Podcasts about enterprises

In 1994, Daragh Mahon won the green card lottery and moved from Ireland to Atlanta. His first American job was driving an eighteen-wheeler for Schneider. Thirty years later, he runs IT for Werner Enterprises, one of the largest trucking companies in North America, with 12,000 drivers moving freight across the country every day.In this episode of Kill Chain, host Terry Reinert sits down with Daragh to unpack a career arc most CIOs never take and a perspective on cybersecurity, AI, and the future of transportation that most haven't earned.What you'll hear:Why Daragh asked every autonomous trucking company the same security question and never got an answerThe 3 critical infrastructure sectors a foreign adversary attacks first (and why transportation is on the list)Why he wants the tech industry to "stop talking about AI"The AI backlash brewing in colleges that CIOs aren't trackingThe story of signing a contract he had no authority to sign and getting 12 months to make it workWhy real innovation only happens at the startup level, and what the big software companies stopped doingBuilding security into corporate DNA instead of bolting it onThe Werner Accelerator and why every corporation should run onePredictions for the next ten years (and why he refuses to make them)About the guest:Daragh Mahon is the EVP and CIO of Werner Enterprises (NASDAQ: WERN). Before Werner, he led IT at Vonage and held senior roles at Sage and Peachtree Software. He emigrated from Ireland to the US in 1994 through the Morrison Visa Program.About the show:The Kill Chain Podcast is a conversation series about cybersecurity, transportation, and the future of fleet operations, hosted by Terry Reinert, CEO of Fleet Defender. New episodes drop every other week.Want to learn more about securing your fleets, platforms, or mission critical systems? Contact us at FleetDefender.com.

Tata Consultancy Services (TCS), a global leader in IT services, consulting and business solutions, who operate a Global Delivery Centre in Letterkenny, Co. Donegal, today announced a landmark strategic partnership with Mistral, one of world's leading AI companies. As part of this collaboration, TCS has become the first global systems integrator partner for Mistral Forge, Mistral's advanced system for enterprises to build frontier-grade AI models grounded in their proprietary enterprise knowledge and domain-specific data. The partnership combines Mistral's frontier AI capabilities with TCS' deep context of enterprise customers, domain knowledge and engineering excellence, to help organisations scale enterprise AI responsibly with greater speed. As part of this strategic collaboration, TCS will leverage Mistral Forge to build custom AI models for enterprises. It will help customers deploy their data and enterprise context to improve decision outcomes. This collaboration draws on TCS' strong global presence across North America, the United Kingdom, Europe, and Asia-Pacific to deliver AI solutions tailored to industry needs, operations and regulatory requirements. The partnership will initially focus on sectors like banking, financial services and insurance (BFSI), manufacturing, healthcare, and the public sector, where trusted AI adoption is becoming increasingly critical. TCS will also establish a dedicated Centre of Excellence for Mistral to drive joint innovation, build industry-specific solutions, support project delivery, and accelerate client value through early access to Mistral's beta models. The Centre of Excellence will serve as a strategic hub for advanced talent, focused training, and the capabilities needed to design, deploy, and govern AI solutions. Arthur Mensch, Chief Executive Officer and Co-Founder at Mistral, said, "TCS' global scale and contextual industry knowledge make them an ideal partner for Mistral. Together, we are enabling enterprises worldwide to move from experimentation to AI deployment with systems that are open, production-ready and aligned with their strategic and operational requirements." K Krithivasan, Chief Executive Officer & Managing Director at TCS said, "The partnership with Mistral reinforces TCS' commitment to scaling enterprise AI with trust, control, and measurable business outcomes at the core. This partnership expands TCS' AI ecosystem, uniquely positioning TCS to create a differentiated solution proposition for our clients. Together with Mistral, we will solve for specific industry challenges, regulatory requirements, and sovereign needs for our enterprise customers." As part of its broader Infrastructure to Intelligence AI strategy, TCS continues to invest across infrastructure, models, data, application, platforms and physical and digital intelligence. This aligns with TCS' ambition to become the world's largest AI-led technology services company, underpinned by a five-pillar strategy focused on embedding AI across the enterprise, scaling AI-led delivery capabilities, and driving measurable business outcomes for clients.

This week we have a technical segment focused on Linux! Paul released a script that helps you get a handle on Linux supply chain security, and new features allow you to assess the state of Secure Boot on your Linux systems (that also use MS certificates, ironically). The script is in his Git repo: https://github.com/pasadoorian/Linux_Hacks. In the security news: The CVE chase The new security basics Enterprises are lacking more than AI Detections are falling behind Why DOOM!?! Chromium vulnerability The ambitious Flipper One I'm still curious who was behind these leaks Mitre moves Caldera to Apache foundation Wind cybersecurity PQC updates YellowKey Bitlocker Bypass updates The software supply chain is in deep trouble Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-928

This week we have a technical segment focused on Linux! Paul released a script that helps you get a handle on Linux supply chain security, and new features allow you to assess the state of Secure Boot on your Linux systems (that also use MS certificates, ironically). The script is in his Git repo: https://github.com/pasadoorian/Linux_Hacks. In the security news: The CVE chase The new security basics Enterprises are lacking more than AI Detections are falling behind Why DOOM!?! Chromium vulnerability The ambitious Flipper One I'm still curious who was behind these leaks Mitre moves Caldera to Apache foundation Wind cybersecurity PQC updates YellowKey Bitlocker Bypass updates The software supply chain is in deep trouble Show Notes: https://securityweekly.com/psw-928

NEAR keeps showing up in strange places: cross-chain wallets, privacy apps, AI infrastructure, and now the emerging agent economy. Sal Ternullo, CEO of SVRN, joins us to explain why he thinks this is not another NEAR pivot, but the original thesis finally coming into focus. They dig into NEAR Intents, AI money, tokenomics, privacy, fee capture, agentic commerce, and why SVRN is trying to commercialize the NEAR ecosystem rather than simply hold the asset. ---

Over the last two decades, Eric Ries's ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader's Guide; and The Startup Way.  As a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; the Lean Startup Co, which teaches and supports the implementation of Lean Startup; Virgil, a legal services startup; and IMVU, where the ideas that became the Lean Startup method were forged. On his podcast, The Eric Ries Show, he talks to guests including world-class technologists, thought leaders, and executives working to build profitable companies for the long-term benefit of society. Eric has served as an entrepreneur-in-residence at Harvard Business School and IDEO. He lives in the San Francisco Bay Area with his wife and three children. This episode is sponsored by the coaching company of the host, Paul Zelizer. Consider a Strategy Session if you can use support growing your impact business. Resources mentioned in this episode include: Incorruptable site Miyoko Awarepreneurs interview The Lean Startup site Long Term Stock Exchange site Paul's Strategy Sessions Pitch an Awarepreneurs episode  

 Ramkumar Narayanan | EVP, Head of India and Philippines,AI technology Enablement,FIS  Ramkumar Narayanan is a global leader focusing on data driven, digital product innovation spanning consumer and enterprise markets. He brings a vast experience in product development, product management and product marketing having led both new market entry and turnaround of existing business areas. He has been an advisor to Enterprises, large and small, in the arena of digital transformation, product strategy and product marketing. Ram is currently EVP Technology & Services at FIS India and Philippines. Prior to joining FIS, he served in global leadership positions at VMware, eBay, Yahoo! and Microsoft. He started his career in the auto industry in US developing software solutions for design and packaging of automotive suspension and powertrain systems. Ram formerly served on the Executive Council of NASSCOM and was Chairperson NASSCOM Product/Deep Tech Council.Ramkumar Narayanan holds a B.E. in Mechanical Engineering from Anna University, Chennai, M.S. in Mechanical Engineering & MBA from University of Michigan, Ann Arbor.  

Are your AI agents truly safe to deploy at scale? In this episode, two founders come together to tackle one of the most urgent questions for every AI startup and enterprise today: how do you build AI systems you can actually trust? Hosted by Preethy Padmanabhan and the 10x Growth Strategies community, this panel brings together Tatyana Mamut (WayFound) and Prukalpa Sankar (Atlan) - two founders redefining how enterprises govern, monitor, and scale AI responsibly. In this episode, we discuss: How supervisor/guardian agents help enterprises reach 3-nines and 5-nines reliability Real-world example: a customer service AI agent secretly offering refunds with perfect guardrails in place Why sampling logs isn't enough and why 100% monitoring is now a legal requirement How the OpenAI and Workday lawsuits are reshaping enterprise AI accountability How to architect AI-ready data systems with lineage, traceability, and explainability built in How enterprises across the US, Germany, and Australia are navigating evolving AI regulation Hybrid deterministic + AI system design for production-grade agents Whether you're an entrepreneur, a corporate executive, or a venture capital investor evaluating AI startup opportunities - this conversation on 10x growth, scaling up, and responsible AI is unmissable. 10X Growth Strategies is a community co-founded by Preethy Padmanabhan, built to bring together founders, investors, and executives for meaningful connection and growth. With thousands of members across LinkedIn, Luma, and Partiful, the community hosts monthly events on timely topics in tech, AI, and entrepreneurship. Chapters 0:00 - 3:32 - Introduction 3:32 - 7:59 - Understanding Enterprise Trust in AI 7:59 - 13:47 - Legal Challenges and Compliance in AI 13:47 - 19:00 - Architecting AI systems for Compliance 19:00 - 21:52 - Benefits of Working with this New Technology 21:25 - 25:21 - Proactive vs Reactive Compliance Strategies 25:21 - 41:35 - Audience Interactions 41:35 - 44:04 - What is your Leadership Principle? Connect:  Website: https://grow10x.podbean.com/ Luma: https://lu.ma/10xgrowthstrategies

Enterprises have agents. Most can't run them at scale. IBM's Suzanne Livingston explains what changes when you have hundreds — not two.Full Show NotesScaling agentic AI is not the same problem as building it. At IBM Think 2026 in Boston, I sat down with Suzanne Livingston, VP of Product for IBM watsonx Orchestrate, to talk about where enterprise organizations actually are on this journey — and what it takes to move from a pilot to a production environment running hundreds of agents across dozens of departments.Suzanne walks through the full watsonx portfolio, then goes deep on the challenge she hears from customers constantly: the agent worked in the demo, but now it needs to run reliably at scale, with proper governance, observable across the estate, and permissioned correctly for every user and every system it touches. That is a fundamentally different problem than building the agent in the first place. The new Orchestrate Agent Control Plane is IBM's answer to it.This episode is for enterprise technology leaders who have moved past "should we do agents" and are now asking "how do we run them well." If your organization is somewhere between first pilot and full production deployment, this conversation is the one to listen to this week.What We CoverWhy the jump from generative to agentic AI changes the operating model, not just the technologyWhat agent orchestration means in practice when you have 40 sub-agents reporting to one master agentWhat the Orchestrate Agent Control Plane does and why cross-estate visibility matters more than per-agent optimizationHow enterprises are treating AI agents like digital employees — with identities, goals, managers, and performance reviewsWhy governance isn't optional in an agentic environment and what "governance light" looks like for organizations just getting started.Guest BioSuzanne Livingston is Vice President of Product Management for IBM watsonx Orchestrate, IBM's enterprise AI orchestration platform. She leads the product team responsible for agent building, orchestration, evaluation, and the recently announced Orchestrate Agent Control Plane. Suzanne presented at IBM Think 2026 in Boston.IBM Think profile: https://www.ibm.com/think/author/suzanne-livingstonResources MentionedIBM watsonx Orchestrate 30-day free trial: https://www.ibm.com/products/watsonx-orchestrateIBM Think 2026 content: https://www.ibm.com/thinkLopez Research blog: https://www.lopezresearch.com/research/

In this Cloud Wars conversation, Bob Evans speaks with Matt Renner, Chief Revenue Officer at Google Cloud, about the explosive acceleration of enterprise AI adoption and how Google Cloud is scaling to meet it. Renner explains why customers are demanding immediate business outcomes, not experimental pilots years down the road, and shares Google Cloud's response through expanded field engineering investments, ecosystem funding, and deeper enterprise co-creation. The discussion also explores Google's differentiated AI stack strategy, the intensifying competitive landscape, and why AI security could become one of the industry's most significant next battlegrounds.Google's AI Scaling Play The Big Themes: AI Demand Has Moved Beyond Experimentation: Matt Renner makes clear that enterprise AI has entered a fundamentally different phase. Companies are no longer satisfied with proof-of-concept experimentation or exploratory pilots. Instead, executive teams want measurable business value quickly. This urgency is reshaping vendor expectations, deployment models, and customer engagement strategies. Google Cloud is seeing demand at a pace that traditional scaling models cannot satisfy, which is driving operational changes. This is not a speculative future trend, it is already happening. The $750 Million Ecosystem Expansion Multiplies Capacity: Google Cloud's $750 million ecosystem investment complements the FDE initiative by scaling partner-led implementation capacity. Renner explains that Google alone cannot meet enterprise AI demand, so partner ecosystems become force multipliers. The strategy is to expand from hundreds of specialists into thousands of technical practitioners capable of building agents, workflows, and AI-powered solutions. This reflects a practical recognition that enterprise AI requires broad execution capability, not just core platform excellence. The AI Market Reset Is Reshaping Cloud Competition: Renner describes AI as a market reset that is materially changing competitive cloud dynamics. Google Cloud's growth rates, contrasted against hyperscaler rivals, are presented as evidence that strategic positioning matters. The broader takeaway is that AI has altered enterprise buying criteria, infrastructure priorities, and vendor differentiation. Long-term investments in chips, models, data infrastructure, and platform integration are beginning to show commercial returns. Rather than incremental cloud evolution, Renner presents this as a structural shift in the market. Enterprises are reallocating attention and budgets around AI capability. The Big Quote: “We're seeing unprecedented demand for Google Cloud products infrastructure, all driven, frankly, from AI." More from Matt Renner and Google Cloud: Connect with Matt Renner on LinkedIn or learn more about Google Cloud AI. Visit Cloud Wars for more.

Enterprises today are managing increasingly complex cybersecurity environments across cloud, AI systems, applications, endpoints, and enterprise networks. As AI adoption accelerates, organizations are under pressure to secure AI-driven environments while responding to faster and more sophisticated threats. In this episode of the Zinnov Podcast, Rajat Kohli, Partner, Zinnov speaks with Michael Khoury, Vice President, Global Ecosystem Partners, Palo Alto Networks, about the shift from point products to platform-led cybersecurity strategies and what it means for enterprises, partners, MSSPs, hyperscalers, and global system integrators. Michael Khoury shares perspectives on how enterprises are navigating cybersecurity complexity in an AI-first world, and how ecosystem models are evolving alongside it. The conversation explores: • Why AI is accelerating platform-led cybersecurity • The rise of MSSPs and cloud marketplaces as key routes to market • How enterprises are reducing security complexity through platformization • What differentiates advanced ecosystem partners in the AI era Tune in now.

Join me, Jenny D. for a special edition of Spill with Me Jenny D. Meet Matt Rohm owner of LeWay Enterprises in the South Hills of Pittsburgh. Matt gave me a tour of his shop and showed me all that goes into screen printing. The people behind the scenes will amaze you! LeWay Enterprises is a family owned Pittsburgh business serving our community for over 26Years. From Custom screen printing, embroidery, team apparel and promotional products, their in-house creative design team has been helping businesses, teams, and organizations throughout Western Pennsylvania and beyond to bring brands to life. Thank you Matt for sponsoring Jenny D's Special Edition Episode with The Steelers Scouting Coordinator Casey Weidl on Wednesday May 20th. Also, a shout out to LeWay for being Jenny D's on-line store for over 4 years now! https://spill.itemorder.com/shop/sale/ To find out more about LeWay click the link below or call 412-942-0740 https://www.leway.com/ #supportlocal #supportsmallbusiness #screenprinting #embroidery #familyowned

 IBM's chief legal officer explains how governance drives AI success through trust, transparency, and employee adoption.  Enterprises are learning that employees won't use AI if they don't trust it. How can the right governance policies help companies improve AI adoption while also increasing innovation? Join Steve Odland and guest Anne Robinson, senior vice president and chief legal officer at IBM, to find out how AI trust ties directly to AI adoption, why companies need to be clear about the problems they're trying to solve, and how the public sector is grappling with AI.

Hi, welcome, I've used Notebook LLM to create this video based on an article I've written about scaling AI projects from pilots to production ready. Read the full article HERE.https://futurestrong.org/2026/04/23/beyond-genai-pilots-how-enterprises-build-scalable-ai-with-governance-and-trust/I'm Rachana, and I write short stories, poetry and essays on our enduring humanity. For 15+ years I've been helping people unlock their highest potential and build lives of purpose, resilience and unstoppable momentum.My big dream? To consciously create a better future where everyone is excited about their own potential – and yes, I'm aiming to win the Nobel in December 2044 for contributions to human development. Crazy? Maybe. But will you join me on this journey of growth and transformation?

Kris Lovejoy, Global Strategy Leader at Kyndryl, has spent her career at the intersection of IT infrastructure and security. Right now, she's one of the people enterprises call when they want to move from AI experimentation to real deployment. Her diagnosis is clear: agentic AI is a bullet train sitting on tracks built for 30 miles per hour. The technology is ready. Most organizations aren't, and the gap between a successful pilot and a production system running at scale is far wider than the hype suggests. In this conversation with Craig Smith, Lovejoy walks through why IT service management is the smartest entry point for agentic adoption, how cost savings of up to 90% in that area can fund broader modernization, and why the security risks in agentic systems are less about sophisticated hackers and more about misconfiguration, bad context, and human error. She closes with a specific prediction: half of traditional IT administration tasks will be handled by AI agents by 2031, and a surprising take on who will actually thrive in the agentic era: not coders, but people trained to ask the right questions. For anyone making decisions about AI adoption, this is the most practical conversation available right now. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

When a company survives an 18 month cease trade order, investors have reason to look closely at what comes next. Fobi AI CEO Rob Anson says the company used that period to reduce costs, maintain operations, raise strategic financing, and reposition the business around enterprise grade agentic AI. With key filings completed and the company continuing through the regulatory review process, Fobi AI is working toward a potential return to trading while presenting a very different company than the one investors last saw.WHAT YOU NEED TO KNOWRegulatory Progress: Fobi says key filings have been completed as the company continues through the regulatory review process required for a potential return to trading.Lower Cost Structure: Operating costs have been significantly reduced from legacy levels, with the company targeting approximately $1.25 million annually for 2026 through automation and a leaner operating model.FIXYR Validation: Fobi's agentic AI platform has been deployed in a live enterprise environment, where the company says it processed more than 20,000 digital tickets and 200 customer inquiries while supporting automated customer workflows.Fobi 3.0 Framework: The company is now positioning around a consulting driven model, “Strategy. Architecture. Execution.”, designed to generate revenue through SaaS licensing and professional services.Shareholder Base: Nearly 30,000 shareholders have followed the company through 18 months of uncertainty, creating a large built in audience as Fobi works toward its next phase.STRATEGIC IMPLICATIONSThe enterprise AI market is crowded with big promises, but many companies are still struggling to turn AI strategy into real business results. That gap is where Fobi AI 3.0 is trying to position itself.The FIXYR deployment is an important early proof point. According to the company, the platform supported more than 20,000 digital tickets and 200 customer inquiries in a live enterprise environment, showing how agentic AI can reduce manual workload and support real customer operations.The timing is meaningful. Enterprises want AI solutions that are practical, secure, and measurable. Fobi is trying to enter that market with a leaner cost structure, live deployment examples, and a clearer focus on turning AI implementation into commercial revenue.CEO Rob Anson:"We've had 18 months to plan this. I'm not coming back to chase headlines or pump news releases. We're gonna do live interactive demos. We'll have customers on those calls. People will understand the products, see the use cases, and watch the traction build. I don't care what happens the first two weeks, I'm building this methodically. The shareholders who stayed deserve to understand what we built, and we're gonna show them every step of the way."INVESTOR TAKEAWAYFobi AI's potential return to trading represents a major inflection point for a company that has spent 18 months rebuilding under difficult conditions. The cease trade order forced a hard reset, including cost reductions, a leaner operating model, and a sharper focus on agentic AI. The message is that Fobi is not preparing to return as the same company investors last saw trading.The opportunity now depends on execution. Fobi must continue through the regulatory process, demonstrate commercial traction, and prove that its Fobi 3.0 model can turn AI strategy and deployment into repeatable revenue. The FIXYR deployment provides an early example of what the company believes the platform can deliver, while the reduced cost structure gives it a more disciplined foundation from which to rebuild.For investors, this is not just a comeback story. It is a test of whether Fobi AI can turn a forced reset into a stronger business model built around automation, enterprise AI implementation, and potential software driven revenue. The next phase will be about proof, transparency, and execution.

Dell's CTO built a 4-category agent framework from real production deployments. Most enterprises are ignoring two of the categories that matter most.Full Show NotesEnterprise leaders are mapping AI agents to org charts — building digital employees, agentic teams, AI workers — and then wondering why the results fall short. Dell's Global CTO John Roese has been running agents in production long enough to know exactly why that framing fails, and what to do instead.In this episode, Roese shares a framework Dell developed from actual production deployments, not pilots. It identifies four categories of AI agents defined by two dimensions: how much autonomy you grant the agent, and how complex the underlying process is. Most enterprises are focused on one category. Two of the four are widely overlooked — and they may represent the fastest path to measurable ROI.This is a practical, grounded conversation about where agents are actually delivering value today, how to think about infrastructure cost in the context of agent economics, and why the sequence in which you deploy agents matters as much as which agents you build. If your organization is trying to move from AI experimentation to production, this episode is required listening.3. Chapter titles:[00:00] — Introduction: Dell's dual role as tech vendor and enterprise AI user[01:38] — Why the org chart model for agents fails[03:12] — Decoupling human capacity from work capacity for the first time[04:23] — The two-by-two framework: autonomy vs. process complexity[06:14] — Productivity agents: what most enterprises already have[07:00] — Hygiene agents: the overlooked category that fixes foundational data problems[08:01] — The CRM data example: why every CRM is inaccurate and how agents fix it[10:05] — Latent infrastructure capacity: running agents in GPU white space to cut costs to cents[13:53] — Facilitation agents: removing entropy from complex cross-functional workflows[17:30] — The sequencing insight: hygiene and facilitation as the path to expert agents[19:24] — Why coordination agents aren't agentic bosses — and where human control actually lives[22:21] — Roese's closing advice: become literate, pick a few, get them into production4. Guest BioJohn Roese is the Global Chief Technology Officer and Chief AI Officer at Dell Technologies, where he is responsible for technology strategy, AI deployment, and research and development across the company. He has held senior technology leadership roles at Nortel, Enterasys Networks, Broadcom, and EMC. At Dell, he operates at a rare intersection: leading AI strategy for a major technology vendor while also deploying AI internally at enterprise scale — which means his frameworks are tested against real production constraints, not just market positioning.LinkedIn: linkedin.com/in/johnroeseDell Technologies: dell.comAbout This PodcastAI with Maribel Lopez is a podcast for enterprise technology leaders navigating AI adoption, agentic systems, AI infrastructure, and AI governance. Host Maribel Lopez covers enterprise technology and advises CIOs, CDOs, CMOs, and technology vendors on how to move from AI experimentation to measurable business outcomes. New episodes published bi-weekly.Subscribe on your platform of choice: buzzsprout.com/1947446

Snowflake is the AI Data Cloud behind some of the world's largest enterprises — $4.68 billion in annual revenue, 29% year-over-year growth, and over 760 Forbes Global 2000 companies as customers. Baris Gultekin, VP of AI at Snowflake, leads the product efforts that sit at the center of how those enterprises actually operationalize AI. Before Snowflake, he co-founded Google Assistant and scaled it from 10 million to 500 million monthly users.What you'll learn:Why our data isn't clean enough is a delay tactic — and the scoped approach to move past itWhat the semantic layer is and how it lets AI answer business questions accurately, not just fluentlyWhy running AI next to data (instead of sending data to models) makes governance dramatically easierHow Snowflake deployed AI internally: a CEO-level non-optional mandate combined with bottom-up access to their own Cortex coding agentWhy context — not just data — is what agents need to operate reliably at enterprise scaleKey takeaways:Start with one scoped use case, build the semantic model around it, layer governance — don't wait for perfect dataContext is a shared reality for agents: unified data + business semantics + codified workflowsAI adoption compounds when leadership sets a hard mandate and simultaneously gives everyone a tool to experiment withCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Baris GultekinSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Welcome to Captain's Pod, a Star Trek podcast presented by Ian and Deneé! Join the crew as Ian over explains Enterprise design, Deneé forgets an episode of Captain's Pod, and they both help history never forget the name Enterprises?Next Week: Star Trek TNG: The Inner Light! (S5E25)1) Ten Forward - Thoughts on the episode; what did the crew love and what can go out the airlock! - (13:38) 2) The Jefferies Tubes- Bloopers and other goodies that didn't make it into the show. Don't tell Section 31! (1:32:43)Want to send us something the old fashioned way?P.O. Box 115Republic, MO. 65738Want early and ad-free access to the show PLUS other perks? Join the Tea-Flingers at the Ian and Deneé Patreon!https://www.patreon.com/iananddeneeConnect with us!Email: iananddenee@gmail.comDiscord: https://discord.gg/cm4nxyKd2SAnd live long and Podsper!Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Podcast: Tech TransformedGuests: Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal TechnologiesHost: Kevin Petrie, VP of Research at BARCArtificial Intelligence (AI) models have been breaking ground in the last three years. In the race to boost capabilities month by month among platforms like OpenAI, Anthropic, and Google's Gemini models. However, for many enterprises, the main challenge is not creating AI prototypes; it's ensuring they can reliably support real business processes.In a recent episode of the Tech Transformed podcast, Kevin Petrie, VP of Research at BARC, hosted a discussion with Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal Technologies. They talked about why enterprises find it hard to transition AI from experimentation to production and how infrastructure must change to support autonomous systems.Why AI Demos Break in the Real WorldAccording to Davis, many organisations make a common mistake: they focus on the "happy path" during experiments and overlook real-world operational challenges. “We have always ignored the non-functional requirements until we go to prod at our peril,” Davis said. “A lot of our experimentation is so focused on the models that we forget about the non-functional requirements.”This means developers often prioritise model performance but neglect reliability, scaling, and system resilience. Agent frameworks used in experiments—usually lightweight Python or TypeScript libraries—add to the issue.“What you're really building is a highly distributed system that's calling Large Language Models (LLMs) that will be rate-limited… networks are going to go down,” Davis explained. “When we move into prod, we haven't considered scale or instability.”As enterprises expand AI into their workflows, these overlooked details become imperative. A single outage, rate limit, or infrastructure failure can disrupt a complicated workflow that involves multiple AI steps.Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?What Risks are Surfacing Since the Rise of Agentic Systems?The transition from simple AI workflows to autonomous agents adds a new layer of complexity. Traditional AI applications have predictable flows—such as summarising documents, tagging data, or creating recommendations. In contrast, agentic systems choose tools and decide on actions dynamically.“When we move from non-agentic to agentic, we introduce unpredictability,” Davis said. “The tools and the order they run in are unpredictable. Whether we go through the agentic loop once or a hundred times is unpredictable.”Such unpredictability creates new governance and compliance challenges, especially in regulated industries. “Enterprises are still responsible for predictable outcomes,” Davis noted. “We need stronger audit trails to understand why the agent made the decisions it did.”For enterprises, this means AI systems must ensure traceability, accountability, and compliance, even when decision paths differ from one interaction to another.Why is Durable Execution the New Foundation for Enterprise AIFateev argues that to manage such newly surfacing risks, enterprises need a new architectural layer focused on reliability. His concept, “Durable Execution,” aims to ensure that complex workflows keep running even when infrastructure fails.“You write code as if failures don't exist,” Fateev explained. “If a process crashes, we recover all the state and continue executing.” In practical terms, Durable Execution allows long-running AI workflows to survive interruptions—from network outages to system crashes—without losing progress or data.This is essential as agents start interacting with real systems and taking real actions. “The moment agents start acting on the external world—changing files, submitting orders—you absolutely don't want those things to get lost,” Fateev said.The Temporal co-founder further emphasised that enterprise AI will not completely replace traditional software systems.“You will always have deterministic code,” he said. “You can't imagine banks dynamically deciding what a money transfer means.”Instead, the future architecture will combine deterministic software with agents that interact through controlled tools and reliable communication layers.Also Watch: How Do You Make AI Agents Reliable at Scale?Key TakeawaysAI projects fail in production when non-functional requirements are ignoredAgentic systems bring unpredictability, making governance, traceability, and auditability essential.Lightweight experimentation frameworks aren't suited for enterprise workloads.Durable execution enables reliable AI workflows, ensuring processes continue despite infrastructure failures.Enterprise AI will blend deterministic software with agents.Chapters00:00 Introduction to AI's Impact on Business03:53 Challenges in Integrating AI into Business Workflows13:00 Understanding Non-Functional Requirements in AI19:14 The Role of Orchestration in AI Systems24:26 Exploring Durable Execution in AI Workflows30:28 Future Architectures for Autonomous AI Systems36:05 Key Takeaways for Executives in AI ImplementationFor more information, please visit em360tech.com and temporal.io.To learn more about Temporal and Durable Execution, follow:Temporal LinkedIn: Temporal TechnologiesTemporal X: @TemporalioTemporal YouTube: @TemporalioEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#DurableExecution #EnterpriseAI #AIToProduction #AIOrchestration #TemporalTech #AutonomousAgents #SystemReliability #LLMs #TechTransformed #AIWorkflows

Ryan Burke, VP of Worldwide Sales at Crogl, joins Sam Jacobs, AJ Bruno, and Asad Zaman on the new economics of enterprise cyber risk. Topics include Anthropic's Mythos model, AI for the security operations center, why vibe-coded apps are far more likely to have security issues, why Claude Design tanked Figma's stock, and what the Elon Musk versus OpenAI lawsuit signals for AI governance. Key takeaways: AI has crashed the cost of running sophisticated attacks, putting nation-state-grade tooling in the hands of low-skill operators. As Ryan Burke, VP of Worldwide Sales at Crogl, put it on Anthropic's Mythos model: "Mythos has lowered the cost to like the dollar menu equivalent of...running an attack...so more people can do it." Enterprises are staring down a multi-year patching backlog that runs from now until the end of time. Non-technical teams in finance, ops, and HR are shipping internal tools using Replit and Claude, and almost none of them are securing what they build. Ryan Burke flagged the research: "vibe-coded software is almost 3 times as likely to have security issues." When the employee who built the agent quits, the agent stays behind with no owner, no documentation, and quiet access to systems it never should have had in the first place. For founders eyeing an exit, security has joined revenue, IP, and hitting your numbers as a non-negotiable diligence pillar. As Ryan Burke explained: "lack of security can kill an acquisition...a fourth pillar now is you're secure." Acquirers like JPMorgan Chase will not buy a fintech startup that turns into a vector for attackers to walk straight into their environment. The market case for NRR-fortress legacy SaaS may be weaker than the last decade made it look. As Asad Zaman, CEO of Sales Talent Agency, argued: "there was a generation of software companies that had signs that they had really good customer relationships...but their customers felt more like prisoners." If AI makes switching cheap and a new generation of software actually delights users, the moats around system-of-record incumbents start to compress fast. Connect with the hosts and guest:  Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/  Guest: Ryan Burke, VP Worldwide Sales at Crogl - https://www.linkedin.com/in/ryan-burke-bos/ Topline is more than a YouTube Channel:  Subscribe to Topline Newsletter: https://toplinemedia.substack.com/  Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast  Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 Introducing Ryan Burke 03:14 Anthropic Mythos and Cyber Risk 04:20 How Attackers Use AI at Scale  07:00 Dollar Menu Attacks Explained  10:41 AI for the Security Ops Center  14:53 Why Claude Tanks Figma's Stock  18:30 Sam's Advice on Falling Stocks  20:50 Are Legacy SaaS Companies Back?  24:04 The Vibe-Coding Risk Surface  27:56 Quiz Pro: Cybersecurity Edition  33:46 Replit Apps Inside Enterprises  40:18 Security as the M&A Fourth Pillar  44:17 Personal Data and Digital Legacy  47:24 Bulls vs Bears: Elon vs OpenAI  52:03 Will ServiceNow Hit $32B?

Podcast Series: Don't Panic It's Just DataGuest: Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead, Stibo SystemsHost: Scott Taylor, The Data Whisperer and Principal Consultant, MetaMeta ConsultingArtificial intelligence (AI) is prevalent in the insurance industry now, but many firms are not seeing the results they expected. The issue isn't with the AI models; it's pertinent to the data.In the recent episode of the Don't Panic It's Just Data podcast, host Scott Taylor, The Data Whisperer and Principal Consultant at MetaMeta Consulting, is joined by Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead at Stibo Systems. The data industry experts address a key misunderstanding about enterprise AI – that companies can innovate their way out of poor data quality. “Some people think AI is a quick fix for data governance,” said host Scott Taylor. “If I need better data, I just use AI.” Experts warn that this belief is what's holding insurers back. How Frankenstein Data is Impacting AI?Despite significant investments in AI, cloud, and analytics, many insurers remain stuck in pilot mode. According to Mark Blake of Stibo Systems, the problem is the infrastructure. “AI itself isn't the challenge,” he said. “It's the ability to scale it, and that comes back to fixing the data.”In reality, most insurance enterprises face fragmented, siloed data across systems. Customer, policy, claims, and product data often don't align. This results in what Taylor calls “Frankenstein data,” where inconsistent records lead to unreliable outputs.For AI to function effectively at scale, insurers need trusted, governed, and unified data. That's where data governance and master data management (MDM) come in.“For us to truly gain benefits from AI, the end user really has to trust the data,” stated Mark Duffy of Cognizant. “That trust comes from having the right data foundation in place.”Also Watch: Can Your MDM Strategy Survive the Shift to Real-Time AI Decision-Making?How Master Data Management (MDM) Unlocks Scalable AI?One of the key drivers of AI success in insurance is multi-domain master data management, a system that connects core business data across the enterprise. “You always have to have a starting point,” Blake explained. “Then you expand horizontally across the enterprise.”The “horizontal data layer” enables insurers to unify key entities like customers, products, and partners—often referred to as the “nouns of the business.” When these are standardised, AI models can work consistently and accurately.The business impact is substantial, including more accurate underwriting decisions, reduced claims leakage, improved customer experience and retention and better cross-sell and upsell opportunities. Duffy shared a real-world example in which enhancing data management directly sped up AI adoption. “It gave them trust in the data,” he said. “They could run models faster and gain more value because they weren't constantly fixing issues.”Instead of spending 80 per cent of their time cleaning data, teams could finally focus on using it.Why AI Is Coercing a Data Strategy ResetFor years, data governance struggled to gain executives' support, but now AI has shifted that.“There's been a refocus,” Blake said. “They're looking at data in a way they maybe haven't done historically.”Today, AI is a priority for boards, driving alignment among CIOs, CDOs, and IT enterprise leaders. “Every C-suite executive wants to do more AI,” Duffy said. “But they've realised they can't do that without the data foundation.”Still, some enterprises believe AI can fix poor data quality. Experts warn that this is a mistake. “You can use AI to support data quality,” Duffy said. “But you're not going to use AI to build an MDM solution.”What's the Solution to Frankenstein DataAs insurers develop their AI strategies for the next 12 to 24 months, one key ideology was spotlighted – success depends less on speed and more on structure. “Go back to the root cause,” Blake said to Taylor. “Fix that, and then you can move forward with confidence.”In other words, AI highlights the need for strong data foundations; it doesn't eradicate them. For insurers serious about AI transformation, that's no longer optional—it's where they must begin.Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn'tKey TakeawaysAI in insurance fails without strong data governance and quality foundations.Master Data Management (MDM) is critical for scaling AI across insurance enterprises.Fragmented “siloed data” is the biggest barrier to AI adoption in insurance.Trusted, unified customer and policy data improves AI accuracy and business outcomes.AI cannot fix bad data—insurers must modernise data management first.Chapters00:00 Introduction to AI Readiness in Insurance03:08 The Importance of Data Foundations06:02 Challenges of Fragmented Data09:06 Modernising Data Foundations for AI11:56 Real-World Use Cases in Insurance15:03 The Role of Master Data Management17:56 Aligning Business and Data Strategies21:06 Final Thoughts on AI and Data GovernanceFor more information, please visit em360tech.com and stibosystems.com.To learn more about AI in the MDM space and how they're progressing enterprise analytics intelligently, follow:Stibo Systems LinkedIn: @StiboSystemsStibo Systems X: @StiboSystemsStibo Systems YouTube: @StiboSystemsGlobalEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#MasterDataManagement #DataGovernance #AIinInsurance #EnterpriseTech #BigData #DataStrategy #AIReadiness #InsuranceTechnology #cioinsights #StiboSystems #frankensteindatamaster data management, MDM, data governance, AI strategy, insurance, enterprise technology, big data, chief data officer, CDO, CIO, data quality, data unification, Stibo Systems, Scott Taylor, Mark Duffy, Mark Blake

Enterprises spent the last decade hardening the front door for human users. Now a new class of worker is showing up to the same applications, asking for the same data, and acting on someone else's behalf. Shreyans Mehta, Co-Founder and Chief Technology Officer of Cequence Security, joins ITSPmagazine to talk through what changes when ten or more agents are operating in your name across email, code repositories, Confluence, Salesforce, and ServiceNow at the same time. For Shreyans Mehta, safe enablement is the central question. Consumer chatbots normalized point-to-point connections into personal inboxes, but enterprise agents are reaching into crown-jewel systems where blanket access is not an option. Cequence Security has spent years protecting applications and APIs for telcos, financial institutions, and retailers, and that history shapes how the team is approaching the agentic shift: how do you let the right work get done without handing over the keys to the building? Identity alone is not the answer. Agents can hallucinate, can be prompt-injected, and will go to great lengths to complete a task. Cequence Security addresses this with what Shreyans Mehta calls an agent persona, a dynamic, job-description-driven scope that limits an agent to exactly what its role requires. An email assistant gets read access and a calendar check, not the ability to send or delete. The job defines the permissions, and the permissions follow the agent through the Cequence AI Gateway platform. This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlight GUEST Shreyans Mehta, Co-Founder and Chief Technology Officer, Cequence Security LinkedIn: https://www.linkedin.com/in/shreyans-mehta-37a529/ RESOURCES Learn more about Cequence Security: https://www.cequence.ai/ Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS Shreyans Mehta, Cequence Security, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, agentic AI, agent identity, AI agents, agent persona, API security, non-human identity, safe enablement, enterprise AI, prompt injection, MCP, AI gateway Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As AI models scale globally, social enterprises are increasingly working with frontier AI labs to test, adapt, and improve the technology in local contexts. This episode explores how those partnerships could determine whether AI reinforces existing inequalities — or helps close them.

Fiber is the key connectivity element that ties together all digital infrastructure asset classes, for consumers and Enterprises alike.Gary Bolton, President & CEO of the Fiber Broadband Association, talks with John Celentano, Inside Towers Business Editor, about the rapid growth, the outlook for the fiber broadband business and the upcoming Fiber Connect event.Support the show

The AI discount is ending as agentic usage drives token consumption through the roof, forcing companies from GitHub to Anthropic to rethink pricing, limits, and compute access. NLW breaks down why usage-based billing is becoming inevitable, what it means for markets and job displacement, and how enterprises can adapt with cheaper models, cost audits, model bake-offs, escape-hatch architectures, and clearer AI cost scoreboards.5 Moves for Enterprises to Reduce the Cost of Agents: https://play.aidailybrief.ai/episodes/the-ai-subsidy-era-ends/April AI Usage Pulse Survey: https://tally.so/r/LZEyGySIGN UP FOR OUR NEW FREE PROGRAM: AGENTOS⁠⁠https://aidbagentos.ai/⁠⁠Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Granola - The AI notepad for people in back-to-back meetings. 100% off your first 3 months with code AIDAILY at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://granola.ai/aidaily⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Drata - The agentic trust management platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://drata.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

We are so proud to announce Ferris Bueller Day At Wrigley Field. Coming June 2027! Hear all about it here first!

Broadcom's VMware Cloud Foundation (VCF) is evolving from a turnkey infrastructure stack into a modern application platform, balancing simplicity with the flexibility demanded by Kubernetes-driven environments. AtKubeCon + CloudNativeCon Europe 2026, Broadcom leaders highlighted how VCF is adapting to support platform engineering teams, cloud-native workloads, and large-scale operations. A key industry shift is the return to private cloud, driven by data sovereignty concerns and the growing impact of AI. Enterprises are bringing workloads back on-premises while still expecting a cloud-like operating model. Broadcom is responding by prioritizing on-prem stability and aligning closely with open source, reflecting its strong contributions toKubernetesand related projects. Kubernetes is no longer a bolt-on but the core control plane of VCF, enabling unified management of compute, storage, and networking through declarative APIs. At the same time, the distinction between virtual machines and containers is fading. The focus is shifting toward application-centric platforms, where developers interact through consistent abstractions, allowing infrastructure to be provisioned seamlessly behind the scenes. Learn more from The New Stack around the latest around Broadcom:  Broadcom ‘Doubles Down' on Open Source, Donates Kubernetes Tool to CNCF Why Broadcom gave Velero to the CNCF Sandbox — and what it means for Kubernetes data protection Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

The floor at RSAC Conference 2026 had one dominant frequency, and it was not subtle. Every booth, every hallway, every late-night conversation kept circling back to the same question: how do enterprises adopt AI agents without losing control of them? In a post-conference follow-up, Itamar Apelblat, Co-Founder and CEO of Token Security, translates what he heard on the ground into what the data now confirms. Token Security arrived at RSAC with a fresh set of findings, produced in collaboration with the Cloud Security Alliance and released alongside the event. The report, Autonomous but Not Controlled: AI Agent Incidents Now Common in Enterprises, puts numbers to what practitioners already suspected: 65 percent of organizations have experienced an AI agent-related incident in the past twelve months, and 82 percent discovered agents running in their environment that no one had authorized. Only 21 percent have a formal process for decommissioning agents — a gap Itamar Apelblat flags as a low-hanging attack path. The short version from the conversation: visibility is the starting line, not the finish line, and the path from discovery to intent-based enforcement is where most programs are stuck. This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlight GUEST Itamar Apelblat, Co-Founder and CEO, Token Security | https://www.linkedin.com/in/itamar-apelblat/ RESOURCES Learn more about Token Security: https://www.token.security/ Download the CSA + Token Security Report — Autonomous but Not Controlled: AI Agent Incidents Now Common in Enterprises: https://cloudsecurityalliance.org/artifacts/autonomous-but-not-controlled-ai-agent-incidents-now-common-in-enterprises Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS Itamar Apelblat, Token Security, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, AI agents, agentic AI, non-human identity, identity security, shadow AI, CSA report, Cloud Security Alliance, intent-based access, AI agent governance, agent decommissioning, RSAC Conference 2026 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Host Brian Walsh takes up ImpactAlpha's top stories with editor David Bank. Up this week: What's significant about Uganda's national pension fund's effort to stand up a locally focused fund of funds; unraveling the controversy surrounding KKR's lucrative employee ownership exit from CoolIT; and, unpacking the failure of the impact advisory firm Align Impact.To try ImpactAlpha Edge yourself, ⁠click here⁠.For more on these stories:“Let KKR do its thing while we convert thousands more companies to 100% employee ownership,” by Aner Ben-Ami"With shared ownership, $4.8 billion sale of CoolIT gives workers a cut of AI-driven gains," by Roody Senatus"Better financing can make employee ownership a game-changer for workers," by Antony Bugg Levine“Pension fund in Uganda readies a $100 million fund of funds to create jobs – and savers,” by Lucy Ngige“After 12 years, advisory firm Align Impact to shut down at the end of the month,” by David Bank

Host Brian Walsh takes up ImpactAlpha's top stories with editor David Bank. Up this week: What's significant about Uganda's national pension fund's effort to stand up a locally focused fund of funds; unraveling the controversy surrounding KKR's lucrative employee ownership exit from CoolIT; and, unpacking the failure of the impact advisory firm Align Impact.To try ImpactAlpha Edge yourself, ⁠⁠click here⁠⁠.For more on these stories:“⁠Let KKR do its thing while we convert thousands more companies to 100% employee ownership⁠,” by Aner Ben-Ami"⁠With shared ownership, $4.8 billion sale of CoolIT gives workers a cut of AI-driven gains⁠," by Roody Senatus"⁠Better financing can make employee ownership a game-changer for workers⁠," by Antony Bugg Levine“⁠Pension fund in Uganda readies a $100 million fund of funds to create jobs – and savers⁠,” by Lucy Ngige“⁠After 12 years, advisory firm Align Impact to shut down at the end of the month⁠,” by David Bank

Podcast: Tech Transformed podcastGuest: John Newton, Chief Innovation Strategist at HylandHost: Dana Gardner, President and Principal Analyst at Interabor SolutionsEnterprise leaders rushing to integrate artificial intelligence (AI) into their operations often think the biggest challenge is the technology itself. In reality, the issue is much closer to home. It's in the piles of unstructured enterprise data spread across documents, systems, and repositories.In the recent episode of the Tech Transformed podcast, John Newton, Chief Innovation Strategist at Hyland, sits down with host Dana Gardner, President and Principal Analyst at Interabor Solutions. They discussed how enterprises can unlock the full value of enterprise AI by addressing fragmented information and building stronger governance frameworks.Their conversation highlights that unstructured data is not an obstacle; it is the foundation for next-generation AI-driven productivity. As Newton stated, “The opportunity to truly use AI and use it effectively in your organisation really depends on that unstructured information.”For companies looking to adopt AI on a large scale, the real work is in organising and contextualising their internal knowledge.Is Unstructured Data the Hidden Fuel for Enterprise AI?Most enterprise data does not sit neatly in structured databases. Instead, it exists in contracts, reports, emails, videos, policies, and operational documents, creating a vast amount of unstructured content.The enormous amount of such unstructured data ends up creating a challenge for AI projects that rely solely on foundation models. Large language models (LLMs) may be trained on public data, but they cannot inherently access proprietary business intelligence.Newton argued that enterprise AI must therefore be built around internal knowledge systems. “Foundation models can't train on your internal information,” he explained. “What you really want is that information to be part of the AI when you're answering questions, doing research, or executing business processes.”This change requires organisations to rethink how information flows across the enterprise. Instead of isolated systems—CRM platforms, ERP databases, content repositories—companies need an interconnected information structure that connects multiple sources in real time.Such a structure enables AI systems and AI agents to find the right data at the right time. This also improves decision-making, automation, and operational intelligence.How to Reorganise Chaotic Unstructured Data?If unstructured data is the fuel, curation is the engine that drives effective AI. Newton emphasised that an enterprise data strategy must start with mapping, organising, and cleaning information assets. The aim is to reduce noise and increase clarity.“I like to look at things from a signal-to-noise perspective,” Newton says. “Curation is the key to removing uncertainty in the information.”The method could typically comprise a combination of several enterprise technologies such as content management platforms with business process management (BPM) and AI agents and LLMs.A pairing of the above strategies is aimed at helping enterprise data become more valuable. Enterprises can implement AI models to automate workflows, enhance knowledge discovery, and speed up processes across departments—from finance and manufacturing to customer operations.Importantly, Newton noted that this work also allows flexibility in the AI ecosystem. With a solid information foundation, companies can use open-source models, hyperscaler services, or internal AI deployments without tying themselves to a single vendor.In other words, an enterprise AI strategy should first focus on data readiness, not model selection.Key TakeawaysUnstructured data is the foundation for effective enterprise AI.Data curation improves AI accuracy and reduces information noise.Connecting enterprise systems enables AI to deliver real-time insights.AI guardrails help manage security, compliance, and data governance.AI automation boosts employee productivity by reducing repetitive work.Chapters00:00 Unlocking AI's Potential with Unstructured Data05:20 Signal to Noise: The Clarity Challenge11:21 Guardrails for AI: Balancing Control and Flexibility14:41 Harnessing the Enterprise Context Engine17:48 Real-World Applications: Case Studies in AI20:37 Curation: The Key to Effective Automation22:21 Future Business Value: Productivity and BeyondFor more information, please visit hyland.comTo stay updated on B2B Tech front and centre, follow EM360Tech:YouTube: @enterprisemanagement360LinkedIn: @EM360TechX: @EM360TechFollow Hyland on all its major platforms:YouTube: @HylandAILinkedIn: HylandX: @Hyland#UnstructuredData #EnterpriseAI #DataCuration #AIGuardrails #LLMs #AIAutomation #FragmentedData #InformationManagement #SignalToNoise #EnterpriseContext #TechTransformedPodcast #Hyland #B2BTech

Enterprise customers demand 99.9% availability, regardless of how the underlying software is built. In this episode, Murali Swaminathan (CTO @ Freshworks) discusses how enterprises actually win with AI! We explore the “Architecture of Predictability” – proactive architectural safeguards to scale “responsible AI by design” across a global organization serving 75,000 customers. Murali shares his leadership playbook for implementing the technical safeguards and product trust controls that empower hundreds of engineers to build safely. We also dive into the shift from deterministic flowcharts to “workflows with a brain” and why backend systems engineers are the secret bedrock of agentic products. Plus, Murali deconstructs the dual evolution required of modern leaders: mastering strategic thinking at the business level while cultivating systems thinking at the engineering level.   ABOUT MURALI SWAMINATHAN Murali Swaminathan joined Freshworks as Chief Technology Officer in September 2024. Murali is responsible for Freshworks' technology roadmap and strategy, leading the company's global engineering and architecture teams. With over 30 years of experience in software engineering, he has held leadership roles at ServiceNow, Recommind (now OpenText), and CA Technologies (now Broadcom), where he delivered scalable, secure solutions that enabled digital transformation and business agility. Murali holds a master's degree in Software Engineering Management from Carnegie Mellon University and a bachelor's degree in electronics and instrumentation from Annamalai University in India.   SHOW NOTES: Freshworks' operating context: Engineering for 75,000 global customers (2:09) Navigating the tension between rapid AI adoption and enterprise-grade reliability (4:58) Breaking the "Positive Scenario" Trap: Using AI to automate negative test cases and corner-case detection (6:40) Why Responsible AI is a competitive advantage: Building "kill switches" and trust gates (8:31) Responsible AI by Design: Moving from reactive compliance to proactive architectural safeguards (10:48) Technical safeguards: Leveraging hyperscaler frameworks for model compliance and data anonymization (13:39) Product Trust Controls: Demonstrating reliability through role-based access and thresholds (16:25) Why engineering leaders should experiment in small teams before global rollout (20:35) Simulating Chaos: Using Business Continuity Planning (BCP) to test AI system resilience (22:13) Workflows with a brain: Transitioning from deterministic flows to agentic runtime decisions (24:16) The AI Team Profile: Why backend system engineers, not just data scientists, are the bedrock of agentic products (29:25) Cultivating a mindset shift toward agentic system orchestration (32:10) The shift to systems thinking: How engineering roles evolve from "building pieces" to managing end-to-end system flows (33:38) How to approach strategic business thinking as an engineering leader (36:43) Rapid Fire Questions: Guy Kawasaki's "Think Remarkable" and the best way to predict the future (38:23)   LINKS AND RESOURCES Think Remarkable: 9 Paths to Transform Your Life and Make a Difference - Tech titan and creator of the Remarkable People podcast Guy Kawasaki delivers a practical, tactical, and sometimes radical discussion of how to make a difference in the world and live a fulfilling life.   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

By Doug Green “Some of these AI-driven attacks are the kind that can stop you in your tracks.” In this Technology Reseller News podcast, I spoke with Chip Witt, Principal Security Evangelist at Radware, about a rapidly emerging challenge: how AI is introducing new, often invisible, security risks into enterprise environments. Witt outlined a fundamental shift. As organizations adopt AI tools across workflows, they are also creating new attack surfaces—many of which are not yet fully understood or monitored. One of the biggest concerns is the lack of visibility. Enterprises often don't know what data AI systems are accessing, how it's being used, or where vulnerabilities may exist. This creates blind spots around data access, compliance, and operational stability. In regulated industries like finance and healthcare, those blind spots can quickly turn into real business risk. A key issue discussed was prompt injection attacks, where malicious inputs manipulate AI systems into exposing sensitive data or performing unintended actions. These attacks are particularly dangerous because they don't look like traditional threats—they operate inside trusted workflows. Witt emphasized that many organizations are still applying legacy security thinking to a fundamentally new paradigm. AI doesn't just expand the attack surface—it changes its nature. Security teams must now account for dynamic, context-driven interactions rather than static perimeters. The takeaway is clear: AI adoption without AI-aware security introduces risk at a pace faster than most organizations can track. For service providers and enterprises, this represents both a challenge and an opportunity. Those who can build visibility, governance, and control into AI deployments early will be far better positioned as these risks evolve. Learn more: https://www.radware.com/

Today we bring back one of our favorite guests: former US most-wanted cybercriminal Brett Johnson. It's been seven years since he was last on the show, and much has happened in the world of cyber. Brett shares how his perspective has changed in the past few years, and gives his thoughts on how new technologies impact cyber crime. Steve and Brett discuss compliance and what Brett's path from prison to helping law enforcement means for other cyber criminals. Brett also answers some rapid-fire questions.Key Takeaways:  Increased ease of access to cybercrime tools and services, along with manpower problems in law enforcement, are key reasons for why cyber crime is one of the world's largest economies today. Enterprises must shift focus from trying to block every attack to protecting their crown jewels for when an attack inevitably gets through.   Bad things happen because good people remain silent.  Tune in to hear more about: Why cybersecurity awareness training often fail (13:32) If Brett's path to redemption is still viable for today's cyber criminals (16:57) Some rapid-fire questions to Brett (21:35) Standout Quotes: “Cybersecurity and security overall is not a romantic thing. It's not an exotic thing. It's simply doing the nuts and bolts of what you need to do. And the problem is that largely that's not happening in the environment. If you've got management that's more interested in butter than they are in guns, you've got those types of issues.” - Brett Johnson “Cybersecurity awareness training or fraud prevention training, scam awareness, anything like that, we tend to educate at a very rational level. For scams and a lot of fraud and stuff like that, it doesn't happen at a rational level. If I'm trying to attack a person and compromise that person, I'm not doing it at a rational level. I'm doing it at an emotional level. I'm trying to get you to set reason and logic aside and to react emotionally. So all that training takes place at that rational level. You can understand it there. That doesn't mean that you understand it at the emotional level whatsoever.” - Brett Johnson “Is it harder? In one respect it is because we now have people that are aware of how money is moved, what criminals seek to do with it. Banks have become more aware of a lot of the new ways to launder and funnel funds. In many ways, it's much harder, but at the same time, criminal networks have adapted to that difficulty.” - Brett Johnson Read the transcript of this episodeSubscribe to the ISF Podcast wherever you listen to podcastsConnect with us on LinkedIn and TwitterFrom the Information Security Forum, the leading authority on cyber, information security, and risk management.

Professor Armando Solar-Lezama, MIT CSAIL Associate Director, says there are currently three camps in AI discourse: the utopian thinkers, the alarmists, and the skeptics. And all of them are wrong. Plus, hear his thoughts on AI Agents, neurosymbolic programming, vibe coding, and more. In this conversation, Professor Solar-Lezama explains how "vibe coding" is transforming daily productivity for those who already know how to code, why software development is becoming a capital-intensive business for the first time in its history, and why the developers who benefit most from AI tools are the ones with the strongest foundations. He also offers a warning on AI agents: that simple attacks have been patched but major vulnerabilities remain, and deploying agents in high-stakes environments without understanding those risks is a gamble organizations shouldn't take yet. Plus, get a closer look at emerging technologies like neurosymbolic programming and areas where human expertise will be more important than ever. Episodes, listener discounts, meet the host, and more can be found here: csail.mit.edu/podcast Connect with CSAIL Alliances: On our site: cap.csail.mit.edu/about-us/meet-our-team On LinkedIn: linkedin.com/company/mit-csail #ArtificialIntelligence #AITrends #MachineLearning #TechPodcast #FutureOfWork #SoftwareDevelopment #AIEthics #Innovation

Over the past year, something has become very clear. AI is not just a technology shift. It is a leadership test. Across enterprises, startups, and even governments, the same pattern keeps repeating: Leaders are being pushed to act fast Teams are overwhelmed with change And yet, clarity is missing From the outside, it looks like a technology race. But from inside organizations, it feels very different. It feels like: uncertainty pressure and a constant question - "Are we doing enough?" In conversations with CIOs, architects, and business leaders, one thing stands out: The real challenge is not adopting AI. The real challenge is leading through it. That's why this episode matters. Chapter List: 00:00 Introduction to Silicon Valley Executive Academy 01:37 Understanding the Silicon Valley Playbook 03:20 The Impact of AI on Leadership 05:25 Leading Through AI Transformation 09:45 Managing Pressure as a Leader 11:21 Driving Growth with a Healthy Culture 13:39 Common Challenges for Executives 16:00 The Role of Emotional Intelligence in Leadership 17:20 Micro Joy Method for Leaders 18:58 Building Trust as a Leader 19:54 Identifying Red Flags in Leadership 21:20 Evolving Leadership Models 23:53 Advice for Emerging Leaders Episode # 186 Today's Guest: Victoria Mensch, CEO & Founder, Silicon Valley Executive Academy An executive leadership coach and strategist with over 25 years of experience in Silicon Valley's high-tech sector. With a PhD in Psychology and an MBA from UC Berkeley. Website: Executive Silicon Valley What Listeners Will Learn: Why AI adoption is fundamentally a leadership challenge How pressure and hype impact executive decision-making The difference between transformation and patching processes with AI Why culture and team alignment matter more than tools How leaders can manage uncertainty without burning out teams What early-career professionals should focus on in an AI-driven world Why trust, courage, and clarity are becoming core leadership traits

AudioCodes at Channel Partners: From Voice Infrastructure to Cloud Integration Platform, Podcast By Doug Green “We're not focused on one platform—we're focused on integration and flexibility across all of them.” At the Channel Partners Conference in Las Vegas, I caught up with Paul Hunsucker and Mitch Hirschkowitz of AudioCodes to talk about how the company has evolved—and where the opportunity lies for channel partners today. AudioCodes is one of those companies that nearly everyone in telecom recognizes, but its identity has shifted significantly over time. What began more than 30 years ago in the early days of VoIP—building media gateways, session border controllers (SBCs), and voice infrastructure—has now evolved into something much broader. Today, AudioCodes positions itself as a SaaS platform company focused on managing and integrating UCaaS and CCaaS environments. Rather than competing as a single-platform provider, the company is leaning into a multi-platform strategy—certified across ecosystems like Microsoft, Cisco, and Zoom—giving customers and partners flexibility in how they build and manage communications environments. That shift reflects a larger industry trend. Enterprises are no longer standardizing on a single vendor. Instead, they are assembling communications stacks that span multiple platforms, carriers, and applications. The challenge—and opportunity—for partners is making those environments work seamlessly. AudioCodes is targeting that exact problem. The company's platform approach allows partners to deliver integrated solutions while still giving customers the freedom to choose their preferred UCaaS or CCaaS provider. It also enables enterprises to retain elements of control they previously had in on-prem environments—such as managing SBCs or bringing their own carrier—while benefiting from the scalability of the cloud. For MSPs and channel partners, this creates a clear path forward. Rather than selling a single solution, the opportunity is to become the integrator—the trusted advisor who can stitch together platforms, ensure interoperability, and deliver a consistent user experience. Visit www.audiocodes.com

By Doug Green “We provide a real alternative to the hyperscalers—with more personality and a true partnership approach to the channel.” At the Channel Partners Conference MSP Summit, I caught up with Tim Mandell of Leaseweb to discuss how the company is positioning itself for MSPs and channel partners looking for more control, flexibility, and partnership in their infrastructure strategy. Leaseweb, now a 28-year-old company, operates as a private, sovereign-by-design infrastructure provider. That distinction—private and sovereign—was central to our conversation and reflects a broader shift in how enterprises and partners are thinking about cloud and infrastructure. Unlike publicly traded hyperscalers, Leaseweb emphasizes independence. “We don't report to the street,” Mandell explained, highlighting the company's ability to focus on long-term customer outcomes rather than quarterly earnings pressure. That independence translates into more flexibility for partners and a willingness to engage in tailored, relationship-driven solutions. From a services standpoint, Leaseweb offers a full stack of infrastructure options, including colocation, private cloud, public cloud, bare metal servers, storage, and content delivery. The goal is not to compete head-on with hyperscalers on scale, but to provide an alternative that prioritizes customization, transparency, and partner alignment. For MSPs and channel partners, that positioning creates opportunity. As Mandell noted, Leaseweb is actively investing in the channel, attending events like Channel Partners to better understand partner needs and refine its go-to-market approach. The company is focused on being vendor-agnostic and enabling partners to build solutions that fit their customers—rather than forcing them into rigid architectures. The concept of “sovereign infrastructure” also resonates in today's environment, where data control, compliance, and jurisdictional concerns are becoming more important. Enterprises are increasingly asking where their data resides, who has access to it, and how it is governed. Leaseweb's model allows partners to address those concerns with greater precision. At the same time, the conversation reflects a broader industry trend: the growing demand for alternatives to hyperscaler dominance. While public cloud remains critical, many organizations are rethinking a one-size-fits-all approach and looking for hybrid or specialized solutions that better align with performance, cost, and compliance requirements. For the channel, this creates a clear path forward. Providers that can offer flexible infrastructure options—combined with strong vendor relationships—are well positioned to differentiate in a crowded market. Leaseweb's message is straightforward: be a partner, not just a platform. And in a market where MSPs are looking for both technical capability and business alignment, that approach may resonate more than ever.

The One Shift Smart Enterprises Are Making with Matthew Aizen - Brainz PodcastIn this episode of the Brainz Magazine podcast, Matthew Aizen, Founder and CEO of Nevari, joins us for a sharp and insightful conversation on AI-first transformation, decision intelligence, and the future of intelligent business. Matthew shares the story behind Nevari, why he chose autonomy over investor control, and how his company helps organisations identify inefficiencies, automate intelligently, and make stronger strategic decisions. The conversation explores what separates true AI-first enterprises from businesses simply buying tools off the shelf, why many leaders are misunderstanding AI adoption, and how the role of leadership must evolve in a rapidly changing world.This is a practical and thought-provoking episode for founders, CEOs, and senior decision-makers looking to understand where AI creates real value and how to approach transformation with more clarity, integrity, and impact.With podcast host Miceal O´KaneHope you'll enjoy the episode! Hosted on Acast. See acast.com/privacy for more information.

By Doug Green “SMS is no longer just a one-way notification channel—it's becoming a real-time conversational platform powered by AI.” In a recent Technology Reseller News podcast, I spoke with Ritwek Swetank about a shift that many in the communications industry may be underestimating: the transformation of SMS from simple messaging into an intelligent, interactive engagement layer. For years, SMS has been viewed as a utility—used for alerts, reminders, and basic notifications. As I framed it in the conversation, most people still think of texting as coordinating plans or sending quick updates. But that perception is rapidly becoming outdated. Swetank argues that AI is fundamentally redefining what SMS can do. Instead of one-way communication, messaging is evolving into a dynamic, two-way conversational experience. This shift is being driven by advances in AI models that can interpret intent, respond contextually, and manage interactions in real time. The implication is significant: SMS is moving closer to the role traditionally held by voice or live chat, but with far greater scalability and immediacy. From a business perspective, this creates new opportunities. Enterprises can engage customers in real-time conversations—handling support inquiries, driving transactions, and delivering personalized experiences—all within a channel that already has near-universal reach and high engagement rates. At the same time, this evolution introduces new challenges. As messaging becomes more conversational and AI-driven, expectations around responsiveness, accuracy, and trust increase. Poorly implemented AI can quickly degrade the customer experience, while well-executed solutions can create a competitive advantage. For the channel, this is another example of how AI is reshaping traditional communications infrastructure. What was once a basic transport layer is now becoming an application platform—one where intelligence, automation, and data integration define value. The takeaway is clear: SMS is no longer just messaging. It is emerging as a core component of the conversational AI stack, and those who recognize this shift early will be best positioned to capitalize on it.

Fresh out of the studio with John Morgan, Senior Vice President and General Manager of Splunk Security at Cisco. The conversation unpacks the AI inflection point reshaping security operations — from the explosion of machine data (set to more than double in three years) to the rise of the agentic SOC, where AI agents handle detection, investigation, and response while humans focus on high-stakes decisions. John breaks down why attackers armed with AI now exploit zero-days in hours instead of weeks, why security must start with observability (including the challenge of "shadow AI"), and how CISOs are evolving from technical gatekeepers into board-level business enablers. His parting message: the entire world is learning AI together — get to it with his perspective on what great looks like for Splunk Security moving forward. "The volume is increasing quite a bit. We expect in the next three years it's gonna double. Attackers do not have a governance of regulatory and compliance restrictions on them. They just go at it and see what works. And so the volume, sophistication, speed of attacks—the only way to defend against it is to automate your responses to it. One thing that folks outside of the industry don't maybe get is just how large the attack surface is. And how hard it is to stop—attackers need to just find one way in, and you're trying to defend all ways in." - John MorganEpisode Highlights:[00:00] Quote of the Day by John Morgan from Splunk Security[00:50] John's path from technologist to cybersecurity leader[01:35] Leading Splunk Security: the mandate and mission[02:20] Why Cisco and Splunk have a disproportionate AI advantage[03:18] It's not the technology — it's the human beings[04:26] Why more data demands better curation and context[05:00] AI as both signal generator and attack surface creator[06:12] Where the bottleneck sits: ingestion, analysis, or response[07:10] Splunk at the intersection of observability and security[08:29] The evolving CISO role: gatekeeper to board-level risk officer[10:22] Defining the agentic SOC and where it's heading[12:00] Alert fatigue and how agentic approaches change the dynamic[13:56] Singapore Airlines: real customer outcomes from AI security[14:47] The AI arms race: who has the structural advantage[16:11] What a mature AI-native security platform looks like[17:19] How AI is changing detection from rules-based to correlation[18:35] Advice to CISOs: observe, trust, automate[19:41] The one question John wishes more CISOs would ask[20:22] The next five years — and why five years is too slow[21:20] ClosingProfile: John Morgan, GM and SVP, Splunk Security, CiscoLinkedIn: https://www.linkedin.com/in/johnmorganinc/Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. This episode is recorded in Poddster Singapore. Here are the links to watch or listen to our podcast.Analyse Asia Main Site: https://analyse.asiaAnalyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasiaSign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Episode 73: Betsy Biemann, CEO of Coastal Enterprises, Inc. (CEI) Investing in Communities: Betsy Biemann on the Mission of CEI In this episode of The Boulos Beat, recorded in December 2025, guest host Drew Sigfridson sits down with Betsy Biemann, CEO of Coastal Enterprises, Inc. (CEI), to talk about her path from a Rotary Fellowship in Kenya to leadership roles at the Rockefeller Foundation—and ultimately to leading one of the country's top Community Development Financial Institutions. Drew and Betsy dive into CEI's mission to support low-income communities through financing, business advising, and targeted investments in sectors like farming and food manufacturing, aquaculture, childcare and renewable energy. They also discuss CEI's impact nationwide, including advancing affordable and workforce housing and rural manufacturing through key tax credit programs. The conversation touches on how CEI has helped small business owners navigate the evolving federal policy and economic landscape and why their work supporting entrepreneurs and local economies is more important than ever.

The growing complexity of messaging compliance and the operational risks facing enterprises, Approved Contact Podcast, This is where structured conversation data becomes critical “Pay attention to why you’re sending the text—operational versus marketing.” In this Technology Reseller News podcast, Doug Green speaks with Tony Nuzzo of Approved Contact about the growing complexity of messaging compliance and the operational risks facing enterprises. A key theme emerging from the discussion is that as customer communications scale—especially across SMS and voice—organizations must move beyond fragmented systems and develop a unified, auditable view of customer interactions. Nuzzo highlights how regulations like the “ten-day revocation rule” are raising the stakes. Once a customer opts out, companies have a limited window to stop all communications—or risk fines and legal exposure. The challenge is not just compliance, but execution: ensuring that opt-outs are captured, understood, and acted upon across all systems in near real time. This is where structured conversation data becomes critical. By centralizing communication records and applying frameworks like vCon, businesses can better understand customer intent, track consent, and reduce ambiguity. The distinction between operational messages (e.g., fraud alerts or payment reminders) and marketing outreach becomes especially important, as each carries different compliance obligations. For enterprises, financial institutions, and service providers, the takeaway is clear: compliance is no longer a back-office function—it is a core operational capability. Those who can manage consent, context, and communication history effectively will reduce risk while improving customer trust. The bottom line: in a world of tightening regulations and rising customer expectations, managing conversations intelligently isn't optional—it's foundational.

At RSAC Conference 2026, the floor at Moscone Center was buzzing with talk of AI -- but underneath the excitement, a sharper question was forming: are enterprises actually ready to secure the AI systems they are rushing to deploy? Ed Wright, VP of Product Marketing at Menlo Security, joined Sean Martin on-site to dig into exactly that question. With 85 percent of knowledge workers now operating primarily through a browser, Menlo Security has spent 13 years building the infrastructure to protect that surface -- and the threat landscape has just taken a significant turn. The traditional browser threat model centers on humans: phishing links, malicious downloads, social engineering, deepfake video scams. Enterprises have spent billions on SSE stacks and endpoint protection stacks. Yet attacks continue to multiply. What Menlo Security is now tracking is a second threat model layered on top -- one designed specifically for AI agents. Agents use browsers to acquire data and complete tasks, often spinning up hundreds or thousands of headless browser sessions outside the enterprise perimeter, invisible to network security tools that only monitor the wire. The threat profile for agents is distinct. Where a human might miss a suspicious link, an agent reads white-on-white text and zero-font-size characters embedded in web pages -- classic prompt injection techniques. Agents are maniacally focused on task completion and do not naturally separate instructions from data. A co-opted agent, redirected through hidden instructions, will pursue its new goal with the same single-mindedness as its original one. Ed Wright notes that the top concern among CISOs at the RSAC Conference CISO bootcamp -- confirmed by a live audience poll -- is data exfiltration from agents: an agent accessing files, scraping internal pages, passing data to external LLMs, and moving sensitive information outside the organization. Menlo Security's response is a unified browser security platform that applies a single policy framework to both human and agentic workloads. The platform is built on four pillars: threat prevention including zero-day protection, secure application access, data security through AI Adaptive DLP, and file security. AI Adaptive DLP is the capability Ed Wright emphasizes most -- it functions as a combination of DLP and DSPM, discovering and classifying sensitive data across the organization and masking it in real time rather than blocking access. When traditional DLP blocks a human, they call IT. When it blocks an agent, the workflow silently fails. AI Adaptive DLP eliminates that failure mode entirely, keeping workflows uninterrupted while sensitive data stays protected at the source. The unification argument cuts through a crowded point-solution market. Rather than deploying separate tools for prompt injection, file security, and application access, Menlo Security delivers a single layer of visibility and observability across the entire workforce. Single policies. Single set of capabilities. No stitching together of forensic data from disconnected systems. Ed Wright points to a Fortune 500 customer that deployed 20,000-plus agents in a short window after a board mandate -- and quickly realized they had no security guardrails in place for browser-based agentic activity. The emergency call to Menlo Security was not the first of its kind, and it will not be the last. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Ed Wright, VP of Product Marketing, Menlo Security LinkedIn: https://www.linkedin.com/in/edwardwright1/ RESOURCES Menlo Security: https://www.menlosecurity.com Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS Ed Wright, Menlo Security, Sean Martin, browser security, agentic AI security, AI agents, headless browsers, prompt injection, data exfiltration, AI Adaptive DLP, DSPM, zero-day threats, enterprise browser, SSE, RSAC Conference 2026, brand spotlight, brand story, brand marketing, marketing podcast Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today we have two guests from two different companies who have one shared conviction: AI works best when it amplifies people, not replaces them. Today we're joined by Rachana Rele, VP of Product Design for AI-native products at Adobe, and David Shim, co-founder and CEO of Read AI. Together, they're building very different products — but they share a vision of AI that removes the drudgery from creative work and makes room for the thinking that actually matters. In this conversation, we dig into some ideas that could genuinely change how you think about your work. David talks about this concept of “storage of intelligence” — the idea that your knowledge, your meeting history, your working style could all be captured and made available as a kind of digital twin that keeps working even when you're not in the room. And Rachana shares how Adobe is thinking about AI not as a one-shot creative output machine, but as a collaborative partner that helps teams break out of their own blind spots. We also push them on the harder questions — the job anxiety that's real right now in tech, the surveillance concerns that come with recording your work life, and where they each personally draw the line. Bios David Shim is Co-Founder and CEO of Read AI, an AI productivity platform focused on helping knowledge workers leverage the power of AI to improve how they collaborate, communicate, and get work done. The platform provides meeting insights, search, chat, and proactive recommendations for millions of professionals, integrating seamlessly with the tools teams already use. Read AI is pioneering the concept of the Digital Twin—AI that serves as a true extension of you, built on deep contextual understanding of how you work. Today, Read AI is trusted by teams at 90% of the Fortune 500 and in the past year, was recognized as a Top 10 AI Vendor for Enterprises by Brex, a Top 50 AI App by a16z and Mercury, and named one of Inc.'s Top 16 Companies to Watch Before founding Read AI, David served as CEO of Foursquare and previously founded Placed, which was acquired by Snap in 2017. In 2025, he was named CEO of the Year by Geekwire. Rachana Rele Rachana has spent 20+ years at the intersection of technology and human experience — figuring out not just what to build, but why it matters. At Adobe, she shapes the direction of new products, nurtures ideas from zero to something real, and helps early-stage businesses find their footing and grow. She's also a perpetual student — currently finishing an MBA at UC Berkeley's Haas School of Business, with an M.Eng. in HCI from Clemson and a B.E. in Industrial Engineering from the University of Mumbai.

On the RSAC Conference show floor, Tony Anscombe shared how ESET has expanded its threat intelligence offering with ECR reports -- designed to give commercial organizations both machine-readable feeds and human-readable analysis. The reason: threat actors are increasingly hard to attribute, they share tools, run coordinated campaigns, and reinvest profits into more sophisticated operations. Having someone do the research and surface actionable intelligence is no longer a luxury. Anscombe pointed to a telling campaign pattern from last year: threat actors refined attack methods against UK retailers, then rapidly adapted those same techniques against US retailers. The implication is clear -- your business may be unique in its infrastructure, but it is not unique in its sector. Understanding how your sector is being targeted is the foundation of a prevention-first posture. Automation came up as equally non-negotiable. If it takes three days to collect all the information needed to make a determination about an incident, the post-attack phase has already begun. ESET Inspect is designed to flip that equation: when an analyst opens an incident, the forensic analysis is done, the evidence is visualized, and the determination can be made on facts rather than gathered through investigation. Anscombe was careful to draw a line between automation as speed and automation as replacement. ESET's position is that AI should operate alongside human expertise -- trust and verify applies to AI-assisted analysis just as it does to any intelligence feed. Oversight remains essential, even as the tooling gets faster. A preview of upcoming survey data offered one of the more striking moments in the conversation. Roughly 35% of SMBs using MDR are sourcing that service directly from their cyber insurer. Anscombe flagged the monoculture risk: when a large share of businesses in the same sector run identical security stacks, a single point of failure becomes a sector-wide vulnerability. His advice after 30 years in the industry -- different organizations should deliberately choose different platforms to maintain diversity. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Tony Anscombe, Chief Security Evangelist, ESET LinkedIn: https://www.linkedin.com/in/tonyanscombe/ RESOURCES ESET: https://www.eset.com ESET Threat Intelligence: https://www.eset.com/int/business/services/threat-intelligence/ Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS Tony Anscombe, ESET, Sean Martin, Marco Ciappelli, brand spotlight, brand marketing, marketing podcast, threat intelligence, cyber resilience, MDR, EDR, XDR, managed detection and response, SMB security, cybersecurity automation, RSAC Conference 2026, prevention-first security, cyber insurance, monoculture risk, ESET Inspect, APT research Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Fresno State Athletics is cutting out the middle man, what is happening to the Baseball team, and we run through the football roster.Tune in on Youtube for the livestream every Monday at 7:30am and subscribe.Go Dogs!

Enterprises are facing an 80% failure rate for AI agents in complex tasks because these systems lack the deep understanding required to navigate established legacy environments and existing internal systems. In this episode, Eran Yahav, CTO and co-founder at Tabnine, outlines how an enterprise context engine acts as a persistent memory and mapping layer that onboards AI systems into specific business logic, security perimeters, and organizational dependencies. The conversation highlights how this infrastructure can double agent success rates and reduce token costs by 80% while allowing technical leaders to establish swim lanes that ensure agents operate reliably within complex software architectures. This episode is sponsored by Tabnine. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner.  Want to share your AI adoption story with executive peers? Click go.emerj.com/expert for more information and to be a potential future guest on the 'AI in Business' podcast!