AI strategy & architecture
Choose a worthwhile AI opportunity, test the assumptions, and design a system the business can operate before committing to a build.
Discuss an AI opportunityTurn an idea into an investment decision.
Consider a team that spends time searching for answers across internal documents. The first decision is whether better search, an existing product, or a custom system is justified.
Map the work
Observe real questions, the sources people use, time spent checking answers, and the consequence of getting one wrong.
Compare the options
Test an existing tool and a small AI-assisted approach against the same questions. Include a process-only alternative.
Draw the boundaries
Name authoritative sources, access rules, the review owner, and what happens when evidence is missing.
Make a funding decision
Agree what the pilot must demonstrate, its operating cost, and the evidence needed to continue or stop.
Start with a testable question
Can people reach a supported answer with less total work, including checking and correcting it? A convincing demonstration alone does not answer that.
Which route deserves the investment?
For a first business case, estimate eligible cases × minutes avoided per case, then subtract review and recovery time. Time released is capacity, not automatically cash savings. Include build, licensing, integration, and operating costs, and make uncertain assumptions visible.
What the work covers
Opportunity and workflow mapping
Map a real workflow, compare alternatives, and separate a useful opportunity from a technology demonstration.
Data and architecture review
Identify source systems, information quality, identity, access, hosting constraints, and the components a first release would need.
Evaluation and delivery planning
Define the manual baseline, representative test cases, cost assumptions, and a staged go/no-go decision.
Possible outputs
- A prioritized opportunity brief
- A proposed system architecture
- Evaluation criteria and operating boundaries
- A staged delivery roadmap
Illustrative outputs. Scope, responsibilities and acceptance criteria are agreed before work starts.
Questions to settle early
- Which workflow is worth changing first?
- What does a useful result look like in the actual work?
- Where is human review essential?
Questions
Does this always lead to an AI build?
No. Plain software, a better integration or a process change may be the better answer.
Do we need all of our data ready first?
No. Data availability, quality and permissions are part of defining a sensible scope.
What should we bring to an initial discussion?
One workflow, a few typical and difficult examples, the tools involved, and someone who owns the outcome. A clear problem is more useful than a finished technology brief.