Do the blank-page work before the AI discovery workshop

Start with an automatic company and market scan. When people cannot give a clear list of problems, use Pain Discovery Studio. Enter the workshop with sourced questions, draft priorities, and the specific evidence gaps that need human judgment.

Open the interview guide Review a sample brief

What this establishes

The decision in view.

Principle 1

Generate a source-linked pre-discovery brief from a company URL.

Principle 2

Use Pain Discovery Studio when operators cannot answer with a clean pain inventory.

Principle 3

Run a 90-minute workshop agenda with questions that force concrete evidence.

Principle 4

Use simple score reasons for value, practicality, and company advantage.

Principle 5

Leave with a live evidence map and ranked roadmap—not only sticky notes.

Why traditional discovery workshops underperform

Workshop time is expensive. Yet many sessions begin with broad prompts—where could AI help?—and spend the first hour reconstructing basic context. The loudest participant often shapes the list, while data access, workflow ownership, constraints, and measurable pain remain implicit.

Automate the preparation

Before the meeting, run a company URL through Foundry's public-evidence scan and auto-map. Review dated market signals, likely business priorities, offerings, customer segments, published case studies, technology clues, and open questions. Upload existing process notes, annual reports, or spreadsheets when available.

Use people where judgment matters

The workshop should validate how work actually happens, quantify pain, test whether data is usable, identify owners and approval boundaries, and capture proprietary know-how. Foundry's gap diagnostics turn missing evidence into specific questions for the right roles.

When not to ask for a pain list

Some stakeholders cannot safely or clearly answer “what are your pain points?” Use Pain Discovery Studio in those cases. It guides the facilitator through relevant interview techniques, suggests possible workflow problems, keeps suggestions separate from facts, and adds only reviewed findings to the company map.

Produce a decision document during the engagement

As facts are reviewed, rankings update and the team can challenge Impact × Feasibility × Moat reasons, identify prerequisites, drop generic ideas, and select which candidates deserve deeper business cases and pilot packages.

A practical 90-minute workshop sequence

10 min — review the pre-discovery brief and mark each suggestion as validate, reject, or investigate.
20 min — confirm the highest-pain workflows, decisions, owners, and constraints.
20 min — map data and system access without requiring sensitive raw records.
20 min — review which ideas the evidence supports, what proof is still missing, and the ranked recommendations.
10 min — challenge the initial ranking on value, practicality, and how hard each idea is to copy.
10 min — assign evidence-closing actions, owners, and next checkpoints.

A simple scoring rubric to bring into the room

Impact — is the pain frequent, measurable, sponsor-visible, or strategically important?
Feasibility — can the team access the evidence, systems, owners, and approval boundaries needed to pilot?
Defensibility — would the outcome strengthen proprietary workflow knowledge, data, trust, or repeatable advantage?

What a good workshop should produce

The output should be a reviewed evidence map, a ranked shortlist, explicit evidence gaps, named owners, and next actions. If the session ends with only a brainstorm board, it has not done enough decision work.

Questions buyers ask

Resolve the practical concerns.

What should an AI discovery workshop cover?

Cover business pain, workflow steps, decision ownership, data and system access, constraints, risk, measurable outcomes, proprietary advantage, and the evidence required to validate each proposed use case.

Can AI discovery happen without a workshop?

Public pages, documents, spreadsheets, and consent-first workflow signals can automate much of the preparation. Human interviews remain important for validating private operating reality, ownership, pain, and risk.

What should an AI discovery workshop produce?

It should produce a reviewed evidence map, ranked opportunities, prerequisites, explicit evidence gaps, owners, and next validation actions—not only a brainstormed use case list.