An AI pilot finishes without a route into production
The demonstration works, but the process owner, data access or acceptance criteria remain unresolved.
AI creates value when the workflow changes.
We help business and technology leaders test where AI can improve a defined workflow, then build the controls and operating model needed to use it.
When needed
A function is asked to absorb growth without more headcount
Manual analysis, document handling or transaction work consumes the capacity needed.
A technology budget grows while the initiative list fragments
Projects compete for funding without a shared architecture or economic baseline.
A use case reaches confidential data
The team needs a defined approach to access, source grounding and human review before proceeding.
Our approach
Four steps take the work from framing the question to executing the agreed changes, with service-specific activities and a decision gate at the end of each step. We agree the scope, evidence requirements and owners for each phase before work starts.
Duration: 1–2 weeks
Step 1: Frame
Observe the work with users; measure volume, effort, quality and cost and identify data restrictions.
Gate 1
Process owner approves the workflow baseline; data access and confidentiality requirements are agreed.
Duration: 8–12 weeks
Step 2: Diagnose
Prioritise a bounded use case, define a test set and compare build, buy and process-only options.
Gate 2
Partner signs off evidence versus assumptions; business and technology owners approve the pilot scope and acceptance thresholds.
Duration: 4–6 weeks
Step 3: Validate
Test quality, failure modes, time saved and user adoption against the starting workflow.
Gate 3
Production requires documented acceptance of quality, controls, operating ownership and unit economics—not a successful demo alone.
Duration: 6–18 months
Step 4: Execute
Deploy accepted solutions in waves, embed them in daily workflows and build the monitoring, support and capabilities needed to sustain adoption.
Gate 4
Each production release requires business and technology acceptance; owners confirm quality, confidentiality controls and realised value before handover.
Our proprietary AI harness manages agreed confidentiality requirements and supports source-grounded analysis. Senior consultants validate findings and distinguish evidence from assumptions.
Deliverables
Step 1: Frame
Workflow mandate and control requirements
Agreed workflow scope, business owner and success measures, with data access, confidentiality and human-review requirements. Define how effort, quality, cost and value will be measured.
Step 2: Diagnose
Use-case portfolio and total-cost model
A workflow baseline and ranked build, buy and process-only options. Quantify potential value against implementation, integration, compute and support costs; distinguish capacity released from cashable savings.
Step 3: Validate
Evaluated pilot and production business case
A working pilot, source-grounded test results and control checks against agreed thresholds. Validate quality, adoption, unit economics and payback, with explicit evidence for the release decision.
Step 4: Execute
Production handover and value dashboard
An owned rollout plan, monitoring rules and support model. Track realised adoption, quality, cost per transaction and net economic benefit after ongoing technology costs, with assumptions reconciled to actual results.
Each phase leaves a working asset with an owner, source references and an update process. Economic estimates distinguish potential value from approved commitments and realised results.
Illustrative example
AI evaluation and release report
Inspect test-level results, source-grounding evidence and confidentiality controls, with explicit thresholds for a production release.
Sample data, not a client deliverable. The format is tailored to the engagement.
View full-size example ↗
Team & Duration
You get direct access to partner judgement, a team working inside your business and a shared focus on economic impact. We work through the difficult questions with you and stay close as decisions become action.
A senior team, working with you
- Partner judgement at the problem-solving table
- 1 accountable partner. Works directly on your hardest questions: shaping hypotheses, examining the evidence and developing recommendations with your team. Regular joint working sessions and partner-led decision gates bring senior judgement into the work throughout the engagement.
- Day-to-day momentum, inside your business
- 1 Engagement manager. Works side by side with your people, including from your offices. Connects the analysis to operational reality, resolves blockers and turns decisions into owned actions.
- Analytical depth focused on value
- 2–3 consultants. Combine rigorous analysis with our proprietary AI harness to test opportunities, quantify the economics and build practical working assets with your team. You gain a stronger fact base and tools your people can keep using.
- Expertise that makes recommendations practical
- 1 expert. Brings relevant industry or service experience to test assumptions, challenge feasibility and identify the constraints that matter before you commit resources.
Duration & Fees
- A clear path from question to impact
- Duration of each step: Frame: 1–2 weeks. Diagnose: 8–12 weeks. Validate: 4–6 weeks. Execute: 6–18 months. We agree the scope, owners and decision gates before work starts.
- Continuity from insight to execution
- The partner and engagement manager are planned to stay from Frame through Execute, carrying the context and accountability into implementation. Any change to the core team is discussed and agreed with you.
- Fee certainty upfront. Alignment on results.
- Steps 1–3 (Frame, Diagnose and Validate) are fixed-fee, with scope and deliverables agreed for each step. Execute can include performance-based fees linked to agreed outcomes, baselines and measurement rules.
- You control the next commitment
- At each decision gate, choose to proceed, adjust the scope or take the working assets forward with your own team. Each further commitment follows an explicit decision, with ownership and handover built into the work.
Cases
Selected anonymised engagements. Many span several services; each case shows the relevant areas of work alongside its outcome.
Consumer & Retail · Technology & AI · Operating Model · Cost Out
AI in finance
European aftermarket group
~20%Targeted effort reduction
Read case →
Transportation & Logistics · Technology & AI · Strategic Decisions
Digital strategy
Passenger rail operator
30 → 12Prioritised digital initiatives
Read case →
Financial Services · Cost Out · Technology & AI
IT cost out and efficiency
European insurance and banking group
~EUR 27mReduction target
Read case →Baselines, timeframes and evidence
European aftermarket group
- Starting position
- Approximately 350 finance FTE across seven countries, with accounts payable spanning four ERP instances.
- Timeframe
- 24-month implementation-roadmap horizon.
- Recorded facts and outcomes
- Use cases were assessed at process level, with data and process standardisation costed and sequenced before the AI build.
- Judgement, targets and limits
- Approximately 20% effort reduction was a target for transactional finance, not all 350 FTE and not a realised result.
Passenger rail operator
- Starting position
- Approximately 30 digital initiatives, a booking system over 15 years old and approximately 60% of bookings already digital.
- Timeframe
- Three-year investment-plan horizon.
- Recorded facts and outcomes
- The portfolio was prioritised from approximately 30 initiatives to 12, linked to customer journeys and a target architecture.
- Judgement, targets and limits
- The approximately EUR 35m investment plan and 85% digital-booking ambition were forward-looking; no achieved increase to 85% is claimed.
Figures are approximate. EUR conversions are documented in the full cases. These engagements illustrate experience, not forecasts for a new assignment.
Other services
The workflow is unclear because ownership and interfaces are unresolved
Operating Model →The immediate need is a cost programme rather than a technology solution
Cost Out →When to do it yourselves
If an existing tool already meets the need and the workflow has a clear owner, test that option internally before commissioning a bespoke AI build.