear in the podcast, Open Source unleashes the opportunity for lower cost AI solutions and more vertical, specialized, application-focused solutions we need. And the business model for these systems moves away from the massive investments of the Frontier providers. The result is more complicated than “open means control.” Model tuning, performance, and optimization could be in your future – as AI moves from a platform to a true layered product set we can use as we need. Lots to learn about here, let us know if you have any questions. Additional Information What's the difference between closed, open source, and open-weight AI? A researcher explains What Is Open-Weights A.I.? Comparison of Open Source Models Chapters (00:00:00) - Open Source and the AI Industry(00:11:46) - The Future of AI Is Fully Integrated(00:15:35) - HR 2030

An estimated 80% of all existing digital data isn't neatly organized in rows and columns, but that unstructured data can...

For episode 758 of the BlockHash Podcast, host Brandon Zemp is joined by Dave Trier where he serves as CEO of ModelOp, where he leads the company with a clear focus on customer value, product innovation, and enterprise execution. A builder at heart, Dave brings deep technical fluency and real-world operating experience to the challenge of helping global enterprises unlock the transformational power of AI.

Enterprise technology has long promised to unlock operational potential, but the trade-offs between monolithic ERP systems and fragmented best-of-breed stacks continue to slow organizations down in ways most leaders don't fully account for. In this episode of Enterprise Unleashed on Supply Chain Now, Scott W. Luton and Wiley Jones (Co-founder at DOSS) are joined by Jindra Zitek (Partner and Head of Scale at Stripes), a former McKinsey consultant and C-level operator whose career spans Chobani, HelloFresh, and a range of board and advisory roles across industry. Together, they unpack what it actually takes to build operations that are truly AI-ready, and why the answer starts with people long before it reaches technology. Jindra draws on firsthand experience leading through hypergrowth and crisis to make the case that both ERP consolidation and best-of-breed ecosystems carry a hidden coordination tax, one that only gets more expensive the longer organizations avoid naming it. He challenges leaders to stop framing AI as a job automation tool and start treating it as a lever for dignity and purpose, giving people the ability to focus on the work that actually requires human judgment. The conversation lands on what separates organizations that experiment successfully from those stuck in pilot purgatory: shared business objectives, cross-functional coalitions, and guardrails that free people to move fast without losing control. Wiley grounds the discussion in what Doss is seeing in practice, where the complexity of edge cases is finally collapsing, and where the real work of comprehension still cannot be skipped. Jump into the conversation: (00:00) Intro  (02:29) Jindra Zitek's background across McKinsey, Chobani, and HelloFresh (04:16) Lessons from family, farming, and leadership (06:09) Why people should come before systems (10:28) The trade-off between ERP and best-of-breed tools (15:24) The hidden cost of technology choices (17:09) How company DNA shapes technology strategy (20:57) Eliminating the human tax of broken systems (21:49) Automation should enable people, not replace them (25:57) The rise of agentic operations (28:57) Building trust and control with AI systems (31:34) Measuring real ROI from AI investments (35:17) Building cross-functional teams for AI success (37:30) Creating guardrails for enterprise AI adoption (40:40) The first workflow to transform with AI (42:10) Finding business problems AI can solve (47:56) How AI changes workflow design and implementation Additional Links & Resources: Connect with Jindra Zitek: https://www.linkedin.com/in/jindrazitek/ Connect with Wiley Jones: https://www.linkedin.com/in/wileycwjones/ Learn more about DOSS: https://www.doss.com/ Learn more about Stripes: https://www.stripes.co/ Learn more about Supply Chain Insights: http://www.supplychaininsights.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- Peak Reality Check: What Shippers, Analysts, and AI Models Are Predicting for 2026: https://bit.ly/4aTlsRv WEBINAR- From Volume to Resilience: How Automotive Supply Chains Are Adapting to a New Market Reality: https://bit.ly/4f6SUGA WEBINAR- The Automotive Industry's Next Digital Breakthrough: https://bit.ly/4vhUwT4 WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt This episode was hosted by Scott Luton and Wiley Jones, and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/beyond-erp-tradeoff-building-ai-ready-operations-1614 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.

What happens when a seasoned hedge fund executive applies decades of institutional investing experience to building AI-powered software for the world's most sophisticated financial firms? In this episode of Silicon Valley Successes, host Shawn Flynn sits down with Jan Szilagyi, Founder and CEO of Reflexivity, to explore the intersection of artificial intelligence, capital markets, and enterprise software. Drawing from his unique career spanning both hedge funds and technology startups, Jan shares what it takes to build software for one of the most demanding customer bases in the world—professional investors managing billions of dollars. He explains why today's competitive advantage is shifting from simply having access to information to asking better questions, and how AI is transforming the investment process. The conversation explores how hedge funds are evolving in the age of generative AI, what institutional investors are actually looking for from AI platforms, and how founders can avoid common mistakes when building and pricing AI-driven software products. Jan also discusses the realities of selling enterprise AI solutions in an increasingly crowded market, why customer education has become one of the biggest challenges for AI startups, and how Reflexivity helps investment firms uncover insights that traditional research methods often overlook. Whether you're building an AI company, investing in technology, or simply curious about how artificial intelligence is reshaping financial markets, this episode offers practical insights from someone who has successfully navigated both Wall Street and Silicon Valley. In This Episode Jan Szilagyi's journey from hedge funds to technology entrepreneur What life inside a modern hedge fund is really like How AI is changing institutional investing Why asking better questions is becoming more valuable than having more data Identifying investment opportunities with AI Building versus buying enterprise software The future of AI-powered investment research Educating enterprise customers on AI adoption Converting AI curiosity into measurable business value Pricing strategies for early-stage AI software companies Lessons learned selling into sophisticated financial institutions How Reflexivity helps investors uncover hidden market insights Key Takeaways AI is becoming a force multiplier rather than a replacement for human judgment. The firms that ask better questions will outperform those with simply more data. Enterprise AI adoption depends as much on trust and workflow integration as model performance. Pricing AI software should evolve as customer value and product intelligence increase. Building for institutional investors requires exceptional reliability, transparency, and measurable outcomes. About Jan Szilagyi Jan Szilagyi is the Founder and CEO of Reflexivity, an AI-powered platform designed to help institutional investors and financial professionals extract deeper insights from vast amounts of information. With a background spanning hedge funds and technology entrepreneurship, Jan combines expertise in investing, data analysis, and artificial intelligence to build tools that improve investment research and decision-making. Who Should Listen This episode is ideal for: Startup founders Venture capital and private equity professionals Hedge fund and asset management executives AI founders and product leaders Financial technology entrepreneurs Enterprise software executives Investors interested in AI applications Anyone curious about the future of intelligent investing Connect with Jan Szilagyi LinkedIn: https://www.linkedin.com/in/jan-szilagyi-12284ab/ Website: https://reflexivity.com/ Disclaimer: The views expressed in this podcast are for informational purposes only. They do not constitute financial, legal, tax, or investment advice, nor do they necessarily reflect the views of Finalis Inc. or Finalis Securities LLC, Member FINRA/SIPC. Any discussion of investment strategies, artificial intelligence, financial markets, or specific technologies is intended solely for educational purposes and should not be considered investment advice or a recommendation to buy or sell any security. #SiliconValleySuccesses #ArtificialIntelligence #AI #GenerativeAI #FinTech #HedgeFunds #EnterpriseAI #InvestmentResearch #MachineLearning #VentureCapital #AssetManagement #SaaS #StartupFounder #TechnologyLeadership #Innovation

Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value? In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valiance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his "value partner" title is deliberate. The company begins by identifying the financial or operational result a client wants and connects its own compensation with achieving that result. Dom argues that many AI initiatives begin without a clear definition of success. The pressure to adopt AI is real, but companies frequently select technology before agreeing on the business problem, desired outcome, or measurement. He identifies three recurring mistakes. The first is framing the project around AI rather than the business need. The second is failing to establish a metric and baseline before work begins. The third is using a consulting model that rewards billable time without connecting payment to the client's result. We also discuss how generative AI is changing traditional build versus buy decisions. Companies historically bought software because custom development was slow, expensive, and difficult to maintain. Coding agents can now reduce the time and cost required to create software for specific internal needs. Dom does not believe SaaS will simply disappear. However, vendors selling convenience, workflow wrappers, or integration glue face new competition from customers who can create similar capabilities themselves. He argues that stronger SaaS positions will depend on assets a model cannot easily regenerate, including proprietary data, networks, regulatory standing, and deep workflow adoption. This leads to a wider discussion about competitive advantage. When companies have access to similar models, generated code begins to converge. Dom believes lasting differentiation comes from company data, institutional knowledge, connected systems, employee experience, and the semantic context surrounding that information. Dom explains why ontologies matter to enterprise AI. Raw data tells an agent what is stored in a particular field. An ontology describes the customers, orders, contracts, payments, relationships, and business rules represented by that data. This context allows people and agents to reason about information in a way that reflects how the company actually works. Governance also needs to be designed from the beginning. Dom argues that security, permissions, accountability, and compliance allow successful pilots to expand without forcing the business to rebuild everything later. How can leaders tell when AI is genuinely being adopted? Dom offers a surprisingly simple signal: people stop talking about AI. The technology becomes part of ordinary Monday morning work, and employees focus on completing the task rather than explaining the tool. Has your company defined the business result, measurement, proprietary context, and governance required to turn AI enthusiasm into operational value? Listen to the episode and share your thoughts with me.

P.M. Edition for July 23. The U.S. plans to impose new tariffs on most trade partners, replacing President Trump's temporary global 10% tariff. Plus, the threat of escalating conflict in the Middle East drove oil prices over $100, and concerns around higher inflation made bond yields surge. WSJ markets reporter Sam Goldfarb discusses how that ripples through the economy. Meanwhile, heavy AI spending from Alphabet and Tesla spooked investors, and the Nasdaq dropped more than 2%. And after IBM issued a rare profit warning last week, the company's earnings shed more light on what went wrong. We hear from reporter Anissa Gardizy about where its business goes from here, while tech columnist Christopher Mims spoke with IBM CEO Arvind Krishna. Alex Ossola hosts. Correction: New U.S. tariffs target 60 economies, or more than 80 countries. An earlier version of this podcast incorrectly said the tariffs target 60 countries. (Corrected on July 24.) Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ben Lorica speaks with Denise Teng, partner at Gradient Ventures, about the real state of enterprise AI adoption, why security and data privacy still slow deployment, and why open weights may become increasingly important for regulated industries.Subscribe to the Gradient Flow Newsletter

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Amit Zavery, President and Chief Product Officer at ServiceNow. The platform runs more than 75 billion workflows a year with around $15 billion in annual revenue growing over 20%. Its market cap is above $100 billion, yet the stock is down more than 30% this past year, while its AI business is on track for $1.5 billion, ahead of a $1 billion plan. Amit previously ran product and platform at Oracle for over two decades and was a VP and General Manager at Google Cloud.What you'll learn:Why the market can't yet tell AI winners from losers, and why companies that don't transform will get killedWhy the idea of one company becoming the single end-to-end enterprise orchestrator is a fallacyThe spare part approach that makes most enterprise AI projects fail, and what pacesetters do insteadWhy access is shifting from user interfaces to agents, and what taking action actually requiresHow to hold long-term conviction on platform bets while the market judges you on short-term sentimentKey takeaways:Transform or die: the market will separate AI-native platforms from legacy vendorsInteroperability beats domination in the agentic eraGovernance only wins when it accelerates innovation, not when it blocks itCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Amit ZaverySocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Plus: Novo Nordisk files a deceptive advertising lawsuit against Eli Lilly. And the latest Chinese AI model launches rattle expectations for the biggest AI players in the US. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI agents can't transform an org they can't see. Albert Strasheim, CTO at Rippling, joins Andrew Zigler to explain why agentic transformation starts with the employee graph, the system of record for who does what. He shares how Rippling assembles teams and primitives across silos, why evals are the new unit test, and how compensating controls keep AI output from turning into slop. When agents do the work, you still have to know who, or what, shipped it. LinearB attributes the work, whether it came from humans, AI assistants, or autonomous agents.Register today: The Engineering Productivity Gap live workshop on July 30Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:Rippling: Explore the workforce management platform at rippling.com Introducing Rippling Data Cloud: AI-powered BI that understands your workforceFollow Albert: LinkedIn OFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Welcome to episode 364 of The Cloud Pod, where the forecast is always cloudy! Justin and Matt are in the studio this week to bring you all the latest in cloud and AI news, including (surprise) astronomical AWS bills, Kimi K3 and what it means for Enterprise AI, and lots of security news! All that and so much more, so let's get started!  Titles we almost went with this week Cache Rules Everything Around NFS Now Lambda Says BYOB, Bring Your Own Bucket Henrico’s Power Struggle: Data Centers 37, Schools 0 Cloud Run Fails Over Faster Than Your Excuses 570 Patches, One Registry Hive Nightmare GuardDuty Gets a Detective Agent, No Trench Coat Required Kimi K3 Aims to Moonwalk Past Opus 4.8 Terraform Gets Policy Muscle, Ditches the Rego Diet CloudWatch Watches Your AI Coders Code Watt A Way To Treat A School District Your Cloud Bill… 1 BILLION DOLLARS Not a way I want to wake up rogue cloud bills Skype is EOL … wait I thought I died 3 times already Our newest superhero CODEMENDER!! A big thanks to this week's sponsors: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our Slack channel for more info. General News  00:49 Amazon fixing bug that billed some AWS customers billions of dollars  A bug in the AWS billing computation subsystem generated inflated billing estimates for some customers, with one Reddit user reporting a quoted estimate near 2.5 billion dollars for a single month. In contrast, others saw figures ranging from millions to hundreds of millions. The issue began late Thursday, and an initial rollback attempt on Friday morning failed to resolve it, suggesting the root cause was more complex than a recent configuration change. Amazon confirmed the billing estimates do not reflect actual usage or charges, meaning affected customers will not be responsible for the inflated amounts shown in the console. Amazon has not disclosed whether any accounts were suspended or paused due to the billing errors, leaving open questions about operational impact during the incident. The event highlights the importance of billing system reliability for cloud providers, since inaccurate estimates at this scale can cause confusion and concern even when the underlying charges are not real. 01:29 Justin – “Amazon doesn’t bill you in the middle of the month, so it’s a pretty low risk that you were gonna get billed or invoice directly on that date, unless you happen to already be overdue on a payment and you were happening to update your credit card at the same time. I don’t think that’s really a big risk for this particular scenario.”  05:04 County With 37 Data Centers Asks Schools to ‘Conserve Electricity' Listener note: Paywall article  H

Most companies say they're doing AI. A surprising number are doing very little — and a Chief AI Officer at one of the world's largest automation platforms has the receipts to prove it. Motley Fool analyst Rachel Warren talks with Adam Field, Chief AI Officer at Tungsten Automation — a company serving 25,000 organizations including 40% of the Fortune 100 — about what separates real AI transformation from expensive spin. They get into why most enterprise AI pilots quietly die before they scale, what "boring AI" actually means and why it's the most important signal investors aren't paying attention to, and why the competitive moat that once made legacy software giants unassailable has effectively disappeared overnight. Host: Rachel Warren Guest: Adam Field Producers: Adam Landfair, Lauren Budabin Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

Today, we are dropping our final episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In our final episode, we are joined by Shayne Higdon, Wallarm CEO, who closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.QuestionsWhy is now the accountability moment for enterprise AI?What has changed between the early days of AI experimentation and today's enterprise AI deployments that makes accountability such a pressing issue?When we talk about AI accountability, what does that actually mean in practical terms? Are we talking about visibility, auditability, enforcement, ownership—or all of the above?As organizations race to deploy AI, how should CIOs balance the speed of transformation with the responsibility to govern it effectively?Why are traditional governance and security models struggling to keep pace with the way AI is being adopted across the enterprise?Given those challenges, how should boards and executive teams evaluate whether their organizations are truly ready to scale AI safely and responsibly?And once an organization believes it's ready, what does a mature AI governance model actually need to prove - not just promise?From an operational standpoint, how do capabilities like discovery, runtime monitoring, and enforcement come together to create a closed-loop approach to AI accountability?Stepping back and looking across this entire conversation, what's the one mindset shift every enterprise leader needs to make when it comes to AI security and accountability?And finally, as listeners think about what's ahead, what should they expect the future of AI security and accountability to look like over the next 6, 12, or even 24 months?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/shaynehigdon/Full AbstractAbstract: Join Shayne Higdon, Wallarm CEO, for this episode, which closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.AI deployment is not waiting for governance to catch up. Across most enterprises, the gap between how fast AI is being adopted and how well it is being governed is widening every quarter. CIOs and CISOs are not debating whether to govern AI. They are trying to figure out how, under real organizational pressure, with tools and frameworks that were built for a different threat model.That pressure is coming from every direction at once. Boards want AI transformation to move fast. Regulators want documented evidence that it is under control. Security teams want runtime visibility and enforcement capabilities that most of their current tools do not provide. And the AI systems themselves are not waiting: they are accessing data, calling external services, and making decisions continuously, in ways that after-the-fact governance cannot meaningfully constrain.This is the accountability moment. Not because the risk is new, but because the consequences of undermanaged AI are now concrete enough to land on a board agenda, an audit report, and a regulatory deadline at the same time. What accountability actually requires in practice is the full AI control loop: knowing what AI is running across the enterprise, seeing what it is doing at runtime, enforcing policy before damage compounds, and generating continuous evidence that the governance is real and not retroactive. Organizations that can demonstrate all four are in a fundamentally different position than those still assembling audit evidence from spreadsheets the week before a review.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy