Automating and Educating Business Processes with RPA, AI and ML Posted on : May 18 - 2020

How are these technologies connected, what are the implementation challenges, and how are companies using them?

Robotic process automation, artificial intelligence and machine learning are all being infused to automate business processes and speed time to decision. What is the "sweet spot" for each of these technologies, and how are companies using them? The common touch point for these technologies is automation.

When you use RPA, you are automating repetitive tasks, so staff doesn’t have to do them. An example is defining and implementing a robotic process automation process that automatically screen-scrapes invoice information from one system and enters it into another system, without an office staff having to manually key information from one system to another.

When you use AI, you are adding automation to decision making. Instead of performing a supply chain risk assessment manually, you enter a diversity of relevant data points into an AI data repository, and then present several what-if risk scenarios that you want the system to analyze and return answers for. The AI system comes back with several different potential outcomes for each risk scenario and then you make the final decision.

When you further augment AI with machine learning, you activate an AI system's ability to detect and analyze data patterns on its own, and to “learn” from those patterns. The advantage of this is the speed at which the system can process data and recognize patterns on its own that a human couldn’t. What the machine learning discovers has the potential to reduce your speed to insight of an important pattern or trend developing in the situation you are studying so you can respond to the situation sooner.

In summary, RPA automates routine, repetitive office tasks; AI adds automation to decision making; and ML is an ongoing educational process for the AI so the AI can “learn” from the patterns and trends developing in the data points that AI is charged to evaluate. Collectively, RPA, AI and ML all play important roles, and must be intelligently orchestrated as tools for business process automation and education to occur.

Overcoming implementation challenges

In working with cognitive automation tools, a major hurdle that many organizations face is understanding which tool to use when.

Here are four common challenges that enterprises face in their adoption of RPA, AI and ML:

1. Unrealistic expectations

In late 2017, a Deloitte survey on RPA revealed that 53% of enterprise respondents had already begun to implement or at least test the waters with RPA. This was a figure that Deloitte projected would grow to 72% of organizations by 2020.

According to Deloitte, most of these organizations were looking for continuous process improvement for their workflows, with automation as a secondary goal. Yet, when Deloitte asked these same organizations about how well they were able to leverage and scale their use of RPA to other areas in their companies, only 3% said they were succeeding in doing this.

The Deloitte report stated: “Many organisations, having started by treating RPA as an experiment, are now “stuck” and are suffering from IT issues, process complexity, unrealistic expectations and a “piloting” approach,” said Deloitte. “Maximising the impact of RPA requires a committed shift in mind-set and approach from experimentation to transformation.…Given the relative immaturity of the automation market, it is taking time for large organisations in particular, to learn about and to adopt RPA at scale.”

The story doesn't change much for AI and ML. Many companies are still working through proofs of concept that characterize early stages of adoption. They are not yet at the stage where these technologies can be broadly leveraged for maximum business benefit throughout their companies.

One element slowing expansion is limited on-staff knowledge and experience with these technologies, and how the technologies can best be applied to business processes and decision making. View More