Research use cases

Using existing use cases when researching the cost efficiency of AI agents provides a reliable benchmark for evaluating 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 outcomes 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:

• Navigate to Search Customer Success Stories from the Learn more section, in the Summary.

• Use the filters on the left-hand side to search by industry, product, region, and more. You can also use the search bar to enter relevant terms and search.

You should then read the relevant use case stories to learn more about how other companies are using AI agents for their use cases. This can help you identify how you might be able to implement something similar for your specific situation.

Collaborate with cross-functional teams

To maximize the cost efficiency of AI agents, you should look at both costs and benefits. To identify the greatest potential benefits, you should collaborate with cross-functional teams to uncover operational pain points, analyze existing workflows to identify automation opportunities, gather customer feedback to find use cases that improve customer satisfaction, and conduct an internal assessment to identify challenges and inefficiencies that AI can address. You can then research industry use cases using defined goals and objectives.