IoT & AI Leaders

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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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 alerts
Reducing downtime through AI-driven diagnostics
Detecting material loss in silos and supply chains
Tracking asset movement to optimize operations
Linking 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.