Our AI work is not delivering
We have run pilots. None of them reached production, and nobody can say what they returned.
What is actually going on
Pilots stall for three reasons, usually together: no business case at real volume, no security answer, and no named owner for after launch. Underneath that, the blocker is almost always the data rather than the model. Fixing the entity resolution, the definitions, or the pipeline is what unblocks the pilot.
Who usually owns this
CIO, COO, Transformation leader
How people describe it
- "is our company ready to use AI"
- "AI pilot not moving to production"
- "measure ROI from AI projects"
What resolves it
- AI Readiness Assessment & Consulting. AI readiness assessment scoring data quality, metric consistency, governance, pipeline reliability, and AI-ready architecture. The score and the fix, in 30 days.
- Generative AI Consulting. Generative AI consultants who pick the use cases that survive production, ground outputs in your own content, and show the real cost per use before you commit.
- Managed Data Readiness Services. Managed data readiness and analytics-as-a-service: continuous metric certification, managed observability, governance operations, and AI-readiness upkeep as a monthly retainer.
- Microsoft 365 Copilot Consulting. Microsoft 365 Copilot deployment consulting: oversharing remediation, Purview sensitivity labels, governance, and adoption. Roll out Copilot safely, not just quickly.
- Data Team Enablement. Data team structure redesign, Tableau and Power BI training, and data literacy programs. Build an analytics team that operates independently.
Read up on it first
- Why AI Pilots Stall Before Production. Start here. 88% of agent pilots never reach production, and the reported blockers are evaluation, governance and reliability rather than model capability. One client had spent $2.4M across three ML initiatives in six months with none live, for three different data reasons. Also the one figure to keep out of your business case.
- Fix Data, Integration or Governance First?. Choosing an approach. One test decides the order: run the pilot against production-shaped data and watch which way it fails. The failure mode names the blocker. Two real assessment rubrics, five dimensions each, and the five engagements where the triage moved money before the build rather than after it.
- What a Pilot-to-Production Engagement Includes. Scoping the work. Seven deliverables, four of which concern what happens after go-live. That ratio is the tell for whether a firm has run production AI or only pilots. Plus eight acceptance criteria, the year-two run cost most proposals omit, and the handover question to ask: what does day 91 look like.
Proof
Three stalled ML pilots restarted, $4.8M recovered
We improved member match accuracy to 94 of every 100 records on the validation set. Within four weeks of delivery, the client restarted all three ML pilots. Reducing misrouting is expected to save $4.8 million annually. The internal ML team now fully owns the pipeline, supported by complete documentation.
- $4.8M, Annual claims recovery.
- 75 to 94 of 100, Member match accuracy.
- 3, ML pilots restarted.
- 14 wks, Delivery timeline.
Getting it approved
Other problems
What is actually going on
Who usually owns this
How people describe it
What resolves it
Read up on it first
Getting it approved
Three pieces, in the order the question usually arrives. Each one is evidence-led and names its sources.
Whoever signs this off is asking a different question from the one you are asking.
is the worksheet for that conversation. Yours whether or not you hire us.