Service / Strategy Sprint
AI Product Strategy Sprint
For the moment your team has explored AI enough to know it matters, and now has to commit roadmap and budget to a specific bet. Four to six weeks. Fixed-price. The output is a defensible product thesis, a technical feasibility read, and an actionable roadmap.
Schedule a ConversationWhen exploration meets commitment
When the team has to actually pick the bet
Every team I talk to is somewhere in the same cycle. Leadership has decided AI is strategically important. The board is asking. Competitors are announcing AI features. There's pressure to move, and it usually manifests as one of two patterns: a series of experiments that never converge into a product, or a major commitment made before the team understands whether it's the right one.
Both patterns share a root cause: no structured product thesis grounded in technical reality. Most product leaders aren't equipped to evaluate technical feasibility. Most engineering leaders aren't practiced at framing technical capability as product value. The result is a strategy conversation where neither side has full information — and the decision gets made anyway.
The Sprint exists to fill that gap before the big commitments are made.
Who it fits
- Founders with a technical thesis who have a product vision but need structured thinking about where AI fits and how to sequence the work before the next funding milestone.
- Product leaders at growth-stage companies under pressure to add AI capabilities and want to commit strategically rather than reactively.
- CTOs and engineering leaders evaluating AI bets and needing a credible outside perspective to pressure-test architecture and team capability before commitment.
- Teams with a working prototype who've shipped a demo, gotten leadership interest, and now have to decide whether the path to production is real or aspirational.
- Teams that have explored and stalled who've done the chatbot work, built throwaway prototypes, and still can't articulate the bet clearly enough to commit.
What changes by the end
From a debated AI ambition to a roadmap your engineers can ship against
Before
Half a dozen possible AI bets, no agreed sequencing, an engineering team uncertain which architecture to commit to, and leadership pressure to move that's about to produce a wrong answer fast.
After
A written product thesis, a phased roadmap with explicit build/buy/integrate calls, and a feasibility read your team helped shape — so the next quarter of work is clear, sequenced, and defensible to the board.
What you receive
Six concrete deliverables, not a deck
Four to six weeks of structured work, embedded with your team. The Sprint produces a working plan your engineers can execute — not a document that gets filed.
Opportunity landscape assessment
An evaluation of the AI opportunities your business can credibly pursue. I look at your data assets, product surface, competitive landscape, and customer needs to identify where AI creates durable value rather than novelty. Not what's technically possible — what's commercially meaningful for your specific situation.
Product thesis
A clear articulation of what you're building, why it matters to your customers, and how it fits your broader strategy. The thesis connects technical capability to customer value and gives your team a decision-making framework for the hundreds of smaller choices that follow.
Technical feasibility read
An honest assessment of what your team can build, what infrastructure you need, and where the real technical risks are. I draw on twenty-plus years of building production AI systems to evaluate feasibility in practice — given your team's skills, your data quality, and your operational constraints.
Actionable roadmap
A phased plan with concrete milestones, clear decision points, and explicit build-vs-buy-vs-integrate recommendations for each component. Designed to de-risk by starting with the highest-uncertainty work, so you learn fast and can adjust before committing fully.
Build / buy / integrate decisions
For each component of the roadmap, a clear recommendation on whether to build custom, buy off-the-shelf, or integrate existing services. Each recommendation includes the reasoning, the trade-offs, and the conditions under which you'd revisit it.
Walkthrough with your team
A live closing session with product, engineering, and leadership. The output is a plan your team believes in because they helped shape it — not a document that gets filed and forgotten.
Not a fit
When to skip the Sprint
- You haven't done any AI exploration yet — start with reading and prototyping. The Sprint is for teams who've explored enough to know AI matters and now have to commit.
- You need a slide-deck strategy document for board optics. The Sprint produces a roadmap your engineers will actually execute against, not a polished narrative.
- You're stuck on a single decision rather than a whole bet. The AI Decision Review is the right shape — and the fee is credited against a Sprint within 60 days.
- Your AI is already live and the question is why it's not improving. That's a Production AI Review, not a Strategy Sprint.
Common questions
What teams ask before a Sprint
-
How long does the sprint take?
Four to six weeks end-to-end. The first week is discovery and access — codebase, data, customer interviews where appropriate, and stakeholder alignment. The middle weeks are analysis and synthesis. The final week is the working roadmap, the build/buy/integrate calls, and a live walkthrough with your team. -
What does the sprint cost?
The sprint is fixed-price, not hourly. The exact figure depends on scope — number of stakeholders, depth of technical review, and whether implementation planning extends across multiple workstreams. I share specifics on the first call once I understand the shape of the work. If a full sprint is more than your team needs right now, the AI Decision Review is a smaller, single-issue alternative — and the fee is credited against a Sprint if you start one within 60 days. -
Who needs to be involved on my team?
At minimum: a product owner, an engineering lead, and an executive sponsor. The sprint works best when your team is in the room with me, not when I disappear and return with a deck. Expect roughly two to four hours per week from each core participant. -
We already have a prototype. Is the sprint still useful?
Often more useful, not less. A working prototype gives me concrete signal — the actual data, the actual edge cases, the actual architecture choices. Most sprints I run are with teams that have something running but can't get it to production-grade reliability or can't tell whether to keep building on it. -
Do you take on the implementation after the sprint?
Sometimes, when it's a clean fit and the work is in my wheelhouse. More often I hand the roadmap to your team or to an implementation partner I trust. The sprint is designed to set you up to execute, not to lock you into a longer engagement. -
What if scope changes mid-engagement?
Small drift is normal — discovery often reshapes the second-week priorities. Material scope changes get a written re-scope and a transparent conversation about timeline impact. I'd rather pause and re-align than deliver work that no longer matches what you actually need. -
Do you sign NDAs?
Yes — happy to sign yours, or use a simple mutual NDA if you don't have a standard.
Start the conversation
Tell me what you're committing to
A few sentences on the bet you're considering, where the team is in its exploration, and what's blocking commitment. I'll respond personally within a day or two.