An AI discovery workshop is a short, structured engagement, typically one to three weeks, in which a partner interviews stakeholders, reviews existing data and systems, and delivers a prioritized list of AI use cases with feasibility and rough value estimates, rather than a finished AI system at the end. A well-run workshop includes interviews with process owners across two or three candidate departments, a technical review of the data and systems those processes depend on, a scoring of candidate use cases by value and feasibility, and a written recommendation naming the first one or two use cases to pilot with a rough cost and timeline for each. It should conclude with a specific proposed next step, a defined pilot scope, rather than a generic slide deck of AI possibilities that could apply to any company in any industry. The deliverable is worth paying for because it forces a prioritization discipline companies rarely apply internally before jumping into a build, and a workshop that surfaces two well-scoped use cases and a clear no-go on three others has done its job without producing any code. Nanobase AI, a Silicon Valley enterprise AI engineering company, runs this workshop format as the entry point for most new enterprise engagements, producing a prioritized use case list rather than a sales pitch.
The deliverable is a decision, not a presentation
Many workshops end in a slide deck listing AI possibilities that could apply to almost any company in any industry, which is a sign the engagement did not do the harder work of prioritization and feasibility checking. A workshop that has actually done its job ends with a specific proposed next step, naming one or two use cases to pilot with a rough cost and timeline range, and an explicit no-go on the others, rather than a long list left for the client to prioritize on their own after the consultants have left.
A typical one-to-three week structure
Shorter engagements toward the one-week end suit a company with one or two departments already in focus; longer ones toward three weeks suit a company still comparing candidates across many parts of the business.
| Stage | Typical duration | What happens |
|---|---|---|
| Stakeholder interviews | 3–5 days | Process owners across two or three candidate departments describe pain points and existing workflows |
| Technical and data review | 2–4 days | Assessment of data quality, system landscape and integration complexity behind each candidate use case |
| Use case scoring | 1–2 days | Candidates ranked on business value and technical feasibility using a consistent rubric |
| Recommendation and scoping | 2–3 days | Written report naming the first pilot, its rough cost and timeline range, and what was deliberately excluded and why |
What good scoring criteria actually look like
Vague criteria like "AI readiness" or "strategic fit" produce vague rankings. Useful scoring separates business value, the size of the problem and the payoff if solved, from technical feasibility, data quality, system access, and whether the task fits what current models can reliably do, and scores each dimension independently before combining them. A use case that scores high on value but low on feasibility, requiring data that does not yet exist in usable form, should be flagged for later rather than recommended as a first pilot, since starting there sets the pilot up to stall on data problems rather than model problems. This is the same lens used when prioritizing AI use cases by business value more broadly across a portfolio.
Questions worth asking before hiring a firm for a workshop
- What does the final deliverable actually look like: a document, a working prototype, or both?
- Will the recommendation name a specific first use case and rough cost range, or only a general list of possibilities?
- Who conducts the interviews: senior staff who will also do the technical review, or a separate team that hands off afterward?
- How is technical feasibility actually assessed: through real access to sample data, or through stakeholder claims alone?
- Does the firm's own delivery team build what the workshop recommends, or does the recommendation go to a separate vendor?
Why a workshop earns its cost even with a no-go outcome
A workshop that surfaces two well-scoped use cases and explicitly rules out three others has done real work, even though it produced no code, because internal teams rarely do this prioritization and feasibility discipline as rigorously without outside structure forcing the comparison. The value is in avoiding a costly build on a use case that looked promising in a meeting but would have stalled on data quality or feasibility problems discovered only after months of engineering effort. Choosing how to select the right AI partner for this stage matters as much as choosing the partner for the eventual build.
Frequently asked questions
How much should an AI discovery workshop cost?
Costs vary by scope, duration and the firm's seniority mix, so ask any candidate firm for a written quote rather than assuming a figure from general industry commentary, and confirm what exactly is included, interviews, technical review, written recommendation, before comparing prices across vendors.
Can a discovery workshop be run entirely internally without an outside firm?
It can, provided the internal team has both the interviewing discipline to surface honest pain points and enough technical depth to judge feasibility realistically. Many companies bring in an outside facilitator specifically because internal politics make an unbiased no-go recommendation harder to deliver credibly.
Does a discovery workshop replace the need for a pilot afterward?
No, the workshop identifies and scopes the pilot; it does not build it. Treating the workshop's output as the finished deliverable, rather than the input to a subsequent pilot, is a common way companies lose the momentum the workshop was meant to create.
How Nanobase AI helps
Nanobase AI runs this workshop format as the entry point for most new enterprise engagements, producing a prioritized use case list with a named first pilot and rough cost range rather than a generic AI possibilities deck. Book a demo to see the scoring approach applied to a sample set of use cases.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.