What's Up with Tech?

Interested in being a guest? Email us at [email protected] 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 showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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 showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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 showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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 showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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 showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] isn’t just changing cybersecurity tools, it’s changing the pace of the entire fight. When attackers can scale campaigns with automation and operate at machine speed, the old rhythm of annual pen tests and quarterly reviews stops making sense. We sit down with Hom Bahmanyar Global Enablement Officer at Ridge Security, to talk about what security teams are up against right now and how continuous security validation can turn an endless backlog of findings into a short list of exposures that truly matter.We dig into the problem every enterprise security team feels: information overload. Vulnerability scanners generate massive reports, but severity scores don’t reliably predict what gets exploited in the wild. Our focus shifts to a more practical approach to vulnerability management and exposure management, prioritizing issues based on exploitability in your specific environment and whether the weakness is actually visible to a threat actor. That perspective helps reduce alert fatigue and aligns remediation work with real-world risk.From there, we explore how agentic AI and autonomous security testing change the workflow. Hom explains why being model agnostic matters, especially for organizations that must run in air-gapped environments and rely on open source or open weight models. We also talk about why remediation is the bottleneck, how compensating controls like firewalls can buy time while patching catches up, and what early results can look like when teams start validating continuously, including uncovering previously missed SQL injection.If you care about continuous security validation, AI-powered penetration testing, and keeping pace with modern cyber threats, subscribe, share this episode with a teammate, and leave a review. What part of vulnerability triage or remediation slows your team down the most?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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is rewriting the rules of healthcare software development, but building a secure scalable healthcare platform still comes down to fundamentals: clear requirements, strong ownership, and serious engineering discipline around HIPAA compliance and PHI protection. We sit down with Ghazenfer Mansoor, founder and CEO of TechnologyRivers, to talk through what’s working right now as digital health teams race from prototype to production.We get practical about how AI fits into the real build process, including rapid POCs, code generation, and stronger testing through AI-assisted unit tests and code coverage. Then we dig into the part most teams learn the hard way: you can’t treat AI like a simple Google search when the data is sensitive and the scale is real. We talk about why “upload your company docs” breaks down, and how patterns like RAG architecture and vector databases can support safer internal search and analysis without losing control of private data.We also cover the repeat mistakes that sink health tech MVPs, especially unclear requirements and fuzzy decision-making. From there, we talk UX and UI in healthcare, focusing on designing for patients, clinicians, and admins based on what they actually need to do. Finally, we look at low-code and no-code in healthcare, how to turn white-coded prototypes into production-ready HIPAA compliant software, and what interoperability and emerging ideas like MCP could mean for the next wave of EHR-connected apps.Subscribe for more conversations on digital health, healthcare IT, and AI in clinical workflows, then share this with a builder on your team and leave a review. What’s the hardest part of shipping healthcare software in your world right now?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] used to be a numbers game. With AI, it’s becoming a personalization game and that’s exactly why “good enough” login security is quietly turning into a liability. We sit down with Dawn Manley, PhD, SVP, Product Management of Yubico to talk about why phishing-resistant authentication is now a must-have for anyone protecting high-value accounts, especially as AI tools become central to daily work and decision-making. We get specific about OpenAI’s Advanced Account Security and why YubiKeys are part of the story. AI accounts can hold an incredible amount of sensitive context: drafts, strategies, internal notes, customer data, and the breadcrumbs of how you think. That makes ChatGPT account security and account takeover prevention far more than a technical checkbox. Dawn explains how hardware-backed security keys work in real life, why they’re considered phishing resistant, and how they avoid the weak points of SMS codes, intercepted OTPs, and push-notification tricks. Then we zoom out to the next wave: AI agents and agentic commerce. When software can act on your behalf, the question shifts from “who logged in?” to “who approved this action?” We talk about proving human intent for high-risk moments, using strong cryptography as a trust anchor, and what it looks like to deploy hardware-backed passkeys across an entire organization without relying on users to spot every scam. If you care about identity and access management, FIDO2 passkeys, enterprise security, or protecting AI workflows, you’ll walk away with a clearer model for what “strong authentication” really means now. Subscribe, share this with someone still relying on codes, and leave a review with your biggest question about phishing-resistant security.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] leadership playbook a lot of us grew up with is starting to fail in the moments that matter most: high pressure decisions, fast growth, founder stress, and team conflict that can’t be solved with a new script. We sit down with Peter Carnochan , an executive coach who is also a psychologist and trained psychoanalyst, to talk about a simple idea with big consequences: the next frontier of leadership development is within, not without.We unpack why “good enough” emotional skills stop working when you’re building at the highest levels, and why surface changes can feel impressive until stress hits and the veneer cracks. Peter introduces what he calls “radiant change” and explains how real, durable growth starts at the center of the self and spreads outward into communication, decision-making, and culture. Along the way, we talk about why a therapeutic approach to executive coaching is less generic, why not everyone should try to lead like the loudest celebrity CEO, and why the future belongs to a plurality of leadership styles that fit the person, not the stereotype.The conversation gets personal through the story behind the documentary “Andre Is An Idiot,” a comedy about dying made after a stage four cancer diagnosis. It becomes a lens for meaning, mortality, and purpose and a reminder that people want leaders who care about the whole organization and community, not only the bottom line at all costs. If you’re a founder, CEO, or operator looking for sustainable high performance, emotional resilience, and deeper leadership presence, this one will challenge you in the best way.Subscribe, share this with a leader who’s under pressure, and leave a review if it helps you lead with more clarity. What inner pattern do you think most limits great leadership?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] isn’t a side project for marketing anymore. It’s becoming the way work gets done, and that shift is happening faster than most teams can measure, govern, or even fully see. We sit down with Ed from Callan Consulting to unpack what he’s hearing directly from CMOs and heads of marketing about real-world AI adoption in tech marketing, from early-stage startups to multi-billion-dollar enterprises. We talk about the move from experimental “skunkworks” use to embedded AI across the marketing tech stack, including LLMs like ChatGPT and Claude, AI features inside core MarTech platforms, and a growing wave of AI-native tools designed for specific workflows. Ed shares why so many leaders report major impact while still struggling to quantify ROI, and how “born-in-AI” companies are rethinking org design and productivity from day one, sometimes even putting agents on the org chart. Then we get into the tradeoffs: token budgets, tool sprawl, and the rising risk of overreliance. If everyone ships AI-generated content at scale, everything starts to sound the same, mistakes slip through, and the internet fills with “AI slop” that models train on again. We lay out a practical path that protects brand voice: keep the hero content human-led, then use AI for atomization, localization, optimization, and distribution. Finally, we look ahead at generative engine optimization (GEO), the early dip in traditional SEO traffic, and why “machine engine optimization” could matter as buyers use agents to research vendors. If you want a grounded, executive-level view of generative AI in marketing, listen now, then subscribe, share with a teammate, and leave a review so more marketers can find it.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is everywhere on stage, but production AI is won in the details. From HPE Discover, we sit down with Jason Schradel, Director of Enterprise Platforms at NVIDIA, to unpack what it really takes to build an “AI factory” that an enterprise can deploy, operate, and scale without turning every upgrade into a science project. If you’re trying to move from AI proofs of concept to real business outcomes, this conversation maps the stack in plain terms. We start with what’s being showcased on the floor and why it matters: next-generation platforms like Vera Rubin and the new Vera CPU, enterprise-ready GPUs including RTX Pro Blackwell Server Edition options, and the networking layer that keeps modern AI workloads moving, from Spectrum-X Ethernet switching to BlueField DPUs. We also talk about how HPE systems, storage, and the private cloud experience come together with NVIDIA accelerated computing and NVIDIA AI software to form a repeatable blueprint for enterprise AI infrastructure. From there, we zoom out to the go-to-market reality: global customers, real deployments across industries like healthcare, manufacturing, financial services, and telecom, plus the growing role of ISVs and partner ecosystems in making AI usable for specific workflows. Jason also shares what he’s watching on the roadmap, especially agentic AI and the importance of confidential computing to protect sensitive data and model weights as hybrid cloud AI becomes the norm. If you’re planning an enterprise AI strategy, you’ll leave with a clearer view of the components that matter most and the tradeoffs you can’t ignore. Subscribe for more conversations like this, share this episode with a teammate building your AI platform, and leave a review. What part of the AI factory stack feels hardest to get right right now?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] hospital breach can turn the network “dirty” in minutes. A fiber cut can take a grocery store offline at the worst possible time. And almost every CIO has said some version of the same thing: “I love the technology, help me find the money.” That’s the tension we dig into with John Tonthat CRO of Cellhub, one of T-Mobile’s longest-tenured agency partners operating right at the intersection of telco, wireless, and IT systems integration.We start with the real-world enterprise problems Cell Hub helps solve across healthcare, retail, and beyond, including how large organizations manage provisioning, procurement, and billing across complex wireless estates. John shares why hospital CIOs are juggling three mandates at once: clinical communications that work inside old buildings, remote patient monitoring and care that can scale safely, and security strong enough to withstand relentless attacks. We get specific about where Wi-Fi struggles and how enterprise 5G can be designed as a resilient backup network to protect continuity of care when primary systems are compromised.Then we shift to the connected grocery store, where uptime, in-building coverage, and refrigerated warehouse connectivity directly impact revenue and customer experience. John explains Super Broadband, combining fixed wireless with Starlink to hit service levels at a compelling price, plus why retail media networks demand “always up” secondary connectivity that doesn’t ride on the core network. Finally, we unpack SCOT, a cost reconciliation engine that uses automation to surface hidden spend across wireline, wireless, and IT, and Design X, a faster way to iterate network designs with a proper system of record. We close with what John sees as the next frontier: securing not just the device, but the communication itself with peer-to-peer encrypted approaches.If you care about enterprise connectivity, 5G transformation, network resilience, and mobile security, subscribe, share this with a colleague, and leave a review so more builders can find the show.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] fastest attackers don’t “hack” like they used to, they drift through systems, blend into normal behavior, and move at machine speed. That reality forces a hard question: if you cannot defend the perimeter anymore, what should a modern security architecture look like?We sit down with James Karimi, CIO and CISO of GTT Communications, one of the world’s largest tier one internet operators, to break down the practical moves behind a containment-first strategy. We talk candidly about the human layer of risk, why awareness training still matters, and why GTT chose a draconian but effective approach: eliminating lateral movement so a compromise stays small. James also shares what it really takes to “unflatten” applications with firewall contexts, explicit network rules, and the painful discovery work most teams underestimate.From there, we zoom into the GTT Envision platform and how software-based service chaining at the edge improves resiliency, agility, and managed security. Then we get into AI governance and operations: how GTT built AI factories with Dell and NVIDIA, why documenting data is the make-or-break step for enterprise AI, and how they designed secure AI operators that are isolated by default. We also explore behavior-based detection and response, CVE analysis with mitigation guidance, real-time topology for threat hunting, and where autonomous mitigation fits depending on a customer’s tolerance.If you care about zero trust, microsegmentation, AI observability, network detection and response, SOC modernization, and measurable AI ROI, this conversation gives you a blueprint you can adapt. Subscribe, share this with a security leader, and leave a review with the one change you think every enterprise should make 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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] two-week simulation was all it took for “autonomous AI agents with rules” to reveal how fragile our current guardrails really are. We sit down with Satya Nitta from Emergence AI, an autonomous AI lab working at the intersection of neural networks and symbolic AI, to unpack the Emergence World Experiment: five virtual cities, ten agents per city, and different frontier language models powering each world, including a mixed-model society where agents influence each other.What we saw is the kind of long horizon autonomy story most benchmarks can’t capture. One world collapses into fighting and resource failure in days. Another becomes eerily stable through near-total conformity. And the most important signal for enterprise AI shows up in the mixed world: agents that look “well behaved” alone can be pulled into unsafe behavior when they interact with other models. If your company is rolling out agentic systems across a messy stack of vendors, tools, and models, that is not an edge case, it is the default reality.We also dig into a concrete safety direction: neuroformal AI, proof-carrying code, and formally enforced constraints using mathematical methods like dependent type theory. The argument is simple and provocative: before an AI agent takes actions that touch production code, sensitive data, or critical operations, it should be able to prove it is staying within constraints, not just promise it in natural language. If you care about AI safety, autonomous agents, multi-agent systems, and real-world deployment risk, this conversation will sharpen how you think about what comes next.Subscribe for more deep dives, share this with a friend building with AI agents, and leave a review with your biggest question about long-horizon autonomy.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] isn’t magic, and it definitely isn’t free. We sit down with Ken from Pega Systems to get brutally practical about the economics of enterprise AI: why token costs are a symptom, why infrastructure spend is so high, and how “murky ROI” happens when companies deploy AI for novelty instead of measurable business value.From Ken’s perspective as a former CFO and current COO, the best mental model is surprisingly simple: treat AI like a utility. If electricity has taught us anything, it’s that the winners don’t just consume more, they manage consumption better. We talk about how to reduce waste, how to avoid paying for frontier-model overkill, and why boards and finance teams are starting to demand tokenomics tied to outcomes. We also dig into a provocative corner of the market: incentives that can turn the AI ecosystem into a circular hype machine unless leaders insist on real examples and hard metrics.We then shift to what this means inside large organizations. Agentic AI can accelerate judgment-heavy work in finance, legal, HR, and marketing, while deterministic workflows still anchor reliability in core operations. Finally, Ken shares career advice for the next generation: as execution gets automated, the premium rises on strategy, product management, and validation skills, plus the curiosity to keep learning as roles evolve.If you care about enterprise AI ROI, workflow automation, and the real operating model behind digital transformation, hit play. Subscribe, share this with a colleague, and leave a review with the metric you think will prove AI is paying off.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] communications is easy to praise and hard to pull off, especially when your reality includes on-prem systems, private cloud requirements, public cloud apps, and a growing buy-in committee that can hit dozens of stakeholders. We talk with Jonathan Buckle, VP of the Americas at Mitel, about what hybrid unified communications actually looks like when you refuse to force customers into a single model and instead design around how organizations really operate. We get concrete about the process: why discovery matters more than demos, how vertical expertise in healthcare, education, hospitality, retail, and the public sector speeds up decision-making, and why workflow integration is often the quickest route to real outcomes. Jonathan shares what he’s seeing in the market as vendors consolidate or exit categories and why that shift is pushing more organizations to rethink voice, UC, and the day-to-day systems their teams rely on. Frontline workers are a major focus, from nurses and operators to school staff and hotel teams. We dig into what changes when you sit next to the people doing the work, how simplicity beats feature creep, and why Mitel’s WX UC client is built to make training easier while surfacing workflow triggers directly in the user experience. If you’re modernizing business communications and you’re tired of “either cloud or on-prem” debates, this conversation will help you pressure-test your plan. Subscribe, share this with your IT team, and leave a review, then tell us: what would “no compromise” need to mean for your organization to believe it?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] in telecom is finally graduating from slide decks to real operational impact, but the jump from pilot to production is where most teams get stuck. I sit down with Guy Lupo from the TM Forum, who leads the trustworthy AI and data mission, to talk about what it actually takes to become an AI native telco and why the industry’s next gains depend less on flashy demos and more on operational proof.We break down where operators are seeing traction right now, like network fault management, faster mean time to resolve, fewer tickets, and churn reduction, and why those wins correlate directly with clean, structured signals. Then we dig into the uncomfortable middle ground: AI that augments people feels manageable, but AI embedded into tools and workflows raises hard questions about governance, monitoring, and accountability. Guy’s point lands hard: trust cannot be claimed, it must be demonstrated continuously, especially as autonomy increases.From there, we connect the dots to risk-based regulation and sovereignty. Frameworks like the EU AI Act signal a shift away from checklist compliance toward auditable evidence over time, with telecom increasingly treated as high risk critical infrastructure. We also explore emerging concepts like agent passports, plus why the industry is asking for a shared “agent factory” reference architecture and practical, no regret patterns such as Model as a Service for consistent, governable model access. We close by looking ahead to physical AI and robotics and the surprising telecom advantage: the operational workforce that can install, maintain, and safely support devices at scale.If you care about AI governance, autonomous networks, agentic AI, and the real-world path to production in telecom, subscribe, share this with a colleague, and leave a review with the one trust gap you want the industry to solve first.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] easiest system to hack is the one that’s always there to be found. We sit down with Steve Visconti, CEO and co-founder of XIID, to talk about a different cybersecurity mindset: architectural invisibility, where the goal isn’t to build a bigger wall, it’s to make the target unreachable in the first place.We dig into what “no inbound communication” really means, including removing public IP dependence, reducing DNS exposure, and enforcing process-to-process connectivity so only the exact executable you approve can talk to the exact service it needs. Steve explains how outbound-only tunnels can be established on both sides, and why strong encryption and post-quantum secure tunneling matter when you’re protecting high-value systems in an increasingly autonomous, machine-to-machine world.We also get practical about where this fits in today’s security stack. Because it operates at the application layer, it can complement existing tools without a rip-and-replace overhaul, and it can roll out one app at a time while still scaling through orchestration. Along the way, we connect the dots to real risks in modern software delivery, like AI-generated code and CI/CD pipelines that accidentally leave behind discoverable test endpoints.Finally, we zoom out to critical infrastructure, including EV charging networks and the growing connection between vehicles, cloud billing systems, and the electrical grid. If you care about reducing attack surface, protecting OT environments, and building zero trust security that survives automation at scale, this is for you. Subscribe, share this with a security-minded friend, and leave a review with your biggest question about making systems “unreachable by design.”Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 2005 malware sample sounds like ancient history, until it looks like cyber sabotage that may predate Stuxnet. We sit down with Jags from SentinelOne’s Sentinel Labs to unpack Fast 16, a rare framework that doesn’t just break computers, it quietly corrupts high precision calculations. If you’ve ever treated simulation results, engineering models, or AI outputs as “the answer,” this conversation will make you pause.We walk through the unexpected discovery path: a curious reference tied to the Shadow Brokers leak, years of researchers staring at a strange sample that “felt important” but refused to give up its secrets, and the moment an internal project using AI for reverse engineering helped unlock what Fast 16 was built to do. Along the way, we connect the dots to the Stuxnet era, cyber threat intelligence “paleontology,” and why truly high end nation state toolkits look like platforms, not one off scripts.Then we get uncomfortably current. Sabotaging calculations is an integrity attack, and integrity is the foundation of modern scientific computing, cloud workloads, and frontier AI model training. We talk about how subtle degradation can waste millions, derail decision making, and even turn teams against their own experts. We close with practical lessons for CISOs and enterprise leaders: invest in visibility, telemetry, and log retention before the crisis, and start treating output verification as a core security problem.Subscribe for more deep dives on cyber sabotage, APT tradecraft, and AI security, and if this made you rethink what “trust” means in computing, share it and leave a review. What system in your world would be hardest to verify?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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] phone rings, you hesitate, and you let it go to voicemail because it might be a scam. Meanwhile your team is juggling Microsoft Teams, Webex, mobile calling, messaging apps and a growing stack of AI tools that promise better customer experience but often add complexity. We sit down with William Rubio to unpack what’s actually changing in cloud communications and what a managed service provider needs to deliver in 2026: not just licenses, but outcomes across UCaaS, CCaaS and AI.We talk through Call Tower’s evolution and the recent strategic investment from Court Square Capital Partners, including how growth, global expansion and M&A fit into a fast-moving market. Then we get practical about mobile identity and eSIM for Teams and Webex: why “clicking the app” is friction, how caller ID consistency affects trust, and why compliance, recording and analytics become more important when work follows you from car to laptop to office to home.On the CX side, we zoom in on conversational AI and agentic AI in the contact center: what major platforms are shipping, where specialized AI vendors can add real value, and why industry-specific AI for healthcare, finance and manufacturing is likely to define the next wave. We also cover WhatsApp integration with Microsoft Teams and what it signals about enterprise communications finally adopting consumer-like channels without giving up security.If you care about cloud calling, AI contact centers, mobile-first collaboration and stopping spam calls from poisoning business communications, hit play. Subscribe, share this with a teammate, and leave a review with the one communications headache you most want fixed next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] phishing is no longer “spray and pray.” It’s targeted, multi-channel, and increasingly powered by exposed employee data that’s sitting in plain sight. We sit down with Paul Mander, Chief Commercial Officer at Optery for Business, to unpack what’s driving the next wave of AI-driven social engineering and why so many security teams are rethinking where the real attack surface begins.Paul walks us through eye-opening survey findings from more than 400 cybersecurity leaders: social engineering attempts are rising sharply, most attacks are moderately or highly personalized, and a large share of those successful attempts lead to credential compromise. We also dig into why there’s no single channel to defend anymore. Email still matters, but attackers are mixing phone calls, SMS, social media, and impersonation to make their stories feel “verified” from multiple angles.The biggest shift is where attackers get their homework done. Data brokers and people search sites compile dossiers that include phone numbers, home addresses, relatives, employment history, and even org chart details that help threat actors pick high-leverage targets. We talk about why IT, HR, and finance often take more heat than executives, and what practical teams can do today: strengthen MFA and training, then get proactive by finding and removing exposed PII through opt-out and deletion workflows at scale.If you’re a CISO, IT leader, or security practitioner trying to reduce phishing risk, social engineering risk, and account takeover risk, this is the playbook for treating privacy exposure as a core cybersecurity control. Subscribe, share this with your team, and leave a review with the one data source you think attackers rely on most.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] product can have a world-class cloud stack and a blazing-fast 5G link, then lose the whole experience in the last half meter. That’s the “last meter” problem, and it’s why we sat down with Dr. Frederic Nabki Co-founder and Chief Technology Officer Spark Microsystems, to talk about ultra-wideband wireless that targets wirelike responsiveness instead of “good enough” latency.We dig into where Bluetooth and Wi‑Fi still shine and where they hit real limitations for deterministic wireless, ultra-low latency, and interference-heavy environments. Frederick explains why Spark’s approach uses impulse radio UWB, how sub-nanosecond-scale pulses change the game for multipath and coexistence, and how wide UWB spectrum enables frequency agility when the airwaves get crowded. If you’ve ever been in a trade show hall where microphones and earbuds fall apart, you’ll recognize why interference robustness is no longer optional for industrial IoT, medical devices, wearables, and robotics.The examples get concrete: a gaming mouse that targets about 150 microseconds end-to-end latency, robots that need fast control loops to avoid collisions, and brain-computer interface systems where cables create infection risk and power budgets are unforgiving. We also cover Spark’s go-to-market details, including transceiver silicon, an SDK, reference designs, antenna guidance for FR4 PCBs, and why modules can simplify certification.If you care about ultra-wideband, UWB data communication, ultra-low power wireless, and real-time connectivity, hit play, then subscribe, share the episode, and leave a review so more builders can find it.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] carrier has more data than almost any company you interact with, yet most telcos still struggle to turn that advantage into growth. We sit down with Miguel Carames, the Chief Product Officer at Mobileum to sort out what’s real, what’s next, and what’s pure hype when it comes to AI in telecom, 5G monetization, and the future of operators as intelligence-driven businesses. Along the way, we get honest about why “we invested billions” doesn’t automatically translate to new revenue and why regulation and privacy expectations reshape every AI roadmap.We also challenge the idea that AI only arrived with generative tools. Telecom has used machine learning for years in automation, anomaly detection, and capacity planning, but the story hasn’t been told well. Miguel shares concrete, production-minded examples: using LLM-style interfaces to make deeply technical testing platforms usable for roaming managers and analysts, moving toward automated root cause analysis, and deploying agent workflows in fraud and revenue assurance so cases arrive pre-analyzed with evidence and a human still making the final call.From there we go into customer experience, where proactive network intelligence can prevent tickets before customers ever feel the pain, and into churn reduction, where the opportunity is huge but the privacy line is delicate. We wrap with fraud and security, the whack-a-mole reality of bad actors, and what it takes to escape pilot purgatory so telecom can move at AI speed. If you found this useful, subscribe, share it with a telecom leader, and leave a review. What’s the best AI use case you’ve seen a telco actually scale?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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] your SOC is buried under alert noise, another flashy AI demo won’t save you. We go deeper into what actually works: starting with data strategy and detection quality so automation has real signal to work with, not chaos to summarize. Our guest CEO and Founder Karthik Kannan from Anvilogic explains what “agentic SecOps” looks like in practice, from data onboarding and normalization to detection engineering, hunting, triage, investigation, and the integrations that move outcomes into your ticketing or case management systems.We talk through why many AI security operations tools jump straight to alert triage and why that can turn into a band aid. The more durable path is end-to-end context: knowing exactly which data sources fed a detection, what logic fired, and how the alert was produced. That lineage supports higher accuracy, cleaner investigations, and consistent mapping to frameworks like MITRE ATT&CK. We also dig into “show your work” explainability, why black box answers stall adoption, and how a decision trace helps teams build trust step by step.On the architecture side, we explore federated security operations across the tools enterprises already run, including Splunk, Microsoft Sentinel, Snowflake, and Databricks. Instead of forcing every byte into a monolithic SIEM, federated queries and data lake strategies let teams correlate where the data lives while controlling cost and complexity. We close with a grounded take on whether AI replaces security analysts and why the real win is reducing burnout and up-leveling people into higher judgment work.If this helped you rethink SOC automation, subscribe, share the episode with your team, and leave a review with the biggest bottleneck you want AI to tackle next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] was supposed to simplify everything. Instead, a lot of CIOs are staring at bills that are far higher than anyone forecast, feeling locked into hyperscalers, and wondering where the business value went. I sit down with David Linthicum, former Deloitte chief cloud strategy officer turned tech influencer, to give an unvarnished reality check on cloud computing costs, cloud repatriation, and what “pragmatic architecture” looks like when budgets are real and timelines are slow.We also get blunt about enterprise AI. David explains why so many AI-driven transformations stall out on two constraints: money and talent. We dig into why AI can cost 10 to 20 times more than traditional software, why “AI-first enterprise” messaging can be dangerous, and how leaders can pick high-impact use cases instead of trying to bolt generative AI onto everything. Along the way, we talk about how AI is reshaping SaaS economics as agents start using systems on behalf of humans, and what that means for vendors and buyers.Then we tackle the loudest buzzword of the moment: agentic AI. Where does it shine as a productivity force multiplier, and where is it mostly hype when you try to deploy it at enterprise scale? We round out with underhyped edge computing opportunities and the growing backlash around data centers, power, and the grid. If you care about enterprise architecture, cloud strategy, generative AI, and what’s actually deployable right now, you’ll get a clear set of takeaways you can use this week. Subscribe, share this with a CIO or architect, and leave a review with the most overrated tech trend you want us to challenge 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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] backup system is sitting on a gold mine and most companies are treating it like a fire extinguisher behind glass. From the floor of Dell Technologies World, we talk with Simon Taylor CEO of HYCU about their next chapter: HYCU Air, an AI resiliency platform designed to turn SaaS backup data into something you can actually interrogate, learn from, and use to stay ahead of cyber risk. We dig into the big idea that the most valuable asset is not the LLM itself, but the unique datasets inside your systems of record. HYCU Air pairs a knowledge graph and context engine with an LLM so you can ask natural-language questions of your backup history, the same way you would investigate a security camera recording. That reframes data protection from “pay for recovery” to “use backup data every day” across modern cloud applications, collaboration tools, and enterprise SaaS sprawl. Then we get practical: cybersecurity posture management when AI agents and integrations are “running amok,” spotting policy drift, and using data classification to find sensitive data like PII that never should have been where it ended up. We also share what we’re hearing from customers, why demand is accelerating, and how this approach starts to look like the “brain of an organization” by connecting corporate memory across dozens of SaaS services. If you want to see where AI resiliency is heading, hit play, share this with a security or IT leader, and leave a review with the one question you wish your backups could answer.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] fastest way to break a modern network isn’t your Netflix download, it’s the quiet, constant upload from sensors, logs, and telemetry that nobody ever reads. We sit down with Julien Dersey from AtomBeam to unpack why data efficiency is suddenly a front-line issue for IoT networking, edge computing, and cloud operations, even in a world with 5G and new satellite options like Starlink. The uncomfortable reality is that bandwidth grows, then data expands to fill it, especially once cybersecurity teams demand near real-time visibility into who connected to what, from where, and when.We get concrete about the uplink bottleneck that hits IoT deployments first, and why “just filter the data” is a risky workaround. Julian shares a field deployment with an oil and gas fracking operator transmitting over Starlink, where compaction reduced traffic dramatically and kept gigabytes per day flowing reliably for months, while also helping identify odd behavior coming from a sensor. From there, we explore how AtomBeam’s lossless “compaction tunnel” differs from traditional compression, how it can run with extremely low CPU and memory, and why keeping applications unchanged is a big deal for real teams.We also dig into enterprise and operator integrations: testing with Ericsson over a 5G router and SD-WAN style network bonding, the latency and performance questions engineers always ask, and the security posture using TLS 1.3 with an added obfuscation effect. Finally, we widen the lens to point-of-sale receipt transmission at scale, disaster recovery replication speedups, and what’s coming as connected vehicles, smart meters, and smart grid AMI 2.0 generate even more machine data.If you care about IoT bandwidth, edge efficiency, secure data transport, and the future of connected devices, subscribe, share this with a colleague, and leave a review. What’s the single noisiest data stream on your network right now?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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] moves fast, and that speed exposes the difference between “cloud by default” and communications that actually hold up under pressure. We sit down with Mitel and Ethan Global to unpack what it takes to deliver unified communications, contact center, and customer experience platforms across Australia, New Zealand, and the South Pacific where geography is huge, budgets demand efficiency, and resilience is non-negotiable.We talk about why Australia and New Zealand are early adopter markets, how hybrid work has shifted to a role-based model, and why complex verticals like government, healthcare, education, emergency services, mining, and transport can’t rely on one-size-fits-all deployments. You’ll hear what customers are asking for right now: redundancy, tight integration into core business systems, managed services, and clear answers on data sovereignty and regulatory expectations.Then we get into the AI reality check. Instead of vague hype, we focus on what’s delivering immediate value in CX and contact centers, including agent assist, conversation summarization, quality monitoring, and AI that improves IT operations through faster issue resolution and smarter provisioning. We also explore cloud-first mandates, the surprising rise of cloud repatriation when organizations move too quickly, and why the partner ecosystem now drives innovation as much as the platform itself.If you’re planning a UCaaS or CCaaS modernization, building an AI roadmap for customer experience, or supporting a hybrid workforce at scale, this conversation will help you pressure-test your strategy. Subscribe, share with a colleague, and leave a review with your biggest question about cloud, AI, or enterprise communications.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] AI assistant is only as smart as the mess behind your dashboards. When business data lives across CRM, accounting, HR, and job systems, “connect ChatGPT to our data” quickly turns into rate limits, broken joins, confusing IDs, and answers nobody trusts.We sit down with Craig Morrall, co-founder of Tugger, to unpack a practical architecture for enterprise AI that actually holds up in the real world: pulling data from many platforms into a warehouse, then layering on a semantic model that explains what the data means and how records connect across systems. That extra context is what turns a chatbot into something you can rely on for revenue questions, profitability analysis, and cross-platform reporting without spending months on custom pipelines.Craig also shares what customers are doing once the foundation is in place, including building interactive dashboards in minutes and generating repeatable board packs that used to take finance teams hours. We dig into time to value, early ROI stories, and how Tugger approaches security and governance with ring-fenced data storage, ISO 27001 certification, and guidance on using business-grade LLM plans to reduce training risk.If you’re evaluating enterprise AI, data warehousing, semantic layers, or secure analytics with Claude or ChatGPT, this conversation will help you separate real capability from hype. Subscribe for more practical AI stories, share this with a friend building on enterprise data, and leave a review with the biggest data problem you want AI to solve.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] agents are starting to do real work inside real companies and they often do it by acting as us. That’s exciting, and it’s also a security wake-up call. We sit down with Matthew Immler Regional CSO, Americas at Okta, to unpack why identity security has become the primary battleground and why attackers increasingly prefer impersonation over breaking through a “front door” with zero-days.We get concrete about what “non-human identities” actually means in plain English, and how agentic AI changes the rules. When employees connect new tools and click consent, an AI agent can gain access not just to a calendar, but to email, files, and other sensitive systems through broad OAuth scopes. From the security team’s perspective, the activity can look like normal user behavior, which creates a visibility problem at the exact moment enterprises are being pushed to adopt AI faster than their controls can mature.We also talk solutions: treating AI agents as first-class identities with owners, managers, and access reviews; spotting non-human behavior through signals like abnormal client secret flows and extreme refresh token patterns; and why blocking AI outright can drive “shadow AI” instead of safety. Matt shares how standards work like cross-app access can shift control from end-user consent to IT policy so teams can approve tools, lock scopes down, and keep tight governance.If you care about AI security, identity and access management, OAuth risk, and practical guardrails for agentic AI, this conversation will help you think clearly and act faster. Subscribe, share this with your security or IT team, and leave a review with the one control you think every AI agent should have.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] keeps getting more expensive, yet most of us feel like we’re doing more work just to get the same care: more portals, more forms, more phone calls, and more confusing lab results. We sit down with Greg Brady the founder and CEO of Connect4Patients to dig into the root cause he’s spent decades solving in other industries: fragmented data. His claim is direct and a little startling. If we can’t assemble a complete, real-time medical record, we can’t reliably reduce errors, we can’t simplify administration, and we can’t move the system upstream toward prevention. We talk about what a patient-centric system actually looks like in practice: one unified “single version of the truth” for your health record that can be shared across providers, while still working with existing EMR/EHR systems. Greg explains how an AI-based network can fuse and cleanse records in a HIPAA-compliant way, then translate medical jargon into plain English so patients can understand what their numbers mean and what actions to take. That shift is bigger than convenience. It’s the foundation for catching trends early, like rising glucose before prediabetes, and for preventing dangerous mistakes, like prescriptions that conflict with other meds a patient is already taking. We also get into the uncomfortable incentives that keep healthcare stuck in a treatment loop: more tests, more procedures, more friction in prior authorization, and a system where insurers can delay care through manual workflows. Greg shares a view of what could change if large employers, cities, or states act as self-insured organizations and reward preventive behaviors directly, using data and personalized guidance to lower chronic disease rates over time. If you’ve ever wondered why healthcare feels “designed” to be hard, this conversation offers a concrete infrastructure-level answer and a practical path forward. Subscribe, share this with someone who’s tired of managing their care across multiple portals, and leave a review with the biggest healthcare friction you want fixed 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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is finally forcing a scoreboard moment in telecom: some organizations are seeing massive productivity gains, while others are stuck between fear of missing out and fear of getting it wrong. From the floor at IBM Think in Boston, we sit down with IBM’s global CTO for telecom, media, and entertainment Eoin Coughlan to get practical about what separates “AI pilots” from AI that actually lands in production and earns trust.We start with the hard truth that hasn’t changed for decades: data is the bottleneck. Clean, timely, governed data determines whether AI helps you run a network or quietly amplifies bad decisions. From there we move into telecom operations where fragmented observability makes it hard to see what’s really happening. We talk about pulling signals into a unified view, using AI to correlate root causes, and keeping control as you introduce agentic AI. Autonomous networks come up as a real journey, not a magic switch: time series models for network telemetry, multiple agents that can read tickets and vendor manuals, and then automation that begins with humans in the loop and expands as trust grows.Then we zoom out to the ecosystem: hyperscaler dependence, rising sovereignty requirements, and what it means to run compliant, air-gapped platforms that enterprises can rely on. One of the biggest opportunities may be hiding in plain sight: SMEs often trust their telecom provider more than software vendors or hyperscalers, opening the door for CSPs to deliver packaged AI assistants and managed platforms. We also hit legacy modernization and 5G monetization realities, and finish with what might surprise us next, including early quantum computing use cases. If you found this valuable, subscribe, share it with a telecom leader on your team, and leave a review with the AI or automation challenge you’re tackling right now.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] didn’t just change how enterprises innovate, it changed how they get breached. One month you’re racing to deploy new copilots and agentic workflows; the next you’re asking a harder question: did we build any of this to match our risk posture?We talk with Chris Bonavita, Vice President of Strategy and Technology Adoption at GTT, about what he’s hearing from enterprise security leaders right now and why the mood has shifted from excitement to panic. We dig into the real-world convergence of CIO and CISO responsibilities, and how a unified data view across network operations and security operations can replace the “swivel chair” handoff between teams. When netflow, logs, identity, device posture, and edge behavior get correlated in one place, you can finally decide faster whether you’re looking at a performance issue, a resiliency gap, an optimization opportunity, or a malicious actor.Chris also shares a sneak peek at GTT’s direction with AI factories, GPU-enabled capabilities, and AI-driven correlation that can shrink vulnerability and CVE matching from weeks to near real time. The bottom line is simple and practical: security wins on time to recognition, time to categorization, and time to action. We close with grounded advice for leaders who feel overwhelmed by the pace of change: stay curious, keep learning, and keep the human conversation alive alongside the machines.If you found this useful, subscribe, share the episode with a colleague, and leave a review with the one security metric you’re trying to improve most.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] browser is no longer a passive window to the internet. It’s becoming a worker with autonomy and that single shift changes everything about cybersecurity, identity, and data protection.We sit down with Anupam Upadhyaya SVP, Products, SASE and Network Security, Palo Alto Networks to unpack what “agentic browsing” really means: the jump from an AI copilot that helps you to an agent that works for you by clicking, filling forms, and moving across tabs with your permissions. When the browser becomes the office for SaaS, cloud apps, and AI tools, it also becomes the most important place to enforce modern security controls. We dig into how the definition of “user” expands to include agents and even sub-agents with delegated access, and why that creates both huge productivity gains and real operational risk.We also map the threat landscape: AI models that can surface hidden vulnerabilities, chain simple issues into complex exploits, and compress attacker speed to near instant. Then we bring it down to earth for SMB cybersecurity, where most teams don’t have a CISO or a SOC. You’ll hear practical steps to reduce blast radius, when to keep a human in the loop, and why incognito or logged-out research can reduce accidental AI memory of sensitive info. We close with what to look for in a secure enterprise browser, including last mile data controls and protections that understand user-to-agent interactions, plus how Prisma Browser for Business aims to deliver enterprise-grade browser security with simpler deployment for small and mid-sized businesses.Subscribe for more conversations on AI security and modern work, share this with a founder or IT lead, and leave a review if it helps. What’s the first rule you’d set for employees using AI agents in the browser?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is closer than most people think, and the biggest surprise is that the headline might not be “faster.” We sit down with Mats Karlsson from Ericsson to talk about the move toward physical AI, where networks help systems sense and act in the real world. When robots, vehicles, and digital twins depend on connectivity for safety and performance, “more bandwidth” stops being the product and guaranteed outcomes become the real promise.We unpack what outcome based services actually mean in practice: collision avoidance, factory uptime, immersive experience quality, and other measurable KPIs that enterprises can justify paying for. That naturally leads to the toughest question for telecom operators and service providers: monetization. Matt explains why the business model has to evolve along with the network, translating intent into offerings, pricing, and even revenue sharing in real time, while still being able to prove the network can fulfill what it sells.From there, we get practical about AI in telecom, OSS/BSS transformation, and where ROI shows up today. The message is blunt: don’t start with AI, start with trusted data. We talk about common OSS and BSS pain points like siloed datasets, uneven data quality, and limited end to end visibility, plus real examples of value like revenue assurance, billing anomaly detection, predictive operations, and faster root cause analysis. We also dig into agentic AI and why industry collaboration through TM Forum and open standards is key to making autonomous networks work at scale and unlock new revenue streams, not just cost savings.If you care about 6G, autonomous networking, AI in telecom, and the future of outcome based connectivity, hit subscribe, share this with a colleague, and leave a review. What outcome would you pay for first: uptime, safety, or experience quality?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] isn’t “soft” when you can tie it to the numbers that run the business. Evan sits down with Meghan Keough, a modern, business-first CMO with decades in enterprise tech, to unpack how marketing leaders can operate at the intersection of brand, revenue, and transformation without losing the plot. We get specific about the metrics that matter most in B2B go-to-market strategy: pipeline by source, cost per pipeline, win rates, sales velocity, and the margin impact behind the dashboard.From there, we zoom out to the reality every team is facing: constant change with imperfect information. Meghan shares a practical approach to transformation that favors fast learning over perfect plans, plus the discipline of revisiting decisions, running experiments, and being ruthless about what’s actually working. If you’re trying to modernize demand generation or reposition a company upmarket, you’ll hear why quick wins build credibility and why foundations still matter even in a world moving at AI speed.AI comes up as more than a shiny tool problem. We talk marketing operating models and end-to-end workflows, where AI can streamline steps and even enable more autonomous execution. That leads to a candid look at martech stack complexity and why many organizations are at a consolidation tipping point, along with a clear way to balance experimentation versus scaling: dedicate a small slice of quarterly capacity to pilots, then operationalize the winners across the team.If you want fewer silos, better alignment with sales and product, and a marketing strategy that holds up under revenue scrutiny, this conversation delivers. Subscribe for more, share this with a growth-minded leader, and leave a review with the one marketing metric you think deserves more attention.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] season is back and so is the annual question: why does keeping water clean still feel so manual? We sit down with Patrick from Beatbot to talk about what changes when a robotic pool cleaner stops being a “dumb” tethered machine and becomes a cordless AI pool robot that can map your pool, plan an efficient route, and adapt when it hits real-world obstacles like ladders and tight corners.We dig into what modern smart pool cleaning actually looks like: cleaning the floor, climbing walls up to the waterline, scrubbing that ring that never goes away, and skimming the surface for floating debris. Patrick also explains how app control and scheduling fit into everyday pool maintenance, plus why sensors matter more than buzzwords when you just want consistent results and fewer headaches. If you’ve been comparing options for a robotic pool vacuum, this conversation helps you separate must-have features from marketing.Then we look forward. Beatbot’s newest direction includes a dock that can flush debris out of the robot’s filter basket into a larger base, aiming to eliminate one of the most annoying parts of pool ownership. We also talk about the longer-term future of smart home integration, weather-aware cleaning, solar-friendly charging timing, and how pool service pros can use robots to work more efficiently while they focus on water testing and chemicals. Subscribe, share this with a pool owner, and leave a review with the feature you most want in the next generation of pool robots.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] in telecom sounds like a pure technology race until you look at where operators actually get stuck. It turns out the models aren't the bottleneck. The humans around them are.We're joined by @Ibrahim Eldeftar, who leads Cognitive Software and Services at Ericsson, to unpack the real path from partial automation to Level 4 autonomous networks, and why the hardest part is often the human system around the tools.Ibrahim walks us through the two hurdle categories every CSP runs into. The first is the technology foundation: multi-vendor support, scalable AI platforms, data management, deployment at scale. The second is the organizational side: change management, upskilling, new ways of working, and breaking down silos that have been cemented in place for decades. The industry keeps underestimating that second category, even when the AI roadmap looks finished on paper. Ibrahim explains why, and what it actually takes to move an operator forward.From there we get concrete. rApps and a service management and orchestration platform can replace the fragmented automation stack most operators are living with today, giving teams a common SDK, consistent interfaces, and an ecosystem model where operators build apps themselves or source them from partners. Ibrahim shares real proof points from live networks, including modernizing worst cell hunting with AI anomaly detection and root cause analysis, and taming massive MIMO complexity where the search space is simply too large for humans to tune in any reasonable timeframe.Then we get into what changes when GenAI and agentic coordination enter the picture on public cloud with AWS. Natural language "talk to the network" interfaces. Orchestrating dozens of RApps at once. A shift toward RApps as a service and SaaS delivery, where operators pay for outcomes rather than software licenses.Subscribe for more deep dives on telco AI and network automation, share this one with a colleague who's living the automation grind, and leave a review if it landed. And think about this while you listen. What would you automate first if you could truly trust the outcome?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is where the fight is moving fastest, and RSAC 2026 proved it. Fresh off the show floor, we sit down with Jim Taylor, President, Chief Product and Strategy Officer at RSA Security, to break down what’s truly changing in identity security as AI reshapes both the threat landscape and the defenses enterprises rely on.We dig into why “sovereign” and “deploy anywhere” identity deployments are suddenly mission critical. Cloud convenience can quietly trade away resiliency and control, and recent disruptions show how quickly authentication outages can become business outages. Jim explains what customers are asking for now: the same identity platform capabilities whether it runs as SaaS, in a private cloud, on-prem, or in highly constrained environments where failure is not an option.Then we get practical about modern identity attacks beyond phishing. If passkeys and phishing resistant MFA harden the front door, attackers pivot to the session with token theft, adversary-in-the-middle scams, and help desk bypass that exploits people and process. We also explore agentic AI and the rise of non-human identities, including how to inventory agents, set entitlements, and apply identity governance so “mini workers” don’t inherit unlimited permissions.We close with a grounded take on passwordless authentication as a step-by-step journey and what we hope the industry looks like by RSAC 2027 and 2028. If this helped you rethink IAM strategy, subscribe, share with your security team, and leave a review. What identity risk are you most worried about right now?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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] AI is no longer a side experiment running in a lab. It’s starting to look like the next operating layer inside the enterprise, and that raises a hard question: do you want a scattered collection of point tools, or a standardized agentic operating system that your teams can actually run?I sit down with Anurag Gurtu Chief Executive Officer @ AIRRIVED to unpack what “agentic OS” means in practical terms. We get specific about the three pillars that make agentic AI useful at scale: adapting a language model to your enterprise data, adding deep reasoning so it can synthesize and rationalize like a real analyst, and then deploying autonomous agents to take controlled action. We also dig into real enterprise cybersecurity needs across security operations, identity management and governance, risk, compliance, vulnerability management, and the growing challenge of shadow AI.We zoom out to the messy reality of adoption: too many pilots, too many vendors, and too many tools designed for developers instead of practitioners. Anurag explains why objective-driven automation beats brittle playbooks, why governance and auditability have to be built in, and how fast proof-of-concepts can turn “AI hype” into measurable ROI. We also touch on open source momentum and why Arrived is building in a more secure, governed direction with Etherclaw.If you’re building an enterprise AI strategy, leading a security program, or trying to prove value beyond demos, this conversation will sharpen how you think about standardization, productivity, and control. Subscribe, share this with a colleague, and leave a review with the biggest hurdle you’re facing in adopting agentic AI.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is everywhere in IT operations right now, but most sysadmins don’t need more buzzwords. We need fewer tickets, cleaner patching, faster deployments, and a clear view of what’s happening across every device. That’s why we sat down with Jaren Nichols, President and COO of PDQ to talk about modern enterprise device management the way admins actually live it: too many endpoints, too many apps, constant updates, and zero tolerance for security gaps.We get into the real pressure point behind “software sprawl” as every department adopts new tools, including AI-driven apps, and IT inherits the responsibility for uptime, support, and security. Jaron breaks down three practical places AI can help right now: faster how-to research, smarter reporting that surfaces outdated versions and risks, and higher-level support for creating policies and workflows. We also dig into the part that matters most when automation gets powerful: transparency. If you can’t see permissions, execution order, and outcomes, you’re building a black box that will fail at the worst time.From there, we zoom out to the bigger trends shaping endpoint management and AIOps: the shift toward a single pane of glass, the consolidation of roles across Windows, Mac, networking, DevOps, and security, and why legacy tools won’t disappear as fast as people claim. We close with what the next generation of sysadmins looks like when things go right: more visibility, policy-driven objectives, and faster execution without sacrificing control.Subscribe for more conversations like this, share the episode with the admin who owns patch Tuesday, and leave a review if it helped. What’s the biggest “this shouldn’t be that hard” moment in your IT environment right now?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] martech stack used to feel complicated. Then generative AI showed up and turned “complicated” into “constantly changing.” We sit down with Scott Brinker, the analyst behind ChiefMartec, to unpack what’s really happening as marketing teams move from a familiar martech stack to a broader AI ecosystem filled with new tools, copilots, and early stage agents layered on top of the systems we already depend on.We get concrete about what still anchors modern marketing technology: a system of record for customer data (CRM in many B2B orgs and often a CDP in B2C), a platform for orchestration through marketing automation and messaging, and a web layer like a CMS or DXP. From there, the stack diversifies fast based on industry, maturity, and team bandwidth, which explains why some organizations can experiment aggressively while smaller teams are still holding marketing ops together with sheer willpower.From a leadership angle, Scott makes a blunt point: you can’t automate what you can’t define. If you want AI automation that protects authentic brand voice, you need clear guardrails, documented standards, and real ownership, not vague “it’s in our culture” assumptions. We also look ahead to a shift that may surprise a lot of marketers, AI used by customers, including AI search behavior and the possibility of inbox agents that reshape email marketing and customer engagement.If you care about AI strategy, marketing operations, martech governance, and what skills the next generation of marketing leaders will need, this conversation will sharpen how you think. Subscribe, share this with a marketing leader who’s drowning in tools, and leave a review with the biggest AI change you’re navigating right now.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] SOC work is collapsing under its own weight. After RSAC, we sit down with Dave Mcginnis, who leads IBM Consulting’s threat management practice, to get brutally practical about what “autonomous security operations” really means when you strip away the marketing. The headline is simple: humans can’t be the bottleneck in threat monitoring anymore, and “AI-assisted” alert triage won’t cut it when machines can generate more detections than teams can ever click through.We talk through the hard parts that decide whether autonomous SOC automation helps or harms: investigation depth, evidence, and accountability. Dave explains why the new problem isn’t finding a needle in a haystack, it’s finding a needle in a stack of needles and why autonomous investigation has to examine every IP, domain, email, and hash, then document the reasoning for forensics. From there, we explore how response can move past traditional SOAR runbooks toward agents that can connect directly to identity systems, cloud controls, and application platforms.The conversation also turns to people and risk. What happens to SOC roles when tier-one work fades, where domain expertise still matters, and why tuning, threat intelligence, and integration become the real jobs. Finally, we look at the uncomfortable truth: adversaries use generative AI too, lowering the barrier to sophisticated attacks. If you’re building a modern cybersecurity program, this is a roadmap for thinking end to end, not tool by tool.Subscribe for more, share this with a security leader on your team, and leave a review with your biggest question about autonomous security operations.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] businesses keep our world running, but most of them operate on painfully thin margins. We talk with Fred Voccola CEO from Simpro Group about what happens when you bring practical AI to the job site and the back office, not as hype, but as an operating platform for the trades that helps contractors finish more work right the first time.We get specific about where the ROI shows up fast: cutting “retreads” (repeat visits) by improving job prep, making sure the right technician arrives with the right materials, and optimizing routes and scheduling. Fred breaks down how AI agents can act like affordable digital staff, doing the kind of job-prep, collections follow-up, documentation, and optimization work that only huge companies can normally afford. The payoff is better job profitability, fewer wasted truck rolls, and a real chance to move from 5% to 7% profit margins toward something closer to 20%+.We also dig into customer experience and why service expectations are rising. With ambient listening and automated documentation, the system can capture the “little” details that matter, then prep the next technician with the right context. Finally, we look at the next generation of skilled trades workers, including real-time training support via wearables, plus a preview of Simpro Lightning and its new AI brain and agents.If you care about AI in construction, field service management, job site productivity, and the future of skilled trades, listen, share this with a contractor friend, and leave a review with your biggest question about AI on the job.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   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] zero to reach a human should not be the default plan. We talk with Fred Fontes CEO from Acclaim about what’s finally making enterprise voice-first AI work in the real world, especially inside regulated industries like banking and financial services where compliance, auditability, and data security are not negotiable. For teams burned by old IVR trees and brittle chatbots, the conversation gets practical fast: what has changed in the underlying models, and what has to change in how we deploy and control them.We dig into the idea of sovereignty and why many CIOs and CTOs feel trapped between the need to innovate and the risk of sending sensitive customer data through multiple third-party clouds. Fred explains how controllable voice AI agents, strong guardrails, and enterprise-grade orchestration can turn “cool demos” into dependable contact center automation. We also get into domain-specific benchmarking, because a universal speech-to-text score does not matter if you cannot accurately transcribe a noisy telephony call about banking topics.Then we go beneath the hood on outcomes: banking collections use cases showing six to eight percentage points higher recovery rates, the ability to A/B test messaging quickly, and why interaction costs can drop dramatically when conversations are faster, more accurate, and handled in parallel. We also talk about the human side, shifting agents toward higher-value customer experience work, and the hardest obstacle left: integration with systems of record and enterprise workflows.If you’re building or buying conversational AI, listen closely, share this with a teammate who owns CX or security, and subscribe, leave a review, and tell us what your biggest blocker is to deploying voice AI at scale.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is making a weird problem even worse: it’s getting harder to tell what’s authentic. That’s why we sat down with Russ from Business Intelligence Group to talk about the We Love Tech Awards ( https://welovetechawards.com/) and what real, transparent judging looks like when trust is on the line. We get specific about how awards can be more than marketing, especially when real people review nominations, score the work, and give feedback that founders and product teams can actually use.We also zoom out to what we’re seeing across the tech landscape right now. From CES to Mobile World Congress, HIMSS, and Enterprise Connect, the energy isn’t just “more AI.” It’s the shift from trials and proofs of concept to real deployments in hospitals, warehouses, and frontline environments. We talk MedTech and digital health, customer experience and contact center technology, cloud apps, and why this moment feels like a true burst of innovation even with macro uncertainty hanging over everything.Then we go where awards don’t go often enough: people. Russ shares a striking stat that fewer than 10% of business award nominations are for individuals, and we make the case that recognition should match the humanity behind the work. We also cover digital certificates, including blockchain-based credentials that can live on LinkedIn, and we lay out the practical timeline: the nomination deadline is March 27, followed by a judging window supported by thousands of judges worldwide.If you’re building a product, leading an innovation team, or know someone who deserves real recognition, listen now, share this with your network, and leave a review. Who are you nominating this year?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] hype is everywhere at Mobile World Congress, so we went looking for something rarer: real operational proof. From the Ericsson booth in Barcelona, we sit down with Ibrahim Eldeftar, who leads Cognitive Network Solutions product and portfolio for telco AI software, and Claudia Muñiz Garcia, Global Head of Sales for the same unit, to talk about what it actually takes to run autonomous networks.We break down the autonomy journey in plain language: moving from manual ways of working to systems that can sense, analyze, decide, and execute, with intent-based networking as the layer that helps unlock Level 4 autonomy. Ibrahim shares where carriers really are today (around Level 2 on average) and why many are publicly targeting Level 4 by 2028 to 2030. The driver is network complexity plus rising expectations for “networks for AI” that can support new AI workloads and millions of connected devices without brittle operations.Claudia explains why Ericsson is being recognized for 5G RAN automation platforms, from standards compliance and security to commercial deployments and scale, and how an open rApp ecosystem accelerates innovation. Then we get into the part operators care about most: outcomes. You’ll hear concrete results from trials and deployments, including major OPEX efficiency gains, fewer issues through anomaly detection and root-cause approaches, spectral efficiency improvements, and a standout uplink story where AI optimization drives meaningful uplink quality and throughput gains.If you care about network automation, 5G RAN, telco AI, rApps, and the practical road to Level 4 autonomous networks, hit play. Subscribe, share this with a network leader on your team, and leave a review with the one autonomy question you want answered next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is about to feel less like a database and more like a conversation. We sit down with Brevo to unpack how a fast-growing customer engagement platform thinks about the next era of CRM, where large language models change the “entry point” for customer data and where the old battle over slick UI starts to disappear.We walk through Brevo’s evolution from an agency to an email automation tool (many will remember the Sendinblue days) and then into a broader customer engagement and CRM platform built for B2C brands. The big idea is the engagement layer: the system that turns customer data into action across channels, agents, and third-party tools. As LLMs become cheaper and more interchangeable, the winners won’t be the tools with the flashiest interface, they’ll be the platforms that orchestrate workflows, permissions, integrations, and real outcomes.Then we get practical about ROI. Customer engagement software should be measurable because it exists to move KPIs tied to revenue: subscriber growth, net new customers, repeat purchase, and bigger baskets. We also break down how to evaluate AI inside CRM without hype by looking at efficiency, automation, and output per marketer so you can grow without constantly adding headcount. To round it out, we talk martech consolidation, what M&A looks like as valuations reset, and a smart loyalty and advocacy approach that turns happy customers into demand generators.Subscribe for more conversations on AI in CRM, customer engagement, marketing automation, and martech strategy and if this sparked a new idea, share it and leave a review. What part of your customer lifecycle would you automate first?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] fastest way to understand agentic AI is to stop thinking about chatbots and start thinking about outcomes. From Enterprise Connect, we sit down with RingCentral’s John Finch to talk about agentic voice AI that can handle real customer work end to end: answering the call, connecting to back-end systems, completing a transaction, confirming the result, and closing the loop without bouncing the customer between departments.We also get specific about what makes this hard and why it matters. Voice is still the most demanding channel in the contact center, and RingCentral’s view is that a strong communications layer unlocks everything else: omnichannel customer engagement, smoother handoffs, and higher containment where it actually helps the customer. From there, the CX revolution becomes orchestration. We talk about scheduling AI agents alongside human agents, tracking performance across both, and using signals like CSAT and NPS to continuously improve. The goal is not “AI replaces people,” but “AI removes the repetitive parts so humans can do higher-value work.”Healthcare raises the bar even further, so we dig into how agentic AI can validate patients, schedule appointments like imaging, and operate with strict guardrails that prevent unsafe medical advice. With templates and deep integrations, teams can deploy faster in complex environments while keeping compliance and safety in view. If you’re evaluating agentic AI, voice AI platforms, or contact center automation, you’ll walk away with a clearer picture of what’s real today and what’s coming next.Subscribe for more conversations like this, share the episode with a CX leader on your team, and leave a review with the biggest question you have about agentic AI.Support the showMore at https://linktr.ee/EvanKirstel