AI strategy that ends in a funded first project, not a slide deck.
We assess where AI can change real work in your business, check feasibility against your data, systems and risk appetite, and hand over a prioritized roadmap with the first build scoped and ready to start.
- Opportunity inventory and scoring
- Data and systems readiness review
- Feasibility spikes
- Value and cost model
- Sequenced roadmap
- First project brief
Where this helps
What we deliver
How it works
- 01
Frame the goals
We agree the business objectives, constraints and decision-makers, and gather existing plans, systems diagrams and prior AI attempts.
- 02
Discover the work
Structured interviews and workflow walkthroughs with front-line teams uncover where time, errors and delays actually occur.
- 03
Assess and test
We review data and systems for the shortlisted use cases and run feasibility spikes on the ones with the most uncertainty.
- 04
Prioritize together
A working session with your stakeholders scores and ranks the options, so the roadmap reflects your judgment and not just ours.
- 05
Hand over the plan
You receive the roadmap, value model and first project brief, walked through with the people who will fund and run the work.
Design decisions we make with you
Build, buy or wait
Some use cases are best served by a feature already in software you own; some need custom work; some are not ready yet. We say which, and why.
Where value is measured
Every recommended project comes with a baseline and a metric the business already trusts, so results can be checked after launch.
Risk appetite
We map each use case to the level of human oversight, data sensitivity and regulatory exposure it carries, and recommend controls to match.
Platform versus point solutions
If several projects need the same retrieval layer, model access or monitoring, we recommend building it once and say when that pays off.
Internal capability
The roadmap names which skills you should build in-house, which to rent, and what a realistic hiring or training plan looks like.
Applications
Related capabilities
- Responsible AI & GovernancePractical ownership, review, privacy and audit controls that let teams ship AI without losing track of what it does.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- AI AgentsAgents that take approved actions across your tools, with permissions, review points and monitoring built in.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
Questions buyers ask
It depends on scope. A focused assessment of one function is shorter than a company-wide roadmap. We agree the scope, participants and outputs up front so the timeline is fixed before we start.
No. Some recommendations will be to buy an existing product, use a feature you already license, or not use AI at all for a given task. The roadmap is useful whoever builds it.
A sponsor who can make decisions, access to the people who do the work, and read access to the relevant systems or data samples. We keep time demands on your teams to structured sessions.
We set a baseline for each use case using metrics the business already tracks, then estimate the change and the cost. Our article on measuring AI value explains the approach.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.