Podcasts about enterprises

Kevin Wright raises top quality mules in Fountain Green, UT. In this episode we talk about his experiences, education, and thoughts on what it takes to make a nice saddle mule.Find more on social media by looking up Diamond W Enterprises on Facebook

Enter to win WBE's Ranger Z521R 55th Anniversary Limited Edition 2023 bass boat (Brian Bird's personal boat worth an estimated $84K!). Enter with a qualifying merch purchase:https://wbe-the-champions.checkoutstores.com/SWEEPSTAKES INFORMATION: NO PURCHASE NECESSARY TO ENTER OR WIN IN THE WORLD BASS ENTERPRISES “MERCHANT STORE” SWEEPSTAKES. Open to legal residents of the 48 US & DC, 18 or older. Void in AK, HI & where prohibited. Sweepstakes starts 12:00 AM ET 7/6/26 and ends 11:59 PM ET 9/20/26. For details on how to enter without purchase and complete Official Rules, which govern, visit https://www.worldbassenterprises.com/merchant-store-sweepstakes.Here's that link to the Hookset Hoodlums X Jigs & Bigs BCRF Charity Slot Tournament! https://app.fishingchaos.com/tournament/EMMhJZlBMtgYH1sKJ8Z7Online

We speak with Italian duo Mind Enterprises. The former post-punk musicians, Andrea Tirone and Roberto Conigliaro, create feel-good Italo disco – a genre full of Mediterranean escapism.See omnystudio.com/listener for privacy information.

Most AI agent conversations start with what the agent can do. Very few focus on how you manage, monitor, and trust those agents once they're in production. At Cisco Live, I sat down with Kamal Hathi, SVP & GM of Splunk at Cisco on The Ravit Show, to discuss what enterprises need beyond AI models to make agents reliable, secure, and trustworthy.A few key takeaways from our conversation:* Moving AI agents from demos to production requires visibility into how they operate, make decisions, and interact with enterprise systems.* As organizations deploy more agents, observability becomes critical. Without it, AI can quickly become a black box.* Data remains one of the biggest challenges. Enterprises are looking for ways to reduce tool sprawl while maintaining a unified view across their environments.* Security and observability are no longer separate conversations. The faster teams can connect operational issues with security events, the faster they can respond.* Making AI accessible is important, but governance cannot be an afterthought. Innovation and control must go hand in hand.One thing that stood out to me:The future of AI isn't just about building smarter agents. It's about creating the trust, visibility, and governance needed to operate them at enterprise scale. Great conversation with Kamal on the next phase of enterprise AI and the role observability will play in making it successful.#data #cisco #ciscolive #ai #theravitshow

Enterprises are running more AI agents than their security teams realize, and attackers only need to be right once. Anand Oswal, EVP of Network Security at Palo Alto Networks, explains how to secure agents across four surfaces: enterprise, SaaS, endpoints, and the browser. With host Michael Krigsman, he covers shadow agent discovery, MCP and browser risks, prompt injection, agent identity, and why a unified platform beats a stack of point products.YOU'LL DISCOVER✅ The four agent surfaces every CISO must secure at once: enterprise, SaaS, endpoints, and the browser✅ Why discovery comes first: you cannot secure agents, models, tools, and plugins you cannot see✅ The Palo Alto Networks finding that one third of public MCP servers carry takeover level vulnerabilities✅ How vibe coding agents demand privileged access to local files, terminals, and cloud credentials✅ How browser agents inherit your session and cookies and can perform identity impersonation✅ Runtime threats to know: prompt injection, memory poisoning, tool misuse, and model DoS✅ How MCP and A2A protocols expand the attack surface, and why a centralized AI gateway anchors identity, runtime, and observability controls✅ The case for zero trust, an AI-driven SOC, and one unified platform over point products, and where Prisma AI fits⏱️ TIMESTAMPS0:00 Introduction0:22 Agent memory poisoning and tool misuse0:59 Discovering shadow agents across four surfaces2:32 Vibe coding agents and MCP risk4:46 Browser agents and session misuse6:20 Runtime threats and prompt injection7:17 Agent-to-agent protocols and attack surface8:04 Agent identity and the control plane9:16 Centralizing control at the AI gateway10:23 Zero trust and an AI-driven SOC11:29 One platform, not point productsSubscribe for weekly conversations with the business and technology leaders shaping the enterprise. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/palo-alto-networks-evp-securing-ai-agents-in-the-enterpriseEpisode 924#CXOTalk #EnterpriseAI #CIO #AIGovernance #AgenticAI #IBM #DigitalTransformation #AIStrategy #AILeadership

It's been a crazy summer for AI. AI enthusiasm remains high, but the market is starting to ask far harder questions. Semiconductor stocks are in a bear market. IBM's shares fell sharply. Data-center projects are facing political and community resistance. Enterprises are adopting AI, but many are struggling to turn experimentation into measurable returns.In this Freestyle Friday episode, I look at why these signals do not necessarily mean an AI winter. Instead, they suggest that AI is moving from a "possibility market" into an "accountability market", where capability, economics, infrastructure, and organizational reality finally have to reconcile. Put another way, things are going to get real.-----------------------Sponsor: FivetranWith the rise of AI and agents, having centralized, trustworthy data is the absolute foundation for building tools and training models. Fivetran automates your data pipelines, removing the need to build fragile connectors so your data arrives clean and reliable. By handling the messy infrastructure behind the scenes, Fivetran allows your team to focus on building the future. Visit ⁠fivetran.com⁠ to learn more.-----------------------Sponsor: Revefi Save serious money on your cloud costs with Revefi's new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure. Start saving money today at ⁠⁠revefi.com/ai-dba

Safe, reliable, production-ready AI agents don't come from the model. They come from everything built around it.Rasmus Hauch is CTO of Boost.ai, one of the enterprise platforms banks, insurers and public sector organisations use to build customer-facing AI agents. His work sits one layer below every deployment: the orchestration, the guardrails, the testing infrastructure and the model choices that decide whether an agent holds up in front of millions of customers.In this episode, Rasmus opens that layer, explaining why conversational AI is shifting away from traditional conversation flows towards orchestration, testing and continuous evaluation. He walks through how Boost approaches it in practice: persona-based simulation across thousands of digital and voice conversations, predefined test suites for jailbreaking attempts and financial advice, layered guardrails inside and outside the agent, and a trust loop that feeds production results back into the build.Rasmus is direct about the hard parts. We touch on why the big LLM providers can't yet deliver the consistent response times voice demands, which is why Boost runs fine-tuned smaller models on its own infrastructure for latency-critical voice work. Rasmus explains why he advises clients against plugging in their own models. We also discuss where liability sits under the EU AI Act when an agent gets it wrong, and why a volcano eruption in Iceland is a good example of what no model can be trained for.Towards the end, Rasmus shares what's exciting him right now, including adaptive voice, real-time language switching and where agent-to-agent protocols are heading.Show notes
Discover more about boost.aiFollow Rasmus on LinkedIn:https://www.linkedin.com/in/rasmushauchFollow Kane on LinkedIn:⁠⁠⁠https://www.linkedin.com/in/kanesimms⁠⁠⁠Subscribe to VUX World: ⁠⁠⁠⁠https://vuxworld.typeform.com/to/Qlo5aaeW?utm_source=podcast&utm_medium=audio⁠⁠Subscribe to The AI Ultimatum Substack: ⁠⁠⁠⁠https://open.substack.com/pub/kanesimms⁠

Rime is handling over 100 million calls each month across multiple companies Also, Anthropic-backed Ode launches as AI labs bet that embedding forward-deployed engineers inside enterprises is the key to accelerating enterprise AI adoption. Learn more about your ad choices. Visit podcastchoices.com/adchoices

More than 48,000 vulnerabilities were disclosed in 2025, yet only about 1% are actively exploited. However, you're expected to mitigate all vulnerabilities, or at least critical and high. But what if there is no patch to fix the vulnerability or the software is unsupported? Ben Lipcynski, Director Security and Regulatory Services at Optima, joins Business Security Weekly to discuss how organizations can take back control of your enterprise software. OPTAS — Origina Proactive Threat Assurance Service — predicts, validates, prioritizes, and mitigates threats specific to your environment. Unlike AI vulnerability tools that flag everything without context or mitigation guidance, OPTAS cuts through the noise. OPTAS helps security teams focus on the risks that matter instead of chasing the 99% that do not. Segment Resources: - https://www.origina.com/optas#optas-overview This segment is sponsored by Origina. Visit https://securityweekly.com/origina to request a consultation. In the leadership and communications segment, US enterprises incorporate cyber risk into larger strategic focus, 75% of CISOs Fear Executives Don't Understand Cybersecurity Risks, AI agents are not your “coworkers”, and more! Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-456

Companies are spending billions building AI factories, but most of them can't tell you why their AI workloads are failing, whether their GPUs are actually being used, or what their infrastructure is going to cost them when agents start running at scale. Paul Appleby, CEO of Virtana, joins Craig Smith to discuss the findings of their AI Factory Reality Check study, a research report that reveals a striking and underappreciated gap between the pace of AI infrastructure investment and the governance needed to run it safely and efficiently. Six in ten enterprises, the study found, cannot automatically identify root cause when an AI workload fails, a problem that compounds fast once you're running critical services on AI infrastructure at scale. The conversation covers the mechanics of Virtana's observability platform, capturing 20,000 metrics per second across the entire AI stack, correlating them in real time, and increasingly using agentic capabilities to remediate failures automatically, but its most important insights are structural. Appleby makes a sharp observation that cuts through a lot of AI optimism: token costs are falling, but token consumption is exploding, meaning the total cost of running agentic AI systems is still going up even as the per-unit price drops. He also tracks a cultural shift inside enterprises - IT resilience reporting that used to happen annually now happens weekly - as evidence that technology risk has become a board-level conversation in a way it simply wasn't before. The result is a conversation that's less about the promise of AI and more about what it actually takes to make it work at production scale. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

More than 48,000 vulnerabilities were disclosed in 2025, yet only about 1% are actively exploited. However, you're expected to mitigate all vulnerabilities, or at least critical and high. But what if there is no patch to fix the vulnerability or the software is unsupported? Ben Lipcynski, Director Security and Regulatory Services at Optima, joins Business Security Weekly to discuss how organizations can take back control of your enterprise software. OPTAS — Origina Proactive Threat Assurance Service — predicts, validates, prioritizes, and mitigates threats specific to your environment. Unlike AI vulnerability tools that flag everything without context or mitigation guidance, OPTAS cuts through the noise. OPTAS helps security teams focus on the risks that matter instead of chasing the 99% that do not. Segment Resources: - https://www.origina.com/optas#optas-overview This segment is sponsored by Origina. Visit https://securityweekly.com/origina to request a consultation. In the leadership and communications segment, US enterprises incorporate cyber risk into larger strategic focus, 75% of CISOs Fear Executives Don't Understand Cybersecurity Risks, AI agents are not your "coworkers", and more! Show Notes: https://securityweekly.com/bsw-456

More than 48,000 vulnerabilities were disclosed in 2025, yet only about 1% are actively exploited. However, you're expected to mitigate all vulnerabilities, or at least critical and high. But what if there is no patch to fix the vulnerability or the software is unsupported? Ben Lipcynski, Director Security and Regulatory Services at Optima, joins Business Security Weekly to discuss how organizations can take back control of your enterprise software. OPTAS — Origina Proactive Threat Assurance Service — predicts, validates, prioritizes, and mitigates threats specific to your environment. Unlike AI vulnerability tools that flag everything without context or mitigation guidance, OPTAS cuts through the noise. OPTAS helps security teams focus on the risks that matter instead of chasing the 99% that do not. Segment Resources: - https://www.origina.com/optas#optas-overview This segment is sponsored by Origina. Visit https://securityweekly.com/origina to request a consultation. In the leadership and communications segment, US enterprises incorporate cyber risk into larger strategic focus, 75% of CISOs Fear Executives Don't Understand Cybersecurity Risks, AI agents are not your "coworkers", and more! Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-456

More than 48,000 vulnerabilities were disclosed in 2025, yet only about 1% are actively exploited. However, you're expected to mitigate all vulnerabilities, or at least critical and high. But what if there is no patch to fix the vulnerability or the software is unsupported? Ben Lipcynski, Director Security and Regulatory Services at Optima, joins Business Security Weekly to discuss how organizations can take back control of your enterprise software. OPTAS — Origina Proactive Threat Assurance Service — predicts, validates, prioritizes, and mitigates threats specific to your environment. Unlike AI vulnerability tools that flag everything without context or mitigation guidance, OPTAS cuts through the noise. OPTAS helps security teams focus on the risks that matter instead of chasing the 99% that do not. Segment Resources: - https://www.origina.com/optas#optas-overview This segment is sponsored by Origina. Visit https://securityweekly.com/origina to request a consultation. In the leadership and communications segment, US enterprises incorporate cyber risk into larger strategic focus, 75% of CISOs Fear Executives Don't Understand Cybersecurity Risks, AI agents are not your "coworkers", and more! Show Notes: https://securityweekly.com/bsw-456

Enterprises rushing agentic AI into production are running it through approval gates, batch windows, and audit systems built for human speed — and the gap is where most operational risk lives. In this episode, Chris Caldwell, President and CEO at Concentrix Corporation, examines how machine-scale transactions break processes designed for human pace and why bounded digital delegates outperform unrestricted digital twins in the enterprise. The discussion covers compliance bots that check other bots, the cost reality of poorly tuned agentic agents, and what leaders need to stop doing if they want a defensible AI roadmap. Learn how to evaluate AI vendors by assessing leadership expertise, and why funding benchmarks can signal product maturity and stability. Download our free PDF report, "5 Ways to Select the Right AI Vendor," at emerj.com/aiv2

Are you wondering if entering your manuscript in book awards is truly worth the effort? Join host Juliet Clark as she sits down with the founder of Black Château Enterprises and The BookFest®, Desireé Duffy, to uncover the true value of recognition in the publishing industry. They discuss why awards matter, how to navigate the submission process, and common pitfalls authors face when promoting their work. Desireé offers expert insights into building a sustainable author platform and the evolving role of AI in the writing process. Discover how to bridge the gap between being a writer and connecting with readers in this insightful conversation about the current landscape of the book world.Love the show? Subscribe, rate, review, and share! https://superbrandpublishing.com/podcast/

Lee Plotkin | LP Enterprises, Inc. L.P. Enterprises helps small and medium size restaurant groups streamline purchasing with precision, insight, and proven expertise – saving time, reducing costs, and protecting brand standards since 2001. Chances are you have spent years honing those skills revolving around the ownership, operation and management of restaurants and hotels. An […]

AGENDA: 05:00 Washington Just Put Frontier AI on a Leash 06:30 Sam Altman's Wild 5% Government Stake Idea 19:00 The AI Funding Bubble: Why Founders No Longer Fear Dilution 28:00 Alex Karp's Brutal Warning: Enterprises Don't Trust Frontier AI 33:00 Meta's Shock Pivot: Has Zuck Accidentally Built the Next CoreWeave? 41:00 Nvidia's Dangerous New Game: "Compute Now, Pay Later" 45:00 Anthropic & DeepSeek Go After Nvidia's Crown 48:00 Kling vs Sora: Did China Just Win AI Video? 52:00 Is China Secretly Winning the Open Source AI War? 01:02:00 Microsoft & Amazon's $6B Bet: AI Still Needs Humans 01:11:00 Ashton Kutcher Walks Away From Sound Ventures 01:16:00 The New Startup Talent War: No Liquidity, No Chance 01:20:00 Final Thoughts: Who Wins the AI Endgame?

Microsoft has established some high enterprise pricing and licensing hurdles going into its FY27. Directions' Advisory Services Director Lane Shelton talks with Mary Jo Foley about the obstacles and potential opportunities.

South and Southeast Asia face a different challenge to many developed economies. As the regions continue to grow, so too does demand for energy, transport and industry. The question is whether that growth can be powered by cleaner technologies from the start. In the second of our episodes recorded from Singapore, Michael meets Marie Cheong, founding partner at 100x100, a climate tech venture builder and VC for South and Southeast Asia. Previously, Marie co-founded WaveMaker Impact, where she helped launch climate startups addressing some of the region's biggest opportunities for decarbonisation. Marie explains why climate innovation in emerging South Asian markets requires a different approach. They discuss investing across the region, growing energy demand, using AI to decarbonise manufacturing, and why there are no "silver bullet" solutions to climate change. Marie also takes Michael to the People Bee Hoon noodle factory, which makes rice vermicelli noodles to see how Desmond Goh, the factory's director is using AI to improve his operations. Michael meets to Desmond and Muun AI founder Kathryn Knight about how better use of data can help cut emissions and waste. And at the end of the episode, a very special announcement… stay tuned. Topics Include Building climate startups across South and Southeast Asia Why climate investing needs more than "silver bullet" technologies The biggest opportunities for climate innovation in emerging Asian markets Solving regional pain points as the key to success Can Southeast Asia grow without burning more coal? Investing in emerging markets and navigating regional diversity How AI can help manufacturers cut emissions, costs and energy use The next generation of climate technology in Asia Leadership Circle Cleaning Up is proud to be supported by its Leadership Circle. The members are Actis, Alcazar Energy, Arup, Copenhagen Infrastructure Partners, Cygnum Capital, Davidson Kempner, Ecopragma Capital, EDP, Eurelectric, the Gilardini Foundation, KKR, Mitsubishi Heavy Industries, National Grid, Octopus Energy, Quadrature Climate Foundation, Schneider Electric, SDCL and Wärtsilä. For more information about the Leadership Circle, visit cleaningup.live Links Marie Cheong's bio: https://www.linkedin.com/in/marie-cheong-06141b36/ Wavemaker Impact https://wavemakerpartners.com/ 100x100 https://www.100x100.com/ Muun AI https://muun-ai.com/ Watch our previous episode from Singapore, with Ravi Menon: https://www.youtube.com/watch?v=Xvhd34i6YMk Harish Hande on Cleaning Up https://www.youtube.com/watch?v=jUxdaHFJI68 South Asia Includes: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan,and Sri Lanka. South East Asia Includes:  Brunei, Burma (Myanmar), Cambodia, Timor-Leste, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand and Vietnam. Acronyms FESIA - Future Energy Storage and System Integration Alliance ASEAN - Alliance for Southeast Asian Nations CPTPP -  Comprehensive and Progressive agreement for Trans-Pacific Partnership MT - Megatonne SAF - Sustainable Aviation Fuel SME - Small/Medium-sized Enterprises

On the latest episode of JohnWallStreet Presents: Big Business on Campus, a college sports podcast powered by Playfly Sports, JohnWallStreet Founder Corey Leff and Playfly Sports Chairman Michael Schreiber sit down with Rutgers Director of Athletics Keli Zinn and Scarlet Knight Enterprises Chairman Oliver Luck. In this 50-minute conversation, the four discuss: - A Pro Sports Approach to Revenue Generation - Managing Costs Through Structure - Building Regional Identity - and more

This is a special episode produced by the collaboration between TiE San Diego and Tantra's Mantra.In this episode, I speak to Mark Budgen of Lenovo and Rajnikant Gupta of TCS on the current status of AI adoption in enterprises. We start by discussing which segments and which functions within organizations have seen deeper adoption of AI, whether AI projects have moved from PoC and trials to production, what the major challenges are in that transition, how the thinking and approach of enterprises toward AI have changed over the short period, the need for “Human in the loop,” and the necessity of Hybrid AI. We discuss key takeaways from Lenovo's “CIO Playbook 2026” report and how enterprises are now more focused on improving and accelerating business outcomes rather than simply improving productivity.Finally, we delve into what to expect in the near and far future, moving from Generative and Agentic AI to Physical and Embodied AI.Index:00:00 - Intro01:05 - Guest intro (Mark Budgen, CTO, Global Technology Partners at Lenovo, & Rajnikant Gupta, Global Head - Partner Ecosystems and Alliances, TCS)02:28 - Current status of AI adoption in Enterprises, how AI can improve business, not just efficiency, risk considerations06:30 - Difference in level of AI adoption between various functional groups within Enterprises - IT, Coding, Security, Supply Chain, Sales & Marketing13:30 - Evolution of Human involvement in AI processes. Humans in the Agentic AI loop will be required for a long time, because of the risk of failure and its costs16:56 - Key takeaways from Lenovo's "CIO Playbook 2026" report: AI is not the entire toolbox, but one of the tools; focus is on how to use AI to improve and accelerate business and finally the outcomes; mapping IT KPIs to Business KPIs22:16 - Current status of AI projects, in "Production" vs. "PoC" or "Trials" stage. The majority still in the process of moving to Production. Execs now better understand their business value; Focus on platforms to scale rather than simple point solutions28:13 - Challenges in implementing AI: Employee fear of replacement, lack of domain experts with AI knowledge,31:56 - Hybrid AI: How to decide where to run AI: Cloud or Edge? Dependencies: Sovereignty, access to data, power, and cooling; Token use optimization38:27 - What to expect to see in the near and far future: Physical AI, Embodied AI, AI becoming ubiquitous and41:20 - Closing

In this episode of Agentic Conversations, we're joined by Shaun Smith, software engineer, open source advocate, and contributor at Hugging Face, to explore how AI coding has changed almost overnight.We dive into reinforcement learning, MCP (Model Context Protocol), Fast Agent, Claude Code, open source AI, and why today's language models have become so capable that many traditional software libraries are becoming "liquefied." Shaun explains how reinforcement learning unlocked long-running autonomous agents, why ideas are becoming more valuable than code, and how developers should think about building software in an era where AI can generate entire applications.Along the way, we discuss Hugging Face's MCP server, Fast Agent, AI-powered developer tools, multimodal applications, MCP Apps, context windows, coding assistants, Rust, Python, TypeScript, open-weight models, software architecture, and what the future of programming looks like when humans increasingly focus on design instead of implementation.Shaun Smith: https://www.linkedin.com/in/smithshaunDemetrios: https://www.linkedin.com/in/dpbrinkmHugging Face: https://huggingface.co⏱️ Timestamps[00:00] Introduction[01:56] The State of Open Source AI[05:18] Reinforcement Learning Changed Everything[07:50] Fast Agent Explained[10:18] Fast Agent as an MCP Reference Platform[12:20] Building Smarter AI Tools at Hugging Face[15:17] Natural Language Search Instead of APIs[17:46] Why MCP Apps Matter[20:06] The Evolution of MCP Apps[23:05] Building AI-Native User Interfaces[26:12] Context Is the New Programming Language[28:00] The End of Code Libraries[29:50] Why Developers Aren't Writing Code[31:25] AI Changes Software Engineering[33:05] The Future of Open Source AI[35:43] Claude Skills That Save Hours[38:02] Training Models with AI[39:05] Building Your Own AI Tools[40:50] MCP for Consumers, Enterprises, and Developers[43:42] Why Shell Access Makes Agents Smarter[45:18] Secure Agent Workflows[46:08] The Future of AI Interfaces[47:02] Outro#HuggingFace #MCP #OpenSourceAI

Autonomous AI and agentic AI are moving from experiments into real enterprise workflows. But are businesses truly ready to let AI systems plan, decide and take action on their behalf?In this interview, I speak with Shayan Mohanty, Chief Data and AI Officer at Thoughtworks, about the next phase of enterprise AI and what leaders need to do now to prepare.We explore the shift from AI assistants and copilots to autonomous agents, why governance and accountability need to be built into the architecture, and how enterprises can move beyond proof-of-concept projects toward scalable, production-grade AI systems.Shayan also explains why competitive advantage in the AI era may come less from access to the latest model and more from orchestration, AI-ready data, responsible engineering and the ability to redesign work around intelligent systems.This conversation is essential viewing for CEOs, CIOs, CTOs, CDOs and business leaders who want to understand what autonomous AI means for enterprise transformation, governance, software development and the future of work.If you'd like to explore how enterprises are preparing for this next wave of agentic AI, Thoughtworks' latest white paper, The Agentic Enterprise, offers practical perspectives and real-world insights.  https://www.thoughtworks.com/about-us/partnerships/cloud/aws/building-the-agentic-enterprise-ecosystem?utm_source=organic-influencer&utm_medium=influencer-marketing&utm_campaign=eai_tsi_rp-gl-pspt_rewire-for-agents_2026-05&utm_term=bernard-video-interview&utm_content=video#Sponsored Autonomous AI in the enterpriseAgentic AI and AI agentsAI governance and accountabilityEnterprise AI readinessMoving AI from pilots to productionAI-ready data and orchestrationThe future of software development#AutonomousAI #AgenticAI #EnterpriseAI #AIagents #ArtificialIntelligence #AIGovernance #AITransformation #DigitalTransformation #Thoughtworks #AIworks #FutureOfWork #BusinessTechnology

Harness has introduced Autonomous Worker Agents, a new capability that allows enterprises to replace rigid CI/CD pipeline scripts with AI agents that can deploy applications, run tests, and perform security scans while operating under existing governance, security, and audit controls. Unlike Harness' existing expert agents, which assist developers with coding and pipeline creation, Worker Agents autonomously execute pipeline tasks within customer-controlled infrastructure. Agents are defined using simple Markdown files, draw context from the Harness Software Delivery Knowledge Graph, and run in sandboxed environments with scoped permissions and policy enforcement.  Harness also provides built-in audit trails that record prompts, decisions, and outcomes, along with token budgets and approval gates to control AI costs. The launch includes an Agent Marketplace featuring Harness-managed, certified partner, and community-built agents. CEO Jyoti Bansal said production AI agents require far stronger safeguards than coding assistants, positioning Harness' governance and knowledge graph as key differentiators. Looking ahead, the company envisions fully autonomous software engineering, where AI agents manage the software lifecycle while humans oversee high-risk decisions. Learn more from The New Stack around AI software delivery: AI won't speed up software delivery - nothing has How to solve the AI paradox in software development with intelligent orchestration  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Enterprises have been slogging through the AI adoption journey, but cybersecurity threat actors have been too. As the time from vulnerability discovery to breach effectively goes to zero, the new cybersecurity playbook is all about how fast you can recover. We Meet:  Anneka Gupta is the Chief Product Officer at Rubrik Credits:This episode of SHIFT was produced by Jennifer Strong with help from Emma Cillekens. It was mixed by Garret Lang, with original music from him and Jacob Gorski. Art by Meg Marco.

https://youtu.be/Ji1OZYu1r1Y Charles Fry, Founder and CEO of CODE Éxitos, is helping businesses hire AI in enterprise to transform software engineering, modernize product development, and build intelligent connected systems.  In this conversation, Charles introduces The Agentic Org Chart Framework: Hire Systems Thinker, Look for Failed Entrepreneurs, and Have AI Replace “Trade Skills”. He explains why AI is fundamentally changing software development, how organizations must redesign their structures for an AI-first workforce, and why systems thinking is becoming more valuable than technical specialization. Charles also discusses how the rise of agentic software development is reshaping the future of SaaS, why combining AI with connected hardware creates a stronger competitive advantage, and what business leaders must do to successfully navigate AI-driven transformation. — Hire AI in Enterprises with Charles Fry  Good day. Steve Preda here, and I’m talking with Charles Fry, the Founder and CEO of CODE Éxitos, building cyber-physical systems for mid-market and enterprise companies, as well as full-stack web, mobile, and SaaS development. Charles, welcome back to the show.  Hey, I’m happy to be here. It’s always great to see you, and I’m looking forward to our chat. Yeah. It’s so interesting to talk to you because the last time we had you on the show, three or four years ago, it was still before the AI age was fully upon us.  Right.  Your business was kind of a different business. I’ve been following you on LinkedIn, and I see that you’ve evolved your approach, and now you’re an AI-first company. So tell me a little bit about how that came about, this whole evolution, and how you found your new focus?  Yeah. Wow. It’s been that long since we were on the show. It was a lot of fun, but here we are. You’re right. AI really sort of came out of left field. I’ll skip all the technical things that suddenly made AI an achievable thing. But really, at the beginning of 2024—and somebody can fact-check my timeline—ChatGPT, if you were aware of it, was kind of passing the Turing test. It was giving pretty reasonable answers to natural-language questions.  And we were like, “Wow, this is interesting.” At first, we were helping our clients think about how to build those capabilities into their products, something we still do.Share on X But by mid-’24, late ’24, it became pretty obvious that one of the best applications of large language models—and expert systems, we used to call them that—is writing software. And so by early to mid-’25, the systems were suddenly not novelties. They were credible at what they were doing, and they were gaining momentum in the quality and credibility of the software they were producing.  Now, CODE Éxitos was started largely to create an opportunity for entrepreneurs and enterprises that needed, let’s call it, garden-variety, well-done software. We built that through the Americas to arbitrage labor rates in Latin America and leverage the time zones. So it was essentially an offshore, blended, hybrid-team model. Honestly, by the middle of 2025, the AI tools for software engineering were as good as, or better than, 50 percent of the human developers that we employed.  And at some point, as a business owner, when you’re out there representing yourself, and your product is yourself, and you’re representing that to clients, you have a moral and ethical obligation to say, “Hey, I think I can still give you the best product that you’re looking for, but I’m going to do it in a different way.” So beginning in late 2025, we were hard into the pivot. Today, all of our software development is done agentically. There are still people. It’s not a complete dark factory. But our mid-level performers and below, we exited them from the business, which caused a lot of human turmoil. I mean, we were at around 100 people. A lot of human turmoil. Our clients were going through the same thing. We were watching what was happening in their organizations and what the leadership demands were.  We just made the decision to lean into it. And here we are, almost mid-’26 now, and it’s actually going really well. Now, AI, of course, anyone who opens an internet browser anymore sees it. AI is everywhere. You read the newspaper. AI is going to change everything.  Every application has an AI layer to it.  Yeah.  Right? Every SaaS application has a button that says, “Use AI here.”  My view, at least—and these are the things people should probably give me some credibility on—I’m going to keep my views focused on how AI applies to the software industry, my industry, and its direct impact. We see, and we have clients working on, things like AI in customer service, the legal department, and the finance department. We do all of those things internally—agentic first, AI first. But those really aren’t my industry. I’m not ready to make a sweeping prognosis about how AI is going to change capitalism in the United States. But in the software industry, it’s a fundamental change. And it’s not done yet.  So what’s your vision? Where is everything going? What’s it going to look like three years from now?  Well, I’m not smart enough to know that. But I think the patterns we've seen—and again, within the world of writing software, and making that a broad category of activitiesShare on X —are going to continue to consolidate and converge to where humans are important, but they might be only 10 to 20 percent of the input to the process. I really do think we’re going to see a day, sometime in the three-to-five-year time horizon, where a large amount of software will be written and managed by other software systems.  There’s no technical reason to prevent that. For example, I was talking to one of our clients today, a CIO at a great company. A couple hundred million dollars in revenue. A really well-run business. A sizable internal IT team. But there’s a lot of ongoing maintenance and attention required. Building the software is just the beginning of a five-to-ten-year life cycle. So I think in the near term we’re going to see that building the software becomes, “Okay, we got that figured out.” That’s a largely solved problem.  We’ll then progress to the problem of: “Hey, this software has been in production for five years.” “It needs updates.” “It needs attention.” “It needs maintenance.” “It needs to scale.” More software systems will take care of that. Fewer and fewer humans will be involved in that kind of work. So I think that’s where the software industry is headed. I think it’s going to be 60 to 80 percent smaller in human capital than it is today. Sometime soon. Yeah. I really do think it’s about as close as I want to get to calling it an extinction event. Let’s put it that way. Some people say SaaS companies are going to go out of business, and it’s all going to be agents running around doing things for us. But other people say SaaS companies are actually the SOP for whatever activity is out there, and you need that structure. The SaaS application provides that structure. You don’t want agents running in an unstructured way. You’d rather have these SaaS applications. What’s your view?  I think that’s a good way of looking at it. If you’re a dinosaur like I am, back in the late 1980s or early 1990s, when you wrote software for a company, everything was custom software because there were no packaged software products, no SaaS platforms. But over the last 20 years, I think SaaS companies have become, for a lot of businesses, exactly what you said. They’re the embodiment of best practices. If you take something like HubSpot, which I’m sure you and many of your audience are familiar with, you really don’t need to customize it.  You just need to follow its baseline processes because they have thousands of customers who have helped refine the sales motions that work. So I think there’s some truth to that. The problem SaaS systems face is that the barrier to competitive entry is much, much lower. If you look at a company like Salesforce—and I think I’ve led three different Salesforce deployments back when I was a CIO or CTO—that software really shows its age. It’s layers and layers of complexity built to serve a wide audience. It’s great. It’s expensive. Emerging companies don’t need that. They can effectively vibe-code their own CRM system, and it works. I think the threat for big SaaS companies is twofold.  One is that the next generation of customers is going to onboard very differently into those systems than the previous generation.Share on X I don’t know what that onboarding ramp is going to look like. The second problem is there’s very little defensibility in having a pure software product. And the other part of our intro—you talked about cyber-physical systems. We’re spending more and more of our product development cycles on hardware-related products, things that have a nexus in the physical world. Here’s a good example. I’m wearing one of these health rings. This Oura Ring. Oura, yeah.  There’s a lot of amazing hardware in here that justifies my monthly subscription for the app. The app we could recreate pretty easily. But the development, manufacturing, and distribution of this physical item create a much higher barrier for a competitor to overcome. So more and more of our clients are companies that have a physical product they either want to make smarter or make more connected.  That’s really what it comes down to. And that’s a pretty exciting space. But for a pure-play SaaS company, I think it’s going to get tough. The competitive pressure is going to be intense. And the barrier to entry is going to be really low. It’s not even about engineering cost anymore because the cost of engineering has dropped so much with AI. It’s almost like it went full circle. You had all these product businesses that wanted to become service businesses to create recurring revenue. And now the service businesses—the SaaS businesses—want to become product businesses to create a barrier to entry, improve retention, or reduce disruption. Isn’t that interesting?  Yeah. I hadn’t thought about it exactly that way. Maybe the pushback would be that these professional services businesses wanted to have a technology play or a platform. That’s interesting. But I think we’re going to see AI, at least in technology, continue to lower the barrier to entry. It will allow much faster experimentation with pure software ideas. And we’re focused on the things where the AI robots can’t play. They’re not going to cut your grass.  They might guide the machine that cuts your grass, but they’re not going to cut your grass. So I think that while the turmoil is still sorting itself out in the pure software world, we’re going to see a whole new set of opportunities open up. We’ll be able to build truly smart devices. Devices that think for themselves. Devices that are aware of the world around them. They can participate with us in our day-to-day work. That’ll be a lot of fun. I think we still have some rough sailing ahead of us as AI sorts itself out.  Isn’t it true that people prefer to interact with a purpose-designed device rather than a software product? And maybe an app is kind of a device that is software, or maybe that’s the overlap there. But I know there are some things I’d rather have on my phone, even though it’s complicated because there are so many other things on it. But if I have a single-purpose device, like you have your Oura Ring, it’s easier to interact with. There’s no complexity, and then it lowers the accessibility.  Yeah. An area of active study is something called HMI, or Human-Machine Interface. Again, back in the ’80s and ’90s, it meant things like: Are the buttons big enough for someone to push? Does a red light always mean a bad thing, and a green light always mean a good thing? But now that’s expanded into the kind of research in psychology and sociology that you’re talking about, Steve. Some of that is really amazing.  I’m sure you’ve seen them, and your audience has seen them. You can find these videos on YouTube. There are humanoid robots. It took a while for researchers to figure out that a robot doesn’t actually need a head. It can have what is essentially a torso with arms and legs. The head doesn’t really need to be there. But a robot with no head freaks people out. Yes. People don’t like it. So the robotics engineers put heads on them. Then they thought, “Well, if we’ve got a head here, we’ll just put a face on it.” But if the face is too realistic, it gives people the creeps.  Yeah.  So people didn’t like faces on them. If you look at the current generation of humanoid autonomous robots, they have these, I don’t know, sort of pseudo-faces. They kind of look like Halloween jack-o’-lanterns or something. They’re not scary, but they’re somewhere in the middle. Anyway, the things you’re talking about are really fascinating.  I mean, Isaac Asimov wrote about humanoid robots and all the challenges that come with them. What happens when they have a human-like appearance? What happens when people think they are actually people, but they just don’t age? All those things have been explored. But listen, I’d like to switch gears here and ask you this. Right now, in this AI age, what drives your business? What drives growth in your business?  This part is truly fascinating to me. Everyone is working off the same timeline now, which isn’t a very long timeline. We don’t have a lot of experience to draw on. Much more quickly than when the internet became commercially available—I was there when that happened too— the adoption of AI as a fundamental change happened in a matter of months, compared to several years for the internet.Share on X Some people also compare it to the adoption of mobile phones, which you mentioned.  But this has happened very fast. A year ago, we were talking to sophisticated technical buyers who said, “Yeah, I’m still on the fence about whether I like agentic software development.” That doesn’t happen anymore. Everybody says, “Yeah, we’re using it too.” What we’re seeing now is that it’s evolved so quickly and had such a fundamental impact that people don’t know not only how to manage it inside their business, but also how to deploy it. It’s really disruptive. And this is where you’re a pro.  It’s really disruptive to organizations. So in less than a year, we’ve gone from, “Hey, should I let my developers use AI?” to now everybody using AI. And the leading teams, including ours, can produce high-quality commercial code faster than organizations can absorb it, and faster than org charts can adapt to the change. Our engineering teams have to adapt to the pace of the business, not the other way around. Because we get clients saying, “Hey, you guys are producing too much.” “We can’t check everything.”  “We haven’t finished testing last week’s new features and capabilities yet.” “We can’t take another batch of features and capabilities this week.” So we’re seeing a lot of organizational behavior change starting to come out of this. When we’re talking to C-level executives and senior leaders, that’s where most of the conversations are today. “How do I retool my organization to capture the benefits?”  Yeah. Because what I see is that the more AI you apply, the faster decision velocity becomes. And the complexity of understanding the whole picture, connecting the dots, increases. So you need a different kind of person who can operate at that higher level of contextualization. Do you see the same thing?  We do. Software engineering and product development had matured into a pretty predictable set of job descriptions and capabilities. The business processes were really well worn. We knew what the product owner did. We knew what a project manager did. We knew what a tech lead did. Et cetera, et cetera. A lot of these, frankly, became trade skills. “Hey, I’m really good at writing code.” Or, “I’m really good at doing QA, but I’m not really a systems thinker.” “I’m not an entrepreneur.” “I’m not a creator.” Pick your fuzzy lens of choice.  That’s really what AI displaces right now. AI displaces those trade skills and, frankly, does them better and cheaper. There’s no way to dispute that. What we look for now, and I think where the trend is going with our clients, is systems thinkers. We have enough agentic tooling built on our own internal platform that the people operating and building products for our clientsShare on X —we refer to our team as digital creators—come from a variety of backgrounds. You don’t have to be a computer science major. You do have to have some domain experience. You do have to be a systems thinker. You do have to understand what business value you’re trying to create. But as far as actually writing really good code, nobody’s really doing that now. It’s being done automatically.  If you have a couple of gray hairs like I do, you’ll remember back 20 years ago when we talked about the war for talent. That’s what everybody was looking for. They wanted people with these highly specialized engineering skills. I think there’s a new war for talent. It’s going to be harder to pin down because we’re going to be looking for whole-systems thinkers as opposed to technical specialists. Because AI will be the technical specialist we need, regardless of the business domain. So what do you do to infuse systems thinking in your business?  Wow. I wish I had a good answer for that. I don’t even know how to recruit these kinds of people right now. I’ll be that candid with you and your listeners. I was talking to a couple of my senior people, and I said, “Maybe we should go look for failed entrepreneurs.” Which is kind of a heretical thing to say. But as an entrepreneur, I know firsthand that it doesn’t always work. The fact that the business fails doesn’t necessarily mean you, as an entrepreneur, are a personal failure.  Entrepreneurs are about the only, I don’t know, primary source I can think of for people who have done a little bit of everything. They’re systems thinkers. Maybe they didn’t get it right, but they could. So we talked about that. I don’t know if Disney still does it, but Disney was phenomenal at producing these kinds of people through its internal training programs. We’re not big enough to compete with Disney. But to answer your question, how do we teach it? I can’t honestly say that we do. Because we’re still figuring out what it is that we would even teach.  Well, it’s a new type of SOP that’s needed in the business. So what are the best practices for building an AI workforce? That still needs to be defined.  Yeah. For larger organizations, our clients that are running larger organizations have the same problem. All of a sudden, their org chart is broken. What I mean by that is, if you look at the way we’ve traditionally built and scaled businesses, you have this pretty large cadre of managers who give you what Eliyahu Goldratt called the span of control, your degree of leverage. When you’re younger, you hear things like, “Ah, my manager doesn’t even do anything.” You’ve probably heard that before. “Oh, my manager doesn’t really do any work.” “He just comes in and bugs me.”  It’s not entirely wrong because we rely on that manager’s experience to be spread across six, eight, or ten other people and supervise their work. So managers don’t really do a whole lot of delivering the work themselves. I think AI is going to change that. I know AI is already changing that inside technical teams. All of a sudden, we have clients saying, “My org chart doesn’t translate to the way my business operates under this new agentic model.” That’s problem number one. Problem number two is, “I have people on my team who are good people and good contributors, but there’s no box for them in the new org chart that I think works with an agentic workflow.” Does that make sense?  Yeah.  So two things have happened suddenly. We see a lot of press—although I think it’s moderating a little bit now—about how kids coming out of university are having trouble getting entry-level jobs. True enough. I think the other area where org charts are collapsing is managers who don’t actually produce any output. Supervising other managers and compiling the weekly report of reports just isn’t a valuable job function, even at the best of times. And now—I hate to say this—but it’s useless. So I think there’s a lot more work we’re going to have to do with our clients, and ourselves, on what an org chart should look like. And what the expectations are for people doing work inside a company that’s moving aggressively toward leveraging AI capabilities in what we would normally call white-collar job functions. Yeah. Some years ago, I thought about the org chart being broken. The top-down hierarchical org chart—I think it’s completely broken. In my head, the org chart is more like an amoeba, where you have the entrepreneur and the manager in the middle as yin and yang. Different departments report to different people. And you’ve got these pizza teams all over, actually delivering teamwork. But that’s fascinating. Yeah, fascinating topic. So who are the ideal customers for you? If they have the right kind of projects, what are the right kinds of customers and the right kinds of projects for CODE Éxitos that would be interesting to look at?  There are two types of clients that we focus on and that we can help quite a bit. The first type of client is one that has a large, established internal software development and product development process, and they’re trying to figure out how to adapt, modernize it, and harness AI. We can come in, and we have a very opinionated point of view. We can have a couple of conversations, and they either like the direction our telescope is pointed in and want us to help, or they say, “No, we think we’re going to do it a different way.”  And that’s okay, too. Because right now, nobody really knows the final answer. We call those engineering transformation projects. Somebody says, “Hey, we have a team. Can you help us get better?” The second type of client we like is one that has this physical connection challenge. I’ll give you an example. We have a client that primarily makes pumps and motors. They put those pumps and motors into very specific industrial applications across North America. They wanted those pumps and motors connected to a network.  They wanted to collect data from those pumps and motors to help their customers. Once we built the data collection, the electronics, and the connectivity, the data started coming in. Now there are a lot of AI-related things we can do with that data. We’re beginning to work on what you can think of as supervisory agents that watch what’s going on. They’re much more robust than the old filters that just looked for exceptions and red lights. Those are the kinds of clients we really help on the product side.  Sometimes they come to us with an idea scribbled on a napkin. It’s like, “Hey, we have this system or this process that we envision, and we need somebody to help us build it.” We’ll do the electronics, the AI, and the software engineering. And that becomes a complete system. So, more systems thinking. Yeah. And then you combine software with hardware. Then you have AI agents doing much of the coding, management, and maintenance of these systems.  Yeah, that’s right. This is not a client of ours, by the way, but the story I’m going to tell is fascinating. It’s a second-degree connection that I chatted with. He runs a $100 million-a-year manufacturing company. I think he’s second or third generation—I can’t remember which. He knows the business. He grew up in the industry. He’s in the process of transforming the company so that he can basically run the whole business from his phone.  He’s applying AI to his internal business functions like finance and accounting. He’s done some really amazing stuff. He’s automating the manufacturing process, the machines, and the feedback systems. It’s just stunning what this guy is already able to do. He just picks up his phone and says, “Yeah, I want to run my company from here.” I think he’s going to be able to do it. I think we’re going to see more of that emerge. Yeah. That’s fascinating. The race is for the first one-person unicorn, right?  I think that’s already happened. I don’t know if you’ve seen this. I can send it to you. There was a New York Times article a couple of months ago about a guy—not a tech guy, a marketing guy. He and his brother run an online company that generates $1.8 billion a year in sales. And it’s just the two of them. Wow!  Spoiler alert: As I remember, they sell weight-loss drugs. He’s a marketing guy with a tech background. He figured out all these marketing channels where, if you order Ozempic or whatever online, it basically drop-ships from the pharmaceutical company to you, and he gets a cut. But he said in the article, “At one point, we were doing $300 million a month in sales.” He goes, “Well, technically, I’m not a one-man company because I had to hire my brother to help me out.” So it’s the two of them.  That’s pretty great. Yeah. That’s definitely a unicorn. So if you’re listening to this and you have an enterprise company, and you want to harness AI in your business, create agentic systems, streamline your operations, and make your company more efficient and productive, then reach out to Charles Fry at CODE Éxitos. Any last thoughts for listeners who are thinking about building a business in the AI age? What advice would you give them? I don’t know that it’s changed a whole lot. Building a business is always hard work. For any listener who wants to chat about any of these topics or see if we’re the right fit, they can always reach out and contact me. The conversation is free, and I usually learn something from it. But no, I would say that things are different. It doesn’t mean they’re wrong or better. I think they’re just different. It’ll be interesting to see how all of this unfolds over the next few years.  Yeah.  I’m in it for the journey.  We’re living in interesting times. So, Charles Fry, Founder and CEO of CODE Éxitos, thanks for coming on the show. And if you enjoyed this show, make sure you follow us on YouTube and Apple Podcasts. Give us a review, and stay tuned because every week I bring an amazing entrepreneur onto the show. Thanks for coming. Thanks for listening.  Thanks, Steve. Important Links: Charles's LinkedIn Charles's Website

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Arnab Bose, Chief Product Officer at Asana. Asana is the work management platform built for human and AI collaboration, trusted by over 170,000 customers including Accenture, Amazon, and Anthropic. The platform's Work Graph maps goals to portfolios to projects to tasks and serves as the foundation for Asana's AI Teammates: collaborative agents that operate inside the graph, learn from human decisions, and compound their intelligence with every cycle. What you'll learn:Why enterprise AI spend keeps returning zero productivity gains, and what is structurally breaking the loopWhy every employee approval, correction, or rejection of AI output is training data that makes the system smarter over timeHow Asana wires its own processes through the Work Graph so that AI decisions write back automatically and compound rather than resetHow PLG, forward-deployed engineers, and AI agents all report to the CPO, each under a GM who owns a revenue numberWhy the future of AI at work belongs to whoever has the richest shared context, not whoever has the best modelKey takeaways:Individual AI productivity gains compound into zero enterprise ROI when decisions never write back into a shared systemEvery human approval or correction is training data. The companies that capture it structurally will pull ahead of those that don'tPLG is an acquisition funnel, not a sales motion. Giving it a GM with a revenue number inside product changes the incentives entirelyCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Arnab BoseSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

The Power of Intentionality: Millions Made, Lessons Learned with Brian House In this episode of The 360 Electrician Podcast, Jeff sits down with Brian House, Vice President of Mike Holt Enterprises and former highly successful electrical contractor. Brian pulls back the curtain on the real story behind building, losing, and rebuilding multimillion‑dollar electrical businesses. He sums it up simply: “I have made millions of dollars, and I have lost millions of dollars. You're only a winner if you made more than you lost.” Together, Jeff and Brian break down the biggest mistakes electrical contractors make and how to run your business with absolute intentionality instead of chasing the next shiny object. In this episode, you'll learn: The “One Thing a Month” Rule How Brian fixed a broken electrical contracting business by choosing one critical thorn in his side each month Real examples: invoicing, material procurement, truck layouts, and more Why focusing on one theme for 30 days outperforms trying to fix everything at once The Reality of Software, Apps, and Tools Why stacking 10–20 software subscriptions destroys your ROI The danger of forcing old habits into brand‑new estimating or project management tools How to choose technology that actually supports your field operations and profit margins Beating “Perfection Paralysis” in Your Electrical Business Why an imperfect job done today by your team is better than a “perfect” job done by you that never happens How to let go of control, delegate, and still maintain quality The mindset shift every growing electrical contractor must make to scale past being the “army of one” Managing the Two Ultimate Scarcities: Cash & Labor How to calculate the true return on investment (ROI) for big decisions When it makes sense to upgrade your fleet, buy new tools, or invest in systems How to think about tools and processes that reduce injuries, downtime, and worker's comp exposure From Reactionary to Intentional Leadership The trap most 1–15 person electrical shops fall into How to stop running your company on emotion and start leading with data and discipline Practical steps to move from “putting out fires” to building a resilient, profitable contracting business Who this episode is for Electrical contractors running small to midsize shops Solo “army of one” electricians ready to grow a team Field electricians thinking about starting their own business Any contractor who feels stuck, overworked, and underpaid Connect with Mike Holt Enterprises Website: mikeholt.com Questions about code, estimating, or business management? Email Brian: brian@mikeholt.com Email Mike: mike@mikeholt.comThey actually answer their own emails. Enjoyed this episode?Like, comment, and subscribe for more in‑depth conversations on electrical contracting, business systems, and building a real, profitable electrical business.

On the KMOJ Morning Show, Dr. Janice M. Williams-Porter joins Freddie Bell to discuss the inspiration behind her new book, The Women's Leadership Blueprint, and why effective leadership begins with self-awareness, clarity, and strong personal values. Drawing from more than three decades of experience in education and executive leadership, she shares practical strategies for helping women develop confidence, strengthen communication, make purposeful decisions, and lead authentically without imitating others. Dr. Porter also explains how meaningful relationships, mentorship, and professional networks contribute to long-term leadership success while overcoming challenges with integrity. The conversation highlights how The Women's Leadership Blueprint serves as both a guide and a leadership development pathway for women at every stage of their personal and professional journey.

SUMMARY: As AI within the Enterprise matures, we look at 10 concerns and challenges that are still causing Chief AI Officers to worry about success in the future. SHOW: 1040SHOW TRANSCRIPT: The Enterprise AI Show #1040 TranscriptSHOW VIDEO: https://youtu.be/RyB4m17YK_4SHOW SPONSORS:Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:THESIS: After spending time with a number of Enterprise companies, what are a list of challenges and concerns they still have in implementing GenAI across a broad set of use-cases within the Financial Services industry?Everybody started with what was available (e.g. CoPilot)Enterprise implementations (now) aren't autonomousRising costs are the looming concernGovernance is a rising concernMeasurements of improvement are available, but variedExplaining measurements is complicatedExplaining trust is more complicatedUse-cases are fragmented, but there if you apply the technology, but not always obviousDe-centralized (shadow AI) to Centralized to De-centralized (semi-controlled) The learning curves are very asymmetrical across teamsNot everyone has access to Mythos or GPT-5.5-Cyber (yet)FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

By Doug Green “We want to do for assets what CRM did for customer records.” In this episode of the Technology Reseller News podcast, Doug Green speaks with Tim Harris, CEO of SoloTruth, about the company's new asset relationship management platform for enterprises and the opportunity it creates for asset-intensive organizations, MSPs and channel partners. Harris says SoloTruth bridges the gap between the physical assets a company owns and the financial system of record, typically the ERP system. The goal is to make sure that what a company actually owns is accurately reflected in its books. SoloTruth calls this asset relationship management, or ARM. Just as CRM evolved from a system for tracking contacts into a broader platform for managing customer relationships and the sales cycle, SoloTruth aims to provide a 360-degree view of enterprise assets. The podcast explores a common problem in asset-heavy businesses: asset drift. A company may purchase a forklift, server, laptop, piece of telecom equipment or other capital asset, but over time that asset may be moved, misplaced, retired or replaced without the financial system being updated. Harris says this creates “ghost assets,” which no longer exist but remain on the books, and “zombie assets,” which exist in the field but are not reflected in the ERP. For companies with large fixed-asset bases, the financial implications can be significant. Harris notes that for many manufacturing, logistics, healthcare, telecom and banking organizations, fixed assets can represent one of the largest categories on the balance sheet. Yet many companies only get an accurate view of those assets during periodic audits. SoloTruth combines IoT-derived location data, including RFID and GPS technology, with human inspection data captured through a mobile app. That combination gives companies better visibility into both where an asset is located and what condition it is in. Harris says those two factors are critical to useful life, depreciation schedules, valuation, maintenance planning and proof of existence. The company recently won an award for its approach, which combines IoT-based location tracking with human condition capture. Harris says that combination can help organizations keep their books in order continuously, rather than waiting for an annual audit or emergency review tied to financing, collateral, or M&A activity. Harris says SoloTruth typically looks at payback period as the key customer metric. For companies with $100 million in fixed assets, he says the platform can generate roughly $1.5 million to $4 million per year in cost savings, with a payback period often below six months. The podcast also looks at the channel opportunity. Harris says MSPs and channel partners already have relationships with customers and are often involved in placing assets into enterprise environments. SoloTruth can give partners a way to extend that relationship by helping customers track, inspect and manage assets after deployment. For enterprises, the message is clear: better asset intelligence can reduce unnecessary capital spending, improve maintenance planning, support financing and M&A activity, and help ensure that the financial system reflects the real operating environment. Learn more at solotruth.com  

today we examine the shifting landscape of artificial intelligence, specifically comparing Small Language Models (SLMs) against Large Language Models (LLMs). Research highlights that SLMs consume 60-70% less energy and water, offering a more sustainable alternative for straightforward tasks without sacrificing accuracy. While LLMs remain superior for complex reasoning and abstract puzzles, they demand significant computational infrastructure and financial investment. Enterprises are increasingly adopting SLMs for specialized applications in healthcare and finance to enhance data privacy and operational efficiency. To balance performance with environmental costs, experts suggest a context-aware deployment strategy that switches between models based on task difficulty. Ultimately, the transition toward right-sized AI reflects a maturation of the industry toward pragmatic, governed, and resource-efficient solutions.

What does it take to turn a high school side hustle into a thriving retail brand, wholesale business, and nationally recognized name? Ashley Alderson sits down with John Mark Sharpe (John Mark Enterprises) as he shares the incredible journey from selling wreaths out of a guest bedroom to building a multi-location business, launching a successful wholesale ribbon line, and leading a team of nearly 40 employees. Along the way, he learned one of the most important lessons in business: sustainable growth happens when you're willing to start small, stay consistent, and keep reinvesting in what works. You'll learn: How a simple wreath business sparked an entrepreneurial journey Why he believes most business owners overthink growth How a mission trip to India led to a successful ribbon line The mindset change that helped him rethink debt and growth Join The Boutique Hub Get Swym John Mark & John Mark Enterprises: Instagram:@JohnMarkEnterprises Website: JohnMark.com  The Round Top Collection: shop.thertc.com                       ____________________________ Ashley Alderson: Instagram     The Boutique Hub: Website | Facebook | Instagram | Pinterest | TikTok | YouTube

Let us know how we're doing - text us feedback or thoughts on episode contentMost companies think of AI's carbon footprint as a data center problem. But the infrastructure powering your AI queries is rapidly diversifying — and each deployment model carries a very different sustainability profile. In this episode, Paul breaks down five emerging AI infrastructure options and what they mean for corporate emissions accounting.Paul makes the case that as AI deployment shifts away from traditional cloud infrastructure, the CSO has to be at the table with the CIO and CFO. The deployment decision is one of the most consequential choices a company can make for its GHG profile — and right now, most organizations are making it blind to the emissions implications.Follow Paul on LinkedIn.

In part two of my two-part field trip to Livermore, California, I sit down with Inertia Enterprises' third co-founder — Annie Kritcher, the chief scientist at Inertia and a longtime physicist at Lawrence Livermore National Laboratory.Annie was the lead designer behind the December 2022 National Ignition Facility (NIF) shot that achieved ignition — a self-heating fusion “burning plasma” that produced more fusion energy out than was delivered to the target. In this episode, Annie walks us through what it took to get there (spoiler: not one magical breakthrough), what “the ignition cliff” actually means, and why Inertia is betting that manufacturing and economics — not brand-new physics — is the fastest path to commercial fusion.We talk about:Annie's path from nuclear engineering + plasma physics to becoming NIF's lead designer on ignition platformsWhy the historic ignition shot was the result of years of iteration (and a few duds along the way)The “pancake” shot (and the lab's surprisingly extensive breakfast-food vocabulary for failed plasmas)What it felt like to get the 3:00am text — “I think we got ignition” — and why relief came before celebrationWhy many in the field had nearly given up on laser fusion ignition — and how close the NIF campaign came to being cutWhy other fusion companies pursued different approaches: history, uncertainty, and the (very real) cost problem for lasers + targetsThe core commercialization challenge: scaling to high-repetition-rate shots (think “engine cycles”) and producing targets cheaply enough to fire millions per dayLinks:Inertia Enterprises: https://inertia.com/Everybody in the Pool: https://www.everybodyinthepool.com/Subscribe to the Everybody in the Pool newsletter: https://www.mollywood.co/Become a member for the ad-free version of the show: https://everybodyinthepool.supercast.com/Join our Discord: https://discord.gg/2EsDhwQC2z Hosted on Acast. See acast.com/privacy for more information.

The conversation around artificial intelligence often creates the impression that software development has already been transformed beyond recognition. Social media feeds are filled with stories about AI agents replacing teams, generating applications automatically, and eliminating the need for traditional development processes. The Enterprise AI Reality is much more nuanced. While AI has become a valuable tool inside software organizations, large enterprises are approaching adoption far differently than many public conversations suggest. The gap between experimentation and production remains significant, especially when millions of dollars, regulatory requirements, and customer trust are involved. About Samuel Otero Samuel Otero is a Software Solutions Specialist with Deloitte US and a technology consultant with nearly 14 years of experience spanning enterprise software development, government projects, commercial consulting, and large-scale digital transformation initiatives. His career began with an early Microsoft internship that shaped his approach to continuous learning and technical humility. Since then, he has worked across media, public-sector, and enterprise environments, helping organizations deliver complex software solutions while mentoring the next generation of developers. Based in Puerto Rico, Samuel is also an advocate for developer growth, career development, and practical AI adoption in modern software engineering. Links LinkedIn Enterprise AI Reality Is Different from Social Media One of the strongest observations Samuel shared was the contrast between what people see online and what happens inside large organizations. Social media often highlights extreme success stories. Teams appear to build entire products using AI agents. Individual developers showcase impressive workflows that dramatically accelerate delivery. Those examples are real. However, enterprise software operates under different constraints. Systems support financial transactions, critical business processes, compliance requirements, and large customer bases. Mistakes carry significant consequences. As a result, organizations are adopting AI incrementally rather than replacing existing development practices overnight. Enterprise AI Reality Requires Trust Before Automation Every technology faces a trust curve. Before organizations automate critical workflows, they need evidence that systems perform reliably under real-world conditions. Samuel described how enterprises often use AI first in lower-risk scenarios before allowing it to influence more critical components of a platform. Features with limited business risk become testing grounds for new approaches. This pattern mirrors previous technological shifts. Cloud adoption happened gradually. DevOps adoption happened gradually. AI adoption is following a similar trajectory. The technology may be powerful, but trust must be earned through consistent results. Enterprises don't adopt technology because it's impressive. They adopt it because it's reliable. Enterprise AI Reality Still Depends on Human Expertise One misconception surrounding AI is that generated code eliminates the need for technical understanding. In practice, the opposite may be true. The more organizations rely on AI-generated outputs, the more important validation becomes. Developers must understand architecture, business requirements, security concerns, and implementation details well enough to verify what AI produces. Samuel emphasized a simple but powerful habit: asking AI to explain exactly what it did and why it made certain decisions.   That approach transforms AI from an answer machine into a learning tool. Developers who understand generated solutions become more effective. Developers who blindly accept generated solutions create risk. Never merge AI-generated code until you can explain its behavior to another developer. Enterprise AI Reality Is Creating New Skill Gaps The rise of AI is changing how developers gain experience. Historically, growth came from solving difficult problems manually. Developers researched documentation, struggled through debugging sessions, and built mental models through repetition. AI reduces much of that friction. While this increases productivity, it also creates new challenges. Developers may complete tasks successfully without fully understanding how those tasks were accomplished. Over time, this can create a dangerous gap between perceived capability and actual expertise. Organizations must address this by emphasizing understanding rather than output alone. The future belongs to developers who combine AI acceleration with deep technical comprehension. Enterprise AI Reality May Increase Software Complexity An interesting prediction from the discussion involved software quality. As AI accelerates development, more software will be produced. More features will be released. More experiments will reach production environments. That acceleration creates opportunity. It also creates risk. Samuel suggested that many organizations are still learning where AI performs exceptionally well and where it struggles under enterprise-scale conditions. During that learning period, users may experience more bugs, patches, and corrective updates as teams discover limitations. This isn't evidence that AI has failed. It's evidence that every transformative technology goes through a maturation phase before reaching stability. Faster development cycles can produce bugs faster if organizations don't maintain engineering discipline. Enterprise AI Reality Still Comes Back to Problem Solving Perhaps the most important lesson from the entire conversation is that technology itself is rarely the source of professional value. Languages change. Frameworks change. Platforms change. AI models will change. The underlying business need remains consistent: solving problems. Samuel's closing advice focused on developing problem-solving skills rather than attaching identity to a specific technology stack. That mindset provides resilience regardless of how quickly tools evolve. Developers who can understand problems, communicate solutions, and create business value will remain relevant long after today's AI tools are replaced by tomorrow's innovations. The most durable technical skill isn't coding. It's problem-solving. Conclusion The Enterprise AI Reality is neither the dystopian future predicted by skeptics nor the fully automated paradise promised by enthusiasts. Instead, it's a period of careful experimentation, measured adoption, and ongoing learning. Organizations are discovering where AI delivers value, where human expertise remains essential, and how both can work together to build better software. The developers who succeed during this transition won't be the ones who resist AI or blindly trust it. They'll be the ones who learn how to use it responsibly while continuing to strengthen the problem-solving skills that define great engineers. Stay Connected: Join the Developreneur Community

The Wall Street Journal reported that a $13 billion AI startup is betting on cheaper alternatives to OpenAI and Anthropic. Enterprises are shifting from pilots to production and seeking to control inference costs across support, copilots, and content workflows. Open source options such as Meta's Llama and models from Mistral enable targeted deployments with retrieval and fine-tuning to improve cost predictability. Procurement teams weigh SLAs, latency, security certifications, data retention, indemnity, and regional hosting against premium providers. Vendors distribute through AWS, Microsoft Azure, and Google Cloud marketplaces, while access to Nvidia accelerators influences performance and cost. Pricing includes per token and per seat plans, with some platforms routing simple tasks to lower cost models and reserving premium models for complex work. Founders are advised to build evaluation harnesses, track cost per outcome, and negotiate for predictable terms.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Expereo: Enterprises Are Racing Into AI, But the Network Still Has to Carry the Load, Podcast, “AI is no longer a debate. Enterprises are already using it. The question now is whether the network is ready to support what comes next,” says Marek Wasilewski of Expereo @Doug Green “AI is no longer a debate. Enterprises are already using it. The question now is whether the network is ready to support what comes next,” says Marek Wasilewski of Expereo. In this Cisco Live 2026 podcast, Doug Green speaks with Marek Wasilewski of Expereo about the company's 2026 Enterprise Horizons report and what it reveals about enterprise AI adoption, network readiness and the growing pressure on global connectivity. This is one of several Cisco Live podcasts worth revisiting after the initial wave of show coverage. The conversation provides interesting insights into how AI is changing the network conversation for enterprises, service providers and channel partners. According to Expereo's research, 92% of enterprises are using AI in some form, while 30% are already using it extensively. At the same time, 70% are investing in AI without carefully measuring ROI. That combination creates both opportunity and risk: enterprises are moving quickly, but many are still building on networks that were not designed for the scale, performance and resilience demands of AI-driven operations. Expereo, a global Network-as-a-Service provider, helps enterprises simplify and manage connectivity across complex international environments. In the podcast, Wasilewski explains why AI success depends not only on models, applications and cloud platforms, but also on the underlying network that connects users, data, workloads and business locations. For channel partners, MSPs and enterprise technology leaders, the message is clear: AI is making the network strategic again. Connectivity is no longer just plumbing. It is becoming a core part of digital transformation, customer experience, automation and business continuity. The conversation also explores how enterprises can think more clearly about AI investment, how global connectivity strategies are changing, and why network visibility, flexibility and reliability will matter even more as AI moves from pilot projects into production environments. Learn more: expereo.com

Network Is Hot Again: Stackpane's Sarbjeet Johal on Cisco Live, AI Infrastructure and Systems Economics, Podcast, Johal brings a unique mix of technical, business, and economics experience to the discussion @Doug Green, Publisher, Technology Reseller News “The network is hot again because a lot more data is traversing on it, and a lot more data will traverse on it,” says Sarbjeet Johal of Stackpane. In this Technology Reseller News podcast, Doug Green speaks with Sarbjeet Johal, Founder and CEO of Stackpane, technology analyst, cloud strategist and go-to-market specialist, about why the network has moved back to the center of the enterprise technology conversation. Johal brings an unique mix of technical, business and economics experience to the discussion. A veteran of VMware, Oracle and Dell EMC, Johal has spent decades building and deploying enterprise systems, advising technology providers, and helping buyers understand cloud strategy, infrastructure modernization and digital transformation. At Cisco Live, Johal says the biggest theme was simplification. Cisco, he argues, is working to reduce infrastructure complexity for customers operating across public cloud, on-premises systems, AI workloads and increasingly distributed environments. That includes a renewed focus on infrastructure as code, platform control, and AI-assisted tools such as Cisco IQ, which Johal says can help customers and partners troubleshoot, configure and operate Cisco environments more effectively. The conversation then turns to Johal's recent observation that “the network is hot again.” His point is that AI, real-time systems, digital services, autonomous agents, and API-driven interactions are all placing new demands on network performance. As more data moves across enterprise systems, latency, memory, compute, and connectivity become more strategically important. Johal notes that during AI inference, latency is especially critical, making the network, chips, CPUs, and memory all part of the same infrastructure story. Johal also frames the issue through systems economics. Enterprises are not simply buying more technology for the sake of modernization. They are being forced to ask whether cloud, AI, on-prem infrastructure, and automation actually pencil out in terms of total cost of ownership, return on investment, and operational value. For MSPs, channel partners, and technology providers, that creates an opportunity to help customers make better architecture decisions, not just consume more tools. The podcast closes with a look ahead to a future conversation on token economics, AI infrastructure costs, and where MSPs and channel partners can find real business opportunity as enterprise technology becomes more automated, more data-intensive, and more dependent on resilient network infrastructure. Editor's note: This podcast was recorded at Cisco Live in Las Vegas and is being posted later. With time, the conversation has become even more notable. Johal's central point — that the network is “hot again” — has only gained relevance as AI, automation, cloud infrastructure and real-time digital services continue to place new pressure on enterprise networks. Learn more: Stackpane at stackpane.com

Enterprises rushing agentic AI into production are running it through approval gates, batch windows, and audit systems built for human speed — and the gap is where most operational risk lives. In this episode, Chris Caldwell, President and CEO at Concentrix Corporation, examines how machine-scale transactions break processes designed for human pace and why bounded digital delegates outperform unrestricted digital twins in the enterprise. The discussion covers compliance bots that check other bots, the cost reality of poorly tuned agentic agents, and what leaders need to stop doing if they want a defensible AI roadmap. Learn how to evaluate AI vendors by assessing leadership expertise, and why funding benchmarks can signal product maturity and stability, download our free PDF report, "5 Ways to Select the Right AI Vendor," at emerj.com/aiv2.

Joe Beda and Craig McLuckie co-created Kubernetes, the infrastructure standard that became the default for cloud native computing. Now running Stacklok, they're watching enterprises hit the same identity, permissions, and security problems with AI agents that took the container ecosystem years to resolve, and they're building tools to compress that timeline. In this episode of Founded & Funded, Madrona's Tim Porter sits down with Joe and Craig to talk through what AI adoption actually requires: why MCP is the Docker moment for AI-native applications, how the LLM gateway is becoming a strategic chokepoint for cost, safety, and model flexibility, and why enterprises that don't get the architecture right early will face a familiar trap: vertical integration that looks like productivity and acts like lock-in. They cover: Why the developer workflow is the template for knowledge worker AI adoption, and where the analogy breaks down The mainframe vs. open platform question that will define the AI infrastructure era Why the knowledge worker transition is harder than it looks — and what has to be built differently before developer-grade AI tooling can scale to the rest of your organization The governance gap between human accountability and AI behavior, and what enterprises actually need to build to close it Where to start: MCP controls first, LLM gateway second, and why deploying a platform without staying to close the loop consistently fails Transcript: https://www.madrona.com/the-best-infrastructure-moment-since-cloud Chapters: (0:00) – Introduction (1:04) – Why the Kubernetes Creators Are the Right People to Read This AI Moment (2:18) – Joe's Lesson from Cloud Native: Ignore Conventional Wisdom, Except When You Shouldn't (4:16) – Craig on Enterprises and the Chaos of a New Infrastructure Era (5:32) – Why Joe Rejoined Craig at Stacklok: The Engineer's Case for Getting Your Hands Dirty (7:05) – Developers as Agent Orchestrators: How the Knowledge Worker Transition Will Follow (10:10) – MCP Explained: Craig Sees Docker in 2013 When He Looks at the MCP Spec 1 (7:53) – The Mainframe vs. Open Platform Question That Will Define the AI Era (20:24) – LLM Lock-In Is the Wrong Worry: The Real Risk Is Left of the Model (25:19) – Where Enterprises Actually Start: Developer Posture First, Knowledge Workers Second (29:10) – MCP First, LLM Gateway Second: The Concrete Technical Starting Point (31:19) – How Stacklok Builds Software Now: Agents, Smaller Teams, the Unrecognizable Developer Profile (38:07) – The Recruiter Who Started Building Agents: What AI Tools Do to Role Boundaries

Most enterprises are renters, not owners, of their technology and AI. Raffi Krikorian, Chief Technology Officer of Mozilla, explains why dependence on a handful of closed model providers means losing control over model behavior, pricing, and your own data.In CXOTalk episode 920, Krikorian lays out where open-source AI actually wins in the enterprise, how lock-in happens quietly, and what CIOs and CTOs should do about it now. Krikorian draws on his experience building infrastructure at Twitter and running the self-driving division at Uber to ground the discussion in real engineering and economic tradeoffs, not hype.YOU'LL DISCOVER✅ Why 85% of enterprises believed they could switch AI vendors, but only about 30% actually could when they tried✅ The "renters vs. owners" framing and what it means to control your AI destiny✅ Why Krikorian wants data "protected by architecture, not legal handshakes"✅ How Pinterest reportedly saved on the order of $10 million in a single quarter by switching from closed to open models✅ Why IT is becoming "the HR team for agents," and the read/write "dangerous triangle" of agentic permissions✅ The case for recording your prompts and running your own evaluations instead of trusting public benchmarks✅ Why roughly 70% of enterprise GPUs sit idle, and the missing "LAMP stack for AI" that could put them to work✅ How closed "validation machines" can quietly steer answers toward sponsored outcomes⏱️ TIMESTAMPS (estimated, verify before publishing)0:00 Renters vs. owners: who controls enterprise AI2:26 The risks of depending on closed model makers6:23 How lock-in happens and where open source fits9:53 Regression testing and building your own evals13:24 Pricing instability and the post-IPO cost question23:31 Governance: IT as HR for AI agents32:38 Can a small organization own its AI stack end-to-end?38:47 Validation machines, trust, and sponsored answers43:39 Keeping humans at the center, not in the loop47:23 Can open source beat big tech in AI?51:39 Inside Mozilla.ai: Otari, CQ, Octanus, Thunderbolt55:21 The "rebel alliance" strategy

PagerDuty SVP Rukmini Reddy explains why AI is making software operations exponentially more complex — and why the companies that learn and recover fastest will be the ones that win.Topics Include:PagerDuty powers critical digital operations for enterprises and AI-native companies.Founded by early AWS employees who experienced always-on system failures firsthand.The platform evolved from simple alerting into a full operational intelligence platform.Complexity exploded with microservices, cloud-native infrastructure, and multi-cloud environments.Reliability must be a core value — not an operational afterthought.PagerDuty's culture champions the customer above everything else.Employee recognition extends beyond sales to celebrate the whole business.AI is accelerating software creation but making operations far more complex.AI fails differently — silently, unpredictably, with a much larger blast radius.Enterprises should leverage their operational history as a competitive AI asset.AI-native companies must build operational resilience early, not bolt it on later.The winners won't build fastest — they'll learn and recover fastest.Participants:Rukmini Reddy – Senior Vice President of Engineering, PagerDutySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Get the free Core Drives in the Wild guide, behavioral design applied to real corporate cases: professorgame.com/WildCD Episode Summary Rob breaks down why enterprise AI adoption stalls even with paid licenses and training, while a group of students beat a locked, proctored exam with ChatGPT and no support at all. Reading both cases through the Octalysis Framework, he shows how the exam accidentally stacked Core Drive 8 (Loss & Avoidance), Core Drive 6 (Scarcity & Impatience), and Core Drive 2 (Development & Accomplishment) into a ferocious, if mispointed, motivation engine. The enterprise bought the most capable tool and surrounded it with zero motivation, so nobody opened the app. Listeners learn why AI adoption is a motivation problem wearing a tooling costume, and leave with a two-part diagnostic question to ask of any AI initiative. About the Host Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia. Key Takeaways Students beat a lockdown, proctored, face-to-face online exam by getting ChatGPT to answer questions live through a Chrome extension, with no license, no training, and no change management. Adoption was instant, total, and creative enough to defeat the security. The exam accidentally stacked three Black Hat Core Drives: Core Drive 8 (Loss & Avoidance, failing is high-stakes), Core Drive 6 (Scarcity & Impatience, one timed shot), and Core Drive 2 (Development & Accomplishment, clearing the hurdle to the grade). Enterprises buy the paid license, training, IT support, and a leadership mandate, then adoption stalls because none of those things are motivation. There is no personal loss for ignoring the tool and no personal win for using it. Motivation pointed at the wrong goal produces flawless adoption of exactly the behavior you did not want. The students aimed AI at passing, not learning, and got it. As AI removes capability constraints, the human motivation layer becomes the only constraint left, which is why behavioral design matters more in the AI era, not less. The diagnostic: ask what your team personally gains by using the tool and what they personally lose by ignoring it. If the honest answer is "nothing much either way," no rollout plan will save it. Topics Covered 0:00 - Students hacked a locked exam 0:52 - Same tech, opposite outcome 1:44 - Adoption was never the problem 2:39 - The exam's accidental motivation engine 4:31 - Almost entirely Black Hat motivation 5:18 - Why the funded enterprise stalls 6:30 - Adoption and direction both matter 7:41 - Why behavioral design matters with AI 7:55 - Your diagnostic question for today Mentioned in This Episode The Octalysis Framework, developed by Yu-kai Chou ChatGPT (OpenAI) Core Drives in the Wild, the Professor Game free guide Free Resources and Get in Touch Core Drives in the Wild: Professor Game Free Guide Get Daily Value on Your Email Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question

Vieth v. MOM Enterprises, LLC

Individual AI productivity gains are already here, but they are uneven, and they are not the main event. In this episode, Tim Sears, Chief AI Officer at HTEC, argues that the real transformation in software development will arrive when AI becomes a catalyst for teamwork rather than an enhancer of individual performance. The conversation examines why software development is the clearest available model for how AI will eventually reshape every business function, how the developer role is being elevated from syntax and grunt work toward architecture, security, and client judgment, why the traditional build-versus-buy decision is being replaced by a build-versus-build reality, and what it will mean when perfection in enterprise software becomes the expected standard rather than the exception. For senior leaders trying to move from supporting AI in principle to actually delivering change, Sears offers a direct and practitioner-grounded view of what needs to change in teams, in expectations, and in the way business processes are understood and redesigned. AI is moving fast — new tools, new research, new use cases every week. Emerj synthesizes what matters most, so senior leaders and practitioners can stay ahead without getting buried. Join 85,000+ subscribers and get the most useful AI business insights delivered to your inbox. Visit: http://emerj.com/ad1

Wipro Brings Enterprise Perspective to Cisco Cloud Control, Podcast Wipro's Uday Kiran discusses what Cisco's new platform means for enterprise customers, global partners and the shift to unified, AI-ready operations By Doug Green “Cisco Cloud Control unifies all of these domains.” In this Technology Reseller News podcast, recorded virtually during Cisco Live, Doug Green speaks with Uday Kiran of Wipro about Cisco Cloud Control and what the announcement means when viewed from the front lines of enterprise transformation. For Wipro, the announcement represents a logical evolution in Cisco's portfolio. Kiran says enterprise customers are often managing separate domains across networking, security and observability. Those domains have historically operated as “multiple islands,” creating complexity for IT teams that need visibility, speed and control across distributed environments. Wipro brings a global systems integrator's view to the conversation. The company serves enterprise customers in more than 64 countries, has more than 250,000 employees, works with more than 1,000 enterprise customers, and has partnered with Cisco for more than 30 years, according to Kiran. That scale gives Wipro a practical view of what customers are asking for now. Enterprises are not simply looking for another dashboard or another tool. They are looking for ways to simplify operations, improve resilience, bring security and networking closer together, and make AI useful inside complex production environments. Cisco Cloud Control is important because it points toward a more unified operational model. Instead of treating network, security and observability as separate disciplines, the platform is designed to bring those areas together. For partners such as Wipro, that creates a larger opportunity than product deployment. It creates a consulting, integration and managed services opportunity around helping enterprises modernize operations, rationalize toolsets, and prepare for AI-enabled infrastructure. The discussion also reflects a broader Cisco Live theme: AI is moving from concept to operations. As enterprises adopt agentic AI, infrastructure must become more observable, more secure and more automated. Wipro's role is to help customers make that transition in real environments, where legacy systems, global operations and business continuity all matter. In this podcast, Kiran offers a partner's view of Cisco Cloud Control: not just what was announced, but why it matters to enterprise customers trying to turn fragmented IT operations into a more unified, intelligent and resilient operating model.

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

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