Robotic Process Automation (RPA) has helped businesses reduce the tedium of mundane tasks for people, giving them the opportunity to work on more rewarding tasks. However, RPA has limitations. The intelligent application of machine learning (ML) and artificial intelligence (AI) can take RPA to the next level.
Software developers have been creating programs to automate tasks for many years, but RPA tools have democratized automation, bringing it into the reach of almost everyone. Drag and drop tools allow frontline workers to take everyday tasks and automate them.
For example, a human resource (HR) officer might receive emails each day for leave requests. She may need to open those emails, extract particular information and compile it into a report that needs to be sent to specific people. It is a repetitive task that is carried out daily and may take a couple of hours. However, an RPA tool can create an automated workflow that scans the email account, extracts the relevant data, compiles it into a report and sends the email faster without hands-on intervention. This frees the HR worker up to focus on higher value activities.
Taking RPA to the next step
The capacity for RPA to be applied is significant – most workers have tasks that are the same each day and are simply carried out by following a series of well-defined steps.
However, what happens when the task requires specific human input? It might be a repeating task but with enough variations each day that human intuition and knowledge are needed. RPA allows variables to be used at each step. For example, it could be used to compile a finance report and the variables could be altered to look at a different account code or financial entity. But what if the list of variables changes every day? What if the report needs to cover the top 10 cryptocurrencies by market capitalization – something that changes daily? Or if that report also needs to alert someone of a budget item that suddenly moved from under-budget to overspent?
It is relatively easy for a person to decide and add an extra parameter or variable to a search. AI will enable tools to learn what we want and augment queries and actions themselves. Instead of RPA routines being specific to a task, they can be more generalized and automatically adapt according to the data they are working with.
Many actions, while repetitive, require insight and consideration by a human with knowledge and experience. And this is where the next generation of RPA tools can leverage AI. Humans are very good at answering the question of “What else is important or interesting?”. AI will help RPA tools go further than simply adding more variables to a query. AI will allow RPA to take the next step and answer the question of “What else?”. In effect, the application of AI to RPA will allow these tools to expand the scope of what they can do.
Leveraging data and AI for RPA
The emergence of GPT-3, Generative Pre-trained Transformer 3, is a powerful technology that uses AI to leverage the vast amount of language data on the internet. By training an incredibly large neural network, GPT-3 can understand and generate both human and programming languages with near-human performance. For instance, given a few pairs of legal contracts and plain English documents, it can start to automate the task of writing legal contracts in plain English. This kind of sophisticated automation was unimaginable with classical RPA tools without leveraging data and state-of-the-art AI.
This demonstration of the power of data and AI leads us to a new question. What else can be improved by leveraging data using AI in RPA?
One illustrating example is sending emails in digital marketing. Marketers often send automated emails to customers and prospects. Collating a list of targets and then creating and sending the email is a common use case for RPA, but AI can be used to analyze data and optimize those messages in order to maximize the chance of a message leading to a successful conversion. By applying AI to RPA, marketers can better target their customers with personalized messages, so they are more likely to respond positively.
Next generation RPA will grow from digital transformation
This relies on having enough data and powerful analysis tools that can create models of what to send to whom and the best times to do so. This will see closer relationships emerge between line-of-business managers and IT teams working together to identify opportunities to use RPA.
In the past, humans would analyze this data and form hypotheses about when to send the messages. With AI, you can now conduct the analysis, build the hypothesis, test it and then refine it to ensure the next campaign is more successful.
While RPA has delivered significant benefits when it comes to automation, the next generation of RPA will deliver more benefits through the use of AI and machine learning through optimization. This is not about faster automation but about better automation.
It is likely that as RPA becomes more widespread, businesses will also look at how they can source software bots quickly. Some experts posit that we will see the emergence of “Robotics as a Service” as companies look for ways to deploy RPA through subscription models and so they can access expert assistance.
With businesses undergoing digital transformation, and perhaps accelerating their efforts as they come to grips with the impacts of Covid-19 on their workforces, data is becoming increasingly important. The optimization of RPA will benefit greatly from increased digitization in businesses. As businesses develop data lakes and other new information repositories that are accessible through APIs, it is important to allow RPA tools to have access so they can be optimized.
There are some challenges though. Automation delivers many benefits, but it may not be worth automating every process. Businesses should target the repetitive processes that are most valuable as this is where the most business benefit will be derived.
There also needs to be a plan in place for people. When a task that was previously carried out by a person is automated, a new role or activity needs to be found for that person. While it is true that automation can reduce or remove mundane and unsatisfying tasks, the time that is created through automation needs to be used. Making someone available for higher value work is irrelevant if there is no higher value work for them to do.
When automation is targeted and executed well, the business benefits are quickly realized. The next generation of RPA will go further by not only automating routine tasks but by finding opportunities, through the use of AI, to optimize automation and deliver better outcomes. The application of neural networks will allow RPA to go further and deliver better outcomes for businesses, large and small, as they seek to deliver greater value to their customers.
Dr. Min Sun, Chief AI Scientist, Appier