Focused engagement · Typically 3–5 weeks

Test an AI use case before investing in rollout.

Test one task with representative data and the people who do the work. Compare quality, time, cost and risks with the current process, then decide whether to proceed, revise or stop.

Discuss the AI proof Explore enterprise AI services
DecisionUser, action, baseline and measurable outcome
EvidenceData fitness, provenance, access and limitations
ControlEvaluation, safeguards, human review and audit
ScaleValue, risk, architecture, operating cost and ownership

What you receive

A working test and a clear recommendation.

The proof focuses on one bounded use case such as knowledge assistance, document processing, workflow automation or predictive analysis. Production deployment and ongoing support are separately scoped after the proof.

01

Agreed use-case brief

The task, users, current process, data sources, responsibilities and success measures.

02

Working proof

A representative workflow tested with realistic cases, including difficult and failure scenarios.

03

Recommendation and next steps

A recommendation to stop, improve the test or prepare for deployment, supported by test results, costs and remaining risks.

Decision gates

Four questions the proof must answer.

Agree the success measures before testing and record both useful results and limitations.

  1. 01

    Is the decision worth improving?A named user action and observable operational effect.

  2. 02

    Can the evidence support it?Representative data, known limits and appropriate access controls.

  3. 03

    Can people remain accountable?Review, abstention, escalation and audit designed into the workflow.

  4. 04

    Can it be operated responsibly?Monitoring, security, cost, ownership and change control beyond the proof.

Stage the decision

Agree the conditions for proceeding.

Agree the evidence needed at each gate, then assess both lifecycle costs and potential value. Scope, data access, timing and commercial terms are confirmed after discovery.

Decision gates, lifecycle costs and value evidence Assess, prove, scale and sustain with an explicit next-step decision. Compare total lifecycle costs and potential value against a baseline and alternatives. No savings curve or guaranteed return is implied. EVIDENCE BEFORE EACH COMMITMENT 01 Assess02 Prove03 Scale04 Sustain Problem, baseline and fitGate: a testable use case Evidence and evaluationGate: an owned decision Controlled deploymentGate: operational acceptance Monitoring and improvementGate: continued value Understand the lifecycle costEstablish the value evidence Data preparation and integrationLicensing, hosting and model usageSecurity, change and trainingSupport, monitoring and retirement Service performance and reliabilityDecision quality and work timeRisk treatment and cost avoidanceBaseline, measure, owner and review period Compare alternatives—including keeping the current approach. Record assumptions and uncertainty. Test whether the benefit justifies the cost and residual risk.
Decision gates, lifecycle costs and value evidence Assess, prove, scale and sustain only when the evidence supports the next step. Compare alternatives using explicit costs, value measures, assumptions and accountable owners.

Talk to us

Discuss an AI use case worth testing.

Tell us about the task you want to improve. We will assess whether an AI proof is useful and what it would take to run it.

Discuss the operational AI proof