All work
AI strategy · Adoption · Platform

One AI assistant, adopted org-wide

AI was fragmented across the company. I built a prototype that unified specialized agents into a single experience — and took it from my laptop to an org-wide rollout.

Role
Product lead & prototyper
Context
Enterprise data & AI platform
Audience
Internal teams + clients
Outcome
Org-wide adoption
When
2024 – present

The challenge

AI was fragmented — finance, customer success, and product each ran their own experiments with no central strategy. There was no unified way for internal teams or clients to get answers about the platform, and every new point solution made the sprawl worse.

What I did

I built a working prototype: a multi-agent chat interface that unifies specialized agents — analytics, monitoring, onboarding — into one experience that follows users across the platform. Then I took it to the CPO with a clear case for a single, governed AI surface instead of scattered, ungoverned experiments.

From a personal prototype to executive buy-in to organization-wide adoption.

The outcome

It was approved, adopted, and expanded across disciplines org-wide — now used by 3,500+ people across the business — the clearest example I have of taking something from a personal prototype to executive buy-in to organization-wide adoption.

3,500+

Active users across the business

Org-wide

Rollout across disciplines

CPO

Executive sponsor

The hard call

On the same platform, I led the MVP for our first end-to-end monitoring capability. Engineering velocity meant we couldn't ship the full suite by the date — and both user groups, Operations and Services, wanted all of it. Working with the feature's PM, I ran every feature through three tests: does it serve all clients or just some, is there an acceptable short-term workaround, and can we build it reliably by the date. That gave us a defensible core to ship and a clear set to defer. Both groups pushed back; we walked them through the reasoning and gave them a dated roadmap for the rest. We shipped on time — and delivered every deferred feature on the committed schedule.

What it proves

AI product vision, stakeholder alignment, change management, and the architecture sense to unify fragmented efforts into one coherent, governed surface.