Service Strategy & Readiness

Know what to build before you pay to build it.

Most enterprise AI budgets die between the offsite and the roadmap — twenty use cases, no business case, and a pilot chosen because it demos well. I run the assessment that ends that: what your organization is actually ready for, what each candidate is worth in dollars, and which one to ship first. You get a roadmap with numbers on it, not a slide deck with sparkles.

2–4 wks Assessment to roadmap

$ first Business case before build

1 KPI Per initiative, no exceptions

Every AI strategy engagement I take over starts the same way: a long list of use cases collected from department heads, ranked by enthusiasm instead of economics. Nobody has asked what the data can support, what the org can absorb, or what any of it is worth. So the pilot gets picked politically, it stalls, and the budget gets quieter next year.

My assessment works the other way around. I start with the money — where hours, errors, and cost-per-task actually concentrate in your operation — then test each candidate against three gates: is the data ready, is the process stable enough to automate, and does the value clear the build cost with room to spare. Most candidate lists shrink by two-thirds. What survives is worth building.

What you walk away with

A readiness scorecard — data, infrastructure, security posture, and team capability graded against what each use case actually requires.
A ranked roadmap — every initiative with a dollar estimate, a KPI, an owner, and a build-vs-buy call, sequenced so early wins fund later ones.
A defensible business case — the document your CFO signs off on, built by someone who spent fifteen years attaching dollars to outcomes.

Strategy that doesn't name a number isn't strategy — it's a mood board. Mine ends with a figure, a date, and the first thing to ship.

What the assessment actually covers.

Four lenses, one ranked roadmap — delivered in weeks, not quarters.

01 / Value

Where the dollars are

Process mining and cost analysis to find where hours, errors, and spend concentrate — the places AI pays for itself fastest.

02 / Readiness

What you can support

Data access, quality, infrastructure, and security posture graded against what each candidate use case actually requires.

03 / Sequencing

What to ship first

Initiatives ranked by value-to-effort and sequenced so early wins generate the proof and budget for the harder ones.

04 / Case

The number that survives finance

A business case per initiative — cost, KPI, payback window — written to survive CFO scrutiny, not just a steering committee.

Readiness assessmentUse-case rankingBusiness caseBuild vs buyRoadmapGovernance

Questions, answered.

The ones every buyer asks first.

How long does an AI readiness assessment take?

Two to four weeks for most organizations. Week one is discovery — systems, data, and the processes where cost concentrates. Weeks two and three test candidate use cases against readiness gates. You get the ranked roadmap and business case at the end of week four at the latest.

What does AI strategy consulting cost compared to a build?

A fraction. The assessment is fixed-price and typically costs less than one month of a stalled pilot. Its job is to prevent the six-figure build that never should have started — one killed bad initiative pays for it many times over.

Do you implement the roadmap too, or just deliver it?

Both. I build agents, RAG systems, workflows, and infrastructure myself, so the roadmap is written by someone who has to live with it. You can also take the roadmap to any vendor — it is deliberately vendor-neutral.

What if the assessment finds we are not ready for AI?

Then you hear that, with the specific gaps and what it takes to close them — usually data access, quality, or process instability. That answer costs you weeks instead of the year and budget you'd spend discovering it in production.