The IoT promise was simple. Connect your machines. Get the data. Make better decisions. A decade later, most enterprises have the first two parts. The third one is still largely a human problem sitting on top of a very expensive data pipeline.

In this episode of Stratola Spectrum, Dinesh sits down with Dr. Jürgen Krämer, CPO and MD, Cumulocity, to talk about what it actually takes to close that loop in the AIoT era:

• How agentic AI is replacing dashboards and rule engines with digital workers that manage themselves.

• Why the gap between monitoring and operating is where most industrial AI value sits unclaimed.

• What a maintenance technician's job looks like before and after AI gets involved, and why the difference is striking.

• How digital twins evolved from 3D visualization into the contextual layer that makes AI decisions reliable.

• What state and context mean in a cyber-physical system and why most enterprises are still missing both.

No hype, just clarity.

🎙️ Guest: Jürgen Krämer, CPO and MD, Cumulocity, https://www.linkedin.com/in/juergenkraemer⁠🎙️ Host: Dinesh Chandrasekhar, Chief Analyst, Stratola, https://www.linkedin.com/in/dineshc/⁠ 📌 Chapters timestamped below – 00:00 — Introduction: Dinesh introduces Dr. Jürgen Krämer, his journey from founding RTM in 2007 through Software AG to leading Cumulocity today.00:15 — Meet Dr. Jürgen Krämer, Cumulocity: PhD from Marburg University, patent holder in complex event processing, veteran of industrial IoT since before it had a name.01:36 — What Cumulocity Actually Does: Real customer examples from ABB, Energon wind turbines and Eaton, and what connecting industrial assets at scale looks like in practice.03:10 — From M2M to IoT to AIoT: How Cumulocity evolved through three platform waves and what each transition demanded from customers.04:54 — Agentic AI Is Shifting SaaS From Tools You Use to Digital Workers You Manage: Why dashboards and rule engines are on their way out faster than most people expect.07:40 — Monitoring vs. Operating: The Line Most IoT Platforms Cannot Cross: Passive observation versus active management, with real wind turbine examples from Jürgen.10:27 — When Do You Automate and When Do You Keep a Human in the Loop: How a human-governed rule gets defined, approved and then automated at scale.14:15 — What State Actually Means in a Cyber-Physical System: Why sensor data alone is not enough and how metadata, asset hierarchies and semantic layers make AI decisions reliable.19:39 — Digital Twins Are Not 3D Models: The SAP partnership linking ERP master data to physical OT reality and why this bridge makes AI on industrial assets possible.25:38 — The Pump Four Moment: What AI-Assisted Field Service Actually Looks Like: Vague ticket in the old world. Diagnosis, part on order, repair guide and shutdown window in the new one.28:14 — You Cannot Trust the AI: Why human oversight in physical systems is not optional and what trustworthy AI actually requires in practice.31:01 — Edge vs. Cloud Intelligence: Latency, data sovereignty, air-gapped deployments and the develop-once-deploy-everywhere paradigm.34:38 — Intermittent Connectivity and Why Apache Pulsar Is Part of the Answer: The data broker approach that keeps industrial IoT reliable when connectivity is not.38:02 — Policy-Based Automation: How You Let AI Act Without Losing Control: Parameter changes, firmware updates, versioned and auditable human-approved policies.43:13 — The Competitive Landscape and Why Bob the Builder Is the Real Competitor: What happens when enterprises build their own IoT stacks and someone says ”now do the global rollout.”47:46 — The Moat for the Next Few Years: Semantic Layer on Top of Hyperscalers: Why buy and build gets stronger with agentic AI and what hyperscalers alone cannot provide.#AgenticAI #IoT #AIoT #EdgeAI #IndustrialAI #EnterpriseAI #DigitalTwin #AI #MLOps #StratolaSpectrum