IoT & AI Leaders

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 Takeaways
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.

Key Topics & Chapters
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.

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