AI on quality foundations
Take one AI use case through four gated phases
AI Pilot-to-Production takes one use case through four gated phases on governed data: data preparation, build and validation, deployment and monitoring, and handover. At each gate, you decide whether to proceed, revise or stop, based on documented evidence.
A senior lead replies within one business day. No obligation.
Why pilots don't reach production
Many pilots are built on a one-off data extract. The demo works, but the data behind it is scattered, its quality is unknown and access is unclear, so it can't run reliably. Pilot after pilot hits the same wall.
What it costs: Budget spent on demos, the board's trust in AI, and time.
What it sounds like:
“Our AI pilots never seem to reach production.”
“The model worked in the demo. Then nothing happened.”
“Our pilots have been running for a year, and nothing is live.”
“Nobody knows who owns the data the model needs.”
Data first, then the model
Reliable AI needs more than a model. It needs governed, traceable data that can run in production, and monitoring after go-live. So we prepare the data foundation first, then build, validate and deploy, with a decision at every gate.
The data foundation required depends on the use case and your existing environment. We identify the controls and capabilities needed for reliable operation, then agree what must be implemented within the project scope.
Examples of what it may need:
- Governed data sources with clear owners
- Automated data-quality controls
- Metadata and end-to-end lineage
- Secure, reusable data pipelines
- Versioning and reproducibility
- Monitoring and feedback after go-live
The AI path
Two steps: AI Use-Case Discovery & Prioritisation, then AI Pilot-to-Production.
Four gated phases
| Phase | Purpose | Decision |
|---|---|---|
| 1. Data readiness and preparation | Validate data availability, quality, access and technical dependencies, and prepare the data | Gate 1: proceed to build? |
| 2. Build and validate | Develop the solution and validate it against agreed business, technical, security and operational acceptance criteria, including data protection and appropriate human oversight | Gate 2: ready for deployment? |
| 3. Deploy and monitor | Controlled deployment with your IT team, operational validation and monitoring | Gate 3: accept operational performance? |
| 4. Hand over | Documentation, knowledge transfer and transfer of agreed responsibilities to your team | Gate 4: final acceptance |
Typical first result
Validated use-case requirements, an assessment of data readiness and dependencies, and a documented decision on whether to proceed with development.
A senior lead replies within one business day. No obligation.
How it works
Four phases in a row, each ending in a gate where the decision is to proceed, revise or stop.
- Phase 1Data readiness and preparationGate 1: proceed to build?ProceedReviseStop
- Phase 2Build and validateGate 2: ready for deployment?ProceedReviseStop
- Phase 3Deploy and monitorGate 3: accept operational performance?ProceedReviseStop
- Phase 4Hand overGate 4: final acceptanceProceedReviseStop
Each gate gives a documented decision to proceed, revise or stop. Phase commitments, and any move of budget to another use case, follow the agreed engagement terms.
Typical use cases
| Predict and optimise | Detect and protect | Understand and automate |
|---|---|---|
| Demand forecasting · Resource planning · Churn or propensity · Predictive maintenance | Fraud and anomaly signals · Risk scoring · Quality exceptions · Compliance checks | Document extraction · Assistants that answer from company documents · Classification and routing · Agentic workflows |
Who does the work
A senior DataOfis lead coordinates the solution design, delivery oversight and gate reviews. DataOfis presents the evidence and recommendations; you make the investment and continuation decisions through your agreed governance process. The build is done by the DataOfis team or by a delivery partner under DataOfis design and oversight, and can include your own engineers.
Working together
- Indicative duration: 8–16 weeks for a defined use case with suitable data and infrastructure readiness. Complex integrations, security approvals, extra data engineering and production requirements may extend the timeline. Each phase is scoped before it starts.
- What DataOfis is responsible for: the agreed project deliverables within the agreed scope. Where a partner builds, DataOfis is responsible for design and oversight.
- A decision at every gate: each phase ends with a documented decision to proceed, revise or stop.
- Acceptance criteria: business, technical, security and operational criteria agreed with you before the build, and checked at the gates. Model performance depends on the data and the use case, so we don't promise it in advance.
- Bought on its own: if you have already chosen a use case, you can start here.
- What we need from you: a business owner for the use case, access to the data and its owners, and IT involvement for deployment.
Questions we often hear
Yes. Each gate gives a documented decision to proceed, revise or stop, with the reasons set out.
Not if you have already chosen a use case. If you haven't, discovery is the faster way to choose.
The DataOfis team or a delivery partner, under DataOfis design and oversight. We can also work alongside your own team.
Yes, where your requirements call for it and your infrastructure can support it. The design follows your data residency and security rules.
AI Pilot-to-Production develops and deploys a defined AI solution. Decision Intelligence focuses on improving a business decision and the process around it. Some engagements need both.
Usually, yes. We first review the pilot, then bring it into the gated phases, starting with the data.
Often combined with
Get one use case moving
Tell us about the use case, or the pilot that's stuck. We'll discuss what the first gate would involve.
A senior lead replies within one business day. No obligation.
Haven't chosen a use case yet? AI Use-Case Discovery