01 / Challenge
Challenge
An education-focused NPO has finite resources but ten times the work. If everyone uses their own AI, workflows fragment; if everyone uses just one, the tool's weaknesses become the team's. How do you turn "multi-AI" into "one organic AI team" rather than "a pile of disjointed AI tools"?
02 / Approach
Approach
**Role definition**: Notion AI = the org's memory and project brain; Gemini = intel and fast research; Claude = deep reasoning, product building, demos. **Standardized workflow**: meetings open in Notion (auto-recorded) → action items into Notion projects → external research switches to Gemini → product building switches to Claude → outputs flow back into Notion to settle. **Team training**: every member fluent in all three AIs, with one specialty. **Continuous tuning**: weekly retros surface stuck workflows for targeted fixes.
03 / Result
Result
With resources far smaller than commercial counterparts, the Junyi team's output rivals theirs. The three-AI workflow is not personal know-how — it's organizational infrastructure. New hires learn it in week one. Departing members take their experience, not the workflow (the workflow belongs to the org).
Why it matters
Why it matters
"Knowing how to use AI" is Basic. "Orchestrating multiple AIs" is Proficient. **"Designing the human/AI division so the whole org collaborates effectively" is Advanced** — it demands clarity on what humans do, what AI does, where the boundary is, and how quality is gated. OUTCOME = compounding organizational throughput; IMPACT = an NPO moving at commercial-company speed. This is Manage · Advanced: integrating the human-AI hybrid team.