Everything You Need to Know about AI and RPA

A good rule of thumb for determining whether a process should be handled by RPA or AI is to begin one's automation journey by tackling processes that one can easily mentally map out and then add AI to workflows deemed too complex for RPA alone.

Fremont, CA: Robotic process automation (RPA) and artificial intelligence (AI) have received a lot of attention in recent years due to their ability to drive previously unheard-of productivity, efficiency, and customer satisfaction gains.

Modern enterprises are comprised of both simple and complex decision-making processes, necessitating the use of complementary technologies to handle the full range of their workflows. RPA, on one end of the spectrum, thrives in systems with a clear, step-by-step flow. AI, on the other hand, can augment and improve human decision-making in complex processes.

It takes two, just like in life and tango. In the world of an automation strategy, driving operational enterprise efficiencies requires the collaboration of AI and RPA. By streamlining their respective halves of enterprise operations, these two partner technologies work together to reduce process fat and make people's lives easier.

This is evident in nearly every industry on the planet. Consider the differential diagnosis processes used in hospitals to identify the 2019 novel coronavirus (COVID-19).

Hospitals can use RPA to create software robots that look at a set of COVID-19 symptoms, such as a high fever and body aches, and alert medical professionals to new cases. However, RPA is limited to the initial "yes or no" style intake questions and cannot assess more complex criteria adequately.

RPA, on the other hand, can consolidate this baseline patient data for AI's more advanced, predictive process analysis.

When to Roll out RPA and When to Send in AI

A good rule of thumb for determining whether a process should be handled by RPA or AI is to begin one's automation journey by tackling processes that one can easily mentally map out and then add AI to workflows deemed too complex for RPA alone. This not only provides one and one robotic Center of Excellence (CoE) with early wins, but it also establishes an automation foundation that one can later scale with AI.

RPA cleans up your underlying processes to provide a framework that can be easily integrated on top of one's existing digital systems. The barrier to entry for integrating AI is much higher without this underlying foundation. AI would have to be manually woven into one's core processes without that foundation.

There is one notable exception to this approach: If one has previously invested heavily in business process automation—that is, one has already done the work to ensure process hygiene—one can consider opportunities for AI and RPA in tandem.