IoT & AI Leaders

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.

00:00 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.

• Accurate 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.

00:00 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 chaos
• How weak processes and governance cause most AI failures
• Why regulated industries are feeling the pressure first
• How IoT could increase enterprise data sources by 50x
• Why boards must take accountability for AI risk and reputation
• What ‘operationalising AI’ means beyond chatbots
• Why 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 attacks
• How 80%+ of breaches trace back to identity and access failures
• What “immovable credentials” really mean (and why chip & PIN got it right)
• Real-world IoT security failures—and their systemic consequences
• The rise of autonomous AI agents and “shadow AI” inside organisations

Tune in to hear the full conversation.

• (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 can
• The rise of private AI and IDC’s concept of the enterprise brain
• Why most enterprise data remains on-prem and what that means for AI infrastructure
• How IoT data feeds AI factories, vector databases, and real-time decision systems
• Why 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 LinkedIn
• Follow Nick Earle on LinkedIn
• Follow 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 risk
• How internal processes block AI-led progress
• What teams need before scaling automation
• Where IoT data adds unique value to AI models
• How 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 LinkedIn
• Follow Nick Earle on LinkedIn
• Follow Eseye on LinkedIn

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