Atlassian argues that treating file storage as a substitute for knowledge management wastes time, hides context, and becomes more costly as AI tools rely on findable, trustworthy information.
Document management (versioning, access control) is not the same as knowledge management (capturing messy, contextual work).
Poor knowledge practices create measurable drag: large companies lose billions of hours and employees spend a quarter of their week searching for information.
Generative AI makes the gap between what teams know and what’s findable more expensive, because AI systems depend on accessible, accurate context.
How document management falls short
Document management systems solve important problems: version history, access control, and a single place for audited materials. Those capabilities are necessary for legal, compliance, and formal reviews. But when organizations treat those systems as a stand-in for knowledge management, the informal but critical traces of work are lost. The result: even when teams find an "official" document, it often lacks the backstory — what changed, why it changed, and what was tried and failed.
Why generative AI raises the stakes
Atlassian points out that generative AI amplifies the cost of missing context. AI tools produce better outcomes when they can access accurate, up-to-date, and discoverable knowledge. If essential context is buried in chats, people's heads, or outdated documents, AI systems will either fail to help or amplify incorrect conclusions. That makes findability and freshness of knowledge more than a convenience — it becomes a prerequisite for reliable AI-driven assistance.
Practical implications and next steps (as presented)
Atlassian recommends drawing a clear line between document and knowledge management and building systems that make the messy, in-between work visible and usable. The company promotes solutions that create a single, searchable destination for living knowledge — for example, Confluence's Company Hub is mentioned as a way to centralize knowledge so teams stop relying on memory and ad hoc communication.
If your organization treats storage as the end goal, expect repeated friction: duplicated effort, slower projects, and fragile handoffs. Shifting priorities means designing processes and tools that capture decisions, informal fixes, and the context that explains why documents exist. That work is partly cultural (encouraging capture and curation) and partly technical (making context searchable and updatable).
日頃の業務のなかで、毎回同じことで迷ったり、過去のやり方を参照したりして時間を取られています。遠回りをなくすためにAIでできることはありますか？
「今のやり方でうまくいっているのに、なぜ変える必要があるのか」──DXを進める企業では、新しいツールや仕組みを現場に導入する際、こうした反応が壁になることがある。AI活用推進も、一部のメンバーに頼っていては、かえって属人化が進んでしまうリスクになることも。
An internal knowledge base puts the answers, processes, and expertise your team needs right where they work. Instead of pinging a coworker who is out of office or digging through old email threads, employees find the solution themselves in seconds.
本連載は生成AIをこれから活用しようとしている方たちのために、生成AIの基本やコピペしてそのまま使えるプロンプトなどを紹介。兎にも角にも生成AIに触り始めることで、AIに対する理解を深め、AIスキルを身に着けて欲しい。第73回は、AIを使って学習を支援してくれるGeminiの「ガイド付き学習」について解説する。
[…] 当社の2026年 AI SDLC調査によると、エンジニアリングリーダーの94%がAIを使っていると回答したのに対し、それをソフトウェアライフサイクル全体にわたって実際に拡張するための仕組みを持っているのは、わずか6%にとどまるという結果がでました。ほぼ全員がエージェントを試している一方で、破綻を招くことなくエージェントを大規模に稼働させられる人は、ほとんどいないのです。 […]
[…] より良いコンテキストが、より良い成果を生みます。優れたコンテキストがもたらす効果は、確かなものです。DXによる最近の分析では、AIツールがAtlassian Teamwork Graphのコンテキストを最も多く活用したチームは、開発者1人あたりの出荷量が約64%多いという結果が出ています。 […]
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