When this is the right service
Pick this when AI is more pressure than payback:
- The board has asked for an AI strategy and you are running out of runway to come back with one.
- 40+ AI agents, models, or pilots are running across the org with no governance.
- AI infrastructure cost is climbing faster than the revenue it's producing.
- You're being asked to choose between building, buying, and partnering — without a defendable framework.
- An AI hire is being made and the role definition is still vague.
- A function (sales, marketing, support, finance, ops) wants to deploy AI but no one owns the result.
What you get
A modular practice across the full AI lifecycle. Each workstream is fixed-scope, milestone-billed, and measured against business outcomes — not pilot completion.
- Readiness & Strategy — leadership alignment, AI governance, prioritized use-case backlog with ROI per case, investment-sequencing plan.
- Data Foundations — data quality audit, data maturity assessment, integration readiness, governance, observability.
- AI Cost Management — model selection optimization, vendor consolidation, usage governance, build-vs-buy analysis, infrastructure cost reduction.
- Functional Deployment — AI in product, AI in GTM, AI in operations, AI in finance — function-by-function deployment with named owners and measured impact.
- Cross-functional optimization — agent governance, agent-of-agents orchestration, kill-list discipline for redundant pilots.
- Executive readout — board-ready deck and walkthrough at the end of every workstream.
Methodology
Four phases. Workstreams plug in at Phase 2 and run in parallel or sequentially depending on the highest-leverage gap.
Phase 1 — Readiness diagnostic. Four-pillar audit (organization, data, technology, strategy). Output: maturity scorecard, ranked use-case backlog with ROI per case, sequenced investment plan.
Phase 2 — Workstream selection. Pick the workstream(s) that match the highest-leverage gap from the diagnostic: Data Foundations, AI Cost Management, Functional Deployment, Cross-functional optimization. Output: scoped workstream charter(s) with named owners and milestones.
Phase 3 — Workstream execution. Fixed-scope delivery against named milestones per workstream. Built with your team, not for them. Output: working capability per workstream — kill-list executed, dashboards live, agent governance in place, function deployment shipped.
Phase 4 — Iterate & sustain. Post-deployment measurement, kill-list discipline applied to the next cohort, ongoing optimization. Output: a discipline your team owns, with the option to add new workstreams when the priorities change.
Proof
- Healthcare tech: fragmented modules unified into a Digitize → Analyze → Optimize platform, 80% lift in market alignment, launched in 6 months.
- Healthcare AI: $12M+ pipeline accelerated through unified platform narrative.
- Healthcare platform: 5 enterprise expansions, 80% lift in pilot-to-deployment conversion.
FAQ
Is this just a maturity assessment? No. AI readiness assessment is one workstream. The practice extends through data foundations, AI infrastructure cost management, and function-by-function deployment — measured against business outcomes, not pilot completion.
What if our pilots are already running? Even better. We use existing pilot data to inform prioritization and cost management. Often the answer is to kill some pilots and consolidate redundant model spend across the rest.
Do we need to be in healthcare? No. Healthcare is our deepest proof base, but the framework applies to any company already running multiple AI initiatives without governance, cost discipline, or measured business outcomes.
What happens after the readiness diagnostic? About 40% of clients move into a Product Growth engagement to operationalize the top use case, or a GTM System build if the AI work needs a commercial engine to land in. The other 60% sequence into one of the AI Optimization workstreams (Data Foundations, AI Cost Management, or Functional Deployment). There is no obligation — the diagnostic stands on its own.