We attended KMWorld 2025 and came away with one validating takeaway: AI is only as good as the knowledge that powers it. Over and over again, keynote speakers reinforced the idea that enterprises can’t treat AI as a magic layer on top of chaos. This aligns with how we think about the future of the digital workplace — not AI first, but knowledge first, so that AI can succeed. It confirmed the pain point that we identified early on: AI is revealing a lot of underlying mess, and companies aren’t sure how to move forward. Today, the real gap between organizations using AI for the sake of AI, and those leveraging it as a differentiator comes down to one thing: the quality, accuracy and accessibility of the knowledge underneath.

See what our takeaways are  — and how they validate our mission at Happeo.

1) AI amplifies whatever already exists, whether good or bad

Seth Earley (Earley Information Science) captured the dynamic in one line: “AI amplifies all of them” — meaning the strengths and the weaknesses of your entire knowledge ecosystem.

If your content is fragmented, outdated or inconsistent, AI won’t fix that. It will simply repeat those inconsistencies faster and louder. This came through across multiple sessions: before you deploy generative tools, you need structure, ownership, versioning and clarity. 

At Happeo, we see this every day. When content is unified, governed and trusted, AI becomes dramatically more effective: search improves, answers become more reliable, and employees trust what they find.

2) Data quality aren’t nice-to-haves, they’re survival requirements

Another pattern that came up again and again: not only will AI amplify poor data quality, but without strong KM foundations, organizations end up with compliance risks and lost credibility.

Many KMWorld case studies showed the same pattern: teams that invested in lifecycle management, metadata and stewardship created measurable AI value. On the other hand, teams that skipped these steps struggled with reliability and adoption.

For us, this reinforces why content governance — simple ownership, editorial structure, clarity and freshness signals — must be built directly into the digital workplace. 

3) Search is shifting from “find pages” to “get answers”

Tim Hill (NiCE) presented compelling data: users increasingly prefer generative-style responses. For public content, that means fewer traditional page views and more LLM-powered summarization. For internal content, it means employees want instant answers rather than navigation.

He described a workflow many KM teams are adopting:
capture → cultivate → calibrate → evaluate
to ensure content continues to perform well inside generative systems.

This is a major shift for digital workplace teams. It means content must be structured, up-to-date, and, crucially, fractionable for generative retrieval. We see the same pattern inside Happeo deployments: the more structured and current the content is, the better our search and AI-powered experiences perform.

4) Employee experience determines AI success

Even the best AI fails without trust, clarity and adoption. Several sessions focused on the human challenges: disengagement, outdated information, fragmented tools, and global barriers like language or regional knowledge differences.

Teams reported that when employees don’t trust the knowledge platform, they won’t trust AI either — because AI is trained on the same content.

That’s why embedding knowledge directly into the flow of work is critical. Whether through integrations, search, mobile access or automated content suggestions, people need answers where they already are — not in a separate destination they rarely visit.

5) KM needs to speak the language of business impact

Speakers connected KM to measurable outcomes:

• time-to-answer
• onboarding time
• support deflection
• error reduction
• compliance
• consistency across regions and teams

Tim Hill also described faithfulness and relevancy as useful metrics for evaluating generative answers — two measurements that show whether AI is actually drawing from the right content.

For digital workplace teams, this is the bridge between AI experiments and executive buy-in. At Happeo, we see growing demand for analytics that show search success, engagement and content health, because measurement drives momentum.

You can’t shortcut the foundations

Our takeaway: You can’t AI your way out of a knowledge silo. If your content is fragmented, outdated or untrusted, AI will struggle. If your foundation is strong, AI accelerates everything.

At Happeo, this reinforced exactly what we’re building: a trusted, structured, human-centered knowledge layer that makes AI not just possible, but reliable and impactful.