The scoping trap
Teams pick the AI use case that sounds impressive in a meeting, not the one the data actually supports. The gap surfaces months later, after budget is already spent.
Find where AI actually creates value, before you build anything.
A 2–3 week fixed-fee engagement that pressure-tests every AI candidate against your data, your constraints, and your people, so the project you commit to is the one that can actually survive contact with your operation.
AI Opportunity Sprint
2–3 weeks · fixed fee
Most AI projects fail at scoping, not at engineering. They pick a use case that sounds impressive, discover halfway through that the data isn't there or the workflow can't absorb it, and quietly die.
The Sprint front-loads that risk. In two to three weeks we go through your operation, find where AI can actually create value, and pressure-test it against reality, your data, your constraints, your people. We use Evaluation-Driven Design to force the specific questions that determine whether a project succeeds or fails, before the first line of code is written.
What you get
- A prioritized opportunity heatmap: what to build, in what order, and why
- Feasibility assessment per candidate project, with the ones you should not build called out and explained
- An evaluation framework blueprint, the measurable criteria that would prove each opportunity works
- Stakeholder alignment summary capturing where the organization agrees and where it doesn't
- Honest scope, sequence, and cost estimates
The deliverable is yours whether or not we work together after. If you take the plan and build it with your own team, that's a good outcome. If the Sprint says your project shouldn't be built, that's the cheapest useful answer you'll get this year.
Why AI projects die between idea and production.
No kill criteria
Nobody defined what failure looks like, so a project that should have died at week two survives to week twelve on momentum alone.
Enthusiasm outruns constraints
Leadership commits before anyone checks whether the workflow, the data quality, or the team's capacity can absorb the change.
What you walk away with.
A ranked list, losers included
Not just what to build first, what to rule out, and why, so the conversation about priorities doesn't reopen every quarter.
Confidence before spend
The chosen project has already been pressure-tested against your actual data and constraints, before you've committed a build budget to it.
A shared definition of success
Stakeholders leave agreeing on what "good" means for this project, the argument that usually happens mid-build happens up front instead.
The Sprint's logic in practice.
SunPower
Mapping the whole operation before designing anything
Before Skowak designed Helix, the domain had to be mapped end to end, sales, engineering, installation, sixteen-plus tools, because no single person could see the whole system. That mapping discipline is exactly what the Sprint packages into two to three weeks.
- Design time – hours or days down to 10–25 minutes, once the domain was mapped
- Ecosystem – 16+ products coordinated across three divisions
- Sequence – map first, then design, the same order the Sprint follows
More questions?
The full FAQ, pricing, scope, and how the three engagement tiers fit together, lives on the Services overview.
Not sure the Sprint is the right starting point?
If you're not sure whether you need a Sprint, a Workflow Redesign, or Forward-Deployed Design Engineering, that's a five-minute conversation, not a form to fill out alone.