What's Up with Tech?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Interested in being a guest? Email us at [email protected] was supposed to simplify everything. Instead, a lot of CIOs are staring at bills that are far higher than anyone forecast, feeling locked into hyperscalers, and wondering where the business value went. I sit down with David Linthicum, former Deloitte chief cloud strategy officer turned tech influencer, to give an unvarnished reality check on cloud computing costs, cloud repatriation, and what “pragmatic architecture” looks like when budgets are real and timelines are slow.We also get blunt about enterprise AI. David explains why so many AI-driven transformations stall out on two constraints: money and talent. We dig into why AI can cost 10 to 20 times more than traditional software, why “AI-first enterprise” messaging can be dangerous, and how leaders can pick high-impact use cases instead of trying to bolt generative AI onto everything. Along the way, we talk about how AI is reshaping SaaS economics as agents start using systems on behalf of humans, and what that means for vendors and buyers.Then we tackle the loudest buzzword of the moment: agentic AI. Where does it shine as a productivity force multiplier, and where is it mostly hype when you try to deploy it at enterprise scale? We round out with underhyped edge computing opportunities and the growing backlash around data centers, power, and the grid. If you care about enterprise architecture, cloud strategy, generative AI, and what’s actually deployable right now, you’ll get a clear set of takeaways you can use this week. Subscribe, share this with a CIO or architect, and leave a review with the most overrated tech trend you want us to challenge next.Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

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

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

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

Interested in being a guest? Email us at [email protected] AI assistant is only as smart as the mess behind your dashboards. When business data lives across CRM, accounting, HR, and job systems, “connect ChatGPT to our data” quickly turns into rate limits, broken joins, confusing IDs, and answers nobody trusts.We sit down with Craig Morrall, co-founder of Tugger, to unpack a practical architecture for enterprise AI that actually holds up in the real world: pulling data from many platforms into a warehouse, then layering on a semantic model that explains what the data means and how records connect across systems. That extra context is what turns a chatbot into something you can rely on for revenue questions, profitability analysis, and cross-platform reporting without spending months on custom pipelines.Craig also shares what customers are doing once the foundation is in place, including building interactive dashboards in minutes and generating repeatable board packs that used to take finance teams hours. We dig into time to value, early ROI stories, and how Tugger approaches security and governance with ring-fenced data storage, ISO 27001 certification, and guidance on using business-grade LLM plans to reduce training risk.If you’re evaluating enterprise AI, data warehousing, semantic layers, or secure analytics with Claude or ChatGPT, this conversation will help you separate real capability from hype. Subscribe for more practical AI stories, share this with a friend building on enterprise data, and leave a review with the biggest data problem you want AI to solve.Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts   SpotifySupport the showMore at https://linktr.ee/EvanKirstel

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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