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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