01 Value
Why should this be done at all?
We start from the business case, not technology enthusiasm. Problem, benefit, measurement and strategic fit are defined in numbers.
- Problem: which business problem, how is it solved today, what does it cost?
- Benefit: cost reduction, revenue, risk reduction, experience; which and how much?
- Measurement: target KPIs, expected improvement, ROI and payback period
- Strategic fit: aligned with company strategy, a management priority?
Output A business case that speaks in numbers and a prioritised scenario portfolio
02 Readiness
Do we have the inputs we need?
Even the best idea stalls without data and organisational readiness. We check four axes before starting.
- Data: sources available and digital; quality, freshness and ownership clear?
- Integration: systems and APIs ready; real-time access possible?
- Organisation: does the business unit own it; sponsor and experts identified?
- Technology: cloud/security infrastructure, AI platform and licences ready?
Output A readiness scorecard with gaps closed before the project; no surprises
03 Feasibility
Can we actually build this?
An honest engineering assessment: AI is not always the right answer. Classic software, classic ML, agents or generative AI; which one is needed?
- Technical fit: should AI solve this at all; which approach?
- Complexity: how many systems, data sources and users are affected?
- Model performance: expected accuracy, impact of a wrong output, is human approval needed?
- Project feasibility: MVP duration, team, external dependencies, technical risks
Output A realistic MVP plan with clear scope and identified risks
04 Operations
How will it live after go-live?
AI systems are products, not projects; go-live is a beginning, not an end.
- Operating model: product owner, process owner, operations and support defined
- User management: training, adoption tracking, feedback loops
- Monitoring: performance, metrics and error management in real time
- Financial operations: monthly run cost, cost projection with growth, budget ownership
Output A living, measured, continuously improving AI product
05 Governance
Is it secure, controlled and auditable?
AI must meet the same standards as every other critical system. Governance is not bolted on; it is part of the architecture.
- Security: sensitive/personal data map, data residency, control of data flowing to models, masking
- Compliance: privacy impact, sector regulation, internal policy requirements
- Risk: assessment of hallucination, wrong decisions, data leakage and prompt injection
- Control and auditability: human approval points, automation limits, kill-switch, decision history
Output An audit-ready, controlled, trustworthy AI operation
