AI Security & Compliance Governance
Frameworks for responsible AI, data protection, model risk management, and regulatory alignment.
Read moreApplied AI
AI Strategy Consultancy
Most AI programmes stall for the same two reasons: the use case was chosen because it was interesting rather than valuable, and the data underneath it was never good enough. Strategy work that takes those two questions seriously is worth more than any model selection exercise.
When this helps
You are probably reading this because of one of these.
- The board is asking what your AI strategy is and you need a real answer
- Pilots demo well and never reach production
- You cannot tell whether your data is good enough to support the ambition
- Teams are adopting AI tools with no policy or oversight
How we help
A delivery sequence, not a discovery phase that never ends.
Every stage produces something you can act on independently, so the engagement can stop at any point without leaving you stranded mid-programme.
Identify
Find candidate use cases from real process cost and volume, then rank them by value against feasibility.
Assess
Test whether the data behind the top candidates exists, is accessible, and is good enough to rely on.
Prove
Run one narrow case end to end with success criteria agreed before it starts, not rationalised after.
Scale
Sequence the roadmap, set the guardrails, and plan the change management that adoption actually depends on.
Efficiencies driven
The measurable change this engagement is aiming at.
Where teams usually start
- Use cases chosen because they are interesting
- Data quality discovered during the pilot
- Pilots with success criteria written afterwards
Where the engagement leaves you
- Use cases ranked by value against feasibility
- Data readiness assessed before commitment
- Stage gates with criteria agreed up front
Ranked
Use cases by value and feasibility
1 case
Proven before scaling spend
Stage
Gates with exit points
What you receive
Artefacts that outlive the engagement.
- Ranked use case portfolio with value and feasibility scoring
- Data readiness assessment against the leading candidates
- Proof of value on one case with pre-agreed success criteria
- Sequenced AI roadmap with investment stages and exit points
- Adoption, policy, and change management plan
Next step
Build an AI roadmap.
A short call is usually enough to work out whether this is the right engagement, and what it would cost. If it is not, I will say so.
Engagement
Applied AI
Then data, then models