AI strategy
Where AI belongs, and where it does not.
Before anyone prescribes a solution, we build the picture: what your leaders and teams actually need, what is already running (including the tools people adopted on their own), and what your policies and vendor agreements allow.
What you get
- Interviews with leaders and team heads, and a written synthesis of pain points, perceptions, and appetite for change
- An inventory of current AI initiatives, tools, and vendor terms, including data rights and usage restrictions
- A maturity baseline across people, process, data, and governance
- A roadmap with pilots chosen by criteria, and twelve-month targets set against the baseline
AI enablement
Prompts, policy, and people.
Enablement is the work between licensing a tool and getting value from it. Alongside your team, we engineer the prompts and data formats for the tasks they repeat, write the rules for acceptable use, and train the people who will carry it forward.
What you get
- Engineered prompts and formatted inputs for the tasks your teams repeat, placed where the work happens
- An acceptable-use policy covering privacy, intellectual property, procurement, and regulatory exposure
- A lean operating model: a small center that sets standards and tooling, with adoption led inside each team
- Prompt-engineering training for early adopters, and a communications cadence that keeps expectations honest
Workflow design
The work, redrawn around the decision.
We map a workflow as it really runs, then decide step by step where AI should read, draft, and weigh, where deterministic rules must give the same answer every time, and where a person makes the call. Results land in the screens where the work already happens.
What you get
- A current-state map with the manual effort, hand-offs, and waiting measured
- A target design that separates AI steps, rule-based steps, and human decisions
- Pilot selection by criteria: high friction, low regulatory exposure, measurable effort, and a sponsor who wants it
- Named ownership, so what works stays in production after the engagement ends
- 01
Discover
Listen, map, govern. Interviews, an inventory of what already exists, a review of policy and vendor terms, and a maturity baseline. The picture comes before the prescription.
- 02
Establish
Operating model, pilots, capability. Standards and an acceptable-use policy are written down, pilots are chosen by criteria, and training and a communications cadence begin.
- 03
Scale
Operate, measure, institutionalize. What works graduates to production with a named owner, results are published, a standing business review keeps AI on the agenda, and targets are set for the year ahead.
- Cycle time on routine workflows
- Manual effort displaced, in hours
- Error rate and quality
- Time to information and time to decision
- Adoption among the people eligible to use a tool
- Policy attestation and training completion
- Documented productivity savings
- New opportunities surfaced by the people doing the work
Build the picture before prescribing.
A solution chosen before discovery solves the vendor’s problem. Listen first, map what exists, then decide.
Govern first, then scale.
An acceptable-use policy and clear ownership are what make the second pilot easier than the first.
Pilots should be boring.
High friction, low risk, measurable effort, and a sponsor who wants it. Drama is not a selection criterion.
Ownership stays in the business.
A small center sets standards and tooling. The teams doing the work drive adoption and keep what works.
With you, not for you.
We work alongside your team so the capability is yours when we leave: your people write the prompts, own the policy, and run the pilots, with us beside them.