“The real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound.” This is from a recent article by Satya Nadella.
It is good to see the legendary Microsoft CEO articulate principles that have been foundational to Nextoar from day one. An industrial brain, built on AI systems that keep the frontline workforce in the loop, where that same system leverages codified knowledge to make better and faster decisions.
But for the learning loop to work, you need strong adoption and very high quality interaction between people and AI. Without that, the loop never starts turning.
The other day I sat in on an employee training session run by a customer, part of a companywide rollout of a popular enterprise AI assistant. The trainer was teaching prompt engineering, showing the team why they need to pack in far more detail and context to get good answers. He literally demonstrated how a natural question looks, one simple sentence, against how an AI prompt is supposed to look, a block with lot more context.
Enterprises are buying these licenses by the thousands, yet we rarely see the real business impact. It reminded me of a report showing only 30 to 50 percent active AI assistant adoption at enterprises.
Manufacturing is a sector we care about, and our hypothesis is that adoption could be even lower here. The sector runs on a large frontline workforce, the everyday men and women working hard on factory floors, in warehouses, in logistics hubs and across supplier hubs to ship physical things to us.
Imagine a plant manager facing unplanned downtime on a deposition machine. The AI already understands the context she operates in and who matters for this event, so it quietly pulls in the right people, the equipment engineer and the service technician, without being asked. It reads her intent, helps bring the machine back live fast and protect OEE, and works toward it. That same system can prevent the next outage altogether by tracking early warning signals like the system impedance of the machine’s heating circuit. None of it turns the cross-functional team into prompt engineers. It lets them stay world-class problem solvers in their own domain.
The intent was never to write a prompt. It was to save the shift.
Or take a team running an SMT line. A random quality defect shows up at the AOI stage, traced to inconsistent solder paste viscosity. What normally takes days to root-cause takes minutes, because the AI understands the context and intent of everyone involved, the quality engineer, the line operator, the shift supervisor, and the procurement manager, and connects what each of them knows.
No one engineered a query. They just solved the problem, together and fast with AI .
Do we want our quality engineer to be a world-class quality innovator, or a prompt engineer who crafts perfect prompts just so the AI understands that the yield issue he is describing is First Pass Yield in production, not the bond yield a finance person might mean? This example might be extreme, but the point stands.
Context is everything. Expecting users to supply it every single time will not drive adoption. Context matters, but understanding intent is what puts it to work. The best AI systems read the context and predict the intent accurately, making them feel magical and impossible to work without.
We need intent-driven AI that makes interactions feel natural, where you barely notice AI is involved. It should feel like working with an expert who already understands your context, your priorities, your goals, and the cross-functional nature of solving real problems. That is what produces precise, actionable insight that moves the needle for the business. And it is the only way to drive adoption and build the high quality feedback loop between people and AI that compounds over time.
AI has to blend into the everyday life of an organization and its people, making work effortless, where no one sees the AI at all.
It reminds me of a line from Mark Weiser of Xerox PARC – “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.”
For AI to create true organization-wide impact, including the frontline, we need intent-driven, context-aware, everyday AI that fuses with everyday work.