This is Part 1 of a blog series on enterprise AI use case adoption.
This series is intended for enterprise teams moving from isolated AI experiments toward repeatable, production-scale adoption. It emphasizes trusted data, clear ownership, measurable outcomes, and durable controls because many organizations are still establishing these foundations. Organizations with greater AI maturity can apply the same principles at higher speed—using patterns and templates, modular agentic applications, and rapidly evolving models, tools, and agents.
How to choose the right enterprise AI use case
Most organizations have no shortage of AI ideas. The harder question is which ideas deserve investment and which are likely to become part of everyday work. An AI use case sticks when employees return to it, leaders trust it, and the business can measure its value.
This creates two distinct questions:
- How do you choose the right AI use cases?
- How do you make the chosen use cases succeed and scale?
In this first post, we focus on how to choose the right AI use cases. In Part 2 , we will look at what it takes to make the chosen use cases succeed in practice and scale across the organization.
The following sections outline the criteria for evaluating potential AI use cases.
1. Start with a business problem. Make the business outcome the primary unit of design
Start with the business outcome, not the technology.
A useful test is: Is the use case tied to a clear, measurable business outcome?
For example, a better starting point is:
Reduce the time spent reviewing incoming service requests and determining the correct route.
rather than:
Build a service triage agent.
The first describes what the business wants to improve. The second assumes an implementation before the problem has been evaluated.
The outcome may relate to revenue, cost, cycle time, productivity, quality, customer experience, employee experience, or risk.
Also consider how often the problem occurs and the impact it creates. A few minutes saved across thousands of transactions can add up quickly. A lower-volume process can also be valuable when each occurrence has a significant financial, customer, operational, or regulatory impact.
Look beyond individual tasks. Opportunities can occur between teams, systems, and process steps. Information may not reach the right person, an approval may remain idle, an exception may have no clear owner, or an employee may spend time gathering context from several places.
2. Decide whether AI is the right approach
Not every process problem needs AI.
A report, dashboard, script, rules engine, workflow, or process automation may be a better fit when the work is deterministic and predictable.
AI is more useful when a process requires activities such as:
- Interpreting unstructured information
- Summarizing complex context
- Classifying intent
- Identifying missing information
- Reconciling conflicting signals
- Applying judgment within defined boundaries
- Providing context-aware recommendations or decision support
- Selecting or using tools based on the business context
- Working across multiple data sources or systems
- Coordinating multiple steps or specialized agents
- Recommending, initiating, or performing actions within a workflow
The choice does not have to be AI or deterministic automation. A business process can combine agents, deterministic workflows, business rules, APIs, scripts, and human approvals, using each where it fits best.
Choose the simplest combination of capabilities that can deliver the required business outcome.
3. Find where AI fits in the business process
Once AI appears to be a good fit, look at the process itself.
Start with the event that brings the capability into the flow. It could be:
- A case being opened
- A document arriving
- A request being submitted
- An approval becoming overdue
- An exception occurring
- A scheduled review taking place
Then look at what happens next.
Many useful AI opportunities occur in processes that are structured overall but contain steps where fixed rules are not enough. Examples include reviewing an unstructured document, understanding a request, resolving incomplete information, handling an exception, or making a context-dependent recommendation.
These are often good places for AI because the surrounding process can remain controlled while AI handles the part that requires interpretation.
Also consider where users work today. If employees have to leave their normal process, open another application, provide the same context again, and remember when to use the capability, adoption becomes harder.
A useful AI capability fits into the business process rather than sitting beside it.
4. Choose the level of ambition
The right level of ambition depends on the organization’s AI maturity.
Some organizations may begin with a task that assists an employee or automates a controlled part of a workflow. Others may be ready to address a broader outcome using multiple agents, tools, workflows, and data sources within an agentic application.
Match the scope of the use case to the organization’s ability to deliver and manage it. The scope can expand as value is demonstrated and the organization gains experience operating and governing the capability.
5. Define and Bound the use case
Define what is in scope and what is not.
Identify:
- The user or role
- What starts the process
- The information required
- What AI will do
- What AI will not do
- The expected output
- What actions it can perform
- Where human review is involved
- Which actions are outside the scope
For example, a service use case may review an incoming request, summarize the issue, identify missing information, and recommend the appropriate route. Reassignment or customer communication may remain outside the initial scope.
Clear boundaries make the use case easier to understand, test, measure, and govern.
The scope can be expanded later based on the results.
6.Check data and context readiness
Data and context readiness
AI needs reliable enterprise information to produce useful results.
Check whether the required data, documents, policies, and records are:
- Available
- Current
- Authoritative
- Consistent
- Accessible when needed
- Protected by the appropriate permissions
- Maintained by a clear owner
A valuable business idea may still be a poor use case if the information required to support it is incomplete or unreliable.
Establish source-of-truth rules
Enterprise information often exists in more than one place.
Before implementation, decide what should happen when two systems, records, or documents provide different answers.
Ask:
- Which source is authoritative?
- Which source is most current?
- What happens when sources conflict?
- Should the process stop, escalate, or present both sources?
- Who owns recurring data-quality issues?
Source priority should be defined before the AI use case is implemented. Users should not have to resolve conflicting enterprise information themselves.
7.Establish ownership, risk, and governance
Every use case needs a clear business owner who is accountable for the business outcome and ongoing changes.
Before moving forward, also determine:
- Whether sensitive data is involved
- What access is required
- What decisions AI can make independently
- What actions it can perform independently
- Which actions require confirmation or approval
- When the process must escalate to a person
- How actions and results will be reviewed
The level of oversight depends on the use case and the impact of the decision or action.
For consequential actions, human approval may remain part of the process by design.
8.Assess adoption readiness
A technically sound use case will not create much value if people do not use it.
Consider whether users understand the problem being addressed and whether the capability actually makes their work easier.
Also look at:
- Training
- Management support
- User trust
- Change-management needs
- How feedback will be collected
The use case should have a clear place in the business process so users know when and why to use it.
9.Assess total cost and operational effort
The cost of an AI use case goes beyond the initial implementation.
When evaluating a candidate, consider the full lifecycle effort required to implement, operate, and maintain it, including:
- Integration and setup
- Data preparation and quality improvement
- Model, infrastructure, or service usage
- Security, privacy, and governance reviews
- Human review and exception handling
- User training and change management
- Monitoring and performance management
- Ongoing support and maintenance
- Updates to content, policies, and workflows
The expected business value should justify the total effort required to implement and operate the capability.
10.Define how success can be measured
A use case should have clear measures to determine whether the agent is performing as expected and delivering value.
Define the measures that are available and relevant to the use case before the pilot begins.
The important point is to agree on what success looks like before evaluating the result.
11.Check scalability
A pilot that works for a small team may behave differently with more users, transactions, data, and integrations.
Consider whether growth would significantly change the cost, response time, integration load, human review, security controls, or operational support required.
The pilot does not need to address every future requirement. The goal is to understand whether the use case has a reasonable path beyond the initial implementation.
12.Confirm pilot readiness
A good candidate should be testable in a controlled way.
A pilot should have:
- A defined user group
- A named owner
- Representative data
- Clear test cases
- Success and failure criteria
- Human reviewers
- A fallback process
- A limited timeline
- A decision point at the end
Include representative business scenarios and relevant edge cases.
The purpose of the pilot is not simply to show that AI can produce an answer. It is to determine whether the use case can produce reliable business value.
What to watch for when selecting the use case
A set of conditions that can limit early adoption, regardless of how capable the underlying technology is.
These conditions do not automatically rule out a use case. They identify areas that may need to be clarified or addressed before moving forward.
| Common warning sign | What happens |
|---|---|
| The business problem is unclear | The team starts with a technology idea rather than a measurable business outcome |
| The use case is impressive but rare | Users do not return often enough to build adoption |
| The process is not well understood | More process analysis is needed before AI can improve or automate the work |
| The scope is too broad | The use case becomes difficult to test, govern, and demonstrate value |
| The required data is unreliable | Users lose confidence in the results |
| The agent has too much autonomy | The risk of unintended decisions or actions increases |
| Ownership and human oversight are unclear | Accountability, approval, and escalation become difficult to manage |
| Too many systems are involved too early | Integration and coordination effort can outweigh the business value |
| The capability does not fit the workflow | Users have to leave their normal process or remember to use a separate experience |
| There is no measurable outcome | The organization cannot demonstrate whether the use case created value |
Conclusion
Choosing an enterprise AI use case starts with the business problem and the outcome the organization wants to improve.
From there, the evaluation becomes more specific: whether AI is the right approach, where it fits in the process, how much of the work it will take on, what the boundaries are, and whether the required data, ownership, controls, and operational support are available.
The result is a use case with a clear business purpose, a defined scope, and a way to measure whether it delivers value.
In Part 2 of this series, we will move from selection to execution and explore how to make a chosen enterprise AI use case succeed, scale, and deliver lasting value.