F1000s that rolled out only chat GenAI (no coding agents), what actually stuck?
Looking for stories from F1000-style deployments where:
- Only the chat interface + M365 Copilot–style plugins are available to end users
- No Codex / Claude Code / Antigravity / agentic dev tools
- Users can upload instructions and knowledge files but can't deploy custom apps
- User base is mostly non-technical knowledge workers
- Non-tech companies specifically — where the best engineering talent went to big tech and you're working with what's actually available in the market
Part 1: What actually works post-rollout
1. Which use cases stick vs. quietly die after the novelty wears off?
Long list of things these tools can do — extracting data from docs, comparing documents, generating PowerPoint drafts, data analysis on uploaded files, SQL/VBA/Python help for non-coders, brainstorming, drafting long docs, even building basic HTML dashboards. But "can do" ≠ "people actually do daily." What survives in your org 6+ months in?
2. How are you measuring impact at all?
Chat is notoriously hard to instrument. Self-reported time saved via surveys? License utilization / DAU / message volume? Use case inventory mapped to workflow-level estimates? Anything that actually maps to dollars on an exec readout?
3. Is chat-only honestly enough to move the needle?
Or does it feel like you're leaving meaningful value on the table because you can't put agents / coding tools in users' hands?
4. Rough ROI numbers — % time saved, $ savings, productivity uplift — what's the directional shape if you have it? Numbers finance actually signed off on, not vendor benchmarks.
Part 2: Building the AI org
5. How did you build an AI function that drives real change, without consultants?
Specifically interested in non-tech companies that pulled this off without leaning on implementation consulting engagements or some sort of FDE-style arrangements with the labs themselves. Success stories of in-house teams that actually delivered.
- How is the central AI team structured?
- Where does it sit — IT, Strategy, CDO/CAIO, a specific business unit?
- Headcount profile: engineers vs. product vs. business translators vs. change management?
- Realistic talent strategy when the best ML/AI engineers aren't joining a non-tech F1000 in the first place — are you upskilling existing tech talent? Partnering with universities? Paying outsized comp for a small senior bench?
- Who actually owns the program at exec level — IT leadership, a Chief AI Officer, a business sponsor?
- How do you avoid the failure mode where the team becomes a glorified shadow consultancy running pilots that never scale?
Rough anecdotes welcome. Especially keen on companies 2+ years into this where the honeymoon is over.