Analytics & AI Operations
Give leaders a clear operating picture of AI value: usage, quality, risk, and business impact.
We build the measurement and operating rhythm behind AI programs so executives can see what is working, what needs intervention, and where value is accruing. Dashboards, KPI frameworks, and control-health reporting turn AI from a set of experiments into a managed portfolio.
Who this is for
Executives and operators who need proof that AI investments are adopted, controlled, and delivering business impact.
Approach
- Define the metrics that prove value, quality, and control
- Build executive-ready reporting across initiatives
- Reconcile sources so numbers hold up under scrutiny
- Install a recurring operating rhythm for decisions
What we deliver
- AI portfolio and adoption dashboards
- KPI and ROI frameworks for AI initiatives
- Control-health and operating metrics
- Executive reporting cadence and decision support
Common questions
How do we measure ROI on AI?
Measure two things separately: whether the system is used, and whether the business moved. Usage is runs, completion rate, and how often a human overrode the output. Business impact is the number the workflow was supposed to change, measured the way finance already measures it, against the period before go-live. Most AI reporting fails because it only shows the first and calls it value. A system with high usage and no movement in the underlying metric is a signal, not a success.
How much are we spending on LLM calls, and how do we control it?
Cost per run, broken down by model and by workflow, is the number to instrument from day one rather than after the invoice surprises you. The usual drivers are oversized context, retries hidden inside agent loops, and calling a frontier model for work a smaller one handles. Controls that actually work: per-workflow budgets, model routing by task, caching, and an alert on cost per run rather than on the monthly total, because the monthly total tells you too late.
What should an executive AI dashboard actually show?
Four things: adoption (is it being used and by whom), quality (override and error rates), cost (per run and per workflow, trending), and business impact (the operating metric this was meant to move). Anything else is decoration. If a tile cannot change a decision, it is taking attention from one that can.
Based in Sarasota, Florida. We work with companies anywhere in the United States and internationally; most engagements are remote.
Contact us about Analytics & AI Operations or book a strategy call.