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.
Choose a problem that matters
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 statement describes what the business wants to improve. The second assumes a particular implementation before the problem has been evaluated.
Ask whether the outcome is clear enough to measure. It might relate to cycle time, productivity, cost, quality, revenue, customer experience, or risk.
If the expected outcome cannot be described clearly, the use case probably needs more work before technology choices are made.
2. Align the use case with business priorities
Strong use cases should support a defined objective, such as:
- Increasing revenue
- Protecting renewals
- Reducing cost
- Improving customer experience
- Increasing employee productivity
- Shortening cycle times
- Improving quality
- Reducing operational or compliance risk
Strategic alignment makes it easier to establish sponsorship, ownership, funding, and success criteria.
3. Look for value that compounds
The value of an AI use case is not always measured by the time saved in one interaction.
Saving a few minutes across thousands of transactions can create significant value. A lower-volume process can also be a strong candidate when each occurrence has a high financial, customer, operational, or regulatory impact.
Look for problems that cause enough effort, delay, cost, or business impact to make them worth solving.
Also look beyond individual tasks. Valuable opportunities often occur between applications, teams, and processes. For example, when information does not reach the right team, approvals remain idle, or exceptions have unclear ownership.
Decide whether AI is the right approach
4. Confirm that AI is the right solution
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.
The goal is to choose the simplest combination of capabilities that can deliver the required business outcome.
Define and Bound the use case
5. Identify a clear trigger
A useful AI capability should connect to a recognizable business event.
That event might be:
- A case being opened
- A document arriving
- A request being submitted
- An approval becoming overdue
- An exception occurring
- A scheduled review taking place
The trigger helps establish when the capability becomes involved and what information is available at that point.
This also makes the use case easier to place in the existing business process.
6. Look for structured workflows with moments of ambiguity
Many good AI use cases occur in processes that are structured overall but contain steps where rules alone are not enough.
Examples include:
- Incomplete information
- Recurring exceptions
- Conflicting signals
- Unstructured documents
- Context-dependent decisions
- Manual triage
These are useful points to consider AI because the surrounding process can remain controlled while AI is applied to the part that requires interpretation or judgment.
7. Check whether AI fits into the flow of work
The capability should fit naturally into the way employees already work.
Consider where the AI participates in the process, how users interact with it, and how work moves between the AI and the person.
The user experience should reduce work rather than create another destination employees have to remember to use.
8. Bound the use case
Broad goals are difficult to govern and measure. A use case should make clear what is in scope and what is not.
Define:
- The user or role
- The business problem
- The information required
- What AI will do
- What it will not do
- The expected output
- Where human review is required
- Which actions are prohibited
These boundaries make the use case easier to evaluate and reduce ambiguity during design and implementation.
They also give the business and technical teams a common understanding of what is actually being proposed.
9. 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.
Assess readiness and controls
10. Confirm 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.
11. 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.
12. Establish ownership, risk, and governance
Every use case should have 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
For consequential actions, human approval may be an intentional part of the design rather than something to remove later.
13. 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.
Assess viability and scale
14. 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.
15. Confirm measurability
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.
16. Check scalability
A pilot that works for a small team may behave differently at enterprise scale with more users and higher transaction volumes.
Scalability should be considered early enough to avoid selecting a use case that cannot mature into a sustainable capability.
Decide whether it is ready to pilot
17. 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.
| 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 is not about finding the most impressive idea. It is about identifying the right problem, in the right workflow, with the right data, controls, and measures of success.
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.