Strong chess play depends on recalling whether specific moves worked before and on recognising studied patterns; performance compounds through memory of efficacy.

By contrast, many organisations underuse historical learning: they do not systematically study prior strategies, decisions, and policies for outcomes, which erodes feedback loops and degrades strategy quality over time. Frequent neglect of decision and policy reviews weakens organisational learning further.

There are at least two learning paradigms worth distinguishing. After Deep Blue, chess engines mostly improved by ingesting large corpora of historical human games; later, AlphaZero showed an alternative, learning purely from self-play given only the rules, reaching superior strength without relying on human databases.

Analogies from chess to business have limits: chess is finite and enumerable, whereas business environments are open systems with shifting complexity, so one-to-one transfer of chess methods is constrained.