AI Strategy & Roadmapping
The useful AI opportunities usually sit inside work your team already does: document review, data extraction, research, reporting, quality checks and handoffs. We look at that work with you, rank where AI is worth using, and set out what to test first and how you will judge it.
Decision support before delivery
Best suited toPartners, heads of practice and operations leaders who can give us access to the people and material behind a recurring workflow.
Not designed forA strategy deck built without operators, source material, decision rights, or a realistic path to validation.
Know where to start.
Workflow opportunity assessment
We review the documents, handoffs, tools and exceptions behind a piece of recurring work. The aim is to find where AI could shorten turnaround, reduce rework or make quality more consistent, and where it shouldn't be used at all.
You get a ranked map of opportunities, scored on value, feasibility, risk and how quickly the people doing the work could test the result.
Risk and governance
For each candidate workflow we set out what data it would touch, where it would be processed, who reviews the output and which decisions must stay with a person.
That gives partners, compliance and IT a concrete proposal to assess, rather than a general request to approve AI.
Feasibility on real documents
We test the leading idea against a sample of your own material: can these documents be read reliably, and can the output be checked quickly by the person responsible for it?
AutoBOQ is an example of the same test applied to construction drawings: proposed quantities, each linked to where it was measured, for an estimator to review.
Audit-to-production roadmap
The roadmap is a short sequence of decisions: what to test first, what evidence would justify building it properly, what should wait, and who owns each step.
It names the first document set, the success measures, the data gaps, the integrations, the review steps and the point at which you decide whether to scale.
What the engagement includes
This engagement is for firms under pressure to do something with AI but without an agreed order to do it in. It turns a broad ambition into a few workflow decisions that can be tested, funded, governed and owned.
Delivery process
- 01
Workflow evidence
We review representative documents, systems, handoffs, exceptions, current cycle time, quality checks, and the decisions that still require expert judgment.
- 02
Opportunity and risk map
Candidate workflows are ranked against business value, technical feasibility, data readiness, operational risk, and the speed at which a real user can validate the result.
- 03
System and governance shape
For the leading opportunities, we outline data sources, model or retrieval needs, integrations, human approvals, evaluation criteria, security boundaries, and ownership.
- 04
Roadmap and decision gate
The final roadmap states what to test first, what evidence would justify production investment, what should wait, and who owns each 30/60/90-day action.
Deliverables
- Current-state workflow map and named bottlenecks
- Ranked AI opportunity portfolio with value, feasibility, and risk rationale
- Recommended first use case and narrow validation scope
- Data, integration, governance, and evaluation requirements
- 30/60/90-day roadmap with owners and decision gates
Measurement and engagement shape
Success is not the number of ideas generated. It is whether leadership can make a clear build, test, defer, or stop decision using shared evidence and named success criteria.
Scope and timing are agreed after an initial workflow conversation because stakeholder count, document access, and regulatory context materially change the work. The written proposal identifies participants, inputs, outputs, exclusions, and the decision the engagement must support.
Evidence and preparation
Questions about AI Strategy & Roadmapping
What happens in a workflow audit?
We review real documents, handoffs, tools, exceptions and what the work costs today. The output is a ranked map of the workflows worth testing first, with success measures and a short roadmap from audit to sprint.
Do we need a finished AI idea before starting?
No. The audit suits teams that know a piece of work takes too long but aren't sure whether AI should handle extraction, review, drafting, routing, or only part of the process.
Why start with documents instead of a broad AI strategy?
Documents expose the real operational cost: turnaround time, expert review, rework, missed scope, compliance risk, and handoffs. That makes the first AI opportunity easier to value and easier to test.
Go deeper with the free course:
AI Adoption for Financial ServicesOther services
- AI Agent Development ServicesAgents that read files, extract facts, draft outputs, work with your systems and pass uncertain cases to a person.
- RAG Development & AI Data InfrastructurePipelines, retrieval, evaluation and monitoring that keep AI accurate on your documents after launch.
- Rapid AI MVPA 28-day sprint that proves one AI workflow on your own files before you commit to a larger build.
Start with one workflow.
Tell us which piece of work you’d like AI to take on. We’ll reply with what a system could do, what it should leave to a person, and what a first version would take to build.