Generative AI (GenAI) is an enabler for more automation – and is now getting embedded in many software vendors’ platforms with different names and labels such as AI agents, AI-led micro-automations, and autonomous workplace assistants (AWA). All of these are somewhat overlapping terms for agentic automation that is starting to sprout as generative AI promises to elevate automation to unknown value heights.
Forrester is explicitly encouraging to leverage GenAI in automation use cases of all sorts. However, the recent emergence of agent-based automation also raises a concern: If RPA bots are getting replaced by AI agents or when AI agents are closing the next automation gap, this will lead to an unmanageable number of AI agents with overlapping functionalities, poor governance, high run and maintenance cost. This development highlights many of the other challenges and risks we have seen from scaled-up RPA bot environments. Let’s not repeat that! Instead automation builders should.
- Explore the technology’s opportunities, risks, and adoption challenges. This is the bedrock capability required for any further GenAI adoption. It applies to any emerging technology. We have written a lot about emerging technology experimentation, which can be found here.
- Identify the business problem behind the GenAI automation use case. Very often GenAI use cases seem so obvious. However, when taking a closer look, building an AI agent to address an issue is more like a band aid than a sustainable, long-term solution – similar to some RPA bots in the past. So our recommendation is to first identify and understand the problembefore deciding if the best solution is an AI agent, or an RPA bot, or an API, or a better process.
- Challenge the underlying process before improving a bad process. Auto-generating an email to a client or having an AWA search your product catalogue for the best product match sound like value-adding cases for AI. But wait a minute! What if the reason for still sending emails to clients and looking up products in product catalogues are due to badly connected application systems or information still sitting in documents instead of digital records? Improving the process first to understand if and where there might be a promising case for agentic automation will not only save cost, but quite likely improve the customer and/or employee experience.
- Embed AI agents in orchestrated processes like any other automation technology. Treat GenAI as another component in your automation tool box which you use to orchestrate your processes. Currently, we are observing three patterns as to how agentic automation is used in productive environments: 1) AI agents used in isolation or an AI agent replacing an existing automation, mainly an RPA bot, 2) RPA bots calling AI agents and vice versa along an automated process, 3) several AI agents orchestrated along a process.
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