AI could change your products, internal platforms, user workflows, and the way your engineers build. The hard part is deciding where to start. Oakmont Partners helps leadership teams turn a crowded set of possibilities or a mandate from the board or CEO into clear decisions: where to focus, which assumptions to test, and what needs to change inside the company.
The problem
Every path sounds plausible. Build an internal AI platform. Add a chatbot or copilot. Automate internal work. Put AI into the product. Leadership still needs a shared way to decide which bets deserve attention.
The board or CEO wants an AI strategy. The request creates urgency before the leadership team agrees on the questions the strategy needs to answer.
The roadmap mixes demos, vendor features, and strategic bets. Few are tied to a user workflow or outcome, and the team has no consistent way to compare value, defensibility, feasibility, readiness, and risk.
Engineering teams have AI coding tools, but review, governance, quality, and delivery remain bottlenecks.
A useful AI strategy connects what the business does well, where users get stuck, why the opportunity matters, and whether the organization can deliver. It gives leaders a shared basis for what to build, what to defer, and what has to change inside the company.
How it works
Start with the capabilities that matter to the business and the problems users are trying to solve. Map those needs to what AI now makes possible, then give leaders a shared basis for deciding where to focus, what to defer, and what comes first.
Capability and workflow map · opportunity portfolio · evaluation criteria · executive narrative · sequenced roadmap
A prototype helps when the team cannot settle an important question on paper. Build only enough to test the riskiest assumptions, put the workflow in front of users, and decide whether a larger investment makes sense.
Prototype or reference implementation · validation plan · architecture options · feasibility findings · next decision
Put AI coding tools to work on the software your team already needs to ship. Redesign how teams specify work, gather context, plan, review, test, and retain what they learn. Start with a small group of respected engineers, measure the effect on shipped work, and expand from there.
Capability assessment · AI-enabled SDLC · structured agent workflows · measured pilot · transformation plan
Engineering transformation
AI coding tools get more useful when the work around them changes. Teams need clearer requirements, the right codebase and business context, reviewed plans, reliable validation, and a way to carry learning from one project to the next. That is how the tools help teams ship more without giving up engineering judgment.
Individual engineers use copilots and coding agents inside the existing process. The gains vary, and rework can erase them.
Teams add clearer specifications, codebase research, plan review, context engineering, validation, and workflows they can reuse on the next project.
Review, governance, team structure, knowledge capture, and delivery metrics change to support the new way of working.
Measure what changes: cycle time, completed scope, quality, and rework. Licenses issued and prompts sent are activity counts.
When to bring in Oakmont Partners
Leadership has plausible ideas across internal platforms, assistants, product workflows, and engineering. The team needs a shared way to compare them and choose.
The board or CEO has asked for an AI strategy before the leadership team agrees on the questions it should answer or where the work should begin.
A product or platform idea looks promising, but the expensive assumptions are still untested. A focused prototype can show whether it belongs in the workflow before the investment grows.
The tools are in engineers' hands. Leaders need to know whether they are improving cycle time, completed scope, quality, or rework.
Selected work
Helped a healthcare company bring executive, product, engineering, and data leaders to a shared view of where AI could create defensible value. The work covered product and market direction, the data required to support it, and a measured pilot to improve how engineering teams work with AI.
Led product strategy and team delivery for an AI operating platform that brought data, workflow, and LLM-assisted planning into the daily work of account management, media buying, and finance.
More than 20 years building and leading AI, personalization, experimentation, and platform products; 27 granted U.S. patents and published research in ML and multi-agent systems.
Field notes
Ryan has spent more than 20 years building AI and data products and leading product and engineering teams at Adobe, DoorDash, and OneSpot. He works directly with leadership and engineering teams. Sometimes the work is deciding where to focus. Sometimes the fastest way to answer a strategy question is to build something small and put it in front of users. In either case, the team should be able to carry the work forward without him.
A useful first conversation
Bring the decision that is stuck: a board mandate with no starting point, a roadmap crowded with good ideas, a product direction that needs proof, or an engineering transformation that is not showing up in delivery. Share a little context, then choose a time. Your inquiry is saved before you reach the calendar.
Step 1 of 2 · Share what you are working through
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