Most AI-enablement hires aren't let go because they can't do the work. They're let go because they measured activity instead of outcomes, or chased an org-wide rollout and had nothing concrete to show by month three. This is how I'd avoid both: a 30/60/90 plan built to land one measurable, visible win early — then scale a proven playbook, not a hopeful one.
Why enablement stalls
The reason "we rolled out AI and nothing changed" is so common isn't mysterious. It's almost always one of these three, and they're all avoidable with a plan that's designed around them from day one.
Reporting logins and license counts. Leadership doesn't fund "people opened the tool" — they fund hours saved and work that got better. If you can't connect usage to a result, you have no story.
Trying to enable the whole org at once. Effort spreads thin, no single team gets to a real win, and by month three there's nothing concrete to point at. Breadth without depth reads as no progress.
Starting without measuring where things stood, and without agreeing up front on what "good" means. Success becomes an argument after the fact instead of a number everyone signed off on early.
The principle: land one measurable win in one team first — against a baseline, on a metric leadership pre-approved — then scale the playbook that worked. Depth before breadth. A proven motion before a big rollout.
The measurement stack
Before deciding to build or buy a tracking system, it's worth seeing that adoption is a stack. The first three layers are largely given to you — the enterprise AI vendors already ship the telemetry. The bottom layers are where real work, and a real differentiator, live.
The build-vs-buy call follows from the stack. Don't build what's commoditized — usage telemetry is solved, and bolt-on platforms cover proficiency and roadmap reporting. Spend the build budget on the one layer no tool can sell you: stitching your org's specific data together and tying usage to your outcomes. That last mile is where a custom build beats off-the-shelf — and it's the layer that produces the ROI story leadership actually wants.
The plan
Three phases, each with a single job. Month one finds the target and the baseline. Month two lands the win. Month three turns one win into a repeatable system. Every phase ends in something visible.
Job of the month: know exactly where things stand, and pick the one team where a fast win is provable.
Job of the month: one team, one measurable, visible result — done right, not done wide.
Job of the month: convert one win into a repeatable program that scales without me as the bottleneck.
What I'd report
Two kinds of metrics, reported differently. Leading indicators show momentum week to week and tell you early if adoption is real. Lagging indicators take longer to move but are what justifies the budget — so I track both from day one and never report one without the other.
Moves in weeks · early signal adoption is sticking
Moves in months · what leadership funds against
Why I can actually run this
Sole owner of enablement for an internal GenAI platform rolled out across ~800 staff — guides, group trainings, 1:1s, road shows, train-the-trainer, and a ~30-person AI community of practice. Adoption was the deliverable, not a side effect.
Three years building RAG systems, agents, and automation on the Anthropic API. The "stitch the sources and tie usage to outcomes" layer that nobody sells you — Python, FastAPI, data plumbing — is work I can ship, not just spec.
Nine-plus years inside a government compliance environment — data classification, audit, access control. The instincts responsible, governed AI deployment demands are already mine.
Turning dense technical systems into the trainings and plain-language guides non-technical staff actually use is the core of my current role — and the exact skill that turns a tool nobody opens into one a team relies on.
Open to implementation, enablement, internal-tools, and applied-AI roles in the Boston area. In-office or hybrid preferred; open to remote.