Run an AI Opportunity Discovery Workshop

Introduction

Good discovery starts with a business problem, not a demonstration of a fashionable tool. The workshop should uncover where time, quality or access breaks down in the current workflow, then test whether AI is appropriate. Some problems need clearer policy, cleaner data or ordinary automation instead.

How the topic works

Evaluate each candidate across value, feasibility, data readiness, risk and adoption. Value needs a measurable baseline. Feasibility covers system access and technical limits. Data readiness checks availability, permission and quality. Risk considers errors, privacy and affected users. Adoption examines who must change behaviour and who will support the result.

Practical workflow

1. Interview process owners and frontline users separately about the current outcome and friction.
2. Map inputs, decisions, handoffs, exceptions and existing measurements.
3. Generate candidate interventions, including non-AI alternatives.
4. Score candidates with documented evidence and identify assumptions requiring tests.
5. Choose one reversible pilot with a baseline, owner, success threshold and stop condition.

Tools and technologies

• A workflow map
• An opportunity scoring matrix
• A pilot hypothesis canvas

Example

A support team wants faster ticket handling. Discovery shows categorization is consistent but knowledge search is slow. A retrieval assistant may help, while fully automatic ticket closure is rejected because exceptions and customer impact are too high.

Common mistakes

• Starting with a model and searching for a use
• Estimating value without a current baseline
• Ignoring the people who perform the workflow

Security and best practice

Do not place client data into unapproved tools during discovery. Record access assumptions, regulatory constraints and excluded use cases before proposing a pilot. Confirm important statements with the process owner.

Portfolio task

Key takeaways

• Outcomes come before technology.
• Alternatives make the recommendation more credible.
• A small reversible pilot reduces uncertainty.

Curriculum connection

• Day 061: AI use-case discovery
• Day 071: Pilot scoping and kickoff
• Explore the complete No-Code learning path