Podcast
IoT & AI Leaders
Hosted by Nick Earle, Executive Chairman, Eseye · EN
IoT & AI Leaders is a podcast from Eseye that educates, predicts, and challenges what IoT can become when AI moves to the centre.
Since 2021, we’ve been sharing real-world IoT and AI stories, strategies, and trends from industry leaders. Hosted by renowned tech industry expert and market disruptor Nick Earle, our podcast boasts over 60 unmissable episodes featuring influential guests from leading brands including Microsoft, AT&T, Volvo, Amazon.
Let IoT & AI Leaders be your go-to show for insights, predictions, and big ideas on how IoT is reshaping the world of AI.
24episodes
Episodes
Newest firstAll episodes
Security in the Age of AI Agents
2w ago00:43:08Tap to summarizeAs AI accelerates cyber-attacks and intelligence moves to the edge, security economics are being rewritten.In this episode of IoT and AI Leaders, Nick Earle is joined by Jasson Casey, CEO and co‑founder of Beyond Identity, to explore what happens when AI, IoT, and autonomous agents collide and why identity has become the weakest link in modern systems.The conversation goes beyond device security into the deeper problem of movable credentials, AI‑powered attackers, and agent-driven systems operating at machine speed. From hacked robotic vacuums to compromised payment terminals, Jasson explains why most cyber incidents still share the same root cause and how immovable, cryptographically bound credentials change the game.Key topics include:Why AI makes speed more dangerous than sophistication in cyber attacksHow 80%+ of breaches trace back to identity and access failuresWhat “immovable credentials” really mean (and why chip & PIN got it right)Real-world IoT security failures—and their systemic consequencesThe rise of autonomous AI agents and “shadow AI” inside organisationsTune in to hear the full conversation.Key Topics & Chapters(00:00) Introduction: AI, IoT and security (02:10) Jasson Casey (04:00) AI and the speed of cyber (07:45) Movable credentials and breach (11:20) Chip & PIN and immovable credentials(15:30) IoT device failures and real-world risks(21:10) Hacked vacuums and firmware trust(24:30) Autonomous agents and shadow AI(29:40) AI governance and data-flow control(33:50) Humans, verification, and future skills(38:10) Closing thoughts on security at machine speed
Transcribe →Artificial Humans Are Already Here
Apr 2200:45:03Tap to summarizeArtificial humans are already here. And most organizations are not prepared for what that means.As AI becomes embedded across enterprise systems, the real shift is not just smarter software. It is the emergence of autonomous digital actors working alongside humans, powered by real-time streams of data from connected devices.HiveMQ CEO and Chairman Barry Libert joins the podcast to explore what happens when IoT data streaming meets AI at scale, including:• Why artificial humans are already working alongside real humans• How data streaming becomes the foundation for AIoT systems• Why IoT and AI are no longer separate technologies• How ontologies and real-time operational intelligence reshape enterprise software• Why the next wave of productivity will come from autonomous machines and devicesTune in to hear why the convergence of AI, IoT, and data streaming will redefine how companies operate.Key Topics & Chapters(01:49) Barry Libert introduction(03:46) Why the podcast pivoted(04:30) AI needs IoT data(05:24) Why HiveMQ matters(06:08) Data streaming between devices(07:20) Humans are already devices(08:24) Blurring human device boundaries(10:10) Data streaming changes business(12:04) Data streaming drives AIoT(13:08) Enterprise brain and dashboards(15:23) Machines act autonomously(16:40) HiveMQ builds operational ontology(17:36) Claude Code inside HiveMQ(18:47) Enterprise software faces disruption(20:27) AI deprecates SaaS models(22:28) SaaS versus AI battleground(23:01) Ariba and SaaS lessons(25:04) What happens to humans(26:47) Why Barry remains optimistic(27:27) Framing beats answering(29:10) Artificial humans are here(30:12) Another species enters work(31:11) The jobs gap problem(32:00) National winners and losers(34:16) From outsourcing to AI sourcing(35:42) Speed creates transition pain(36:00) Customers now want ontology(37:44) Healthcare process intelligence example(40:42) Every company needs streaming(41:42) Ontology leads to automation(42:29) Closing reflections on HiveMQ
Transcribe →Why AI Must Move to the Edge
Mar 1800:45:46Tap to summarizeAI is getting smarter but it’s still thinking in the wrong place.Currently too much intelligence sits in the cloud, leaving devices dependent, fragile, and slower than the real world can tolerate. If IoT is going to feed the next wave of AI, the model has to flip. Intelligence needs to move into the device, with the cloud supporting updates and orchestration, not doing all the thinking.David Linthicum joins the podcast for one of our deepest conversations yet, exploring what it takes to rebuild AI for the edge, including:• Why today’s “agents” are not truly autonomous• The case for a client-server style architecture for AI• How small, purpose-built models can live inside constrained IoT devices• Why 5G will not solve latency, reliability, or physics• Why device manufacturers will set the standard, not the cloud giantsTune in to hear why edge intelligence is the reset AI and IoT both need.Key Topics & Chapters(01:58) David Linthicum background(04:02) AI and IoT convergence(07:00) Why AI isn’t at edge(08:03) Problems with cloud dependency(09:02) Small vs large models(11:30) Client server architecture analogy(14:02) Flaws in IoT architecture(18:05) Inefficiency of cloud AI(20:02) Why edge AI matters(22:03) What drives the shift(24:02) Rise of autonomous devices(26:03) Why 5G isn’t enough(28:32) Importance of system decoupling(32:02) Who will drive innovation(35:02) How standards will emerge(36:25) AI impact on jobs(38:32) Limits of AI replacement(40:02) Short versus long term jobs(42:02) Outlook on future work
Transcribe →Can AI Fix IoT Adoption?
Feb 1800:44:45Tap to summarizeIoT promised to transform the physical world. Ten years on, adoption still lags behind expectation.Despite proven technology and successful pilots, most IoT projects never make it to scale, and the reasons are not what many expect.IoT product expert and author Afzal Mangal joins the podcast to challenge how the industry thinks about IoT adoption, and to explore whether AI could finally unlock its potential, including:• Why the device remains the biggest single point of failure in IoT projects• How firmware, not connectivity, determines long-term success• The awareness and cultural gaps still blocking enterprise IoT adoption• Why AI has reached the mainstream while IoT remains invisible• Whether an AI-first approach could finally make IoT stickTune in to hear why rethinking IoT through an AI lens may be the reset the industry needs.Key Topics & Chapters(04:01) Cisco roots and telco beginnings(06:17) Launching narrowband IoT networks(07:16) Early global IoT developer demand(08:34) B2B onboarding breaks IoT scale(10:14) Pilots succeed, organizations resist(11:05) Education missing from IoT adoption(13:29) IoT innovation demands device failure(14:37) Hardware failure destroys time and capital(15:35) Device failure breaks entire IoT stack(16:19) Firmware audits before global connectivity (17:17) Firmware governs SIM and modem behavior(18:10) Awareness blocks enterprise IoT progress(21:37) Proven IoT solutions remain unknown(24:18) AI awareness versus IoT invisibility(26:01) AI prepares workers, IoT surprises them(27:44) Fifty billion things prediction missed(28:43) AI has consumed everything apart from IoT data(29:56) Sound sensors gain meaning with AI(31:40) Can IoT companies afford AI(32:38) AI-first healthcare transformation model(33:31) Smart hospitals track patients, staff, and assets(34:24) AI exposes hospital process delays(35:37) Do AI builders understand IoT(37:24) Can AI simplify IoT integration?(39:12) Humans still define data connections(40:19) LLMs ignore IoT use cases(41:03) AI quality depends on device data(42:27) Selling IoT through AI consultantsShow Links• Follow Afzal Mangal on LinkedIn• Follow Nick Earle on LinkedIn• Visit our website
Transcribe →Building the Enterprise Brain with AI, IoT, and Private Data
Jan 2100:54:27Tap to summarizeAI is moving fast. And most enterprises are not ready for what comes next.As organizations rush to deploy AI, the real constraint is no longer algorithms or compute. It is whether they have the right data, architecture, and operating model to turn intelligence into outcomes.IDC Research Director Rob Tiffany joins the podcast to explain why private IoT data is becoming the foundation of enterprise AI:Why IoT data gives AI real-world context that scraped content never canThe rise of private AI and IDC’s concept of the enterprise brainWhy most enterprise data remains on-prem and what that means for AI infrastructureHow IoT data feeds AI factories, vector databases, and real-time decision systemsWhy IoT leaders now sit at the center of AI-driven competitive advantageTune in to hear how IoT data unlocks enterprise intelligence and reshapes the future of AI.Key Topics and Chapters (01:25) —IoT and AI Leaders Podcast rebrand(03:48) — Rob Tiffany introduction (04:16) — Navy submarines and special operations experience(06:38) — IDC analyst role covering cloud(08:03) — First IoT exposure via submarine sensors(08:54) — Early IoT vending machines in 1994(09:32) — Microsoft era and smartphone revolution(10:21) — Building Azure Cloud and Azure IoT(10:27) — Industrial digital twins at Hitachi(12:32) — Why AI concentrates in hyperscale clouds(13:48) — ChatGPT’s unexpected industry impact(14:14) — Elon Musk rapidly launches xAI(16:25) — Edge computing promise remains unmet(17:32) — Enterprise brain concept explained(19:04) — Most IoT happens indoors(21:18) — AGVs reveal need for indoor cellular(23:39) — Rise of enterprise hybrid AI data centers(24:27) — Samsung data leak into ChatGPT(25:22) — Growing interest in private enterprise AI(27:14) — Fine-tuning AI with company data(28:27) — Building the enterprise brain(29:23) — Hybrid AI and competitive advantage recap(35:28) — Enterprises downloading pretrained LLMs(37:14) — Jensen Huang’s AI factory vision(38:08) — Small language models for domains(41:39) — ServiceNow and agent-driven automation(44:27) — Will agents replace applications?(47:12) — Graduate unemployment and future of work(53:58) — AI disruption moves exponentially(57:51) — AI gives IoT professionals new relevance(58:16) — IoT data powers AI vector databasesShow LinksFollow Rob Tiffany from IDC on LinkedInFollow Nick Earle on LinkedInFollow Eseye on LinkedIn
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The Hidden Dangers of Shadow AI
Dec 2900:48:57Tap to summarizeEnterprises hold growing volumes of connected-device data, yet many are still stuck in early experimentation. The gap isn’t the technology, it’s the readiness of the workflows, processes, and skills that determine whether AI can turn IoT data into meaningful outcomes.This episode explores:Why shadow AI is creating unseen riskHow internal processes block AI-led progressWhat teams need before scaling automationWhere IoT data adds unique value to AI modelsHow leaders can move from experiments to resultsTune in to hear from Nassia Skoulikariti at Apiro Data about the shift from selling raw data to delivering actionable insights and outcomes.Key Topics and Chapters (01:40) — IoT-AI impact, org mistakes, 3-stage implementation framework (04:50) — Sentient IoT, 80% AI training data from content (05:51) — IoT data is real-time AI gold mine (07:01) — IoT-AI enables execution intelligence and coordinated action (07:27) — Apiro Data evolution to execution intelligence pillars (08:41) — Core pillar: prepare internal ops for AI (10:12) — IoT gives data, AI gives speed, execution layer avoids failed pilots (11:05) — 70% test AI in one department only (12:54) — Shadow AI and ungoverned internal AI experiments (14:27) — Individual AI creates silos, not org strategy (15:11) — Parallels to early ungoverned internet experiments (16:10) — Mass AI pilots need policy and governance guardrails (16:46) — Data leak risks and Big Tech policy shifts (18:02) — Innovation vs guardrails balance (19:15) — Three Ds framework: Discovery phase (19:53) — Design phase, prioritize AI workflow impact (21:41) — Internal AI boosts efficiency, protects margins (22:01) — AI differentiates IoT products (23:20) — Amazon and Volvo AI-driven IoT examples (25:47) — Predictive maintenance now conversational and autonomous (26:57) — AI agent autonomy fears and governance risks (27:29) — Human checkpoints required in AI workflows (28:38) — AI augments humans, frees time for strategy (29:28) — IoT firm shift to intelligence services example (30:23) — AI and youth experience gap (35:10) — Practice turns AI knowledge into execution (37:00) — Commodity to outcome-based pricing via AI (38:03) — Outcome pricing precedent example (38:42) — Risks and pricing challenges with outcomes (40:07) — Why buy AI intelligence vs build? (43:06) — IoT roles will evolve to super agents (44:34) — IoT pros will orchestrate AI minions (45:37) — IoT data pricing model is unsustainable (47:40) — Final sign off: podcast evolution to IoT & AI Leaders in 2026Show LinksRead Eseye's 2026 IoT Predictions Report Follow Nassia Skoulikariti from Apiro Data on LinkedInFollow Nick Earle on LinkedInFollow Eseye on LinkedIn Hosted on Acast. See acast.com/privacy for more information.
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From Sensors to Strategy: How AI Unlocks Industrial Insight
Dec 100:47:34Tap to summarizeThe promise of AI starts with the data beneath your feet.In this episode, we explore how connected load cells, long viewed as routine industrial tools, are becoming strategic assets. Jorge Truffin, CEO at Unified Cloud Sensors, is using real-time IoT data to power predictive maintenance, automate diagnostics, and even influence market decisions.This evolution unlocks new possibilities across industries like agriculture, logistics, and construction, including:Transforming sensor data into pre-emptive maintenance alertsReducing downtime through AI-driven diagnosticsDetecting material loss in silos and supply chainsTracking asset movement to optimize operationsLinking sensor output to commodity market insightsTune in to discover how rethinking industrial IoT inputs can reshape outcomes—and build smarter, more resilient business models.Key Topics and Chapters(00:07) Intro to IoT Leaders and transformation stories(00:22) Jorge Truffin’s journey in the global load cell market(01:32) Shift from reactive to AI-powered proactive services(02:42) Traditional IoT companies embracing AI for problems(03:45) What load cells do and how they measure(04:00) Jorge’s 30+ years in weighing industry(05:13) Industrial weighing from 1 kilogram to 600 tons(06:38) Historical weighing data previously lost and underused(07:21) Use case: historical data enables early detection(08:42) Data benefits providers and owners tracking silo flow(09:27) Truck scales with many load cells complicate diagnosis(10:29) Continuous monitoring detects gradual load cell issues(11:35) Traditional reactive maintenance waits for failures(14:58) Monitoring weight, angle, temperature for diagnostics(16:03) Digitizing analog load cells for monitoring(17:04) Why cellular chosen over client networks(18:04) Global connectivity enables expansion with single SKU(19:22) Single SIM solution supports global regulatory needs(20:32) Use case: grain silo environmental and flow monitoring(21:29) Monitoring deters theft and non-technical losses(23:43) Detecting stuck material via center-of-gravity changes(25:45) Center-of-gravity reveals distribution issues remotely(28:12) AI agent giving traffic-light status for scales(30:23) Growth to managed services with multiple agents(31:10) Correlating sensors to reveal hidden patterns(32:14) Detecting structural silo issues via PM10 correlation(33:42) AI agents replace scarce decades-deep expertise(34:49) Start monitoring now to build AI training data(35:46) IoT data from things now essential for AI(36:44) Volvo example: connecting millions to prevent stoppages(37:59) Financial intelligence linking weights and futures prices(38:58) Clients track asset value using weights plus markets(40:47) Construction client uses market rates for pricing(41:38) Hospital case: multi-source IoT enables new services(42:31) Journey from weighing to financial advisory via AI(44:02) Customer-driven innovation using existing data insights(45:16) AI aids talent gap in traditional industries(46:06) Agents train next-generation employees with knowledgeShow Links· Follow Jorge Truffin at UCS on LinkedIn· Follow Nick Earle on LinkedIn· Follow Eseye on LinkedIn Hosted on Acast. See acast.com/privacy for more information.
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The €1 Breakthrough: Scaling Smart Labels for Impact
Nov 500:54:07Tap to summarizeSmart cities are making headlines—but what’s driving real ROI behind the scenes?In this episode, we explore how consultative IoT strategies, not just new tech, are reshaping urban infrastructure and logistics. The conversation centers on smart labels, AI, and sensor-driven solutions that turn everyday operations into measurable value.You’ll hear how municipalities and enterprises are rethinking connectivity by starting with ROI—not infrastructure. Key takeaways include:Why the one-euro price point is pivotal for active smart labelsHow smart parking systems recover costs in under a monthThe hidden cost drivers IoT can eliminateWhy AI models are only as good as the IoT data they feed onWhat consultative selling unlocks that tech-first approaches missTune in for a grounded take on what makes IoT work at scale.Show Links· Follow Silviu Neghina at VizioSense on LinkedIn· Follow Nick Earle on LinkedIn· Follow Eseye on LinkedIn Hosted on Acast. See acast.com/privacy for more information.
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IoT and Agentic AI Power Tomorrow’s Smart Hospitals
Oct 1600:40:08Tap to summarizeHospitals don’t run on hope—they run on data.Today’s healthcare systems are under pressure to do more with less. From equipment shortages to long wait times, inefficiencies are everywhere. But what if AI agents could fix them in real time?In this episode, we explore how a new class of IoT-driven intelligence is transforming hospital operations from the inside out. You’ll hear how real-time data and agentic AI are reshaping healthcare by:Turning physical spaces into connected, trackable ecosystemsAutomating maintenance, cleaning, and resource allocationReducing wait times through dynamic workflow updatesEnhancing patient safety with predictive monitoringEnabling cross-hospital benchmarking with live KPIsTune in to see what happens when hospitals start operating like production lines—with smarter outcomes.Show Links· Follow Dr. Burak Bardak at Borda Technology on LinkedIn· Follow Nick Earle on LinkedIn· Follow Eseye on LinkedIn Hosted on Acast. See acast.com/privacy for more information.
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From Shark Tank to Smart Home Game-Changer
Sep 300:32:39Tap to summarizeThe kitchen is the home's heart—but also its greatest fire risk.In this IoT Leaders episode, Nick Earle explores how technology can make cooking safer without changing how people cook. Unattended stoves remain the top cause of house fires, but smart retrofits and better standards promise a shift from reactive to proactive safety.Akshita Iyer explains how smarter knobs, automation, and data-driven monitoring can reduce risk, while US industry standards like UL push manufacturers toward built-in solutions. They also discuss the role of AI and device integration in the connected home.In this episode, you'll learn:Why unattended cooking is the top fire riskRetrofitting existing appliances for smart safetyHow UL standards can drive industry changeUsing data and AI to spot risky patternsBuilding seamless connections with the Matter protocolTune in to hear how safety is becoming standard in the smart home.Show Links· Follow Akshita Iyer on LinkedIn· Follow Nick Earle on LinkedIn· Follow Eseye on LinkedIn Hosted on Acast. See acast.com/privacy for more information.
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