Your board wants an AI plan. Your teams have tools. What's missing is the middle: which workflows AI pays in, what each is worth, and the order to build. Patrick maps it against your real work — and prices every step in payback, not promise.
Not a workshop. Not a tool pitch. A ranked, sequenced plan you could hand to your board as-is.
Stalled pilots aren't a failure of ambition. They're what happens when nobody has priced the options — so every AI conversation restarts from opinion.
Which workflow, worth what, in which order — that's an answerable question. It just needs looking at properly.
A workshop can't tell you what a workflow is worth. So we work from evidence — real usage, real workflows, real numbers — and hand you a plan priced in paybacks.
Where the org sits on the curve — scored against firms like yours, not against the hype.
Candidate workflows ranked by value and feasibility — seen in your operation, not brainstormed.
Expected time and cost returned, payback per use case — the numbers the board asked for.
How the core processes get rebuilt around AI — not bolted onto the side of them.
Which tools and models for which jobs — and where build, buy, or route is the right call.
What data must be clean, accessible, and structured before each use case can work.
Building real AI skill in the team — beyond the safe-use training.
Champions, incentives, and the resistance plan — so usage survives the demo.
A sequenced 12–18-month plan from first pilots to scale — with decision gates, not faith.
The metrics that prove productivity and ROI over time — agreed before anything is built.
Every recommendation carries its expected payback. If a use case doesn't clear the bar, the roadmap says so — including "not yet" and "never."
Book the roadmap →Ranked use cases with paybacks, a sequenced build order, and the measurement plan that keeps everyone honest. Two pages from a sample:
Each ranked use case carries its evidence, expected hours returned, cost to build, and what must be true for the payback to hold. "Not yet" and "never" are findings, not failures.
Build the first use case. Baseline its numbers.
Data cleanup only where the first build needs it. Measurement plan live from day one.
Scale what measured well. Start use cases two and three.
Champions named per team. Upskilling tied to the tools actually deployed.
Embed into core systems. Re-rank the inventory.
Integration into DMS, CRM, practice systems. The board sees returned hours, not demos.
Already ran the AI Exposure Assessment? Its usage map seeds the use-case inventory — the same evidence, pointed at value instead of risk. If you haven't, the roadmap includes its own usage discovery.
Every use case in the plan is specified tightly enough to hand to any competent builder — your team, your vendor, or Patrick's build arm. The plan doesn't assume you hire us.
If you do: Patrick builds and operates one process at a time, each phase earning the next, measured against the roadmap's own numbers.
Built for firms of 20–250 people where AI enthusiasm has outrun AI evidence — accounting, consulting, legal-adjacent, HR, risk advisory, engineering and design.