01
Define
Clarify the problem, users, constraints, and success criteria before anyone commits to a solution.
Advisory
AI Product Engineering Sprint
Ship important roadmap initiatives dramatically faster.
I work alongside an appointed champion—ideally a product-minded engineer—on a high-value roadmap initiative, from problem definition through working implementation and validation.
The sprint is meant to move quickly, and to show the ownership and resources now available to a single contributor. The aspiration is a much shorter cycle than a traditional process. It is not a guaranteed result.
The Problem
Traditional product development still contains a lot of sequential work and handoffs:
Product discovery → Requirements → Design → Engineering planning → Implementation → QA → Validation
AI can compress many of these activities. Giving engineers an AI coding tool is not enough.
The organization also needs to rethink who frames the problem, how options get explored, and how much of the path from idea to something real a single product-minded engineer can now own.
The Sprint
Five stages. The champion stays involved in each of them. Stakeholders enter when the work needs domain input or validation—not as a standing working group.
02
Explore
Use AI-assisted research, prototyping, and technical investigation to evaluate possible approaches quickly.
03
Build
Work with the champion using AI-native development workflows to turn the strongest concept into a working implementation. I may get hands-on with the code when it is useful. This is not “hire me to write the feature.” It is to show how much one person can now own.
04
Validate
Put the result in front of users and stakeholders and determine what actually works.
05
Productionize
Leave the champion with the technical direction, implementation plan, and workflows needed to take the work into production. Production ownership stays with the organization.
Not an Outsourced Development Team
Your champion stays in the driver's seat.
I don't replace your Product Managers or engineers, and I don't run a parallel squad. I work with one appointed counterpart—ideally a product-minded engineer—to show how AI-native Product Engineering changes what a single contributor can own, and how quickly they can move.
The champion brings the domain knowledge and remains the owner of the work. The organization retains production ownership. I bring the methodology, a product and technical perspective, and AI-assisted execution.
You provide
- An appointed champion—ideally a product-minded engineer
- Access to AI tools the champion can actually use—Cursor, Claude, or equivalent
- Product and domain expertise
- Existing codebase and technical context
- Access to stakeholders when validation is needed
- Production ownership and review
I provide
- Product discovery and problem framing
- Rapid solution exploration
- AI-assisted prototyping
- Product and UX direction
- Technical investigation
- AI-native development workflows
- Architecture and implementation guidance where it helps
- User and stakeholder validation
- A path from prototype to production
The sprint does not have to produce an AI feature
Use AI to build the right thing faster—not build something merely because it uses AI.
AI isn't only useful for building AI products. It can change how quickly organizations build software of any kind.
The initiative might be:
- A new customer onboarding flow
- A major workflow redesign
- A new reporting experience
- An internal operations tool
- A new integration
- A complicated CRUD workflow
- A new customer-facing product capability
- An AI-powered feature
What AI Actually Changes
AI is used across the product engineering loop—not simply the act of typing code.
Research
Synthesize customer feedback, documentation, analytics, and existing knowledge.
Prototyping
Explore multiple product and UX concepts before committing to a larger implementation.
Codebase understanding
Navigate unfamiliar systems, understand dependencies, and identify implementation constraints.
Implementation
Use agentic coding and AI-assisted development to accelerate construction—not as a substitute for engineering judgment.
Testing
Generate tests, surface edge cases, and move QA earlier in the loop.
Iteration
Move quickly between feedback, changes, and validation.
What You Walk Away With
The primary deliverable is progress on a real roadmap initiative—not a pile of consulting documents.
- A working prototype or implementation on a real initiative
- Validated product direction
- Product and UX decisions
- A technical approach the champion can stand behind
- AI workflow recommendations the champion can keep using
- A production implementation plan
- Identified risks and open questions
Who It's For
Organizations with a Product and Engineering function, a real roadmap, and someone who can own the sprint as champion—not a committee.
- An existing Product and Engineering function
- A meaningful software roadmap
- Pressure to increase development velocity
- Some experimentation with AI already underway
- A high-value initiative that has been difficult or slow to move
- A champion who can own the work—not a committee
Typical conversations start with a CTO, VP Engineering, VP Product, CPO, Head of Product, or a founder or CEO of a B2B SaaS company.
Compress the stages. Don't skip the work.
The goal is not to skip discovery, design, engineering, or validation. It is to spend less time moving between those stages.
Traditional approach
- Product discovery
- Requirements
- Design
- Engineering planning
- Implementation
- QA
- Validation
AI-native approach
- Problem
- Rapid research
- Prototype
- Technical validation
- Working implementation
- User validation
Have a roadmap initiative that's taking too long to get moving?
Let's explore whether an AI Product Engineering Sprint could help.