Low-impact work creates more complicated products which, in turn, lead to more dependencies and conflicts to manage. Those dependencies and conflicts discourage teams from taking on work that touches on the product’s commercial core. Which, in turn, encourages more low-impact work.
Some low-impact signs to watch out for: Teams that are only accountable for operational goals like velocity or number of features delivered Teams that reverse-engineer their goals from the work they already have planned Teams that broadly resist estimating impact because it’s “too complicated” or “involves too many things outside of our control”
The proliferation of one-size-fits-all “best practices,” of sanitized case studies from Silicon Valley darlings, of “best vs. the rest” narratives, has created an environment where just about everybody working within the real-world constraints of most companies’ business and funding models will never feel like their companies are doing things “the right way.”
So if I’m correct, then the future of build vs buy will be “yes to both.” Companies will continue to buy complex and valuable component services for important parts of their business, but these components will be designed to be accessed and controlled by both humans and software. Some of that software will be AI agents acting on our behalf, and some will be customer (or system integrator) defined workflows generated from gen AI tools.
Great thinking isn't about getting to the answer fastest. It's about exploring the problem space thoroughly enough to find the best answer—or sometimes, to redefine the question itself. AI allows us to accelerate this exploratory process. It lets us rapidly test multiple approaches, challenge our assumptions, and refine our thinking in real time. But only if we engage with it as a collaborative partner rather than a vending machine.
a lot of professionals operate in a single cognitive gear: convergent thinking. They jump immediately to solutions, rush toward decisions, and mistake speed for intelligence. They've been trained by decades of quarterly reviews and daily standups to believe that having an answer—any answer—is better than exploring the problem space. This isn't intelligence. It's algorithmic behavior. And it's exactly why companies are finding it so easy to replace middle management with AI systems. If you only know how to converge, you're just a slower, more expensive algorithm.
Business Strategy: Start by asking AI to explain market analysis fundamentals and what indicators signal real opportunities versus vanity metrics. Learn what solid business cases look like compared to wishful thinking or incomplete analysis.
Complex Analysis: Always ask AI to explain its methodology step-by-step before it analyzes data so you can follow the reasoning. Have it show you the key assumptions it's making and how they might affect conclusions. Request that complex analysis be broken into smaller parts you can verify independently.
