What's Up with Tech?

Evan Kirstel
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Tech Transformation with Evan Kirstel: A podcast exploring the latest trends and innovations in the tech industry, and how businesses can leverage them for growth, diving into the world of B2B, discussing strategies, trends, and sharing insights from industry leaders!With over three decades in telecom and IT, I've mastered the art of transforming social media into a dynamic platform for audience engagement, community building, and establishing thought leadership. My approach isn't about personal brand promotion but about delivering educational and informative content to cultivate a sustainable, long-term business presence. I am the leading content creator in areas like Enterprise AI, UCaaS, CPaaS, CCaaS, Cloud, Telecom, 5G and more!

  1. 18小时前

    How Broadcom And VMware Protect Dynamic AI Workloads

    Interested in being a guest? Email us at admin@evankirstel.com Agentic AI is showing up everywhere, and it is not waiting for your security program to catch up. From the VMware Explore floor, we talk with Umesh Mahajan, General Manager for Broadcom’s Application Networking and Security Division, about what changes when AI agents become real workloads that talk constantly, call tools through APIs, and evolve by the week. The headline is uncomfortable but useful: AI helps defenders ship faster, but Frontier AI also helps attackers find vulnerabilities and drive more zero-day behavior than many teams are ready to handle. We dig into the security model that holds up when agents are dynamic and mistakes travel fast. Umesh explains why you have to solve a dual problem: protecting agents from attacks while also preventing a compromised or misdirected agent from attacking others. That leads to concrete priorities like lateral segmentation, distributed firewalling, IDS/IPS, and NDR-style behavioral detection, plus network-level controls to reduce the risk of sensitive data exfiltration. We also talk about discovery, because shadow AI is already a reality. If you cannot identify which agents, MCP tools, servers, and models are running, you cannot confidently separate authorized usage from risky sprawl. Finally, we tackle the fear every operator has: security that slows the business down. We cover why performance and latency matter more for AI workloads, how enforcement close to the hypervisor can reduce overhead, and what it takes to deploy protection quickly so “bought but not deployed” does not become your weak spot. If you’re building enterprise AI on VMware infrastructure, this is a practical map for getting safer without stalling momentum. Subscribe for more VMware Explore conversations, share this with the person owning AI rollout, and leave a review with your biggest question about securing agentic AI. Support the show More at https://linktr.ee/EvanKirstel

  2. 18小时前

    How To Secure Enterprise AI Agents With VMware Tanzu

    Interested in being a guest? Email us at admin@evankirstel.com Frontier AI is changing the rules of security, and enterprises feel it first. From the floor at VMware Explore, we sit down with Purnima Padmanabhan Vice President and General Manager, Tanzu Division, to unpack what customers are asking for as AI agents move into real systems and real data. The headline: private AI is getting a serious bump, not as a trend, but as a control strategy for regulated industries and anyone worried about risk, governance, and cost.  We walk through the “threat side” of AI, where advanced models can amplify attacks and push open source security to a breaking point. Purima explains why so many enterprise applications depend on open source, especially Spring and Java, and what happens when vulnerability reports spike. We also dig into TrueSource by Broadcom, the idea of a secure software supply chain for curated open source packages, libraries, and images that enterprises can trust and operationalize.  Then we shift to the “opportunity side”: moving beyond chat and RAG into workflow automation with enterprise AI agents. That raises hard questions about sandboxing, limiting autonomy, and keeping agents secure. Just as important, we talk about why data projects slow everything down, and why governed access to the right data is what makes agents useful at scale.  Finally, we get practical about developer experience and SDLC automation, including Purima’s example of running a swarm of 21+ agents across planning, Jira creation, coding, and testing, plus the caution that not all AI-generated code is equal. If you’re building on VMware Tanzu, thinking about private cloud AI, or trying to balance security, sovereignty, and TCO, this is for you. Subscribe, share with a builder on your team, and leave a review. What would make you trust an AI agent in production? Support the show More at https://linktr.ee/EvanKirstel

  3. 5天前

    Trustworthy AI For High Stakes Decisions

    Interested in being a guest? Email us at admin@evankirstel.com AI is making decisions that shape real lives, yet most people cannot see how those decisions get made. We sit down with Scott Zoldi, Chief Analytics Officer at FICO, to unpack what “trustworthy AI” actually requires when the stakes include fraud, credit risk, and customer outcomes in heavily regulated financial services. If you have ever wondered why black box models create so much fear and backlash, this conversation puts clear language around the real issues: data provenance, explainable AI, ethical testing, robustness, and the ability to audit a decision after the fact.  We go beyond buzzwords and get specific about AI governance. Scott explains why responsible AI starts with a shared model development standard, so a large organization is not running a hundred different approaches that no one can consistently defend. We talk about why monitoring is often the weakest link in real world machine learning, when to retire models that drift, and why enterprises need to stay in control instead of outsourcing critical decisions to models they did not build.  Then we dig into a practical enforcement mechanism: coupling AI governance with blockchain to create an immutable record of requirements, testing, verification, and release decisions. Think of it as an operating manual that travels with the model and can be inspected years later by regulators, customers, or internal teams. We also look ahead to what changing regulation could mean, including the push toward interpretable models, trust scoring for generative AI, and focused language models or small language models built for narrow tasks with auditable data.  If you care about responsible AI, AI transparency, and building systems people can actually trust, hit play, then subscribe, share this with a friend who works in AI or compliance, and leave a review with the one governance rule you think every model should follow. Support the show More at https://linktr.ee/EvanKirstel

  4. 5天前

    How NVIDIA Turns Computing Into A New Lab Partner

    Interested in being a guest? Email us at admin@evankirstel.com Drug discovery is one of the hardest engineering problems on Earth, except it has not always been treated like engineering. Costs can hover around $2 billion per successful drug, timelines can run 10+ years, and too many patients still wait without a cure. We sit down with Rory Kelleher, who leads global business development for life sciences at NVIDIA, to talk about what changes when accelerated computing meets foundation models, generative AI, and agentic AI that can actually do work. We break down how scientific agents differ from chatbots, and why tools matter as much as models. Rory explains NVIDIA’s BioNEMO Agent Toolkit and the idea of turning core life sciences capabilities into “agent skills” so biologists and chemists can run complex workflows through natural language. We talk protein design and protein binder design, co-folding, bioinformatics, target identification, and ADMET prediction for toxicity and safety, plus why this wave can “democratize” computational drug discovery for scientists who were never trained as programmers. You’ll also hear a real example from Bristol Myers Squibb, where foundation models trained on proprietary sequences and compound libraries helped improve a sickle cell molecule profile until it reached first-in-human testing. We dig into what an “AI factory” looks like inside pharma, why teams want to run open models and local LLMs on secure infrastructure, and why scientific judgment becomes more important, not less, as agents increase throughput. If you care about biotech, pharma R&D, life sciences AI, and the future of medicine, this conversation is for you. Subscribe, share the episode with a friend in research, and leave a review with the one workflow you want agents to tackle next. Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts   Spotify Support the show More at https://linktr.ee/EvanKirstel

  5. 8月7日

    How To Store And Serve AI Data At Scale

    Interested in being a guest? Email us at admin@evankirstel.com Everyone is obsessed with bigger models and faster GPUs, but the part that decides whether AI actually works in the real world is the data behind it. We talk with Paul Speciale CMO at Scality, about what happens when enterprises try to store, protect, and serve AI data at petabyte and exabyte scale, and why the rise of huge context windows turns “just a chat” into a massive storage and latency problem. We walk through the full enterprise AI pipeline, from data collection and cleansing to inference and the long archive tail most teams never plan for. Paul breaks down which AI phases truly need the fastest storage, why inference can demand microsecond access, and how tiered designs (hot, warm, cool) align to modern GPU stacks. We also dig into what’s changing in the market right now: flash price spikes, constrained supply, and the reality that power per rack often matters more than raw capacity. From there, we get practical about operations and risk. We explore autonomous infrastructure as “tell the system what you want, not how to do it,” including human-in-the-loop recommendations that can move data to cheaper tiers and cut power bills. We also cover cyber resilient storage, immutability for ransomware defense, and the growing pressure around data sovereignty and even code transparency. If you’re building an AI infrastructure plan, this is the blueprint mindset that keeps GPUs busy and data trustworthy. Subscribe, share this with your infrastructure team, and leave a review with the biggest AI data challenge you’re facing. Support the show More at https://linktr.ee/EvanKirstel

  6. 7月31日

    How Pega Blueprint Changes Who Builds Software

    Interested in being a guest? Email us at admin@evankirstel.com AI is cranking out more code than ever, but that doesn’t mean teams are shipping the right outcomes. From PegaWorld, I sat down with Steph Lewis, Senior Director of Community Developer Programs at Pega Systems, to unpack what’s actually changing for developers, architects, and business stakeholders as AI, agents, and LLMs reshape enterprise delivery.  We get specific about the Solution Designer role Steph helped build and why it’s showing up now. We talk about Pega Blueprint as a practical way to turn business ideas into a live application earlier, and how that changes the skills teams need across low-code development, workflow automation, and digital transformation. Steph explains why the goal never changes (delivering value), but the day-to-day work does, especially as “hands-on keyboard” tasks shrink and the need to challenge AI outputs grows.  A big thread is the human edge: judgment, creativity, and the discipline to align what you build with what stakeholders truly need. We dig into the skills gap Steph sees most often, why workshops and real conversations beat passive training, and what sits behind eye-catching outcomes like 80% of projects going live in 90 days. We also zoom out to what Steph wants for the developer community over the next year: guiding architecture decisions, placing Pega in the right spots, and incorporating other agents only when it makes sense.  If you care about modern software delivery, developer enablement, and the real-world impact of AI on building systems, this one’s for you. Subscribe, share this with a teammate, and leave a review with your take: what “human skill” will matter most as AI becomes default? Support the show More at https://linktr.ee/EvanKirstel

  7. 7月24日

    Why CRM Still Fails Sellers And How AI Fixes It

    Interested in being a guest? Email us at admin@evankirstel.com Your CRM knows a lot about your customers, so why does it still feel like a place you go to do homework? We talk with SugarAI CEO David Roberts about the hard truth many sellers live every week: CRM has mostly evolved into a management tool, not a system that makes reps better in the moment that matters, right before a call, a meeting, or a critical follow-up. We dig into why the “360-degree view of the customer” often backfires. More dashboards and more reports can create more overwhelm, especially when a rep is managing hundreds of accounts, dozens of deals, and a product catalog with thousands of SKUs. David lays out a more practical promise for AI in CRM: move from data straight to action. Instead of forcing sellers through a rigid process, AI can interpret unstructured sales work like emails and meetings, spot gaps like missing executive sponsors, and recommend the next best step that improves retention, expansion, or new business. We also explore contextual intelligence and “precision selling” and why generic AI advice is easy to ignore. The best guidance comes from combining customer data with the real selling context: sales methodology, pitch decks, competitive battle cards, and objection handling. For manufacturing and distribution teams, connecting front-office CRM with back-office ERP data like inventory and shipping can surface reorder opportunities without sending reps into endless spreadsheets. If you care about sales productivity, revenue team performance, AI CRM, and faster sales onboarding, you’ll get a lot from this conversation. Subscribe, share this with a sales leader or RevOps partner, and leave a review with the biggest “next step” you wish your CRM would tell you. Support the show More at https://linktr.ee/EvanKirstel

  8. 7月23日

    When AI Learns Your Workflow Who Wins The Market

    Interested in being a guest? Email us at admin@evankirstel.com Your AI demo is easy. Shipping AI inside a regulated insurance workflow is the hard part and that’s where the real advantage gets built. We sit down with Doug Marquis, CTO at ZyWave, to get specific about what’s changing in insurance technology and why “agentic AI” only matters when it reliably drives growth outcomes for agencies and brokers. We dig into ZyWave’s front office focus from lead identification and marketing to quoting, service, and renewals and why that matters as the insurance workforce ages and capacity tightens. Doug breaks down the two inputs that separate winners from copycats: access to high-quality insurance data and the discipline to encode domain knowledge and real producer workflows into artifacts that AI agents can actually use. We also talk through how automation can cut the administrative load so producers spend more time being consultative with customers. Then we get practical about enterprise AI: compliance and security up front, licensing and data access controls, and the “AI harness” you need around agents, including observability, testing, cost management, and explainability. Doug also explains ZyWave’s insurance MCP server and how plugging insurance-specific data into LLM tools turns generic answers into specialized, contextual guidance. If you’re a CIO, CTO, or product leader trying to move from pilots to production, this is a clear look at what to prioritize next. If this helped, subscribe, share it with a teammate building enterprise AI, and leave a review with the biggest challenge you’re facing right now. Support the show More at https://linktr.ee/EvanKirstel

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Tech Transformation with Evan Kirstel: A podcast exploring the latest trends and innovations in the tech industry, and how businesses can leverage them for growth, diving into the world of B2B, discussing strategies, trends, and sharing insights from industry leaders!With over three decades in telecom and IT, I've mastered the art of transforming social media into a dynamic platform for audience engagement, community building, and establishing thought leadership. My approach isn't about personal brand promotion but about delivering educational and informative content to cultivate a sustainable, long-term business presence. I am the leading content creator in areas like Enterprise AI, UCaaS, CPaaS, CCaaS, Cloud, Telecom, 5G and more!

信息

  • 创作者
    Evan Kirstel
  • 活跃年份
    2024年 - 2026年
  • 单集
    673
  • 分级
    儿童适宜
  • 版权
    © 2026 TechInfluencer
  • 节目网站
    What's Up with Tech?