Knowledge and decisions
Help teams retrieve internal knowledge, synthesise information and support research with evidence they can review.
SERVICES / ARTIFICIAL INTELLIGENCEStrategy / Workflows / Adoption
Artificial
Intelligence.
Identify where AI creates value, redesign the work around it, and put the data, delivery and governance in place to make it useful.
Discuss your requirementsArtificial Intelligence / Our focus
Intelligence,
applied.
Scope of work
Practical AI.
Clear business purpose.
Six connected workstreams, shaped around the organisation and the decisions it needs to make.
Discuss your requirementsAI strategy and investment case
Identify the uses of AI that matter to the business. Assess readiness, costs and commercial value before committing resources. For investors, examine AI dependencies and adoption opportunities within the investment case.
- Working output
- A prioritised opportunity map, readiness assessment and investment roadmap.
- Decision supported
- Where to invest first, what to test, and whether to build, buy or work with a specialist.
Data and technical readiness
Establish the data access, quality, security and system connections each use case requires. Compare model and vendor options against the organisation’s operating requirements.
- Working output
- A data-readiness plan, solution requirements and a model or vendor shortlist.
- Decision supported
- Which foundations and dependencies must be addressed before delivery.
Generative AI and intelligent workflows
Develop the use case for knowledge assistants, document processing and workflow automation. Design controlled AI agents to carry out defined tasks, with clear permissions, human review and escalation routes.
- Working output
- Workflow designs, a pilot brief and criteria for testing accuracy, usefulness and control.
- Decision supported
- Which tasks AI should support, which it may execute, and where people remain accountable.
Implementation and integration
Translate a validated use case into a delivery programme. Coordinate technical workstreams, specialist providers and internal teams around integration, testing and operational handover.
- Working output
- An implementation plan, integration requirements and acceptance criteria.
- Decision supported
- What a solution must demonstrate before moving from pilot to everyday use.
Governance and responsible AI
Set the operating rules for AI: approved uses, accountable owners, access controls, evaluation and incident handling. Identify privacy, security and regulatory questions for the appropriate specialists.
- Working output
- An AI governance framework, control register and review responsibilities.
- Decision supported
- Who approves, oversees and maintains each deployment as its use evolves.
Adoption and performance
Prepare leaders and teams to work with AI through role-based training and redesigned responsibilities. Establish baseline measures and a review cycle so the business can track adoption and practical value.
- Working output
- An adoption plan, training priorities and a performance measurement framework.
- Decision supported
- How to embed the change, evaluate results and decide where to scale next.
Scope, responsibilities and specialist input are agreed for each engagement.
Where to start
The business question comes first.
Processes and operations
Assess document-heavy and repetitive workflows for automation, with exceptions and approvals designed into the process.
Investment and ownership
Evaluate AI readiness, costs and delivery dependencies within a business, transaction or portfolio improvement programme.
Illustrative applications. Feasibility depends on the use case, available data and agreed controls.
The engagement
Start focused.
Build on evidence.
A clear decision at each stage.
- 01
Prioritise
Define the business objective, baseline and use case.
- 02
Validate
Test a focused pilot against agreed criteria.
- 03
Implement
Coordinate integration, controls and team readiness.
- 04
Embed
Review adoption, performance and the case to scale.
Connected expertise
