Discovery / Analysis Sprint
For leaders who know AI matters but do not yet have a defensible answer to where it should matter first.
We study real workflows, people, data, source material, current experiments, and implementation constraints. We build enough evidence to recommend the first useful capability and the operating model that should carry it.
Typical shape: 2–4 weeks, adjusted to the number and complexity of workflows in scope.
Questions It Answers
- Which workflows have enough consequence and source material to deserve attention?
- Where is judgment trapped in people, spreadsheets, handoffs, or disconnected systems?
- What is the smallest capability that could improve the work and teach us something real?
- What evidence would justify implementation, and what evidence should stop it?
- If the evidence is strong, should the customer implement, should Goose Group bring the build path, or should the work remain advisory?
What We Do
| Activity | What It Produces |
|---|---|
| Executive and operator discovery | Goals, constraints, current AI activity, decision-makers, and the workflows that matter. |
| Workflow mapping | Handoffs, systems, rules, source material, exceptions, delays, and customer consequences. |
| Capability identification | Concrete candidates for tools, pipelines, review surfaces, context systems, and operating changes. |
| Light evidence building | Working artifacts, data replays, or a narrow first run in a Skillpad workbench when it will improve the implementation decision. |
| Operating-model recommendation | Ownership, governance, implementation line, and the next investment decision. |
What You Keep
- A capability map grounded in actual work.
- Decision-ready opportunity briefs.
- A first workbench or diagnostic brief, and sometimes a narrow first run.
- Evidence, assumptions, open questions, and known constraints.
- A clear recommendation: build, establish a customer-owned mechanism, bring implementation, wait, or stop.
Good Fit
A useful sprint has a sponsor who can decide, people close to the work, real source material, and at least one implementation path if the evidence is strong. It is not a generic AI workshop or a promise that every idea becomes a system.