Through the real experience of product manager Zhang Wei, this article summarizes three core pain points of AI tool operations in 2025 and proposes a practical roadmap from “cataloging” to “assetization”.
Story Introduction
At the end of 2024, Zhang Wei, product manager of the Alpha team, was managing 120 AI tools at the same time.
At first she believed “the more tools, the better we can cover every scenario”, but quickly ran into three walls: tool explosion, fragmented data, and implementation resistance.
After three months, she used three strategies — capability map + evaluation cards, tool performance dashboard, and implementation review mechanism — to finally turn AI tool operations into reusable assets.
New tools arrive every week and the team can’t tell which ones are worth real investment.
Recommendations:
Test data is scattered across Notion, Trello, spreadsheets…
No one can clearly measure the real value of implementation.
Solutions:
Even great tools won’t be adopted if people don’t trust them.
Tactics:
Cataloging
List all tools, capabilities, and owners to build a clear resource map.
Versioning
Keep evaluation records, change logs, and comparison reports so decisions are traceable.
Scenario-ization
Standardize how outputs are used (who uses it / why / how it lands), turning tools into reusable capability modules instead of isolated experiments.
Create an “AI Tool Asset Map” table
Publish one implementation case per week
Include AI tool capability in OKR / performance
By treating AI tools as operational assets instead of scattered experiments,
teams can gradually build a systematic, reusable AI operations capability that compounds over time.