AI strategy
We help leadership teams decide where AI belongs, what should be built, what should be bought, and how to sequence the investment with eyes open to what a vendor demo doesn't tell you.
A roadmap should settle arguments, not decorate a board meeting. What you walk away with are decisions your executive team can explain and your technical team can act on.
Typical deliverables include technical due diligence, a prioritized opportunity map, build-vs-buy analysis, vendor and model selection input, risk and governance notes, and a roadmap with sequencing and rationale.
This work fits organizations evaluating where AI should enter the product, operations, customer experience, knowledge management, or internal workflow portfolio. It is especially useful before a major vendor commitment, internal platform build, board discussion, or first AI hire.
The hard part of an AI roadmap is not listing use cases. Anyone can name ten. It is deciding which to build, which to buy, which to leave alone, and the order to run them in, with reasons your team can act on. For most of what companies want from AI in 2026, the default is buy, configure, and evaluate; we reserve building for the narrow cases where your data, a specific workflow, or a compliance line makes off-the-shelf genuinely worse for you.
We sequence by value and reversibility: start where the payoff is real, the data exists, and a wrong call is cheap to undo, and defer the expensive, hard-to-reverse commitments until cheap experiments have taught you something. If the question is one scoped decision rather than a full plan, an AI Decision Memo may be the faster path; if it touches an outside product or vendor, see AI technical due diligence. For the longer argument, read how to build an AI roadmap that doesn't end up in a drawer.
Answers
What to build, what to buy, what to ignore, and in what order, each with the reason it ranks where it does and the decision it informs. A ranked wishlist that changes nothing about who does what is not a roadmap.
For most companies in 2026, the default is buy, configure, and evaluate. Building is justified where proprietary data, a specific workflow, or a compliance boundary makes the off-the-shelf option genuinely worse for you.
By value and reversibility. Start where the payoff is real, the data already exists, and a wrong call is cheap to undo; save the expensive, hard-to-reverse commitments for after the cheap experiments produce evidence.
A roadmap covers a portfolio of decisions and their order. The AI Decision Memo is a fixed-scope, time-boxed read on one decision. Many teams start with the memo and grow into a roadmap.
Next step
Share the decision, budget moment, or vendor question that needs a senior technical point of view.
