Bring order to AI adoption and make the value visible. We set priorities, establish practical governance and put AI to work across teams, with clear ownership and a measured link to business and financial results.
12 weeks
from first baseline to an organisation-wide roll-out plan, closed by a board review
Programme design
6 areas
assessed and baselined before anything is built, from tools and skills to governance
Diagnose
100%
of initiatives with a named owner, a success measure and a link to financial value
Accountability
3 stages
diagnose, prove, then scale, each accepted before the next one starts
Our approach
Pro tip:Start with an AI readiness assessment, or go straight to a focused implementation if your priorities are already clear.
About the solution
We help you understand where you stand, decide what matters and put it into practice, with clear responsibilities and success measures at every step.
Let’s talkWorkflows, tools, current AI use and risks mapped. A baseline of adoption, manual effort and costs, with priorities ranked by value, feasibility and risk.
What to do first and why. Each initiative gets an owner, a success measure and a link to the financial outcome it should deliver.
Practical rules for tool selection, data handling, human review and decision rights, refined as adoption grows. We are ISO/IEC 42001 certified ourselves.
Selected workflows rebuilt so AI does the volume and people make the calls, with working pilots compared against the baseline.
Role-specific workshops on real tasks, reusable skills and playbooks, and named owners who keep adoption moving.
An adoption scorecard and a map from each initiative to hours, costs and ROI, reviewed with finance over time.
Platform-agnostic
Whichever platform fits your organisation, we take AI from the first pilot to everyday use across your teams.
What to roll out first, to whom, and how it pays back.
Platforms selected around your workflows, data requirements and existing systems. Your goals and constraints shape the choice.
One process or a whole function redesigned so AI carries the volume and people make the decisions.
Sign-in, workspaces, licences and your first champions set up, with us beside you through go-live.
Role-specific courses on your approved tools, built around what each role should be able to do afterwards.
Named owners and day-to-day help for users, so usage, skills and governance keep moving after launch.
How it works
Department leads interviewed, current AI use and priority workflows measured.
Owners and an internal AI team named, priority use cases chosen and the roadmap agreed.
Approved tools, a tool registry and practical AI-use rules in place.
Role-specific workshops on real tasks, with pilots built alongside each team.
All departments demo together. Results reviewed against the baseline, and the path beyond the programme set.
Example schedule · organisation-wide programme
Workflows, tools, current AI use and risks mapped. A baseline of adoption, manual effort and costs.
Priorities set by value, feasibility and risk, each with an owner, a success measure and an expected financial outcome.
Approved tools rolled out, selected workflows redesigned and pilots built with your teams, measured against the baseline.
Proven practices extended across teams with named owners and playbooks. Adoption, effort and financial results reviewed over time.
The method behind every engagement
Project principles, a rules registry enforced on every pull request and agent skills for your stack.
A written spec and testable definition of done. One scoped task per agent, engineers review and sign off.
Tests run against real data before sign-off.
Each delivery incident becomes a permanent rule, so the same mistake cannot ship twice.
Pattern for the technology department · AI-SDLC inside your own team
Why it holds
From first step to scale
A measured view of where you stand, value proven on a few selected priorities, then a phased roll-out across the organisation.
Readiness assessment
Six areas assessed and baselined, with opportunities ranked by value, feasibility and risk.
Pilot programme
Pilots on the selected priorities, inside real workflows and connected to real systems, measured against the baseline.
Phased roll-out
What worked extended team by team, with named owners, playbooks and regular value reviews.
What you own
A shared baseline of workflows, AI use, risks and success measures.
A prioritised AI roadmap with owners and next steps.
Practical AI-use rules and tool-selection guidance.
Team playbooks, reusable skills and training materials.
Working pilot assets and handover guidance, where in scope.
An adoption scorecard and initiative-to-value map for ongoing reviews.
Our work
From fragmented AI experimentation to a shared operating model: a three-month program spanning company-wide assessment, strategy, harmonized governance, an AI-native product lifecycle and practical team enablement.
Explore case: Bringing structure to AI adoption in PropTechEngagement
Same engineers, either shape. Take a fixed piece of work, or add senior people to your own team.
For the assessment and the pilot. A full programme can run on one fixed fee paid against monthly milestones.
For the rollout and ongoing advisory.
Hourly or monthly, alongside your own AI champions.
We look after what we set up, or run a custom mix as the programme moves from strategy to delivery.
Tell us what you're working on. A senior expert will get back to you.
Next step
