The simplest definition of sensemaking is the ‘deliberate effort to understand events.’ This series examines how to become good at sensemaking rapid shifts in the business environment, so you may separate signal from noise and use that to benefit your business or your career.
The series was written primarily in the context of the AI revolution, which at the time of publication (2026) was a huge source of anxiety for a lot of people. But the techniques described here are more broadly applicable than just the AI shift — they apply to any rapidly developing series of events that may affect the outcomes you care about, whether the source is technological, societal, or (god forbid) geopolitical.
All essays in this series are freely available, as a public service to readers.
The Essays
- How to Make Sense of AI — We live in a hostile information environment. How do you make sense of artificial intelligence without becoming emotionally compromised? This essay proposes a simple method: ignore all opinions and predictions — since most of them are inaccurate anyway — and focus only on detailed field reports of use. That way, you can concentrate on the changes most likely to affect your specific context: your specific business, and your specific career.
- How Experts Sensemake — It turns out the method we outlined in Part 1 isn’t good enough. To improve it, we need to talk about the best theory of sensemaking we currently have: the Data-Frame Theory of Sensemaking, originally developed by Gary Klein and colleagues from a grant by the United States military. What makes this theory so useful is that it explains the differences between how experts sensemake versus how novices sensemake, which gives us a guide for improvement. The theory is load-bearing in more ways than one: as it turns out, the Data-Frame theory lies at the heart of expertise. If you can improve in sensemaking, you can improve at problem solving in your chosen domain.
- How to Improve at Sensemaking AI — Now that we know the best theory of sensemaking available, we may use it to improve the method outlined in Part 1. In this piece we take software engineering as a concrete example, as it is the domain where AI has made the biggest early impact. We examine a controversy between multiple groups in software and use it to answer two questions: how do we prevent frame fixation? And, second, how do we find fragments in order to improve our frame construction? The answers to both questions are presented in the context of the software engineering controversy, but remain highly generalisable.
One conclusion of Part 3 is that, to improve your sensemaking skill in the context of the AI wave, you need to:
- Seek out cases of companies that won (or lost) as the result of new technology.
- Seek out stories from the diffusion of these new technologies. Specifically: what changed, how long did the changes take, and how were various parts of society affected?
Having such fragments in your head will improve your ability to construct useful, well calibrated frames.
As a result of this series, Commoncog will publish cases on both topics for the benefit of its membership. If you’re a Commoncog member, you may suggest topics and cases in the forum here.
Originally published , last updated .