Problem
We feel behind on AI but do not know where to start.
Our pilots do not reach production.
We cannot assess the risk of a use case.
Outcomes
- A funded, prioritised AI plan
- A clear view of readiness and risk
- Confidence in what to build and what to avoid
Scope
In scope
- Use-case assessment
- Readiness and risk review
- AI roadmap
Not included
- Building the models themselves (see generative AI solutions)
- Model governance operations (see responsible AI)
Intelligent automation
Many repetitive processes can be automated with workflow tools, with AI added where judgement or unstructured information is involved. We identify the processes worth automating, design the automation with people in control of exceptions, and build it so it can be supported.
- Process discovery and automation candidates.
- Workflow and robotic process automation, combined with AI where it helps.
- Exception handling and human review built in.
Approach
Discovery
We agree the problem, the data and the constraints, and how success will be judged.
Delivery planning
We shape the approach, sequence the work and set out costs, risks and controls.
Build and test
We build in the open, test against real data and cases, and prove it before it goes near production.
Operational readiness, hypercare and closure
We hand over, support the first weeks of use, and confirm the outcome before we close.
Deliverables
Technologies
Controls
Frequently Asked Questions
Will you tell us not to use AI?
Where that is the honest answer, yes. We would rather save you a failed project.
Do you account for safety and regulation?
Yes. Risk, safety and governance are part of the assessment, not a later surprise.
Ready to scope this?
Tell us what you are hoping AI can do and we will give you an honest plan.