Generative AI (GenAI) facilitates greater automation and is now being integrated into the platforms of many software vendors under different names and labels, such as AI agents, AI-powered microautomations, and autonomous workplace assistants (AWA). These are all somewhat overlapping terms for agent automation, which is beginning to emerge as generative AI promises to elevate automation to heights of unknown value.
Forrester explicitly encourages the use of genAI in automation use cases of all types, but the recent emergence of agent-based automation also raises a concern: whether robotic process automation (RPA) robots are being replaced by AI agents or if AI agents are closing the next automation gap, this will lead to an unmanageable number of AI agents with overlapping functionalities, poor governance, and high execution and maintenance costs. This development highlights many of the other challenges and risks we have seen in expanded RPA bot environments. Let’s not repeat that! Instead, automation creators should:
- Explore the opportunities, risks and challenges of technology adoption. This is the foundational capability needed for any future adoption of genAI. It applies to any emerging technology. We’ve written a lot about experimenting with emerging technologies.
- Identify the business problem behind the genAI automation use case. Very often, genAI use cases seem very obvious. However, if you look closer, building an AI agent to address a problem looks more like a Band-Aid than a long-term sustainable solution, similar to some RPA robots in the past. Therefore, our recommendation is to first identify and understand the problem before deciding whether the best solution is an AI agent, an RPA bot, an API, or a better process.
- Challenge the underlying process before improving a bad process. Automatically generating an email to a customer or having an AWA search your product catalog for the best product combination sound like value-added cases for AI. But wait a minute! What if the reason for continuing to email customers and search for products in product catalogs is due to poorly connected application systems or information still in documents rather than digital records? First improve the process to understand if there might be a promising case for agent automation and where it will not only save costs but likely also improve the customer and/or employee experience.
- Integrate AI agents into orchestrated processes like any other automation technology. Treat genAI as another component in your automation toolbox that you use to orchestrate your processes. Currently, we are seeing three patterns of how agent automation is used in production environments: 1) AI agents used in isolation or an AI agent replacing existing automation, primarily an RPA robot; 2) RPA bots that call AI agents and vice versa, throughout an automated process; and 3) multiple AI agents orchestrated throughout a process.
