Technology before the problem
A model or platform is selected before the user need, workflow, decision, and expected improvement are understood.
01 · AI capability
AI Strategy & Readiness
Turn broad AI ambition into a prioritised, evidence-led plan connected to business value, data, risk, and delivery reality.
The context
Begin with the work, evidence, and consequence.
An AI strategy should explain which decisions or workflows are worth improving, what evidence supports the opportunity, and what the organisation must be ready to own. It should also make clear where a simpler software or process change is the better response.
Unclear readiness
Data access, quality, permissions, technical integration, subject expertise, and operational ownership have not been assessed together.
A roadmap without decision gates
Experiments become open-ended initiatives because evidence thresholds, risk boundaries, and stop-or-proceed decisions are not explicit.
Areas of attention
Connect AI behaviour to a dependable product and operation.
The appropriate techniques, controls, and delivery depth follow the use case. These areas keep the technical work connected to the people and decisions around it.
01
Opportunity portfolio
Frame candidate use cases around people, decisions, workflows, value, feasibility, and the cost of failure.
02
Data and technology readiness
Assess relevant sources, access, quality, integration, security, platform constraints, and the work required to support evaluation.
03
Risk and governance context
Identify affected people, sensitive decisions, regulatory context, human oversight, evaluation needs, and accountable owners.
04
Sequenced roadmap
Prioritise discovery, prototypes, pilots, foundations, and production decisions with dependencies and evidence gates.
Potential outputs
Create evidence and artefacts the team can use.
Outputs are shaped around the decision and engagement stage. Each should have a clear audience, purpose, owner, review criteria, and stated limitation.
- 01Prioritised AI opportunity map
- 02Data, technology, people, and governance readiness assessment
- 03Use-case briefs with assumptions and evaluation criteria
- 04Build, buy, or integrate option analysis
- 05Sequenced roadmap with owners and decision gates
Evaluation and responsibility
Make inputs, useful evidence, and boundaries explicit.
AI quality cannot be separated from its context of use. Representative inputs, subject expertise, realistic scenarios, and accountable owners are part of the engineering work.
What we need to understand
- Business objectives and affected workflows
- People who perform, manage, and receive the outcome of the work
- Relevant systems, data sources, policies, and technical constraints
- Existing AI trials, vendor proposals, risks, and investment boundaries
Evidence of a useful direction
- Each opportunity has a specific user, decision, workflow, and intended improvement
- Data and operational dependencies are visible
- Important risks and oversight responsibilities have named owners
- The roadmap contains proportionate evidence and stop-or-proceed decisions
Important boundary
A readiness assessment cannot guarantee that a use case will be feasible, accurate, adopted, or commercially valuable. Those questions require representative evidence and controlled evaluation at the appropriate stage.
Working path
Reduce uncertainty before expanding commitment.
The exact sequence depends on the use case and current evidence. Each stage should leave a reviewable result and an informed choice about the next step.
Frame
Clarify the business context, affected people, current workflow, constraints, and desired change.
Assess
Review opportunities, data, systems, ownership, risk, and organisational readiness.
Prioritise
Compare options using shared value, feasibility, consequence, and evidence criteria.
Plan
Define a sequenced roadmap, accountable owners, evaluation gates, and the next bounded step.
AI capability questions
Important details to clarify.
Early questions should expose the task, evidence, uncertainty, and responsibilities that shape a safe and useful implementation.
Do we need an AI strategy before experimenting?+
Not always. A small, low-risk exploration can create useful evidence, but it should still have a clear question, representative material, boundaries, and an owner.
How do you decide whether AI is appropriate?+
We compare it with simpler process, rules-based, integration, and software options, then consider the variability of the task, available evidence, consequence of error, and operating cost.
Can you review an existing AI roadmap?+
Yes. We can assess the assumptions, use-case definitions, data dependencies, evaluation approach, governance, ownership, and delivery sequence already proposed.
Does the strategy select vendors?+
Vendor or platform options can be compared where that decision is in scope. Selection should follow the use case, data, security, integration, evaluation, portability, and ownership requirements.
Start a conversation
Let's discuss ai strategy and readiness.
Share the workflow, evidence, people affected, and the outcome you want. We can help frame a responsible first question and bounded next step.