Tung & Roussiere (2026) argue firms should deliberately codify tacit knowledge into private knowledge graphs as their next competitive moat
Writing in California Management Review Insights (2026-03-16), Teresa Tung and Philippe Roussiere argue that as data becomes abundant and frontier models become commodified, a firm's durable differentiator shifts to its tacit, institutional know-how — and the prescribed response is not to protect that knowledge by leaving it unwritten, but to deliberately encode it into a private enterprise knowledge graph, "codifying it without flattening its richness." Both authors hold Accenture roles squarely inside the practice they are recommending (per the article's own bios: Tung is "the global lead for data capability at Accenture," Roussiere "the global lead for innovation and AI at Accenture Research"); the piece functions in part as a pitch for a service their own employer sells, and should be read with that interest disclosed rather than assumed away.
Read against the vault's existing codification thread, the prescription looks at first like a direct inversion of the claim that codifying knowledge is what makes it copyable (claim-a-chain-of-thought-trace-is-codified-so-it-cannot-form-a-tacit-moat, observation-codification-is-the-shared-hinge-of-automation-and-imitation). It is not an inversion once exposure is tracked as a variable separate from codification — a distinction those two notes already carry rather than one this source introduces. A chain-of-thought trace is both codified and externally observable, since it is delivered to every caller and so distillable from roughly 1,000 examples in minutes (claim-s1-distilled-reasoning-from-1000-traces-in-26-minutes); OpenAI's own stated reason for withholding the raw o1 trace names competitive advantage directly (claim-openai-hid-o1-raw-chain-of-thought-partly-for-competitive-advantage). Tung & Roussiere's knowledge graph is codified but, on their own prescription, kept inside the firm's walls. Codification without exposure preserves a moat; the two cases differ on exposure, not on whether codification happened.
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“In a world where data is abundant and models are open, tacit knowledge is your next competitive moat.”
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