Your AI roadmap has more possibilities than priorities.
AI is showing up in product ideas, internal platforms, user workflows, and engineering. The hard part is deciding which ideas to pursue first. Oakmont Partners helps leadership teams turn a board mandate or a crowded opportunity set into a decision they can defend: where to focus, what to test, and what the company has to change to follow through.
Three engagement options
Choose the kind of help you need next.
Decide
Set AI strategy and priorities.
- The problem
- More plausible AI bets than the leadership team can evaluate.
- What I do
- I help the team compare the options and choose.
- You leave with
- A roadmap leadership can explain.
Prove
Test an AI product or workflow.
- The problem
- A promising idea rests on assumptions no meeting can settle.
- What I do
- I build the smallest useful version and test it with users.
- You leave with
- Evidence for the next investment decision.
Enable
Change how software gets built.
- The problem
- AI coding tools are in use, but delivery has not improved in a repeatable way.
- What I do
- I change the workflow with a pilot team and measure what changes.
- You leave with
- An engineering system the rest of the team can adopt.
Decide · AI strategy
Choose which AI bets deserve attention.
A board or CEO can ask for an AI strategy before the team knows which questions it needs to answer. Pressure also comes from below: internal AI platform proposals, chatbots and copilots, product features, and vendor demos all compete for attention.
I bring leadership, product, engineering, and data into the same working sessions. We map the business capabilities and user workflows that matter, then compare each opportunity against value, feasibility, readiness, defensibility, and risk. The team leaves with a defensible answer about what comes first and what can wait.
Prove · working prototypes and evidence
Show what it takes to make the idea work.
Build a small, working version of the idea and put it in front of the people it affects. Learn whether it helps, whether people will use it, what the technology needs, and how the cost changes as the work grows.
The scope stays small: enough to make the question concrete, without building a full product. Then we look at what happened and decide what to do next.
Enable · AI software engineering
Make AI-assisted software delivery repeatable.
A coding agent can make one developer faster and still leave the team with more rework and slower review when it lacks context. Better results require changes to how work is specified, planned, reviewed, and tested.
I start with a small group of respected engineers working on software that is already on the roadmap. We change the workflow around the tools, then measure cycle time, completed scope, quality, and rework before deciding how to expand.
Who you work with
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. Either way, the team should be able to carry the work forward without him.
A useful first conversation
Which AI decisions are expensive to get wrong?
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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