AI strategy consulting that tells you what to build first

What does an AI strategy consulting engagement include?

It includes discovery interviews, a use-case inventory, scoring, vendor testing, a sequenced roadmap and a governance outline. The work is compact on purpose: a strategy that takes longer than the first project it recommends is usually a sign the scope has drifted.

Discovery means sitting with the people who do the work. We ask finance how month-end close runs, ask sales how leads move from the website into HubSpot or Salesforce, and ask operations which reports eat their Monday mornings. Every answer becomes a line in the inventory with an estimated weekly hour count and the systems it touches.

• Leadership workshop to agree goals, constraints and risk appetite.
• Interviews with five to fifteen people across departments.
• System map covering CRM, ERP, document stores and data warehouse.
• Scored use-case inventory and a recommended first project.

How do you score and rank AI use cases?

We score each idea on business value and ease of delivery, then adjust for risk. The result is plotted on a value and ease matrix, and the projects in the high-value, high-ease quadrant become the first wave.

Value is measured in hours, errors or revenue: for example, how many analyst hours a week go into building a status report by hand. Ease looks at whether the inputs are structured, whether the systems expose APIs, and whether staff will change how they work. Risk asks what a wrong answer costs and whether a reviewer can catch it in time. A use case that saves a lot of time but sends unreviewed messages to customers usually moves to a later wave, after the review queue is designed.

What does a good AI roadmap look like?

A good AI roadmap is a 12-month sequence of three to six projects, each with a budget range, owner, success metric and decision date. It reads like a capital plan, not a technology wish list.

Order matters more than most teams expect. If the first project connects your document store to a retrieval layer with citations, the second project (say, an internal policy assistant) can reuse it for a fraction of the effort. We design that reuse deliberately. Each project also has a kill criterion, such as accuracy below an agreed level on the eval set after two iterations, so money stops flowing to ideas that are not working.

How do you pick between OpenAI, Claude, Gemini and Copilot?

We pick by testing the shortlisted vendors on twenty to fifty of your real tasks and comparing accuracy, cost per task and data terms. Benchmark tables rarely match the shape of your documents or the tone your customers expect.

Context also narrows the field. A firm already paying for Microsoft 365 may get most of its early value from Copilot before any custom build. A team with strict data residency rules may prefer Amazon Bedrock or an open-weight model in its own cloud. We also look at the build or buy question for each use case: off-the-shelf software wins when the workflow is common, a custom build wins when the workflow is your advantage.

Why does AI strategy consulting matter when most pilots fail?

Because the failure usually starts with the choice of project, not the code. BCG reported in October 2025 that only 5% of companies get value from AI at scale, and that 60% report no material value at all.

The common thread in stalled programs is a pilot picked for its demo value, built without access to production data, and handed to a team that never asked for it. A disciplined AI strategy fixes those three problems up front by tying each project to a measured baseline, confirming data access before the build, and making the department head who benefits the named owner.

What happens after the AI strategy is approved?

You can take the roadmap to any vendor, give it to your internal team, or ask us to deliver the first project. Many clients choose the last option because the people who scored the use case already know the edge cases.

Our case studies include a regulatory consulting firm where a compliance management tool with recurring tasks, owners and reminders cut status reporting from 4-5 hours to under 15 minutes and saved more than 30 hours a month. That kind of back-office project is often where we recommend starting, because it is measurable and low risk.