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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

Interested in being a guest? Email us at [email protected] phone rings. The screen shows an unknown number or “spam likely.” Do you pick up, or do you protect yourself and ignore it? That split-second decision has become the default for millions of people, and it’s crushing legitimate communication from banks, healthcare providers, insurers, and service teams that genuinely need to reach customers.From the Enterprise Connect show floor, we talk with Nick Moss from TransUnion’s communications product team about how the voice channel lost trust and how to earn it back. We get into verified caller identity, branded caller ID, and the real-world impact of call authentication built on STIR/SHAKEN principles. The conversation breaks down why spoofing is so easy, what it takes to vet enterprises, and how carriers can help deliver clear identity at scale so a legitimate call looks legitimate when it matters most.We also explore practical outcomes across industries: fewer missed pharmacy and provider calls, faster resolution for financial services alerts, and stronger defenses against fraudsters who target contact centers as well as consumers. If you care about robocalls, phone spoofing, call labeling, and the future of trusted telephony, this one connects the dots with concrete examples and a clear direction forward.If you find this helpful, subscribe, share it with someone fighting “spam likely” fatigue, and leave a review with your biggest question about trusted calling and caller ID verification.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] AI sounds like magic until you have to ship it inside a real enterprise contact center where every mistake becomes a compliance problem and every bad interaction hits customer trust. From Enterprise Connect, we sit down with Dialpad to unpack how they’re building agentic customer service on top of an end-to-end UCaaS and CCaaS platform, and why having AI deeply integrated into the communications stack matters more than flashy demos.We dig into the hardest first step: figuring out what to automate. Dialpad explains “skill mining,” a way to analyze your past customer conversations and surface a practical hit list of workflows that are both common and realistically automatable. Instead of guessing which user flows will deliver ROI, you start with the data you already have, then keep refining as new conversations reveal new opportunities.Then we get into the part that makes or breaks adoption in regulated industries like healthcare and financial services: safety. Dialpad walks through Guardian, a live conversation analyzer designed to protect agentic voice and chat with layers for security and compliance, scope and intent alignment, and frustration detection, plus a built-in path to hand off to human agents when the customer needs it. If you’re evaluating AI for contact centers, customer experience automation, or enterprise-ready agentic AI, this is a practical look at what “trust” really requires. Subscribe, share this with your ops team, and leave a review with the workflow you most want AI to handle next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] check-in tablet passes from one patient to the next, and a single leftover photo of an ID or an auto-filled keyboard suggestion becomes a PHI nightmare. From the Samsung booth at HIMSS, we sit down with Ray to unpack how Samsung SDS approaches healthcare IT with a simple goal: make devices and workflows safer, easier, and more useful for the people on the front line.We get specific about what Samsung SDS does beyond distribution, including helping health systems get the right Samsung phones and tablets into hospitals and clinics, and even porting key applications from iOS to Android when teams need the same functionality across platforms. We also talk about Samsung DeX and the bigger operational idea behind it: reducing dependence on costly nursing carts and PCs by enabling a one-to-one mobile device model that follows the nurse, supports real mobility, and keeps care more connected to the patient.Then we dig into privacy, compliance, and practical risk. Ray shares how shared tablets used for patient check-in can accidentally carry over sensitive information, and how an approach like Data Sweeper can reset devices between patients without removing the core app staff depends on. We close with a look at the connected patient room and the potential of Google XR for patient distraction and comfort during difficult moments like burn wound care or long dialysis sessions, plus why Samsung’s broader investment in healthcare shapes this “work backwards from the patient” mindset.If you found this useful, subscribe for more healthcare technology conversations, share the episode with a colleague, and leave a review with the biggest workflow problem you want solved next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] Season Is Here. Your Certificates Expired Last Month.RSAC is coming, and I'll be there — so expect more conversations like this one dropping in the weeks ahead.But here's the thing: the most important security story this season isn't happening on the conference floor. It's already inside your network, running on devices most organizations barely think about.In this episode, I sit down with OmniTrust co-founders David Sequino and Bill Lattin to unpack what "trust" actually means when CPUs are embedded in everything: cars, medical devices, industrial controllers, payment terminals, routers, and the AI agents that are increasingly making decisions in the physical world, not just on a screen.We build the conversation from the ground up — starting at the silicon root of trust, then working through secure boot, operating systems, applications, and the network infrastructure that ties it all together. And we get specific about where organizations actually fail. Expired certificates on a switch that drop a VPN tunnel. Static credentials that never rotate. "Fix it later" thinking that simply does not survive contact with embedded and IoT environments. These aren't edge cases; they're common, and they're preventable.We also draw a line between certificate lifecycle management and identity lifecycle management — and make the case that passwords, secrets, cryptographic keys, and certificates cannot be managed in silos. They're one lifecycle. Treat them as separate problems and you'll have separate failures.The back half of the conversation puts AI under pressure. It can help defenders move faster, analyze more, and surface what matters. But it also hands attackers new tools: prompt injection, automated attack chains, and "vibe coding" that generates plausible-looking cryptographic implementations that don't actually hold up. Nuance matters in cryptography. Vibes do not.We close with what CISOs should be measuring right now: PKI posture, SBOM paired with a cryptographic bill of materials, and a credible roadmap toward post-quantum cryptography. Regulation like the EU Cyber Resiliency Act and DORA is raising the bar, and "we're working on it" isn't a compliance posture.Subscribe for more RSAC-ready conversations, share this with your security team, and leave a review if it was useful. And tell me: what's the weakest link in your chain of trust today?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] gaps don’t feel strategic when they hit, they feel personal: missed deadlines, burned-out managers, and the slow bleed of “we can’t grow because we can’t hire.” From the floor at Enterprise Connect, we sit down with Brett and Sebastian from Gr8 Global to talk about a practical answer: building global capability center (GCC) teams that give companies real access to experienced remote talent.We dig into Gr8 Global’s origin story as a GCC provider created during COVID for eight Top 100 accounting firms, and what it took to stand up around 100 professionals in roughly six months. Then we zoom out into the commercial market: how global staffing can support not only tax and accounting roles (from staff accountant up through manager and director level talent), but also executive assistants, HR support, graphic design, and other remote-friendly positions. The thread running through it all is scalability without the endless recruiting cycle.AI is the turning point in the conversation. We share why the best operators will combine an AI workforce with humans, letting AI agents handle repeatable tasks while people move into higher-value work like review, advisory, and client relationships. We also look at Latin America’s nearshore momentum, where strong talent and time-zone alignment help teams build more with fewer people, especially as companies choose to create tools instead of buying everything off the shelf.If you’re wrestling with hard-to-fill roles, rising labor costs, or a mid-level experience shortage, you’ll leave with a clearer hiring strategy and a more realistic view of how AI and global talent fit together. Subscribe for more conversations like this, share the episode with a leader who owns hiring, and leave a review with the role you’re struggling to fill most.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] create the most valuable raw material in a company: the ideas people say out loud. The problem is that those ideas usually evaporate into scattered notes, half-remembered decisions, and action items no one can trace. From the floor of Enterprise Connect, we sit down with Zoom Chief Product Officer Jeff Smith to unpack how Zoom is trying to close that gap by turning conversation into completed work.Jeff explains why transcription is only the starting point and how “My Notes” is designed to capture interactions across platforms, building a durable repository that feels like perfect recall. We get into the less-hyped but crucial AI work that makes outputs reliable, like noise reduction, voice understanding, and meeting-room identity so tasks go to people instead of “Conference Room One.” Then we zoom out to the big product bet: AI canvases. With Zoom Docs as a text surface and new AI Slides and AI Sheets, AI Companion can translate what you discussed into a project plan, a deck, or a spreadsheet without the blank-page grind.We also explore how customer obsession shapes the AI roadmap, how a unified data layer connects customer experience, revenue, and support insights, and why healthcare could be a breakout use case for AI workflow automation, better clinician preparation, and more focused patient conversations. If you care about AI productivity, meeting intelligence, knowledge management, and practical enterprise AI, this is a clear look at where collaboration software is going next.Subscribe for more conversations like this, share this with a teammate who lives in meetings, and leave a review with your take: what should AI automate first in your daily workflow?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] calls cost more than you think, especially when leads come in after hours or demand spikes without warning. From the Enterprise Connect show floor, we sit down with https://www.girikon.com and their product and innovation leaders to unpack how AI-first customer engagement is changing the way contact centers and sales teams respond in real time.We talk through Girikon’s path from a decade of CRM onboarding and consulting into Girikon AI, including Girik Connect, a unified customer engagement platform that brings telephony, two-way messaging, and chat under one roof. The big promise is practical: stay CRM-agnostic so teams can keep Salesforce, HubSpot, Dynamics, or ServiceNow as the system of record while still connecting every conversation across channels. We explore what it looks like to deploy AI voice agents, AI message agents, and AI chat agents as the first touchpoint, so inbound callers get help fast and sales gets qualified opportunities instead of a messy backlog.Then we get specific about enterprise realities: legacy integrations, multiple platforms, and the need to map conversation history back into the CRM with actionable insights. Think call takeaways, meeting notes, next steps, lead scoring signals, and case trends that reveal the true root cause behind hundreds of support tickets. We also dig into rollout timelines, native integrations, and open APIs, plus the hard parts of making LLMs work on real calls with noise, accents, multilingual speakers, and the risk of hallucinations.If you care about AI customer experience, contact center automation, CRM integration, and faster speed-to-lead, this one is for you. Subscribe, share with your team, and leave a review with the AI use case you want to automate next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] contact center already tells you what’s happening. The harder question is why and what you should do next. From the Enterprise Connect floor, we sit down with Ray Bohoac CEO of Spearfish.ai to unpack a practical approach to contextual intelligence that connects customer conversations to the back-office data that actually explains outcomes.We get specific about how Spearfish moves teams beyond classic contact center KPIs like service level, QA, and CSAT, using a “signals” architecture that learns from calls and correlates them with systems like ERP, sales, and operational data. Ray shares a standout customer story: an apparel brand hears repeated complaints about a shoe wearing out, then traces those calls to the exact product and lot number, tracks the issue into the supply chain, and gets proactive with customers before the problem snowballs. That’s root cause analysis built for speed, not a report you read a month later.We also talk about reality: multi-vendor environments, legacy tech, and a market crowded with agentic AI companies. The big takeaway is the feedback loop. When AI and humans do more work automatically, you still need a way to measure what changed, what worked, and what needs adjusting in real time across support, operations, training, and marketing. Ray also highlights expansion into non-traditional “contact center” settings like 211 organizations, proving the same foundation can drive mission-critical outcomes.Subscribe for more conversations on contact center innovation, customer experience analytics, and applied AI, then share this episode and leave a review. What back-office data would you connect to customer calls first?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] single customer call can reveal why people churn, what drives complaints, and where sales get stuck, but only if you can actually see the patterns. On the Enterprise Connect show floor, we sit down with John from MiaRec to unpack how AI-powered conversation analytics turns everyday contact center recordings into clear actions you can take the same day.We get specific about the biggest wins: automated QA that replaces slow manual scoring, customer experience intelligence that infers CSAT and NPS without relying on biased surveys, and churn-risk detection that can alert a retention team the moment a caller is truly unhappy. John explains how this “visibility first” approach helps teams protect revenue by catching problems early, while also improving coaching and consistency across agents.Then we move into revenue intelligence for sales conversations: spotting buying intent, understanding how reps handle objections, and finding missed opportunities that quietly drain conversions. We also talk deployment realities in complex environments, including cloud integrations with popular CCaaS and UCaaS platforms like RingCentral and Twilio, plus what onboarding and configuration can look like.The most exciting part is what’s new: Ask AI, a chat-style way to query large datasets and uncover trends across negative calls in seconds, and Zapier-powered workflows that can summarize a call, draft a personalized follow-up email, and automate next steps to improve CX and revenue outcomes. If you care about contact center AI, speech analytics, and practical automation, this one is built for you. Subscribe, share this with a teammate, and leave a review with your biggest takeaway.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] native doesn’t mean what it used to. What started as a self-service way to buy compute fast has turned into a full application platform transformation where Kubernetes, desired state, and automation decide how software scales, heals, and lands on compute, storage, and networking.From Mobile World Congress in Barcelona, we sit down with Dilpreet from Broadcom’s VMware Cloud Foundation (VCF) organization to talk about what’s actually changing on the ground. We get into why Kubernetes has moved from a niche tool to a mainstream standard, and why that success brings a new set of problems: operational complexity, painful upgrades, lifecycle management, and the skills required to keep clusters stable over time. If your cloud native journey feels slower than the slide decks promised, this is the part you’ve been running into.We also unpack VCF’s product strategy for simplifying the cloud consumption experience by bringing key capabilities together, including automation and multi-tenant cloud operations. Dilpreet explains how combining ARIA Automation with vCloud Director can create a more unified interface for enterprises and cloud service providers, then tying that experience more deeply to the underlying Kubernetes and infrastructure fabric. The aim is a Kubernetes-like workflow where you declare what you want and the platform does the heavy lifting.Finally, we talk about a big shift we’re hearing from customers: they want that cloud experience on-prem. We connect that to telco modernization as networks move from VNFs to CNFs and the push toward modern telco cloud platforms announced at MWC. If you care about cloud native, Kubernetes operations, VMware Cloud Foundation, platform engineering, and what AI-era apps will demand next, this one is for you.Subscribe for more conversations like this, share the episode with a teammate, and leave a review so more builders can find it. What’s been the hardest part of your Kubernetes or cloud native journey?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] your contact center only finds problems after customers complain, you are operating blind. We sit down with Klearcom at Enterprise Connect to unpack what “CX end-to-end assurance” really looks like when you have voice, IVR, chat, SMS, and now AI-driven journeys all stitched together across multiple regions and vendors.We get specific about the pain points teams keep running into: manual testing that is slow and subjective, migrations from legacy platforms to CCaaS that introduce silent failures, and the growing need to validate experiences before changes hit production. Klearcom explains a non-intrusive approach to contact center testing and service assurance so you can spot routing issues, latency, and broken paths early, then measure performance like a repeatable confidence metric instead of a gut feeling.AI is the turning point. Everyone talks about what chatbots and conversational AI can improve, but far fewer talk about how to test them. We dig into chatbot testing that checks accuracy, brand guardrails, structured and unstructured conversations, and the moment that matters most: when a customer asks for an agent, how quickly do they actually get there, and does it meet your SLA? You will also hear how SaaS on AWS supports global teams while handling data residency needs, and why industries with high downtime costs test on a tight cadence.If you care about customer experience, contact center reliability, and testing AI before it tests your reputation, this one is for you. Subscribe, share with a teammate who owns CX or CCaaS migration, and leave a review, then tell us: what would you automate and test first in your customer journey?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is everywhere right now, but the hard part is turning dozens of pilots into durable systems that actually run a business. We sit down with Tom, SVP of Product Management at GTT, to unpack what changes when enterprises move from “testing AI” to deploying agentic AI at scale across thousands of sites, users, and applications.We start with how GTT thinks about networking and security as a service, and why the promise is not just bandwidth or a product SKU but a simpler experience that helps customers connect, secure, and simplify. Tom explains the Envision platform and how it spans the edge, the core IP backbone, and public cloud so teams can deliver consistent connectivity, SD-WAN, and security outcomes while preparing for new AI workloads that increasingly want compute closer to the premises.Then we get practical about what agentic AI requires: data readiness, trustworthy context, and APIs that let agents act safely without constant human validation. We talk frameworks versus one-time deployments, why vendor lock-in is riskier in a fast-changing AI cycle, and how an “AI factory” mindset brings manufacturing discipline to data pipelines, orchestration, validation, deployment, and continuous improvement.We also share real internal examples, including a cash application agent that helps match remittances to invoices across messy real-world variations, plus how GPU infrastructure supports operational intelligence and proactive network issue detection. If you care about enterprise AI, SASE and SSE, edge computing, and building a scalable agentic architecture, this conversation is built for you. Subscribe, share with a teammate, and leave a review, what part of your AI foundation needs the most work right now?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] AI teams are learning the hard way that dumping more text into a prompt does not guarantee better answers. We sit down with Philip Rathle Chief Technology Officer from Neo4j to talk about the missing ingredient: relationships. When your data is inherently connected, a graph database can turn scattered facts into usable context, so LLMs and agentic AI systems can respond with more precision and less noise.We walk through why graph technology is showing up as a “quiet power layer” behind enterprise AI, from knowledge graphs and digital twins to metadata, lineage, and even relationships between vector chunks for graph RAG. Philip explains the practical difference between raw data and knowledge, why multi-hop reasoning matters in domains like financial services and supply chain, and how an AI system can delegate deterministic parts of a problem to a graph while the model focuses on language and judgment.We also get specific about engineering tradeoffs: why relational databases struggle with constant schema changes, what index-free adjacency means for performance, and how graph queries can run 100x to 2000x faster with less hardware for deeply connected questions. Then we look ahead at where the category is going, including why “graph as a bolt-on feature” often misses the real benefits, plus a roadmap update on Infinigraph for scaling graphs into the 100+ terabyte range. Finally, we cover how AI is making graph adoption easier by inferring graph models from relational sources and helping teams write Cypher queries quickly.If you’re building enterprise AI, graph RAG, or agentic workflows and you care about accuracy, context, and causality, this conversation will sharpen your architecture instincts. Subscribe, share this with a builder on your team, and leave a review. What’s the hardest connected-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] if one install could fix coverage and unlock an entire layer of building intelligence? We sit down with Michiel Lotter of Nextivity at Mobile World Congress to explore how modern enterprise DAS has evolved from “make the bars appear” to a multi-use platform that delivers public cellular, plug‑in private 5G, and sensor-driven insights across the same footprint.We break down the Do More with DAS philosophy and why it changes the ROI math for facilities that once viewed in-building coverage as a reluctant expense. Mikhail explains how Nextivity’s system lets teams map antennas to solve dead zones, then mirror that design for private networking by simply connecting a 5G signal source. From there, smart server antennas act as nodes for panic buttons, leak detectors, asset tracking, and environmental sensors, streaming data to an on‑prem edge compute node for fast, secure action.You’ll hear concrete examples: big box retailers lighting up service in under a week with overnight work; hotel-casino operators meeting staff safety mandates while stopping costly water damage; aged care facilities giving residents dependable connectivity and instant alerts; and hospitals turning their coverage grid into an acoustic awareness network. With embedded microphones and lightweight AI in each antenna, teams can measure noise levels across wards, improving patient experience without deploying a separate sensor mesh.Along the way, we touch on global carrier approvals, a channel-first approach with system integrators, and the operational win of controlling timelines instead of waiting on lengthy builds. If you care about enterprise connectivity, private 5G, and practical building intelligence, this conversation shows how a single DAS can become the backbone for communications, safety, and analytics.Subscribe for more deep dives into real-world connectivity, share this with a colleague wrestling with in-building coverage, and leave a review to tell us which multi-use DAS application you’d deploy first.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] if the fastest way to fix American healthcare is the simplest: put people back at the center. Sitting down with Gil Bashe—chair of Health and Purpose at Finn Partners, editor of Medical Life, and author of Healing the Sick Care System: Why People Matter—we unpack the paradox of a nation that spends more, trains the best, and still delivers poorer outcomes than its peers. Gil brings hard numbers, lived stories, and a clear blueprint for moving from fragmented “sick care” to coordinated care grounded in relationships and results.We explore the rising frustration across every corner of the system: patients feeling sidelined by delays and denials, clinicians burning out under prior auth and paperwork, payers caught between risk and regulation, and innovators struggling to communicate value. Gil argues for a mission-first mindset where business supports care, not the other way around. He shares how continuity of care, team-based primary care, transparent data-sharing, and measurement that values function and time-to-therapy can rebuild trust while bending the cost curve.With a global lens—from integrated systems like Clalit to policy lessons shaped by his work across countries—Gil highlights what the U.S. can adapt without importing entire models. We also get tactical on artificial intelligence: where AI can reduce cognitive load, triage more effectively, and streamline documentation, and where governance, bias checks, and explainability must anchor deployment. Along the way, Gil previews the growing bookshelf of patient-centered reform and reads a striking paragraph from his book that reframes health as mission plus discipline.If you’re hungry for practical hope, this conversation delivers clear steps and a human compass: pay for relationships, simplify the rules, open the data, and design technology that frees clinicians to care. Subscribe, share with a colleague who needs a lift, and leave a review with one change you’d make tomorrow—what would put people first where you work?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] energy on the MWC floor said it all: AI for telecom has moved past “someday” and into “show me how fast.” We sit down with Broadcom’s Anupama Mahabhashyam to unpack what sovereignty really means for carriers, why local control and continuous compliance matter, and how operators can turn regulated infrastructure and rich datasets into a durable AI advantage.We trace the shift from AI hype to execution, highlighting the operational realities telcos face as they build private and sovereign AI clouds. That includes the unglamorous but essential work of model governance—curating approved catalogs, tracking versions and provenance, and enforcing access policies—so teams stop firefighting model sprawl. We also cover the emerging traffic pattern of AI workloads, why early signals may be invisible on traditional links, and how the rise of voice and video inference changes bandwidth planning and east‑west flows across the network.From there, we dig into the architecture: intelligent orchestration that matches models to GPU capacity, keeps workloads close to data, and prevents oversubscription. Anupama explains how AI as a Service abstractions can remove plumbing while preserving control, letting teams focus on high‑value use cases like anomaly detection, automated triage, and customer care copilots. We emphasize data readiness as the make‑or‑break factor—organizing datasets, enforcing metadata standards, and eliminating silos so generative systems don’t amplify fragmentation. Finally, we connect the dots to outcomes: improved reliability, faster MTTR, better customer experiences, and new revenue streams such as GPU‑as‑a‑Service and compliant enterprise copilots.If you’re a carrier leader, network architect, or product owner mapping a path to sovereign AI, this conversation offers a clear blueprint: build on governed data, enforce model discipline, and invest in an intelligent infrastructure layer that scales with demand. Enjoy the episode, then subscribe, share with a colleague, and leave a quick review to help more builders find the show.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] feels fast until memory and storage slow everything down. We sit with Michael Wu to unpack a blunt truth: inference is where value happens, and storage now sits on the critical path. Instead of treating SSDs as cold capacity, Phison’s adaptive middleware turns them into a live cache that expands usable memory and keeps models, embeddings, and long context windows close to compute. The payoff is practical and immediate—lean AIPCs and mini workstations run bigger workloads with steadier latency, and teams can scale inference without waiting for DRAM supply to catch up.We trace the story from CES announcements to real-world deployment. Michael breaks down how OEM integrations and consumer upgrade kits bring adaptive caching to both new and existing machines, why developer and education communities are the first winners, and how this bottom-up momentum seeds better software and on-device AI experiences. For enterprise leaders, we map the route from local experiments to global rollouts: consistent performance across distributed teams, lower cloud egress, and a storage layer tuned for retrieval-augmented generation, fine-tuning, and high-concurrency serving.Zooming out, we explore where we are in the AI cycle—early, hungry, and building—and how edge devices and “physical AI” will broaden demand for fast, cache-aware storage. Michael also shares Phison’s fabless strategy, the new Pascari enterprise lineup, and the push toward Gen 6 performance that aligns with next-gen model serving. If you care about real-world AI velocity, this conversation shows how to turn a bottleneck into an advantage by rethinking the memory hierarchy from the ground up.If this helped you think differently about scaling AI, follow the show, share it with a teammate, and leave a quick review so more builders can find it. What’s the first AI workflow you’d speed up with adaptive caching?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is loud at MWC, but we wanted signal, not noise. We sat down with Angus CEO from BeyondNow to unpack how telcos can turn hype into real revenue by building an “AI factory” for SMBs, digitizing the front office, and orchestrating partner ecosystems that deliver complete, easy-to-buy solutions. The conversation gets practical fast: what it takes to package connectivity with AI-driven services, why data readiness still gates everything, and how to replace price wars with a disciplined upsell motion that deepens relationships and boosts stickiness.We explore the shift from back-office obsession to front-office acceleration—where CPQ discipline, guided selling, and AI agents create a faster, cleaner path from quote to cash. Angus breaks down why B2B is different from B2C, how standard blueprints cut complexity, and where prompts can and can’t launch new services yet. Instead of betting on magic, the focus is on proven building blocks: structured data, simplified migrations, partner co-selling, and outcome-based offers that customers immediately understand.If you work in telco, MSP, or enterprise networking, you’ll hear a playbook for growth: unify internal stovepipes into coherent bundles, add partners to enrich the solution, automate fulfillment end to end, and use AI to amplify—not replace—sound operations. We also touch on Beyond Now’s 45% growth trajectory, portfolio expansion, and plans to bring AI factories to life across adjacent markets. Ready to connect AI buzz to booked revenue? Follow the show, share this episode with a colleague who needs a better upsell strategy, and leave a review with your biggest AI roadblock—we’ll tackle it next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] wonder why your 4K stream freezes even though your plan boasts blazing speeds? We sat down at MWC with Metin from AirTies to unpack the real reason people switch broadband providers—and it’s not the price tag. Drawing on fresh survey data from the US, UK, and Japan, we dig into churn rates, intent to switch, and the number one culprit behind dissatisfaction: inconsistent user experience. From video stutter to choppy video calls, the pain points are surprisingly common, and they point straight at Wi‑Fi as the everyday bottleneck.We break down how AirTies tackles the problem with a lightweight software agent embedded in home and small business routers, feeding real‑time performance data to a cloud platform that troubleshoots and optimizes automatically. Think channel optimization, band steering, client balancing, and policy tweaks that prioritize what people actually feel—smooth video and stable calls—over theoretical peak speeds. When software can’t fix it, the system flags precise next steps for providers, whether that’s an extender, a router swap, or a line investigation, cutting support costs and stopping churn before it happens.The conversation also explores the messy reality inside big ISPs: multi‑vendor fleets, technical debt, and the challenge of deploying at scale. We get into why AI‑driven personalization is the next leap—networks that learn each home’s patterns and adapt on the fly without the user touching a setting. Metin shares a recent US launch of personalized Wi‑Fi with a major provider and the momentum behind smarter networks that measure success by experience, not just Mbps. If you care about reliable streaming, rock‑solid video calls, and a home network that quietly gets out of the way, this one’s for you.Enjoyed the conversation? Follow the show, share it with a friend who battles buffering, and leave a quick review to help others find us.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] happens when connectivity grows from a niche tool into the backbone of modern industry? We sit with Erik Brenneis CEO of Vodafone IoT, to explore how a team that started in the M2M era now supports 230 million connections across 180 countries and 760 networks. From connected cars that update themselves over the air to smart meters reshaping utilities, we trace the systems, standards, and strategy that turned scattered pilots into dependable, planet-scale services.Eric breaks down where the demand is strongest—automotive, energy, industrial equipment, payments—and why connected health is surging with pacemakers, sleep apnea devices, and dialysis machines that need authenticated, encrypted, and reliable links. We go inside the operating model: embedded technical teams near customer R&D centers, direct access to experts, and local solutions for complex markets like Turkey, Brazil, and the UAE to meet data residency and regulatory needs without redesigning products per country.Security takes center stage as we contrast consumer SIM behavior with a closed IoT system that authenticates all traffic and blocks unauthorized access. Then we zoom out to the unexpected: conservation stories from tracking seals, rhinos, and whales, and environmental protection through early forest fire detection. Finally, we look ahead to a major shift—evolving from mobile-only to a hybrid mobile plus satellite network through partnerships with Iridium and Skylo, delivering ubiquitous coverage without new hardware. That leap doesn’t just connect more places; it feeds industrial AI with the steady, trustworthy data it needs to drive real outcomes.If you enjoy conversations that blend real-world deployments with what’s next in connectivity, subscribe, leave a review, and share this episode with a friend who loves tech that actually ships. What would you connect first?Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] if a telecom could think with one brain? We sit down at MWC with Gil Rosen CMO from Amdocs to unpack how the company is shifting from building point solutions to delivering AOS, an agentic operating system that orchestrates many AI agents as a single, contextual layer across the business. Instead of bolt-on automations, AOS connects decisions end to end—governed, explainable, and secure—so outcomes are consistent and measurable.We walk through why domain expertise matters: telecom-specific ontologies, prebuilt process libraries, and open integration with non-Amdocs BSS, financial, and logistics systems. Gil explains how trust becomes non-negotiable when AI touches money and identity, outlining controls for repeatability, policy adherence, auditability, and scale. Then we go practical. With Market One data across tens of millions of subscriptions, operators can see which OTT bundles trend, how device cycles forecast upgrades, and where local behavior shapes winning offers. The result is smarter pricing, higher ARPU, and faster testing without rip and replace.The conversation takes a human turn with “waiting is gone.” Voice-first agents shift service from taps to talk, powered by two new layers: personality engineering to encode brand tone, and a customer digital twin that aggregates thousands of signals to tailor language, depth, and empathy. A techie hears throughput and latency; a casual user gets plain guidance and reassurance. Early results are striking, with personalized agents showing up to 3x NPS gains versus generic interactions. Beyond speed, customers feel understood—and that feeling moves retention, referrals, and revenue.Change is urgent. Three years ago the story was 5G; now it’s outcomes customers can feel. AOS offers a collaborative path forward, coordinating multi-vendor agents and existing stacks while upgrading the experience layer. If you’re ready to reimagine service, unlock new bundles, and replace hold music with helpful conversation, tune in—and then share your take. Subscribe, leave a review, and tell us what part of the customer journey AI should transform next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] hallways at MWC were buzzing, but our conversation cut straight to what matters for operators right now: shipping new services faster without getting boxed in. We sat down with Broadcom to unpack VMware Telco Cloud Platform 9 and how it blends cloud-native speed with the reliability telco networks demand. This release pulls previously separate components into a single, horizontal stack, pairing one automation engine for both virtual machines and containers with an embedded Kubernetes runtime. The payoff is simpler operations, shorter launch cycles, and real freedom to choose best-of-breed network functions.We also dive into AI where talk meets traction. Broadcom is rolling out integrated management for NVIDIA GPUs directly in the platform, making it practical to offer GPU-as-a-service and manage accelerator resources with the same tooling operators already trust. That creates a clean path for enterprise AI workloads and sets the stage for the next leap: virtualization of GPUs. By bringing their virtualization heritage to accelerators, Broadcom is targeting higher utilization, better isolation, and elastic scaling for inference and training across the network and at the edge.Vendor lock-in has long slowed telco transformation, so we explore how a true horizontal platform changes the equation. CSPs can run virtualized and containerized network functions from multiple suppliers on the same infrastructure, swap vendors as needs evolve, and rely on a rigorous ecosystem program for pre-qualification and certification. That means less integration risk, fewer surprises in deployment, and faster routes to revenue. We round out the conversation with Europe’s rising demand for sovereign cloud—country-level compliance, data privacy, and security—and how recent national certifications prove the model is working in practice.If you care about open ecosystems, sovereign-ready architectures, and an AI-enabled telco cloud that actually reduces time to market, this conversation is for you. Follow the show, share it with a colleague who’s building next-gen networks, and leave a quick review to tell us what you want covered next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] if your network could sense a problem, explain it, and fix it before anyone files a ticket? We sit down with Cisco’s to unpack the leap from AIOps and GenAI to true agentic operations—systems that detect, diagnose, and remediate across complex, multi-domain environments where the internet is your backbone and SaaS is your front door.We trace the journey from ThousandEyes’ “Google Maps for the internet” to today’s AI-driven NetOps. You’ll hear how teams move from a find-and-fix mindset to evidence and escalate, using shared telemetry to pinpoint where issues live—whether that’s an ISP interface, a cloud region, or a misconfigured DHCP pool. Joe shares a live example showcased at Cisco Live where a days-long incident class, like DHCP pool exhaustion, is now identified in real time and resolved with guided workflows, shrinking resolution from days to minutes.Then we look ahead. As AI agents proliferate, networks must support machine-to-machine traffic that never sleeps and often surges in bursts. We break down the essentials: identity for agents, least-privilege access, and microsegmentation that aligns with intent. We also explore how an AI canvas brings networking, security, and observability into a multiplayer workspace, replacing giant war rooms with a precise, shared picture and deterministic actions. Trust is the final mile to autonomy, and we detail how deep network models—trained on decades of TAC knowledge—can make automated actions explainable, auditable, and safe for mission-critical environments.By the end, you’ll have a clear playbook: use cross-domain evidence to localize issues, standardize incident response, automate the repeatable, and measure success by tickets avoided rather than just MTTR. If you’re preparing for AI-to-AI traffic, aligning security with agent behavior, and aiming to prevent problems before users ever notice, this conversation is your roadmap. If this resonates, follow, share with your team, and leave a quick review to help more builders find it.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] to move from AI pilots to P&L? We sit down with HCLTech’s Anil Ganjoo to unpack how telecom, media, and technology are converging into an AI-native future where outcomes rule, stacks unify, and partners co-create at speed. The conversation starts with HCLTech’s engineering roots and tracks a bold evolution into an IP-led, platform-driven strategy that spans networks, cloud, edge, data, and silicon—giving operators and media leaders the tools to turn infrastructure into monetizable products.We dig into the realities of 5G monetization, where enterprise use cases like private 5G, network slicing, fixed wireless access, and edge AI are generating measurable ROI, while consumer ARPU remains a longer play. On the media front, Gen AI is transforming vast content libraries into searchable, reusable, and hyper-personalized experiences that raise engagement and cut churn. Anil explains why AI must be embedded across the entire stack—think AI as the brain, with cloud, networks, edge, and data as the nervous system—to unlock dynamic pricing, predictive maintenance, churn prevention, and planet-scale personalization.The shift from telco to techco takes center stage as we explore network APIs, platform ecosystems, and product-aligned operating models. We get practical on commercial innovation too: outcome-based engagements, gain-share structures, and transparent KPIs tied to revenue growth, cost-to-serve reduction, billing accuracy, and NPS. Partnerships power the journey, from co-built AI factories with Nvidia to agentic solutions with Microsoft and Google Cloud, all industrialized into OSS/BSS and modern network architectures to scale beyond proofs of concept.We close with sharp predictions for what will accelerate next: agentic AI in core operations, autonomous and AI-native networks, edge AI plus enterprise 5G crossing the chasm, custom silicon for real-time inference, and services-as-software powered by AI agents. Headed to MWC Barcelona? Come see the demos and strategy sessions at Hall 2. 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] great health tech isn’t about stacking features. It’s about clarity, secure architecture, and the courage to automate the real bottlenecks. We sit down with Technology Rivers founder and CEO to unpack how AI is changing the way we design, build, and scale HIPAA-compliant digital health products without cutting corners on privacy or performance.We talk through the full development lifecycle and where AI actually pulls its weight: rapid proofs of concept, code generation that respects standards, unit testing and coverage, and fast UX prototyping that gets teams aligned. Then we draw a hard line around risk. If you’re uploading sensitive docs to generic endpoints or treating PHI like search fuel, you’re setting yourself up for trouble. We break down practical strategies like retrieval-augmented generation, clean vector design, strict access control, audit logging, and the human-in-the-loop practices that keep systems safe as they scale.From there, we tackle the real reasons projects fail: blurry requirements and no single owner. You’ll hear a playbook for defining outcomes, narrowing scope to a lovable version one, and building for specific users—clinicians, patients, and admins—with interfaces that are simple, informed, and fast. We also explore how to graduate low-code MVPs into production systems without tossing your work: evolve schemas, enforce coding standards, add encryption and RBAC, and ship with CI and observability. On interoperability, we go beyond EHR APIs and highlight model context protocol—the next step in connecting AI agents to your data and workflows in a controlled, auditable way.If you’re planning for VIVE or HIMSS, or you’re mapping your next quarter, this conversation gives you a sharp lens: build automation where it matters, protect data by design, and use AI to amplify well-defined processes. Subscribe, share with your team, and leave a review with the one build challenge you want us to unpack next.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]'s growth model is broken. Here's the blueprint to fix it.5G returns are under pressure. Hardware costs keep climbing. And operators are drowning in complexity they can't monetize.We sat down with VMware by Broadcom leaders to cut through the noise and what emerged is a sharp, actionable roadmap for what comes next.Three big shifts every telco leader needs to understand:**1. The mobile core is your highest-leverage asset right now.**Not the edge. Not the RAN. The core — and the operators who productize it with disciplined lifecycle management will outpace those still treating software as an afterthought on hardware.**2. AI monetization is real — but only if you architect for it.**The team walks through VMware Cloud Foundation as the engine for GPU-as-a-service and AI-as-a-service: model stores, runtimes, vector databases, compliance baked in, data isolation from day one. Not someday. Now.**3. Sovereign cloud isn't a checkbox anymore — it's a revenue line.**Especially in Europe. Jurisdiction and residency are becoming competitive differentiators, not legal formalities. The operators moving fast here will own the enterprise stack.We also go deep on:→ Co-innovation with Nokia, Ericsson, Mavenir, and Oracle — and why full-stack ecosystem orchestration is replacing the old "software on hardware" mindset→ How consolidated dashboards, certification, observability, and license governance cut change risk and accelerate upgrades — and why this matters *more* as Kubernetes complexity stacks up→ Intelligent operations: embedded AI that reads signals across storage, network, compute, and Kubernetes — and recommends next actions while keeping humans in the loop→ Agentic AI traffic: bursty, hard to cache, and arriving fast. Telcos sit at the crossroads of inter-DC connectivity, edge placement, and quality guarantees — and that's a strategic position worth owningThe bold takeaway: stop selling raw connectivity. Start selling trusted AI capacity and outcomes.If you're lowering TCO, launching AI services, or charting a pragmatic path toward autonomous networks — this conversation will help you act with confidence, not just follow the hype.🎧 Listen now. Subscribe for more candid strategy talks.📤 Share with your team if you're rethinking your telco roadmap.💬 Drop a comment: **What's the one business outcome you're chasing in 2025?**Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] do you protect teenagers online without turning every app into an ID checkpoint?That's the question governments, platforms, and parents are all wrestling with right now — and most of the answers so far have been blunt, binary, and broken.We sat down with our guest from TELUS Digital to go beyond the headlines and into the actual design challenge: what age verification gets right, what it gets dangerously wrong, and how to build systems that protect young people without making privacy feel like a casualty.Here's what we unpacked:The smartest approach isn't one-size-fits-all. It's layered and proportionate:→ **Low-risk spaces** (forums, general content) can rely on self-declaration and behavioral signals→ **Medium-risk spaces** use facial age estimation — a quick confidence range, image deleted immediately, no data stored→ **High-risk spaces** (adult content, dating, gambling) justify stronger verification with human-in-the-loop reviewThe architecture matters as much as the intent. Poor design is how safety becomes surveillance.Transparency is the trust engine.Us ers need to know *why* their data is requested, *how* it's processed, and *what* they can do when the system gets it wrong. Appeals aren't a nice-to-have — they're the difference between a system people accept and one they route around.We also got into the real trade-offs nobody talks about enough: accuracy, privacy, inclusion, and the very real risk that blanket bans — like those emerging in Australia, Spain, and across the EU — backfire without safer defaults, stronger parental tools, and genuine digital literacy investment.Our guest walks through how TELUS Digital supports clients across the full stack: content moderation, fraud prevention, bias testing, account security, age estimation models, and verification systems built to correct mistakes at scale.And we close on where this is all heading — zero-knowledge proofs, privacy-preserving credentials, and portable age attestations that raise protections while *reducing* data exposure. The technology is ahead of the policy. The question is whether platforms will lead or wait to be forced.If you're building products that touch teenagers, this conversation is for you.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] IT Asset Inventory Is Lying to YouAsset sprawl isn't coming. It's already inside your organization, quietly growing through every new SaaS subscription, remote device, and shadow tool your team adopted without a ticket.Most IT leaders know this. Few want to say it out loud.In a recent conversation with Peter from NinjaOne, we got into the real reason IT asset management is back on everyone's radar -- and it's not because the category got flashier. It's because the pressure finally got undeniable. Hybrid work exploded device counts. SaaS spending spiraled. Compliance auditors stopped accepting "we think we have about 400 endpoints" as an answer. Spreadsheets, once a perfectly acceptable stopgap, became a liability.That pressure is what pushed NinjaOne beyond endpoint management into a full ITAM strategy. And the conversation that followed is one worth sharing with any IT director who still runs discovery through manual walk-throughs and outdated files.From Guesswork to Ground TruthThe practical core of unified ITAM starts with continuous discovery -- pulling data from Active Directory, Intune, SNMP, network scans -- and pairing it with lifecycle context: who owns this device, what's its status, when does the warranty expire? Software license reconciliation becomes a living process instead of a once-a-year scramble. Asset records stay current. Clean data syncs automatically into CMDBs like ServiceNow and into the ERP systems finance and procurement actually use.Peter told a story most IT leaders will recognize immediately: the pre-audit ritual of hunting down the most recent spreadsheet, calling facilities, walking floors, and still not being confident in the numbers. Automation doesn't just save time. It removes the guesswork entirely.The Business Case Is Hiding in Your Asset DataHere's what changes when hardware and software inventory finally live in one place: you get leverage. Teams can spot noisy OEM vendors, stretch refresh cycles, kill redundant purchases, and surface shadow devices that are quietly sitting outside your security perimeter. Real-time visibility tightens both security posture and compliance footing. Ops teams get cleaner root-cause analysis because they can actually map relationships between network gear, servers, and peripherals.Peter outlined a ROI model worth borrowing for your next exec conversation: measurable cost control, quantified risk reduction, and audit readiness backed by actual numbers rather than estimates and optimism.Where the Category Goes NextThe shift underway isn't just better asset tracking. It's toward a unified IT operations control plane -- broader coverage, deeper integrations, and tools explicitly designed to collapse sprawl rather than add another layer to it. Fewer tabs. Fewer systems of record. One place where asset data, security context, and lifecycle history actually connect.For teams still chasing asset data across tickets, procurement emails, and disconnected portals, this conversation is a practical starting point -- not a vision deck. It's about centralization, automation, and building the kind of business case that gets budget approved.If this hits close to home, follow the show, share it with your team, and drop a quick review. It helps more people find the conversation.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] if your best trainer is the person who’s retiring next month—and an AI that turns their brain dump into accurate, engaging lessons overnight? We unpack how a scrappy chat-with-PDF experiment grew into an enterprise-grade course creation engine that slashes time, cost, and confusion while boosting trust and adoption.Ro shares the early product choices that mattered: attacking hallucinations head-on, grounding content in source material, and designing guided workflows that mirror real instructional design standards. We dive into the moment enterprise demand hit—when a major manufacturer needed to capture critical know-how from a retiring workforce—and how AI made it possible to transform raw voice notes and messy documents into structured training with assessments, examples, and clear outcomes. The conversation widens beyond linear courses to the realities of learning at work: microlearning, deep search across knowledge bases, and just-in-time guidance that supports real performance.Then comes the power move—joining LearnUpon. By pairing Courso’s AI-driven content generation with LearnUpon’s LMS strengths in skills mapping, role-based delivery, and gap analysis, creation finally meets distribution. Think on-the-fly lessons triggered by skill needs, rapid updates that keep content fresh, and spaced repetition that helps learners remember what matters. Ro’s philosophy grounds it all: AI should be invisible, human-guided, and relentlessly practical. It’s not a thinker; it’s a multiplier for experts, a backstage crew that helps knowledge scale beyond silos and survive turnover.If you care about L&D strategy, instructional design, LMS integration, knowledge capture, and personalization that actually helps people learn faster, this story-rich deep dive brings clarity and a real blueprint for action. Subscribe, share with your learning team, and leave a review with one course you’d automate first—we’d love to feature your ideas next.Support the showMore at https://linktr.ee/EvanKirstel