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 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. Define and Bound the use case
Once the role of AI in the process is clear, decide how much of the work the use case will take on.
A use case may assist an employee with part of a task, provide a recommendation, perform a controlled action, or coordinate several steps across tools and systems.
The level of ambition depends on the business need, the process, the associated risk, and the organization’s ability to operate the capability. More mature organizations may address a broader business outcome using multiple agents, tools, workflows, and data sources within an agentic application.
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.
A clear boundary makes the use case easier to understand, test, measure, and govern. The scope can expand later based on the results.
5.Check data and context readiness
A valuable business problem may still be a poor AI use case if the information needed to support it is incomplete or unreliable.
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
Also consider what happens when enterprise information exists in more than one place.
Ask:
- Which source is authoritative?
- Which source is most current?
- What happens when sources conflict?
- Does the process stop, escalate, or present both sources?
- Who owns recurring data-quality issues?
If the use case depends on information that cannot be identified or trusted consistently, that affects its suitability as a candidate.
6.Establish ownership, risk, and governance
Identify who is accountable for the business outcome and who will make decisions about changes to the use case over time.
The nature of the use case also determines the level of risk and control involved. Consider:
- Whether sensitive data is involved
- What access is required
- What decisions AI can make
- What actions it can perform
- Which actions require confirmation or approval
- When the process escalates to a person
- How actions and results can be reviewed
A use case that requires broad access or consequential actions is different from one that summarizes information or provides a recommendation. These differences affect how practical the use case is to pursue.
7.Assess adoption readiness
A technically sound use case will not create much value if people do not use it.
Consider whether the capability reduces effort and has a clear role in the existing process.
Look at questions such as:
- Will users know when to use it?
- Does it remove work or add another step?
- Is the output useful for the task?
- Can users review the result when needed?
- Will users have enough context to trust the result?
Training, management support, and change management become important during adoption, but at the selection stage the main question is whether the use case fits naturally into the work being done.
8.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
9.Define how success can be measured
A use case is easier to evaluate when the expected result is clear. The measures need to connect to the business outcome identified at the beginning.
Define the measures that are available and relevant to the use case.
The ability to measure the outcome is part of deciding whether the use case is a good candidate.
10.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.
It is important to understand whether the use case has a reasonable scalable path beyond the initial implementation.
11.Confirm pilot readiness
The final step is to determine whether the use case can be tested in a controlled way.
A pilot can include:
- A defined business problem and outcome
- A named owner
- A clear scope
- A defined user group
- Representative data
- Clear business scenarios
- Relevant edge cases
- Success and failure criteria
- Human reviewers where needed
- A fallback process
- A defined evaluation period
- A decision point at the end
If these elements are difficult to define, the use case may need more work before it is ready for a pilot.
The purpose of the pilot is not simply to show that AI can produce an answer or complete a task. It is to determine whether the use case can deliver the expected business value reliably enough to justify moving forward.
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.