Where can AI help?
Prioritise repetitive, information-heavy work. Check data quality and evaluate whether AI improves on a simpler workflow.
AI strategy & readinessFrom ambition to a working system
A considered strategy.
A visible way forward.
The best next step is one you can explain, test and build on. Connect your business priorities to an architecture, a delivery plan and evidence that the work is helping.
The delivery journey
Every stage earns
the next investment.
A reference sequence, not a fixed-duration promise. Scope and milestones depend on access, data quality, integrations and the people available to review the work.
- 01
Frame the opportunity
Map the user journey, current process and the cost of the problem. Select a business owner and establish a measurable baseline.
Decision evidence: A problem brief, opportunity ranking and agreed success measures.
- 02
Design the system
Review data readiness, integration options and security boundaries. Compare build, buy and configure options with operating costs.
Decision evidence: An architecture, dependency map and a costed pilot scope.
- 03
Prove the important assumptions
Build a bounded prototype with representative inputs. Compare it to the baseline, test failure cases and involve the people who will use it.
Decision evidence: Evaluation evidence and a decision to proceed, change direction or stop.
- 04
Engineer the release
Implement production requirements, permissions, tests and deployment controls. Rehearse migration, recovery and operational handover.
Decision evidence: Accepted software increments and a reviewed launch checklist.
- 05
Adopt, operate and improve
Train users, assign support ownership and review performance in real use. Prioritise improvements using evidence rather than feature volume.
Decision evidence: A service review, operating runbook and prioritised roadmap.
Three useful starting points
Choose the question
you need to answer.
A well-defined entry point makes scope, ownership and value easier to discuss.
What should we modernise?
Map fragile dependencies, release bottlenecks and operating costs. Choose an incremental change with measurable benefit.
Application modernisationHow do we connect our work?
Find repeated data entry and dropped handoffs. Define a system of record, an integration contract and exception ownership.
Automation & integrationsResponsible AI in practice
Make quality
part of the plan.
- Compare against a useful non-AI baseline.
- Use representative cases and difficult exceptions.
- Define which sources and tools the system may access.
- Require approval for agreed high-impact actions.
- Track quality, usage costs and failure modes after launch.
Agree the total cost of ownership.
Include discovery, engineering, migration, subscriptions, hosting, model usage, security work, training and maintenance.
For AI, measure cost per completed and accepted task. For cloud, attribute costs to workloads. For business software, account for users, data migration and ongoing administration.
Compare technology choicesThe next move is yours