AI Strategy & Measurement
Align leadership on prioritized AI investments and give executives dashboards for adoption, quality, and business impact.
We help executive teams decide where AI creates durable value, what to fund first, and how ownership should work. In parallel we build the measurement rhythm: portfolio metrics, control-health signals, and reporting that turns AI from experiments into a managed program.
Who this is for
Consumer and B2B brands in regulated and fast-moving markets, including fintech, medtech, consumer, and wholesale.
Approach
- Assess opportunity, readiness, and operating constraints
- Prioritize use cases and define success metrics
- Stand up executive reporting across initiatives
- Install a recurring operating rhythm for decisions
What we deliver
- AI opportunity and readiness assessment
- Use-case portfolio and investment prioritization
- Operating model and decision-rights design
- AI portfolio and adoption dashboards
- KPI frameworks and executive reporting cadence
Common questions
Should we hire an AI engineer or work with an outside firm?
Hire when you have a standing pipeline of AI work and someone senior who can direct it. Bring in a firm when you need the first systems in production and an operating model your team can take over. If you cannot describe what the hire would own on day 90, you are not ready to recruit for it.
Where should we start with AI?
Start with a workflow on systems you already have, that happens often, and has a named owner who wants it fixed. Do not start with the most impressive use case; start with one whose success you can measure in a quarter. The first project's job is to prove the operating model. After that, sequencing the next investments becomes an evidence question.
How do we measure ROI on AI?
Measure usage and business movement separately. Usage is runs, completion rate, and how often a person overrode the output. Business impact is the metric the workflow was meant to change, measured the way finance already measures it, against the period before go-live. Reporting only usage and calling it value is a common failure mode.
How do we control LLM spend?
Instrument cost per run by model and workflow from day one. Drivers are often oversized context, retries inside agent loops, and using a frontier model for work a smaller one handles. Per-workflow budgets, model routing by task, caching, and alerts on cost per run catch drift before the monthly invoice.
What should an executive AI dashboard show?
Adoption, quality (override and error rates), spend per run trending, and business impact on the operating metric the initiative was meant to move. If a tile cannot change a decision, it is taking attention from one that can.
Do you work with companies outside Florida?
Yes. We are based in Sarasota, Florida and work with companies anywhere in the United States and internationally. Most engagements run remote, with onsite time in discovery and at go-live when it earns its cost.
Based in Sarasota, Florida. We work with companies anywhere in the United States and internationally; most engagements are remote.
Contact us about AI Strategy & Measurement or book a 30-minute call.