A team can leave an AI brainstorming session with dozens of ideas and no clear next step. Sales wants automated follow-ups, operations wants faster reporting, and leadership wants to know which project deserves funding.
An AI use-case discovery workshop turns those ideas into a shortlist of business problems worth testing. It brings the right people together to map workflows, assess opportunities, identify risks, and agree on owners. The goal is a set of practical decisions, not a long list of tools. A well-run workshop should make it clear what to test first, what evidence is missing, and what each next step will cost.
Quick Answer
An AI use-case discovery workshop helps a business find and prioritize practical AI opportunities. A focused session may last 90 minutes to half a day, while cross-functional discovery can take one or more days. Typical outputs include a workflow inventory, ranked use cases, feasibility notes, risk checks, owners, and pilot recommendations. The cost depends on preparation, participant count, and technical depth.
Table of Contents
- What happens in an AI use-case discovery workshop?
- What agenda should the workshop follow?
- Who should attend the workshop?
- What outputs should you expect?
- How much does an AI discovery workshop cost?
- How should you prioritize the ideas?
- How do you turn workshop ideas into a pilot?
- What does a successful workshop look like in practice?
- How should you prepare for the session?
- Frequently Asked Questions
- Conclusion
What happens in an AI use-case discovery workshop?
An AI use-case discovery workshop identifies business workflows where AI may save time, improve quality, or support better decisions. Participants examine real work, define the problem, compare options, and select a small number of opportunities for further testing.
The session should begin with business tasks rather than AI products. Otherwise, the group may spend its time discussing chatbots, agents, and software instead of solving the problems that affect daily work.
Start with real workflows
Participants describe work that is repetitive, slow, inconsistent, or difficult to complete. They explain who does it, what triggers it, which tools they use, and what the finished output must contain.
For example, “use AI for sales” is too broad to evaluate. “Draft a first response to each inbound enquiry using approved service information, then let a salesperson review it” is specific enough to discuss.
This level of detail helps the group see where AI may fit. It can also reveal that a clearer process or ordinary automation would solve the problem at lower cost.
Separate ideas from testable use cases
An idea becomes a use case when the team can describe the task, inputs, expected output, user, and success measure. It should also identify the human review needed and any access or data limits.
Consider an internal reporting task. The team might test whether an approved AI tool can draft a weekly summary from project notes, while a manager checks the facts and missing actions.
The workshop does not prove that the tool will work well. It creates a specific question that a small test can answer.
Make decisions before the session ends
The group should leave with a short list of priorities and named owners. It should also record ideas that need more evidence or are not worth pursuing.
OpenAI Academy's Use Case Discovery Workshop Playbook recommends grounding the session in real workflows and defining clear next steps. That principle keeps the discussion connected to work people actually do.
What agenda should the workshop follow?
A useful AI discovery workshop agenda moves from shared goals to workflow discovery, prioritization, and commitments. The timing should leave enough room for decisions, not just brainstorming.
For a focused 90-minute session, use this agenda:
Minutes 0 to 10: Set the goal and boundaries
The facilitator explains the business objective and the decisions the group must make. Participants agree on what data and tools they may use, which workflows are in scope, and who can approve priorities.
Without this shared frame, teams may discuss unrelated problems. A clear scope also prevents the session from becoming a general introduction to AI.
Minutes 10 to 25: Map workflow problems
Each participant shares one or two recurring tasks that cause friction. The group records frequency, current effort, errors, handoffs, and the people who depend on the output.
Ask for examples rather than broad opinions. A sample report, anonymized enquiry, or process document can make the discussion more concrete.
Minutes 25 to 45: Develop use-case candidates
Small groups turn the workflow problems into specific opportunities. They define the inputs, desired outputs, user, human review, and the possible role of AI or standard automation.
The facilitator should challenge vague ideas. If nobody can explain what the tool receives or produces, the idea needs more definition before it can be ranked.
Minutes 45 to 60: Assess value and feasibility
The group compares candidates using the same criteria. It considers business impact, frequency, effort, data readiness, technical dependencies, and risk.
A high-value idea may still be a poor first project if it depends on inaccessible data or complex integration. Record those dependencies rather than hiding them inside one overall score.
Minutes 60 to 75: Select priorities
Choose a small number of use cases for follow-up. For each one, record why it ranked highly and what evidence is still missing.
Do not force a decision when the group lacks key facts. Label the candidate as needing further review and assign someone to collect the missing evidence.
Minutes 75 to 90: Assign owners and next steps
Each selected use case needs an owner, a first action, and a review point. The group should also agree on what result would justify a pilot or further investment.
The facilitator closes by separating approved decisions from suggestions. That distinction prevents an idea raised during discussion from being mistaken for an authorized project.
For a multi-department workshop, extend the session or run separate discovery meetings before a leadership review. More participants usually mean more workflow detail and more competing priorities, so the same agenda may need additional time.
Who should attend the workshop?
Include people who understand the work, people who can assess technical constraints, and someone who can make decisions. A workshop made up only of executives may miss workflow details, while a session made up only of technical staff may miss business priorities.
A useful participant group includes:
- Executive sponsor: Sets the business goal and resolves priority conflicts.
- Process owners: Explain how the work is done and where it breaks down.
- Frontline users: Identify manual steps, repeated questions, and hidden work.
- IT or data lead: Reviews system access, data quality, integration, and security.
- Risk or compliance representative: Flags privacy, legal, and control requirements where needed.
- Facilitator: Keeps the session focused and records decisions.
For a small company, one person may cover more than one role. However, the business owner and the person responsible for technical feasibility should both contribute before the group approves a pilot.
Keep the group small enough to make decisions
A focused group of five to eight people can work well for a single department or closely related workflows. Larger groups may need breakout discussions, followed by a smaller decision meeting.
Invite people because they hold useful knowledge or authority, not simply because they are senior. If a key decision-maker cannot attend, record the shortlist as a recommendation until that person approves it.
What outputs should you expect?
An AI use-case discovery workshop should produce a decision pack that someone can act on without repeating the entire discussion. The output needs to explain the opportunity, the evidence, the constraints, and the next step.
1. Workflow and pain-point inventory
This record describes the tasks discussed, who owns them, and what makes them difficult. It should capture frequency, current tools, handoffs, and the business effect of the problem.
This inventory helps prevent the team from selecting an idea based only on enthusiasm. It also gives the business a useful record of the process issues that may need attention even without AI.
2. Use-case register
Each candidate should have a clear description and enough detail for comparison. Include the target user, inputs, expected output, business goal, dependencies, and any human review required.
For example, an enquiry-handling use case might specify approved service information as input, a draft reply as output, and a salesperson as reviewer. This is more actionable than a note that simply says “AI sales agent.”
3. Prioritized shortlist
The workshop should produce a small set of candidates to investigate or test. It should also explain why other ideas were deferred or rejected.
A useful shortlist may include one low-complexity test, one larger strategic opportunity, and several ideas that need better data or process clarity. The exact mix depends on your budget and readiness.
4. Feasibility and risk notes
Record the systems, data, approvals, and technical work each use case may need. Note risks such as sensitive data exposure, inaccurate outputs, unclear ownership, or excessive human review.
For higher-risk use cases, refer to established guidance such as the NIST AI Risk Management Framework. The workshop should identify which questions need a deeper review, rather than claiming that a brief session completes risk assessment.
5. Pilot brief and action log
For each selected use case, capture the owner, first milestone, required support, success measure, and decision date. If the group is not ready to approve a pilot, list the evidence needed to reach that decision.
The action log should distinguish between a suggestion, an approved next step, and a funded project. This keeps workshop outputs from turning into an unowned backlog.
How much does an AI discovery workshop cost?
The cost depends on how much preparation, facilitation, technical analysis, and follow-up the engagement includes. A short remote workshop may cost a few thousand dollars, while a multi-team engagement with detailed use-case analysis can cost substantially more.
The following figures are illustrative budgeting ranges in USD, not verified market averages or SEQUIANT prices.
- Self-facilitated internal session: $0 to $500 in direct costs. Staff time is the main expense. This works when the team has a capable facilitator and needs only an initial shortlist.
- Focused remote workshop: $1,000 to $4,000. This may include preparation, facilitation, and a concise output document for one team or a narrow business problem.
- Facilitated cross-functional workshop: $4,000 to $10,000. This may cover interviews, several teams, use-case prioritization, risk notes, and a decision pack.
- Workshop plus technical discovery: $10,000 to $20,000 or more. This may include deeper data and integration checks, business-case estimates, pilot design, and additional leadership sessions.
These ranges are broad because providers use different definitions of a workshop. A single facilitated session is not the same service as a discovery project with interviews, technical reviews, and detailed cost estimates.
What makes the fee go up?
Preparation often takes more work than participants expect. The facilitator may need to review workflows, interview process owners, and prepare candidate materials before the group meets.
Technical depth also changes the fee. If the business needs an assessment of data access, APIs, system integration, or governance, that work should be clearly listed in the proposal.
The number of teams matters as well. Each added department brings different goals, processes, tools, and approval needs, which can extend discovery and follow-up.
What should be included in the quote?
Ask the provider to specify:
- Pre-work and stakeholder interviews.
- Workshop duration and participant count.
- Facilitation and technical support.
- The number and format of deliverables.
- Whether use-case scoring and cost estimates are included.
- Whether a pilot brief or proof of concept is included.
- Follow-up meetings and revisions.
- Travel, taxes, and other expenses.
A low quote may cover only the live session. If you need a decision-ready shortlist, compare the total cost of preparation, workshop delivery, documentation, and follow-up.
For a broader view of the work after discovery, review SEQUIANT's services. The right next step may be a narrow implementation, a deeper readiness review, or further strategy work, depending on what the workshop reveals.
How should you prioritize the ideas?
Use the CLEAR framework to compare workshop candidates without letting enthusiasm decide the outcome. CLEAR stands for Contribution, Likelihood, Effort, Access, and Risk, with each dimension answering a different decision question.
Contribution: What business outcome could improve?
Define the outcome in terms the business already understands. Examples include reducing handling time, improving response speed, lowering error rates, or helping staff complete more work with the same resources.
Where possible, record a baseline before estimating gains. If the team does not know how much time a task takes, measuring it may be the first action rather than making an unsupported savings claim.
Likelihood: How often could the value occur?
A workflow performed every day may offer more repeated value than a task performed once per quarter. Frequency alone is not enough, though, because a frequent task may have little business impact.
Ask how often the problem occurs and how many people it affects. Then consider whether an AI-assisted workflow could improve the result consistently.
Effort: What would it take to make the use case work?
Consider process changes, integrations, testing, training, maintenance, and human review. A quick demonstration may hide work that becomes clear only when the system is used in normal operations.
If a use case needs several systems and a custom integration, it may be better as a later project. Start with a smaller workflow when it can test the same business assumption at lower cost.
Access: Are the required inputs and permissions available?
Check whether the necessary data exists, whether it is accurate enough, and whether the team can use it for the intended purpose. Also confirm that the output can reach the system or person who needs it.
If access is unclear, the use case should remain conditional. Assign someone to confirm permissions and data quality before the team approves implementation.
Risk: What could happen if the output is wrong?
Assess the consequences of inaccurate answers, exposed data, unfair decisions, or a missed handoff. The required safeguards depend on the task and the people affected.
For example, an internal draft that a staff member checks may need less oversight than an automated decision affecting a customer. The workshop should flag the difference and identify who must review the higher-risk option.
Make the final decision explicit
Use the five CLEAR dimensions to support discussion, not to create a score that hides uncertainty. A candidate with high potential value should not move forward if it lacks an owner, permitted data access, or a workable test.
Place each idea in one of three practical groups:
- Ready to test: The problem, inputs, owner, and success measure are clear.
- Needs evidence: The idea may be useful, but a key question remains unanswered.
- Defer or reject: The value is too low, the effort is too high, or the risks cannot be managed within the current scope.
This approach creates a defensible shortlist. It also tells leadership what must change before a deferred idea becomes viable.
How do you turn workshop ideas into a pilot?
The workshop creates candidates; a pilot tests whether the best candidate works under real conditions. Keep the first test narrow enough to learn something useful without committing the full implementation budget.
Define one testable question
Choose a single workflow and state what the test must prove. For example, can an approved assistant draft a useful first response from permitted information while a staff member checks every message?
Avoid testing several changes at once. If the workflow, data, tool, and review process all change together, it becomes harder to identify what caused a good or poor result.
Set a baseline and success measure
Record how the task works without AI. Depending on the workflow, this may include completion time, error rate, number of manual steps, response time, or the amount of rework required.
Then set a threshold for continuing. Include human review time and correction effort, because a fast first draft may create little value if employees spend longer fixing it.
Define the test boundaries
Specify what data the system can use, who can access it, what output it may produce, and when a person must intervene. Decide how to handle errors and when the test should stop.
Use synthetic or redacted data during early exploration when appropriate. Before using real customer or employee data, confirm permissions, security controls, and any relevant legal requirements.
Assign a decision owner
One person should own the test and collect the evidence. A separate sponsor should decide whether to continue, revise, expand, or stop the project.
Agree on the next review point before the pilot begins. That prevents an experiment from becoming a permanent workflow without a clear business case.
If the workshop identifies an opportunity in lead response or follow-up, compare the findings with the AI Lead Conversion System. The aim is to check whether a defined workflow fits the business need, not to assume every sales problem requires the same solution.
What does a successful workshop look like in practice?
Consider a hypothetical B2B services company with 30 employees and a sales team of four. The company receives enquiries through its website and WhatsApp, but sales staff manually enter details and follow up when time allows.
Assume leadership has a $10,000 budget for its first AI project. These figures are illustrative assumptions, not actual client results or a forecast.
The workshop findings
The team identifies three candidates: automated lead qualification, AI-assisted proposal drafting, and an internal support assistant. It finds that lead records are inconsistent, proposal templates vary between salespeople, and support documents have no clear owner.
The group ranks lead qualification as the first candidate because the workflow is frequent and the business can define what a qualified lead looks like. Proposal drafting remains on the shortlist, while the support assistant is deferred until its source documents are reviewed.
The agreed next step
The team assigns the sales manager to define qualification rules and the operations lead to check data quality. The technical owner must confirm the CRM integration and the permitted use of customer information before a pilot is approved.
Assume the workshop costs $3,000 and takes half a day, including preparation and a written decision pack. The remaining budget is reserved for a scoped pilot, subject to a separate cost estimate.
The team does not assume that automation will increase sales. Instead, it measures response time, qualification accuracy, follow-up completion, and staff effort during the test. Leadership can then decide whether the evidence supports a wider rollout.
How should you prepare for the session?
Preparation makes the workshop more useful because participants can discuss actual work instead of guessing. You do not need perfect documentation, but you should bring enough detail to explain the current process.
Before the session, gather:
- One or two examples of recurring tasks from each participating team.
- A short description of each workflow, including inputs and outputs.
- The main business problem and its impact.
- Existing software and known integration limits.
- Any data access, privacy, or security constraints.
- A rough budget and the person who can approve spending.
- The measures you would use to judge success.
Also appoint a facilitator and a note-taker. The facilitator guides the discussion, while the note-taker captures decisions, owners, open questions, and next steps.
Share only information participants are authorized to use. Redact sensitive details from examples and agree on data handling before any live testing begins.
After the session, set a review meeting and send the decision pack to all owners. If the next step involves implementation, SEQUIANT's delivery process provides an example of how discovery can lead into design, build, testing, and optimization.
For a business that needs help identifying its first practical AI opportunity, book a free AI audit to discuss the business problem and possible next steps.
Frequently Asked Questions
How many AI use cases should a workshop produce?
Aim for a short list rather than an exhaustive catalog. One to three candidates with clear owners and next steps are more useful than dozens of ideas nobody is ready to test.
Should participants use AI tools during the workshop?
A live demonstration can help test a narrow assumption, but it is optional. Confirm tool access and data permissions first, and do not treat a convincing demonstration as proof of business value.
Can an AI discovery workshop be held remotely?
Yes. A shared document or virtual whiteboard can capture workflows, decisions, and owners. The facilitator should ensure that quieter participants can contribute and that everyone can access the materials.
Is an AI workshop suitable without a technical team?
Yes, especially for early discovery. However, technical feasibility, data access, integration, and security should be reviewed before a business commits to implementation.
What is the difference between an AI workshop and an AI readiness assessment?
A workshop helps surface and prioritize candidate use cases with the team. A readiness assessment examines the data, systems, processes, skills, and controls needed to carry out those use cases.
Should the workshop include an ROI calculation?
It should capture enough information to estimate potential value, but detailed ROI may require more data. If the baseline or implementation cost is unknown, record the uncertainty and assign someone to investigate it.
What should happen if the team cannot agree on a priority?
Record the competing views and the evidence needed to resolve them. The executive sponsor should make the decision once the key business, technical, and risk questions have been addressed.
Conclusion
An AI use-case discovery workshop earns its value when it turns discussion into clear decisions and owned next steps. The most useful output is a small, testable shortlist that explains what to try, why it matters, and what must be confirmed first.
Before approving a pilot, make sure the business can measure the result and stop the test if it fails its agreed criteria. If you need help choosing where AI can create value, book a free AI audit with SEQUIANT.