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Deploying AI at the edge is no longer limited to organisations with vast engineering teams and multimillion-pound budgets. In this episode, Nick Earle speaks with Neil Cresswell, Co-founder and CEO of Portainer, about how containerisation is making it easier to distribute, update and govern software across fleets of connected devices.  Neil explains why containers are well suited to IoT environments, where devices may be numerous, geographically dispersed and intermittently connected. The conversation explores the technical realities of running applications and AI inference at the edge, the operational challenge of maintaining consistency at scale, and the value of solving one measurable problem before attempting a wholesale transformation.  From AI-enabled agriculture and production-line monitoring to the detection of unexploded ordnance in potato processing, the episode shows how edge intelligence can turn previously impractical ideas into deployable use cases.

Artificial intelligence is rapidly changing what video surveillance can achieve. In this episode, Nick Earle welcomes back David Ly, Founder, Chairman and CEO of Iveda, to explore how AI-powered video intelligence has evolved from traditional object detection into real-time situational awareness. David explains how advances such as zero-shot learning, natural language search and vision-language models are dramatically reducing deployment time while enabling cities, retailers and public agencies to solve practical operational problems from e-bike enforcement and retail theft to wildfire detection, environmental monitoring and public safety. The discussion also explores why AI adoption remains more of a business education challenge than a technology challenge, and why future success will ultimately be measured by improvements in everyday human experiences rather than technical sophistication. Key Topics & Chapters00:00 Introduction and episode overview 02:00 David Ly’s refugee journey and entrepreneurial story 06:00 The evolution of Iveda since the previous interview 08:00 Zero-shot AI and natural language computer vision 12:00 Smart city applications and e-bike enforcement 17:00 Retail theft detection and behavioural AI 20:00 Why AI adoption remains challenging 26:00 Selling business outcomes instead of technology 30:00 Fire detection and emergency response 34:00 Environmental monitoring and ocean protection 39:00 The future of AI and improving everyday life 44:00 Closing thoughts and where to learn more

Indoor location technology has traditionally been viewed as little more than asset tracking. But what happens when precise location data becomes contextual intelligence? In this episode, Nick Earle speaks with Samuel Van de Velde, Founder and CTO of Pozyx, about why the future of industrial AI starts with understanding not simply where people and assets are, but what their movements actually mean. From dairy farms and manufacturing plants to AI-generated factory applications, Samuel explains how location data evolves into semantic understanding, enabling automation, process optimisation, predictive operations, and eventually software that builds itself around users in real time. This conversation explores how IoT, AI, real-time location systems (RTLS), and enterprise software are converging to reshape industrial operations. Key TakeawaysAccurate positioning is only valuable when transformed into operational context.Semantic understanding creates significantly greater business value than simple location tracking.AI performs best when IoT data is enriched with contextual meaning rather than raw telemetry.Manufacturing is emerging as one of the strongest convergence points for AI and IoT.Dynamic AI-generated enterprise applications could fundamentally reshape industrial software over the next decade. Key Topics & Chapters00:00 Introduction: Why location is about much more than tracking 02:00 Samuel’s background and founding Pozyx 04:30 Ultra-Wideband versus Bluetooth positioning 07:00 Why precision creates entirely new business opportunities 09:00 Dairy farming, AI and behavioural analytics 15:00 From cows to healthcare and industrial applications 19:00 The evolution of Pozyx into industrial software 21:00 Manufacturing workflows and real-time operational visibility 27:00 IoT meets AI: building semantic context 33:30 The next generation of factory software 40:30 AI-generated enterprise applications 43:30 Closing thoughts

AI adoption is accelerating faster than most organisations can control and the result is growing chaos. In this episode of IoT and AI Leaders, Nick Earle is joined by Santosh Kaveti, CEO of ProArch, and Jim Spignardo, Director of Cloud Strategy and AI Enablement, to explore why ‘AI-first’ thinking without foundational discipline is creating serious operational, security, and governance risks. Rather than chasing hype, ProArch argues for a foundation-first approach: slow down, define processes, establish governance, and build trust before embedding AI into workflows, especially as IoT expands the data surface dramatically. The conversation covers: Why cheap, accessible AI tools are amplifying organisational chaosHow weak processes and governance cause most AI failuresWhy regulated industries are feeling the pressure firstHow IoT could increase enterprise data sources by 50xWhy boards must take accountability for AI risk and reputationWhat ‘operationalising AI’ means beyond chatbotsWhy trust, auditability, and explainability matter more than intelligence Episode Highlights 00:02:15: Introduction to ProArch 00:04:30: Realising the potential of AI 00:06:45: What are the 'unknown unknowns' of AI? 00:12:20: The "Donald Rumsfeld era" of AI 00:18:00: ERP/MRP analogy 00:22:15: Balancing visibility and control with innovation 00:28:45: The "trust layer" or "truth layer" 00:35:30: Real-world power plant example 00:42:15: Ontology mapping 00:48:30: Three board-level risks identified 00:52:00: Final takeaways

As 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 organisations Tune 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

Artificial 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 devices Tune 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

AI 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 giants Tune 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

IoT 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 stick Tune 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 consultants Show Links • Follow Afzal Mangal on LinkedIn • Follow Nick Earle on LinkedIn • Visit our website

AI 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 advantage Tune 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 databases Show Links Follow Rob Tiffany from IDC on LinkedInFollow Nick Earle on LinkedInFollow Eseye on LinkedIn

Enterprises 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 results Tune 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 2026 Show Links Read 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.

The 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 insights Tune 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 knowledge Show 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.

Smart 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 miss Tune 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.

Hospitals 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 KPIs Tune 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.

The 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 protocol Tune 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.

The next wave of IoT connectivity is here. Enter eSIM orchestration. In this episode, Eseye Executive Chairman Nick Earle is joined for a third time by Matt Hatton, Founding Partner and Analyst of Transforma Insights, to explore the market forces driving this shift. The rise of eSIM technology specifications like SGP.32 offers more flexibility, but it also brings greater fragmentation. Enterprises now face a tangled web of regulations, latency constraints, and diverging network standards across the globe. That's where eSIM orchestration comes in. Matt explains why a new orchestration layer is emerging—not just to simplify profile management, but to unify billing, compliance, and connectivity in a single managed service. Without it, global IoT remains a patchwork of complexity and compromise. In this episode, you'll learn: - What's driving the shift from roaming to eSIM orchestration - Why eSIM orchestration fills the gap between flexibility and reality - What it takes to deliver true global IoT at scale Whether you're in telecoms, product development, or enterprise IoT strategy, this episode offers a clear view into what's changing, why it matters, and how orchestration might be the missing piece. Show Links · Read the whitepaper discussed in this episode: eSIM Orchestration: Driving the Next Wave of IoT Connectivity · Follow Matt Hatton on LinkedIn · Follow Nick Earle on LinkedIn · Follow Eseye on LinkedIn Hosted on Acast. See acast.com/privacy for more information.