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knowledge-management
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Heuristic Make the firm itself a Claude project A shared Claude project loaded with the firm's policies, values, governance and knowledge sources, queryable as the first-line answer to internal questions, has emerged as a reproducible deployment pattern across engagements. Heuristic Structure documents for AI consumption, not just human reading Human-formatted documents obstruct AI consumption; plain-text formats such as Markdown let AI work with the underlying knowledge efficiently. Heuristic Useful AI is a context problem The difference between useful AI and dangerous AI is almost entirely about the context it has; output quality is bounded above by input quality. Pattern AI treats documentation as authoritative In the pre-AI world, messy or incomplete documentation was tolerable because humans interpreted around it; AI does not, and instead surfaces and propagates errors, stale content, and inconsistent processes — which changes the maintenance burden of every document an AI can see. Case study An 'Ask the Org' knowledge-base rollout in a mid-sized organisation A mid-sized national organisation deploys an "Ask the Org" Claude project as a retrieval layer over its existing knowledge stack rather than migrating platforms; the architecture and pilot decisions, and the cluster of principles they instantiate. Heuristic Define a dedicated AI-facing knowledge manager role The work of curating AI's inputs needs an owner; treating it as a side activity reliably loses to the primary role's deadlines. Pattern Defensibility lives in what AI can't access What survives AI disruption sits in three categories AI cannot access without human participation — privileged client knowledge, trust, and institutional memory. Heuristic A document store is not a knowledge management system Shelving documents in a repository is storage, not knowledge management; the presence of the repository often produces false confidence that the problem is solved. Heuristic Users assume AI has access to information it does not have Users routinely overestimate the information AI has access to, treating it as if it were working from a complete picture; this overestimate compounds with AI fluency to produce misplaced trust. Heuristic Make tacit knowledge explicit, or AI cannot use it AI cannot interpret the unwritten assumptions that shape how an organisation actually works; operational self-description is precondition, not polish. Heuristic Frame content-and-data programmes by change-cost tier When proposing a content or data architecture programme, separate it into a backend-only tier (no ask of staff), a modest-asks tier (light behavioural change), and a full-reset tier (enforced behaviour change) — the tiers expose the trade-off and most engagements settle in the middle. Case study A tools-first AI rollout that plateaued An abstracted composite showing what happens when a mid-tier firm buys AI tools without putting its information in order first. Heuristic Declining AI engineering commits you to content discipline The argument for deferring a custom AI build — pipeline, integration, evaluation harness — because content quality is the real leverage point only holds while someone is actively doing the content work; declining the engineering is a commitment to the discipline, not a free deferral. Heuristic Start with knowledge management, not tools Audit and structure what the organisation knows before selecting AI tools; the limits of AI output are set by the limits of its input context. Pattern Context rot As AI-generated content feeds back into the organisation's context — documents, transcripts, summaries — today's hallucinations become tomorrow's training data, and the quality of the context degrades over time unless the cycle is actively broken. Pattern Knowledge management becomes an M&A and partnership signal As AI pervades professional services, acquirers and partners are likely to treat the target's knowledge management as a due-diligence signal because poor KM implies unreliable AI-assisted work product downstream. Pattern Surveillance-chilled collaboration degrades knowledge work The collaborative behaviours that produce good knowledge work — thinking aloud, proposing imperfect ideas, showing uncertainty, offering dissent — depend on low-observation conditions that AI-enabled monitoring degrades.