AI could reshape your products, internal platforms, user workflows, and engineering organization. The possibilities multiply faster than the priorities. Start with business capabilities and user outcomes. Oakmont Partners helps leadership teams decide where to focus, prove the riskiest assumptions, and build the capability to act. The work may begin with opportunity overload or a mandate from the board.
The problem
Every path sounds plausible: build an internal AI platform, add a chatbot or copilot, automate internal work, or create AI product features. Leadership still lacks a shared way to decide where to focus.
A board or executive mandate adds urgency before leaders have agreed on what the strategy must decide or where the work should begin.
The roadmap mixes capability demos, vendor features, and strategic bets without connecting them to user workflows and outcomes or evaluating value, defensibility, feasibility, readiness, and risk.
Engineering has AI coding tools, yet review, governance, quality, and delivery bottlenecks remain.
A coherent AI strategy connects business capability, user workflow, market advantage, and execution capacity. It gives leaders a basis for choosing what to build, what to defer, and what must change inside the organization.
How it works
Start from business capabilities, customer problems, user workflows, and outcomes. Connect them to what AI makes newly possible, then align leaders around where to play, what not to build, and what comes first.
Capability and workflow map · opportunity portfolio · evaluation criteria · executive narrative · sequenced roadmap
Resolve the highest-risk assumptions before committing to a larger build. Use workflow prototypes, reference implementations, design-partner work, and customer validation to create decision evidence.
Prototype or reference implementation · validation plan · architecture options · feasibility findings · next decision
Turn AI coding-tool adoption into execution capacity. Redesign the software-development lifecycle around real work, begin with a credible engineering cohort, and expand from demonstrated results.
Capability assessment · AI-enabled SDLC · structured agent workflows · measured pilot · transformation plan
Engineering transformation
Redesign the engineering system so requirements become explicit, context is assembled deliberately, plans are reviewed before code is generated, output is validated, and learning persists across projects. Those changes turn AI coding tools into durable delivery capacity.
Individual engineers use copilots and coding agents inside the existing process. Gains are inconsistent, and rework can erase them.
Teams introduce specification, research, planning, context engineering, validation, and reusable workflows around real codebases.
Governance, code review, quality controls, team structure, knowledge capture, and delivery metrics evolve around 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 sees opportunities across internal platforms, assistants, workflows, products, and engineering but needs a shared method to evaluate them and choose where to focus.
A board or executive mandate has created urgency, but leadership still needs a shared definition of the strategy, an opportunity set, and a credible place to begin.
Product and engineering need to test a strategic direction before scaling investment or defaulting to a bolt-on feature.
Engineering leaders need a measured path from individual AI coding-tool use to better cycle time, completed scope, quality, and rework.
Selected work
Helped a healthcare company align executive, product, engineering, and data leaders around where AI could create defensible value and what had to change internally to deliver it. The work connected market and product strategy, data foundations, and a measurable engineering-enablement pilot.
Led product and team delivery for an AI operating platform integrating data, workflow, and LLM-assisted planning across account management, media buying, and finance.
Built and led AI, personalization, experimentation, and platform capabilities; 27 granted U.S. patents and published ML and multi-agent research.
Field notes
Ryan has spent more than 20 years building and leading AI, ML, personalization, and product organizations at Adobe, DoorDash, and OneSpot. He works directly with leadership and engineering teams to make the strategic choices, test them against technical reality, and build the internal capability to carry them forward.
A useful first conversation
Bring the board mandate, the blank page, the crowded roadmap, the bolt-on feature, or the engineering transformation that has stalled. We will clarify the next decision and whether Oakmont Partners is the right partner for the work.
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