AI Product Strategy
Every AI initiative on one page, ranked, with the ones you should stop marked as stopped.
Engagement length · 2-4 weeks as a diagnostic sprint, 8-12 weeks for a full strategy engagement
Outcomes
What the engagement creates.
ProductExec engagements are built around the artifacts, decisions, and operating habits your team needs after the advisory work ends.
ProductExec works with CEOs and product leaders who have more AI initiatives than owners.
Prioritized AI bets that can survive executive scrutiny.
A concise AI product opportunity map focused on durable customer value.
Operating model decisions — decision rights, rituals, and scorecards that reduce drag.
A practical roadmap from thesis to shipped capability.
Diagnostic sprint
Two to four weeks, when the question is where to start.
The diagnostic sprint is the short form of this engagement. It turns scattered AI ideas into a focused product strategy, operating model, and execution roadmap before delivery capacity is committed.
Teams that already know their thesis usually skip it and run the full 8-12 week engagement.
Highest-value AI product and workflow opportunities mapped against customer value and operating readiness.
Useful automation separated from low-impact experiments.
Data, process, governance, and adoption constraints identified before delivery capacity is committed.
A prioritized execution path with the roadmap, decision cadence, and leadership ownership needed to move from idea to execution.
Customer workflows, product strategy, operating constraints, data readiness, and AI pressure points reviewed in one pass.
Opportunities ranked by customer value, feasibility, operating readiness, risk, and leadership urgency.
Proof
What changed, and what stayed in place.
Context
European automotive retail SaaS, four product tribes, dealer and OEM customers.
Situation
AI, machine learning, and conversational AI work was running across product lines without a shared thesis or a single owner.
Intervention
Consolidated the AI work behind a smaller set of product bets and named an owner for each.
Outcome
A single AI thesis with a named owner behind each bet, and a shorter list of committed initiatives.
Engagement Model
A senior, decision-oriented working rhythm.
This engagement runs 2-4 weeks as a diagnostic sprint, 8-12 weeks for a full strategy engagement. Pricing is by scope, not by hours.
Find the bets
Evaluate where AI changes the customer experience, cost structure, or competitive position.
Align the leadership team
Clarify decision rights, tradeoffs, risks, and investment posture before teams commit.
Translate to a roadmap
Translate strategy into operating cadence, metrics, delivery flow, and accountability.
The first step
Ninety minutes on the decision you are actually stuck on.
Most advisory relationships start with a discovery call that discovers nothing. This one starts with work. Send the context beforehand — the roadmap, the org chart, the three AI initiatives nobody owns. We spend ninety minutes on the single decision blocking the others. You leave with a written point of view: what you should stop, what you should own, and what the next ninety days look like.
Format
90 minutes, one decision
Deliverable
Written point of view within 48 hours
Price
$1,500, credited against any engagement
Not a fit if you want a vendor evaluation, a staffing plan, or a deck to circulate. If ninety minutes shows this is not a fit, Kevin will say so and point you somewhere better.
Fit
Best fit scenarios.
ProductExec works with CEOs and product leaders who have more AI initiatives than owners.
Leadership teams that need to know which AI bets are worth building and what operating changes are needed to ship responsibly.
Product organizations that want to choose fewer bets and ship the right ones.
Leaders who want operating judgment, not framework theater.
Start
Send the context.
Share a little context and Kevin will respond directly.