Using existing use cases when researching the cost efficiency of AI agents provides a reliable benchmark for assessing potential return on investment. These use cases offer concrete examples of how AI agents have been deployed to reduce operational costs, improve productivity, or streamline workflows in comparable environments. By analyzing results such as time savings, automation rates, and resource utilization, organizations can make informed projections about cost efficiency without relying solely on theoretical models. This evidence-based approach not only strengthens financial justification but also helps identify the most cost-effective configurations and deployment strategies for AI agents.

Find Existing Use Cases

Steps to find more AI customer stories:

Go to Search for Customer Success Stories from the Learn More section in the Summary.

Use the filters located on the left side to search by industry, product, region, and more. You can also use the search bar to enter relevant terms and start the search.
You should then read the relevant use cases to learn more about how other companies use AI agents for their own use cases. This can help you identify how you might implement something similar for your specific situation.

Collaborate with Cross-Functional Teams

To maximize the cost efficiency of AI agents, you need to examine both costs and benefits. To identify the most significant potential benefits, you should collaborate with cross-functional teams to:

• Detect operational pain points,
• Analyze existing workflows to identify automation opportunities,
• Gather customer feedback to find use cases that improve customer satisfaction,
• Conduct an internal assessment to identify challenges and inefficiencies that AI can solve.

You can then search for industry-specific use cases using defined objectives and goals.

Next unit: Identify Business Needs and Define AI Use Cases