Every business wants to use AI, but very few know where to begin. The challenge is not finding AI tools. The challenge is identifying where AI can create real, measurable value. Without clarity, AI becomes a wish list rather than a strategic initiative. Teams start experimenting in silos, vendors pitch solutions that do not fit, and leadership struggles to connect the technology to business outcomes.
This guide is meant to solve that problem. Identifying your AI use case is not only the first step in your AI journey. It is the most important one. When done correctly, it prevents wasted money, eliminates confusion, builds internal confidence, and accelerates results. When skipped, projects fail, expectations collapse, and AI becomes more of a burden than a benefit.
Let’s walk through a simple, structured, and repeatable process that any organization can use to identify its first AI use case.
Step One: Define What the Business Is Trying to Achieve
AI is not the goal. Business outcomes are the goal. AI is simply one of many tools that can help organizations reach those outcomes more efficiently.
Before trying to find AI opportunities, ask the leadership team a single question:
What is the business trying to accomplish over the next twelve months?
There are only a few possible answers:
• Increase revenue
• Improve customer experience
• Reduce costs
• Improve productivity
• Strengthen security and compliance
• Improve decision making
• Modernize outdated processes
Your first AI use case should support one of these goals directly. If it does not, the excitement will fade quickly because no one will understand why the project matters.
This step ensures alignment. It also prevents random experiments that lead nowhere.
Step Two: Observe How Work Actually Gets Done
Most of the best AI use cases do not come from leadership discussions. They come from watching how people work. This means spending time with frontline employees, managers, and teams that interact with customers or systems every day.
Ask three simple questions:
• What takes the most time?
• What work is repetitive or manual?
• What tasks are people doing that software should handle?
You will uncover far more opportunities than you expect. In almost every business, the pain points are the same. Manual data entry. Repetitive emails. Scheduling. Documentation. Customer inquiries. Reporting. Searching for information. Switching between systems.
The frontline always knows where the problems are. Their insights become the foundation for potential use cases.
Step Three: Document the Current Workflow
A workflow is the actual series of steps required to complete a task. Most people think they understand workflows, but the truth is that workflows change over time and usually become more complicated than anyone realizes.
To identify use cases accurately, document the workflow in detail. Write out every step from start to finish. Include the people involved, the systems used, and the decisions that must be made.
A simple workflow might look like this:
Customer email comes in.
Employee reads it.
Employee searches for the answer.
Employee writes a reply.
Employee updates the CRM.
Employee closes the ticket.
Once the workflow is documented, inefficiencies become obvious. Steps that add no value. Tasks that can be automated. Bottlenecks caused by waiting for another team. Data that must be copied manually. Outdated systems slowing everything down.
Workflows reveal the truth. They show which problems AI can realistically help solve.
Step Four: Identify the Tasks That Are Suitable for AI
Not every task is appropriate for AI. Instead of trying to use AI everywhere, focus on three categories where AI consistently produces strong results.
Routine and repetitive tasks
Anything that happens the same way every time is a strong candidate. Examples include:
• Drafting customer responses
• Classifying emails or tickets
• Summarizing documents
• Extracting data from files
• Scheduling or routing requests
These tasks take time but require little creativity.
Tasks that require pattern recognition
AI excels at identifying trends and anomalies in large datasets. Examples include:
• Finding unusual expenses
• Predicting equipment failures
• Identifying sentiment in customer messages
• Detecting inconsistencies in insurance claims
• Analyzing sales trends
Any task that improves with more data is ideal for AI.
Tasks that require decision support
Many employees waste time gathering information before making decisions. AI can simplify this. Examples include:
• Providing recommended next steps
• Highlighting missing information
• Ranking priorities
• Predicting outcomes
• Suggesting corrective actions
The goal is not to replace decision makers. It is to give them better information faster.
Once you categorize the tasks, you begin seeing clear use case candidates.
Step Five: Estimate the Value of Solving the Problem
A use case is only strong if it produces measurable value. The value can be financial or strategic, but it must be tangible. Ask three questions to estimate the impact.
First, how much time does the task consume per week?
If employees spend ten hours per week on a task, and AI can reduce it to two hours, the value is clear.
Second, what errors or risks exist in the current process?
AI often reduces mistakes that create downstream costs.
Third, what is the broader impact on customer experience, revenue, or efficiency?
Some tasks have a multiplier effect, such as improving response times or speeding up sales cycles.
If the value is easy to identify and measure, the use case is worth exploring.
Step Six: Evaluate Whether the Required Data Exists
Data is the fuel for AI. Without good data, even the best use case cannot move forward. Evaluate three aspects of your data.
First, availability. Do you have the data needed? If the task requires access to customer history or ticket logs, make sure the data exists.
Second, accessibility. Can the data be connected, exported, or integrated? Many legacy systems make this difficult.
Third, quality. Is the data accurate, complete, and consistent enough to be used? Messy data often delays projects more than any other factor.
If the required data is missing or inaccessible, the use case should be placed on the future roadmap while foundational work is done.
Step Seven: Score and Prioritize Your Use Cases
By this stage, you will have several possible use cases. Some will be easy. Some will be complex. Some will deliver high value. Others will deliver moderate value. To avoid overwhelm, score each use case using four criteria.
Impact. How much value does the solution create?
Feasibility. How easy is it to implement based on data, systems, and complexity?
Risk. Does the solution affect sensitive data or critical operations?
Speed. How quickly can the solution be deployed and tested?
Your first AI use case should have high feasibility, low risk, and fast time to value. You want a quick win that builds confidence across the organization.
Step Eight: Define What Success Looks Like
Before implementing anything, define success clearly. Success should be measurable and simple. Examples include:
• Reduce time spent on a task by 50 percent
• Increase customer response speed by two minutes
• Decrease manual errors by 30 percent
• Shorten onboarding time for new employees
• Increase completed tickets per rep per day
Success metrics are the difference between experimentation and strategic improvement.
Step Nine: Validate the Use Case With the People Who Will Use It
AI projects fail most often when teams do not adopt the solution. Before building anything, validate the use case with the people who would use the AI tool.
Ask them three questions.
• Does this actually solve your problem?
• Would this realistically fit into your workflow?
• Is anything missing that would make it more useful?
This step ensures the solution is grounded in real-world experience rather than theoretical design.
Final Step: Build, Test, and Iterate
Once the use case is validated, start with a small pilot. Do not aim for perfection. Aim for proof. Test the model or tool, observe how employees use it, gather feedback, and refine the approach.
AI adoption succeeds through iteration. Every improvement builds momentum. Every win builds trust. And every use case builds toward broader transformation.
Final Thought
Identifying your AI use case is not a technical exercise. It is a business exercise. It requires clarity, observation, and collaboration. When done correctly, it becomes one of the most powerful steps an organization can take.
Companies do not succeed with AI because they have the most advanced tools. They succeed because they know exactly what problem they are trying to solve.
