6. Beyond Tools: Standards and Responsibility
Toss’s commerce domain found that reliable organizational knowledge cannot be created by writing more documents or adding automation alone. Sustainable knowledge management requires clear standards for what should be documented, who owns it, how it is maintained, and which sources can be trusted. The proposed solution combines AI-assisted documentation with domain-level responsibility and company-wide governance. ## The Limits of Writing Alone - A commerce wiki consolidated terminology, onboarding material, code references, and policy documents. - This reduced confusion over terms such as “seller” and “store” and gave teams a shared starting point. - However, product and policy changes happened faster than one Technical Writer could document them. - Important knowledge also appeared in policy changes, temporary experiments, and chat discussions that were difficult to track manually. ## Why Culture and Participation Were Not Enough - The team promoted documentation through: - A weekly “Commerce Wiki News” newsletter - A policy-question channel and bot - AI documentation workshops - A documentation guild - These efforts increased requests, wiki usage, and adoption of official terminology. - Participation rarely continued beyond an individual’s first document because documentation was not part of normal work priorities. - Writers lacked guidance on: - What information to preserve - How much detail to include - Which audience to target - How to verify whether a document was correct - Documentation became sustainable only when it was treated as a team responsibility embedded in existing workflows. ## AI Automation Reveals the Governance Problem - AI now creates draft documents nightly from two signals: - Product deployment and policy-change announcements - Questions that the commerce Q&A bot cannot answer - AI gathers supporting context and produces drafts, while humans verify the evidence and approve them. - This removes the burden of starting documents from a blank page. - Automation also exposed new problems: - Duplicate or overlapping documents - Unclear authoritative sources - Outdated policies being used in bot answers - Difficulty distinguishing current policies from completed experiments - Automation can collect and draft information, but it cannot decide who owns a policy or whether a document should still be trusted. ## Knowledge Standards and Governance - The focus shifted from “How do we create more documents?” to “How do we create knowledge people can trust?” - Toss’s knowledge-management standards state that teams should: - Preserve recurring questions, important decisions, and information needed by newcomers. - Organize knowledge so both people and AI can find it. - Connect documents to work tools such as Q&A bots and GitHub. - Assign owners and review cycles to keep information accurate and current. - Possible classification systems include: - **Technical layers** for teams with clear data or system flows - **Service domains** for teams responsible for multiple service areas - **Functional units** for systems with distinct feature boundaries - Information becomes organizational knowledge only when it helps people understand situations and make better decisions, with sufficient context and verification. ## The Role of the Knowledge Committee - The Knowledge Committee defines and maintains company-wide documentation standards and resolves conflicts between organizational rules. - Unlike a voluntary guild, it has designated members with decision-making authority. - Governance operates at two levels: - The Technical Writing Chapter manages shared standards for sources, ownership, document status, and lifecycle. - Individual domains decide how those standards apply locally, including ownership, update schedules, and retirement rules. - This balance prevents both inconsistent practices across teams and overly centralized rules that ignore local realities. - For example, commerce teams may need separate handling for permanent deployments and temporary experiments so expired policies do not remain authoritative. The practical recommendation is to treat knowledge management as an operating system for the organization, not a documentation project. AI can reduce the effort of capturing knowledge, but clear ownership, review processes, lifecycle rules, and governance are necessary to keep that knowledge reliable and useful.
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