The leadership problem
Move from a catalogue of ideas to an investment decision
AI programmes often start with enthusiasm but weak selection. Teams collect use cases, run disconnected proofs of concept and discover governance or data constraints after time and budget have already been committed.
An opportunity assessment creates a common decision frame. It starts with the business problem and the current operating reality, then tests whether AI is appropriate, whether another form of automation or redesign would be better, and whether the organisation can implement the change responsibly.
What we assess
Value, feasibility and control in the same conversation
Business outcomes
Goals, process friction, service constraints, financial impact and the measures that would justify change.
Current AI use
Existing tools, pilots, shadow AI, duplicated activity and where people are already working around the formal process.
Data and systems
Available sources, permissions, quality, integrations, security boundaries and the systems of record that must remain authoritative.
Operating model
Accountability, human decision points, workflow ownership, skills, adoption barriers and the support needed to sustain the change.
Economics
Baseline effort and cost, plausible benefit, delivery dependencies and how value can be tested before wider investment.
Risk and governance
Privacy, security, legal, model, supplier and reputational considerations, aligned to the organisation's risk appetite.
A practical scorecard
Prioritise the strongest opportunities, not the loudest ideas
Each candidate is considered against four connected questions. The purpose is not to manufacture a single false-precision score. It is to expose the assumptions and trade-offs behind the investment decision.
The output may be “not yet” or “use a simpler approach”. A credible assessment prevents weak use cases from absorbing the budget needed for stronger ones.
What leaders receive
A usable decision pack, not a generic maturity score
Opportunity map
A clear view of candidate use cases, the problems they address and the expected source of value.
Prioritised portfolio
A ranked backlog with value, feasibility, risk, adoption needs, dependencies and named decision owners.
Readiness and control view
The material data, security, privacy, governance and operating-model gaps that must be addressed.
30, 60 and 90-day roadmap
Sequenced actions, decision gates and a recommended first pilot or foundation step.
Measurement plan
Baseline measures, pilot KPIs, unacceptable errors and the evidence needed for a scale or stop decision.
Executive narrative
A concise explanation of the recommendation, trade-offs, investment logic and decisions required from leadership.
How it works
A short path from business context to governed action
The work is adapted to the organisation. Existing governance, assurance, architecture, procurement and risk processes are used where they are effective rather than replaced with a parallel AI bureaucracy.
Standards and evidence
Grounded in recognised guidance, applied to the actual decision
The assessment can draw on relevant parts of recognised frameworks without treating any framework as a one-size-fits-all checklist.
- NIST AI Risk Management Framework Playbook ↗, including Govern, Map, Measure and Manage.
- UK Government AI Playbook ↗, including use-case selection, business cases, governance and quality assurance.
- ICO AI and data protection risk toolkit ↗, where personal data and individual rights are relevant.
Start with the decision
Which AI opportunities deserve investment now?
Bring your strategic priorities, current AI activity and one process where the organisation believes AI could create value. We will identify the most useful scope for an opportunity assessment.
Book a confidential 30-minute scoping call ↗