The future is future fitness. The advantage isn’t prediction. It’s how fast your team can notice change and respond. Pick one mindcandy prompt below. Discuss it. Decide your moves.
Do you want you a repeatable operating rhythm to sense signals early, decide priorities faster, and act with real strategic options? Click here.
Today’s prompt: navigating the convergence of AI and robotics
Physical AI is transforming traditional robotics into adaptive, learning machines capable of operating in complex and unpredictable environments. Software ate workflows. Now it’s coming for worksites, warehouses, labs, farms, and factories. Robots are shifting from “repeat the script” machines to learning systems that adapt.
If you treat physical AI like a normal automation project, you’ll get surprised. The winners will build learning loops: instrument the environment, capture edge-cases, retrain fast, and govern like it’s a product — not a one-off install.
CEO/founder question:
If a competitor could add 20–30% capacity without hiring (by deploying learning robots), where would your margin collapse first — delivery, quality, or cost-to-serve?
The business owner question
Other prompts:
My latests book about books about AI
Data, acceleration, and the future of intelligence: Lessons from 25 core books about AI, technology abstraction, and consciousness.
https://www.ronimmink.com/product/a-book-about-books-about-ai/
Beyond the AI hype: Building real‑world intelligence loops
As we look ahead to 2026, the next competitive edge will come from creating continuous feedback loops that link every step of the customer journey: from product data to store operations to mobile engagement.
https://nrf.com/blog/beyond-the-ai-hype-building-realworld-intelligence-loops
Architecting Data Ecosystems For Agentic AI
The structure of business data estates plays a huge role in AI’s success or failure. Legacy architectures—which already have governance, quality and data silo issues—were built for human-paced reporting, not for autonomous decision-making. As unsupervised AI agents start operating at scale, they will widen existing gaps and could cascade errors throughout data systems, causing damage across multiple processes and increasing business risk.
AI goes physical: navigating the convergence of AI and robotics
Physical Artificial Intelligence (AI) is transforming traditional robotics into adaptive, learning machines capable of operating in complex and unpredictable environments, driving significant advancements in safety, precision, and efficiency across multiple industries.
How data lineage became a boardroom metric
Data lineage has moved beyond a technical function, becoming a board-level signal of how well organizations govern, audit and explain their data across complex environments.
https://www.techtarget.com/searchdatamanagement/feature/How-data-lineage-became-a-boardroom-metric
Transforming Maintenance with Artificial Intelligence
With little to no capex, companies can turn maintenance into an engine of cash flow.
Survey: How Executives Are Thinking About AI in 2026
Heading into 2026, leaders are still bullish on AI despite worries about a bubble and struggles to demonstrate value with AI investments. According to a survey of digital leaders at leading global companies, the vast majority of leaders believe that AI is a high priority for their organization, have plans to spend more on it, and report that their company is getting measurable business value from their AI investments
https://hbr.org/2026/01/hb-how-executives-are-thinking-about-ai-heading-into-2026
4 Core Principles for Scaling Your API Engineering Practice
When your API landscape grows from a few to hundreds, lightweight engineering patterns may not be able to handle the mounting complexity. Read https://www.ronimmink.com/apis-as-an-automatic-innovation-ecosystem/
https://thenewstack.io/4-core-principles-for-scaling-your-api-engineering-practice/
