Hi there

A mental model I keep coming back to is Gray Thinking. It’s the ability to resist the urge to judge something as purely black or white too quickly. Instead, you stay in the gray longer. You hold multiple possibilities open, gather more information, and look for the nuance that binary thinking usually misses. Most things in life are not so cleanly black or white. Yet our minds and many of our workplaces prefer the simplicity of either/or. When I catch myself forcing a problem into a strict binary frame, it’s usually a warning sign that I’ve taken a cognitive shortcut. It simply feels easier and less overwhelming to say “it’s either A or B.” That’s an illusion of simplicity. The binary view may not be wrong, but in my experience it almost always means something important is still missing. ...

As the strategy definition process for next year begins, and as a current student in the Stanford LEAD program, I find myself reflecting on lessons from Professors William Barnett and Jesper Sørensen in their Strategic Leadership course. One simple definition has stayed with me: strategy is the logic that explains why we might win. This definition took me a while to internalize. For much of my career, the strategy we received looked more like a to-do list: cut costs by 10%, invest more in marketing, grow market share by 10%, add 10 new team members, and close 100 projects by year-end. We were told the what, but rarely the why. When people on the execution side receive only a list of actions, they are forced to connect the dots themselves—and that makes it far more likely they will take steps we never intended. ...

There’s a growing pressure to turn every process into an AI agent. Probably because the technology is new and hot—but that is not the right approach for everything. Using AI should be strategic. It should not be treated as a silver bullet that can be thrown at every organizational problem. We already have different solutions for different problems: Humans — for high-stakes decisions Rule-based systems — when the logic is known and explicit Machine learning — for predictions GenAI — for unstructured inputs that need interpretation Each comes with its own trade-offs in accuracy, cost, complexity, and risk. ...

I’ve witnessed again and again companies trying to implement a RAG solution only to fail in the process. Most of these projects don’t fail because the technology is immature. They fail because the documents fed into them are contradictory, outdated, or simply shouldn’t be there. The oversimplified flow of a RAG (Retrieval-Augmented Generation) solution looks like this: a user asks a question, the system retrieves relevant passages from a vector database, and those passages plus the question are sent to an LLM to formulate an answer. Why such initiatives most often fail is quite obvious to the external eye but not so obvious to the people leading them from within. ...

In my day-to-day work I see firsthand what companies are planning as part of their AI transformation. Some projects are genuinely exciting and could reasonably be called moonshots. Others, in my view, rest on a basic misconception about what an AI agent actually is. What I keep finding is that many companies are not really looking for an AI agent. They are looking for a business rules engine. When you ask them to describe the specific use case, they describe a deterministic process: a fixed set of steps and rules that must be followed the same way every time. Think of an invoice under a certain amount that needs three matching approvals and an approved vendor before it can be paid. That is classic rules-engine territory—predictable, auditable, and repeatable. ...