AI strategy and roadmaps
A sequenced plan tied to business priorities, with owners, dependencies and decision points.
- Vision and principles
- Value-based prioritization
- Roadmap and investment sequencing
AI consultancy
We work with leadership and delivery teams to build AI capability that is strategic, governed and embedded in how the business operates.
Most organizations are experimenting with AI.
Far fewer have changed how they work because of it.
The gap is rarely the model. It is strategy, ownership and structure.
That is where our consultancy work sits: deciding what to pursue, setting up who owns it and how it is governed, and helping people adopt it.
Eight services, grouped by the question they answer
Most engagements combine services from more than one group. We scope to the gap you have, not to a package.
Where AI should go first
Direction before delivery: what to pursue, in what order, and why.
AI readiness assessments
A candid view of data, platforms, skills, governance and culture, so the roadmap starts from where you actually are.
- Data and platform readiness
- Skills and operating-model gaps
- Regulatory readiness
Workshops and use-case discovery
Facilitated sessions that surface candidate use cases, test them for value and feasibility, and align the people who must back them.
- Opportunity mapping
- Prioritization
- Executive alignment
Who owns it and how it is governed
The structures that let AI work repeat without starting from zero.
AI Center of Excellence
Design the central team, funding model and platform choices, and how the CoE works with business units.
- Operating model
- Talent strategy
- Platform selection
Governance and responsible AI
Policies, risk controls and review processes mapped to the regulations and standards you are subject to.
- Responsible AI policy
- Risk and security frameworks
- Compliance mapping
AI team setup and practice building
Roles, toolchain and ways of working for a first AI team, or a stronger one.
- Role design
- Toolchain
- Delivery model
How it sticks
Tools only matter once people use them well.
AI Champions program
Identify and train people inside business units who carry AI into everyday work and support their peers.
- Champion selection
- Curriculum
- Community of practice
Adoption strategy
Change management, communication and usage measures, so tools get adopted and not only launched.
- Change management
- Stakeholder communication
- Adoption metrics
Roadmaps are owned by the people who will run them
We build in working sessions with leadership and delivery teams together, so priorities, trade-offs and ownership are settled in the room and not handed over in a deck.
Five steps from first conversation to lasting capability
The same path whether you are starting from nothing or fixing a stalled program. Each step builds on the one below it.
Step 1 of 5
Discover
Understand the business context, the current level of AI maturity and the strategic priorities.
Typical focus
- Stakeholder interviews
- Maturity, data and platform review
- Longlist of candidate use cases
Step 2 of 5
Align
Bring leadership and teams to one vision, one roadmap and one set of success measures.
Typical focus
- Shared vision and principles
- Prioritized roadmap
- Success measures and named owners
Step 3 of 5
Build
Stand up the teams, tooling, governance and processes the roadmap depends on.
Typical focus
- CoE or team set-up
- Governance framework
- Platform and toolchain decisions
Step 4 of 5
Activate
Launch pilots, train champions and turn early results into working practice.
Typical focus
- Pilots with clear exit criteria
- Champions program
- Feedback loops with users
Step 5 of 5
Scale
Expand what worked, embed it in operations and build capability that outlasts the engagement.
Typical focus
- Playbooks for repeatable delivery
- Operating cadence
- Capability handover
Two directions, one destination
Leadership sets direction while people inside teams build momentum. Running both at once is what keeps a program from stalling in either place.
Top-down
Direction, policy and structure from the leadership level.
- Executive vision and strategy
- AI governance and policy
- CoE set-up and leadership
- Enterprise roadmap
Bottom-up
Discovery, experiments and adoption from inside the teams.
- AI Champions inside teams
- Workshop-driven discovery
- Rapid proofs of concept
- Grassroots adoption
An organization that can plan, build and govern AI
without starting from zero each time.
Ten themes the practice is organized around
- AI strategy and transformation
- Adoption and change management
- AI literacy and capability building
- Data foundations and data governance
- Intelligent automation and agentic systems
- AI governance and responsible AI
- Security, risk and compliance
- AI CoE and operating models
- AI engineering and production operations
- Business value and ROI realization
Start with a conversation about where AI should, and should not, go
Tell us what you are trying to change. We will say plainly whether and how we can help.