The knowledge management tooling category has been through a turbulent period since 2022. The wave of AI-enhanced features arriving in every product in the space, the consolidation of several smaller vendors, and the maturation of the customer expectations have produced a clearer market in 2026 than the category has had for several years.

What the May 2026 market looks like:

Notion remains the dominant horizontal knowledge management platform for small-to-mid teams. The 2024 and 2025 product investment in AI features, in the database functionality, and in the team-collaboration workflows has continued to strengthen the product. The competitive position for general-purpose knowledge work in the 5-500 person organisation is strong.

Confluence remains the standard for larger organisations with significant Atlassian footprints. The integration with Jira, with the broader Atlassian ecosystem, and with the enterprise security and compliance frameworks supports its position in enterprise. The product investment has continued and the user experience has improved meaningfully through 2024 and 2025.

Coda has maintained a smaller but committed user base around its document-as-application model. The product is most successful in teams that have a use case where the document-application boundary is genuinely productive.

Obsidian has continued its growth in the individual-knowledge-management and small-team space. The local-first, file-based approach has appealed to a specific user community that prioritises portability, longevity, and tool ownership over the cloud-platform convenience of the larger tools.

Logseq, Roam Research, and the smaller PKM-oriented tools continue to operate with smaller user bases focused on specific knowledge-work patterns.

The AI features that matter:

The AI-assisted writing, summarisation, and search features have become standard across the category. The differentiation is no longer about whether the tool has AI features but about how well they are integrated into the actual workflows.

The AI-assisted writing in Notion, Confluence, and the other major tools is now genuinely useful for the routine writing tasks — meeting summaries, project status updates, draft documentation. The output quality is consistent enough that it is part of many users’ daily workflow.

The AI-assisted search across organisational knowledge is the feature that has produced the most visible productivity improvement. The ability to ask a natural language question against the team’s knowledge base and get a synthesised answer with citations to the source documents is materially better than the keyword search of three years ago. The teams that have invested in document quality and metadata are getting better results than the teams whose knowledge bases are messy.

The AI-assisted writing of structured content — meeting minutes that conform to a template, status reports in a specific format, technical documentation in a defined structure — is the area of fastest improvement in 2025 and 2026. The combination of template definition, source content, and AI synthesis is producing serviceable first drafts that the human author then refines.

The patterns that have not stuck:

The AI as autonomous knowledge worker that browses the team’s knowledge base and proactively generates content has not stuck. The early 2024 pitches around this were not matched by user adoption, partly because the autonomous content was not consistently useful and partly because users prefer to direct the AI rather than receive its output passively.

The fully-generative documentation systems that promised to maintain organisational documentation automatically have not stuck. The reality is that documentation requires human judgment about what to document, what to leave out, and how to structure it. The AI can assist these decisions but cannot make them.

The team-wide knowledge graphs that promised to connect every document to every other document and to enable structured reasoning across the knowledge base have not stuck at the scale the early pitches suggested. The work of creating the structure required to support these graphs is more than most teams will do, and the value of the graphs at smaller scales is unclear.

The structural patterns that are working:

A primary knowledge base in the team’s chosen platform (Notion, Confluence, or equivalent) with disciplined document quality, clear ownership of major content areas, and regular maintenance.

A search layer — either the native search of the chosen platform or a dedicated search product like Glean — that allows AI-assisted retrieval across the organisational knowledge.

Integration with the other tools where work happens — the calendar, the project tracker, the code repository, the communication tool — so that the knowledge base is not isolated from the work itself.

A document hygiene practice that includes regular archiving of stale content, regular review of high-traffic documents for accuracy, and clear conventions for document creation and maintenance.

For organisations evaluating their knowledge management stack in May 2026, the read is that the tooling is mature, the AI features are useful but not transformative, and the bigger lever is the practice discipline around how the knowledge base is maintained. The teams that have invested in the practice are getting value from the tools. The teams that expect the tools to compensate for missing practice are not.

For larger organisations considering deeper AI integration into knowledge management — combining the knowledge base with the customer support content, the sales materials, the engineering documentation, and the broader corporate content — the work moves into the AI implementation space. Team400 is one of the Australian AI consultancies that has done work in enterprise knowledge management AI, which is the conversation to have for organisations approaching this kind of integrated capability.

The 2026 read is that the knowledge management category is in a better place than it has been for several years. The tools are good, the AI is helpful, and the practice discipline is the differentiator.