AI Use Case Discovery: How to Identify and Prioritize High-Value AI Opportunities

An AI Use Case Discovery should begin with a meaningful problem or opportunity—not simply with enthusiasm for what AI can do.

AI creates no shortage of ideas. A leader sees a competitor launch an AI assistant and asks whether the organization should build one.

An employee discovers a repetitive task and wonders whether AI should automate it.

A technology team proposes an agent. A business unit wants to add AI to an existing product.

Before long, the organization has dozens, or hundreds, of potential AI Opportunities competing for attention.

The challenge is not generating ideas.

It is determining: Which problems are actually worth solving with AI?

Without a repeatable discovery process, organizations jump directly from: "AI could do this."

to: Experimentation → Pilot → Investment

without clearly defining:

• The problem
• Who experiences it
• Its significance
• Expected value
• Technical feasibility
• Data requirements
• Strategic relevance
• Organizational readiness
• Risk and governance considerations

Microsoft's Cloud Adoption Framework recommends evaluating potential AI use cases through dimensions such as strategic value, business impact, technical feasibility, available resources, and alignment with organizational goals.

Google Cloud similarly recommends identifying AI Opportunities through a business-value-driven approach and considering business value, feasibility, and actionability.

Organizations don't simply need more AI ideas.

They need a repeatable capability for AI Use Case Discovery.

 

What Is AI Use Case Discovery?

AI Use Case Discovery is the organizational capability to intentionally identify, define, evaluate, and prioritize valuable opportunities to apply AI.

That starts with an important distinction: An AI idea is not necessarily an AI Use Case.

"Build an AI agent" is an idea.

"Reduce the time account managers spend preparing for customer meetings by using AI to retrieve and summarize relevant account information" is much closer to a defined AI Use Case.

The second example establishes:

• A user
• A workflow
• A problem
• A potential role for AI
• An expected improvement

Effective AI Use Case Discovery helps organizations answer questions such as:

• What problem are we trying to solve?
• Who experiences the problem?
• How significant is it?
• What part of the workflow creates friction?
• What role should AI play?
• Why is AI appropriate?
• What should improve if the use case works?
• How will improvement be measured?
• Is the opportunity strategically important?
• Is the necessary data available?
• Is the use case technically feasible?
• What organizational change is required?
• What risks or governance considerations exist?
• Is the expected value worth the investment?

NIST's AI Risk Management Framework similarly emphasizes understanding intended purpose, expected impact, context of use, users, potential benefits, and risks before an AI system is implemented.

The objective is not to find as many places as possible to insert AI.

It is to repeatedly identify AI Opportunities where a meaningful problem, appropriate AI capability, organizational readiness, and potential value come together.

 

Figure Out Where You Are

Before improving AI Use Case Discovery, identify how your organization currently moves from a business problem or AI idea to a decision about whether the opportunity deserves further investment.

LAI's AI Use Case Discovery Maturity Model uses five stages to describe that progression.

It is to understand how reliably the organization separates high-value AI Opportunities from ideas that do not deserve further investment.

 

The AI Use Case Discovery Maturity Model

LAI's AI Use Case Discovery Maturity Model describes how organizations progress from opportunistic AI idea generation toward a repeatable system for identifying and prioritizing AI Opportunities based on meaningful problems, expected value, feasibility, readiness, and learning.

The five stages are:

Starting → Emerging → Enabling → Operationalizing → Optimizing

The progression moves from:

Ad Hoc Ideas → Individual Exploration → Shared Discovery Practices → Workflow-Embedded Discovery → Continuous Learning

At first, AI ideas appear wherever employees or leaders see a new technology or capability.

Individual teams become more intentional about exploring potential use cases.

Shared discovery practices establish common definitions and evaluation criteria.

Workflow integration makes AI opportunity identification part of normal business and product discovery.

Implementation evidence then improves how future AI Opportunities are evaluated.

The goal is not: More AI ideas.

It is: Better decisions about which AI ideas deserve attention.

 

Starting: AI Ideas Emerge Without Structured Discovery

At the Starting stage, AI Opportunities are identified opportunistically rather than through a repeatable discovery process.

Someone sees a new AI capability and asks where it could be used. Leaders brainstorm applications. Vendors demonstrate solutions.

Teams start experimenting before clearly defining the underlying business problem.

The conversation often begins with: "What can we do with AI?"

rather than:

"What problem or opportunity is important enough that we should consider AI?"

 

What This Looks Like

Common signals include:

• No structured AI Use Case Discovery
• Ad hoc idea generation
• Problems not explicitly defined
• Teams moving quickly from idea to experiment
• No common AI Use Case evaluation criteria
• Technology capabilities driving opportunity selection
• Different leaders using different definitions of value

This often produces solution-first AI:

Choose the technology → Search for somewhere to use it

A stronger approach reverses the sequence:

Identify important work → Understand the problem → Consider whether AI belongs

 

How to Progress to Emerging

Start by changing the question.

Instead of beginning with AI, begin with:

• Friction
• Constraints
• Unmet needs
• Opportunities
• Strategic priorities
• Desired outcomes

Microsoft's AI planning guidance similarly recommends identifying business scenarios first and then determining whether AI is an appropriate way to address them.

Look for work where:

• Employees spend significant time on repetitive cognitive tasks
• Information is difficult to find or synthesize
• Decisions require analysis of large amounts of information
• Manual handoffs constrain flow
• Customers experience avoidable delays
• Quality is inconsistent
• People repeatedly create similar first drafts
• Existing workflows do not scale economically
• Expertise creates a bottleneck
• Important information is fragmented across systems

Then ask: Is there a meaningful role AI could play in improving this part of the workflow?

 

Practical Example: Run a Problem-First AI Discovery Session

Choose one business function and run a 60–90 minute discovery workshop.

Do not begin by asking: What AI ideas do we have?

Instead, create four columns: Workflow → Friction → Business Impact → Potential AI Opportunity

For example:

Then ask the most important question in the session:

If AI did not exist, would this still be a problem worth solving?

If the answer is no, the proposed use case is probably being driven more by interest in AI than meaningful business need.

The objective at Starting is to create:

Better problems before creating more AI solutions.

 

Emerging: AI Opportunities Are Explored Independently

At the Emerging stage, employees and teams intentionally explore AI Opportunities, but their discovery practices remain inconsistent.

Teams brainstorm possibilities. Individual contributors test ideas. AI Opportunities appear in intake documents and meetings.

Functions discuss where AI should improve their work.

This represents progress because discovery becomes broader and more deliberate.

But practices vary significantly. One team creates a detailed AI Use Case definition. Another moves directly into a pilot. Different teams define value differently.

Similar AI Opportunities are explored independently across multiple functions.

The organization is gaining experience but remains dependent on the people involved.

 

What This Looks Like

Observable signals include:

• Independent exploration of AI Use Cases
• AI brainstorming sessions
• AI Opportunities appearing in intake documents
• AI discussed during business and technology planning
• Different teams using different evaluation approaches
• Duplicate AI ideas appearing across functions
• Early experiments beginning without consistent definition

The organization is becoming more deliberate.

It does not yet have a common answer to: "What information do we need before an AI idea deserves experimentation?"

 

How to Progress to Enabling

Capture the questions strong teams already ask when exploring AI Opportunities.

Microsoft provides structured approaches for comparing AI use cases through considerations such as:

• Business impact
• Technical feasibility
• User desirability

Google Cloud similarly emphasizes:

• Business value
• Feasibility
• Actionability

Use those principles to define the minimum information required before an AI idea becomes an experiment. Do not create a full business case.

Create enough clarity to distinguish: An interesting AI idea

from: A potentially valuable AI Use Case

 

Practical Example: Create an AI Opportunity Card

Ask anyone proposing an AI Use Case to complete one page.

AI Opportunity

• Problem: What problem are we trying to solve?
• Who Experiences It?: Which employees, customers, or stakeholders are affected?
• Current Workflow: How is the work performed today?
• Current Friction: What makes the work difficult, slow, expensive, inconsistent, or difficult to scale?
• AI Opportunity: What role could AI play?
• Expected Outcome: What should improve if the use case works?
• Potential Measure: How would we observe that improvement?
• Data Needed: What information would AI require?
• Known Risks / Constraints: What makes the opportunity difficult or inappropriate?

Keep the card deliberately lightweight. The objective is not approval.

It is definition.

Collect AI Opportunity Cards in a shared repository.

Over time, patterns become visible around:

• Common problems
• Duplicate ideas
• High-frequency workflows
• Data limitations
• Governance concerns
• Repeated AI Opportunities
• Valuable areas for investment

 

Enabling: Shared AI Use Case Discovery Practices Are Defined

At the Enabling stage, AI Use Case Discovery becomes a shared organizational capability supported by common discovery methods and evaluation criteria.

Guidelines exist. Workshops follow common approaches. Employees formally submit AI Opportunities. Potential use cases are evaluated using a shared framework.

The organization moves from: "I have an AI idea."

to: "I have a defined AI Opportunity we can evaluate consistently."

 

What This Looks Like

Evidence includes:

• Published AI Use Case guidelines
• Repeatable discovery workshops
• Standard AI Use Case submissions
• Common evaluation criteria
• Shared AI Opportunity repository
• Defined minimum information requirements
• More consistent prioritization discussions

The goal is not to make every use case identical.

It is to make the decision process consistent enough to compare opportunities intelligently.

 

How to Progress to Operationalizing

• Establish a common AI Use Case evaluation model.
• A strong model prevents two common mistakes.

Value Without Feasibility

• A strategically attractive AI Opportunity that the organization cannot reasonably implement yet.

and:

Feasibility Without Value

• A technically easy AI solution that does not improve anything important.
• A useful evaluation framework examines multiple dimensions.

Business Value: How meaningful is the problem or opportunity?

Strategic Alignment: Does the AI Use Case support an organizational priority?

User Value: Will employees or customers actually benefit?

Feasibility

• Do the required:
  • Technology
  • Data
  • Integrations
  • Skills

exist?

Readiness

• Can the organization absorb the workflow, process, and behavior change required?
• Do not simply add the numbers and automatically choose the highest score.
• The scorecard should structure a better decision.

 

Practical Example: Create an AI Use Case Evaluation Scorecard

Score each candidate from 1–5 across five dimensions.

The purpose is not:

Support assistant scored 23, therefore we must build it.

The purpose is to create questions such as:

The autonomous pricing opportunity has significant potential value but low feasibility and readiness. What would need to become true before it deserves investment?

That turns AI Use Case Discovery into: Evidence-based prioritization

rather than: Enthusiasm-based selection

 

Operationalizing: AI Use Case Discovery Becomes Part of Standard Work

At the Operationalizing stage, AI Use Case Discovery becomes embedded into the normal workflows through which the organization identifies problems, improvement opportunities, product opportunities, and strategic investments.

The organization no longer runs isolated AI brainstorming sessions simply because someone decides it needs more ideas.

AI Opportunities are intentionally considered within existing:

• Product discovery
• Business planning
• Transformation
• Process improvement
• Strategy
• Technology planning
• Operational improvement

Use cases connect to strategy. Governance considerations appear early. Standard intake mechanisms capture opportunities.

The organization measures how efficiently it moves from a meaningful problem to an evaluated AI Opportunity.

 

What This Looks Like

Observable evidence includes:

• Standard AI Use Case intake templates
• AI Opportunities connected to strategic priorities
• Governance and risk considered during discovery
• AI consideration included in normal discovery workflows
• Shared use-case portfolio visibility
• Improving discovery lead time
• Duplicate opportunities identified earlier

At Enabling: The organization knows how to discover and evaluate AI Use Cases.

At Operationalizing: AI Use Case Discovery reliably happens as part of how the organization identifies and prioritizes improvement opportunities.

 

How to Progress to Optimizing

Embed AI consideration into existing discovery workflows rather than creating a separate AI innovation process.

For example:

Business Problem Identified
→ Understand Current Workflow
→ Identify Friction or Opportunity
→ Consider Whether AI Is Appropriate
→ Define AI Use Case
→ Evaluate Value and Feasibility
→ Apply Initial Governance and Risk Screening
→ Experiment / Hold / Reject

NIST's AI RMF similarly emphasizes understanding intended purpose, users, potential impacts, context, and risk early rather than discovering those issues after implementation begins.

Microsoft's AI Center of Excellence guidance also emphasizes working with business leaders to identify AI Opportunities aligned with organizational priorities and validating promising ideas through focused pilots.

 

Practical Example: Build AI Into Your Existing Discovery Template

Add an AI Opportunity Check to the discovery process already used by product, operations, transformation, or technology teams.

AI Opportunity Check

• Problem
  • What outcome or workflow requires improvement?
• Current Constraint
  • What prevents better performance today?
• Could AI Help?
  • Yes / No / Needs Exploration

If yes:

• Potential AI Role
  • AI could:
    • Assist
    • Generate
    • Analyze
    • Retrieve
    • Recommend
    • Automate
    • Act
• Expected Outcome
  • What should improve?
• Strategic Alignment
  • Which organizational priority does this support?
• Initial Feasibility
  • High / Medium / Low
• Initial Risk
  • High / Medium / Low
• Decision
  • Explore / Experiment / Hold / Reject

Then route promising opportunities through the standard evaluation process.

Measure:

• Discovery Lead Time
  • How long does it take to move from identified problem to evaluated AI Opportunity?
• Qualified Use Case Rate
  • What percentage of submitted AI Use Cases meet the organization's minimum evaluation criteria?
• Duplicate Rate
  • How frequently are teams identifying AI Opportunities already being pursued elsewhere?
• Strategic Alignment
  • What percentage of qualified use cases support an established organizational priority?
• Time to Decision
  • How long does the organization spend deciding whether an opportunity deserves experimentation?

The objective is not to discover more AI Use Cases.

It is to make valuable AI Opportunities easier to identify, compare, and evaluate consistently.

 

Optimizing: AI Use Case Discovery Improves Through Implementation Learning

At the Optimizing stage, AI Use Case Discovery becomes a learning system that uses implementation evidence to improve how future AI Opportunities are identified and evaluated.

The organization no longer assumes its discovery criteria are permanently correct.

It looks at what happened after opportunities were selected. Some promising ideas produce limited value. Some technically simple AI Use Cases become valuable.

Some initiatives expose hidden:

• Data limitations
• Workflow dependencies
• Governance constraints
• Integration challenges
• Adoption barriers

Others should have been stopped much earlier.

Those outcomes become inputs into future discovery.

 

What This Looks Like

Observable signals include:

• Reviews of implemented AI Use Cases
• Low-value opportunities abandoned earlier
• Evaluation criteria refined based on evidence
• Portfolio-level learning across AI initiatives
• Readiness assumptions updated
• Better visibility into recurring barriers
• Weak opportunities stopped before significant investment

The critical shift is:

AI Use Case Discovery becomes a learning system.

The organization gets better not only at finding AI Opportunities.

It gets better at determining:

Which opportunities deserve further investment.

 

How to Sustain and Continuously Improve

Create a feedback loop between discovery and implementation:

Discover → Evaluate → Experiment → Implement → Measure → Learn → Improve Discovery

Review successful and unsuccessful AI Use Cases.

Ask:

• Which evaluation criteria were useful?
• Which criteria were misleading?
• What did we overlook?
• Which use cases should have been stopped earlier?
• Where did expected value differ from observed value?
• Which feasibility assumptions proved wrong?
• Which readiness assumptions proved wrong?
• What organizational dependencies repeatedly affect implementation?
• Which AI Opportunities scaled more easily than expected?
• Are we improving our ability to identify viable use cases?
• Are weak ideas being abandoned earlier?

Google's guidance emphasizes measuring AI initiatives through their lifecycle rather than treating prioritization as a one-time decision.

Microsoft similarly recommends using pilot evidence to validate AI approaches, understand business value, refine operational practices, and improve future investment decisions.

 

Practical Example: Run a Quarterly AI Use Case Portfolio Review

Once per quarter, review the AI Use Cases that moved through discovery.

Group them into:

• Implemented
  • Produced enough evidence to continue or scale.
• Experimenting
  • Still being validated.
• Paused
  • Blocked by readiness, capacity, dependencies, or timing.
• Rejected
  • Did not meet evaluation criteria.
• Stopped
  • Experimented with but did not produce enough value to warrant continued investment.

Then examine what was learned.

For example:

Then ask:

What Should Change in Discovery?

Possible improvements include:

• Increase the weight of data readiness
• Add workflow frequency as a value factor
• Require a measurable baseline earlier
• Strengthen governance screening for autonomous use cases
• Add employee or customer desirability
• Identify change-management requirements earlier
• Eliminate ideas with no identifiable outcome

Turn those findings into an AI Use Case Discovery Improvement Backlog.

A particularly important measure at this stage is: Time to Abandonment

How quickly does the organization stop investing in an AI Use Case when evidence suggests it is not worth pursuing?

Finding out quickly that an idea should not be pursued is valuable.

The objective is not a 100% implementation rate.

That could indicate the organization is:

• Not experimenting enough
• Selecting only obvious ideas
• Reluctant to stop weak opportunities

Optimization means becoming faster and more disciplined at separating valuable AI Opportunities from ideas that do not warrant additional investment.

 

Key Takeaway

AI Use Case Discovery maturity isn't measured by how many AI ideas an organization generates. It is demonstrated by how consistently the organization identifies meaningful problems, defines AI Use Cases clearly, evaluates value and feasibility, advances promising AI Opportunities, and stops weak ideas before significant investment is committed.

 

From AI Ideas to AI Operationalization

AI Use Case Discovery becomes operationalized when identifying and evaluating AI Opportunities moves from isolated brainstorming into the normal way the organization finds and prioritizes valuable improvement opportunities.

AI adoption creates an almost endless stream of possible ideas.

Operationalization requires discipline about which ideas move forward.

The maturity model progresses from:

Ad Hoc Ideas → Individual Exploration → Shared Discovery Practices → Workflow-Embedded Discovery → Continuous Learning

Initially, organizations become more deliberate about identifying meaningful problems.

Early exploration creates learning.

Shared criteria make AI Use Case definition and evaluation repeatable.

Workflow integration makes discovery part of normal business operations.

Implementation evidence improves how future opportunities are identified and prioritized.

That is the difference between: Brainstorming ways to use AI

and: Building an organizational capability for repeatedly discovering valuable AI Opportunities

The goal is not to put AI everywhere.

It is to repeatedly answer: Where can AI create meaningful value—and which opportunities deserve our attention now?

 

Evaluate Whether Your Organization Is Finding the Right AI Opportunities

Generating AI ideas is easy.

The more important question is whether the organization has a repeatable way to identify which ideas represent meaningful AI Opportunities and which should never move beyond discussion.

Lean Agile Intelligence helps organizations establish a baseline across AI capabilities and determine whether AI Use Case Discovery remains dependent on individual enthusiasm, has become a shared practice, or is embedded directly into standard discovery and planning workflows.

For AI Use Case Discovery, that means looking beyond the number of ideas generated and evaluating whether the organization:

• Starts with meaningful problems
• Defines AI Use Cases consistently
• Connects opportunities to strategic priorities
• Evaluates business value
• Evaluates user value
• Assesses technical and data feasibility
• Considers organizational readiness
• Identifies risk early
• Prioritizes opportunities through shared criteria
• Stops weak ideas before significant investment
• Uses implementation learning to improve future discovery

Establish where your AI Use Case Discovery capability is today and identify the next practices needed to consistently find and prioritize high-value AI Opportunities.