Understanding the difference between RPA and AI

CIOs are implementing both automation and AI at a faster pace and are eager to expand and accelerate deployments to ensure the speed and cost savings they each offer.

Recently published figures quantify the accelerated adoption rate. According to a January report by the Everest Group, more than 72% of global companies have already started implementing AI. The company expects global spending on AI services to accelerate 32% from $ 25 billion in 2019 to $ 95 billion in 2024.

The global Robotic Process Automation (RPA) software market was $ 1.2 billion at the end of 2019, growing more than 75% year over year. Although the expansion rate slowed after the pandemic in 2020, it will increase until 2021. Everest Group predicts that pent-up demand for automation will drive the RPA market, with an expected average annual growth rate of nearly 50% over the next two years.

This is not surprising, as RPA and AI bring a number of benefits to a company: streamlined processes, reduced cycle times and, ultimately, better business results. According to experts, the overarching return is not only faster processing of the end-to-end business cycle, but also a more responsive business overall.

While companies can expect benefits from using each technology independently and independently, experts stressed that companies using RPA and AI technologies together can expect higher returns and a more competitive stance in the modern digital marketplace.

“For us it’s always AI plus RPA. It’s part of the end-to-end transformation,” said Bhooma Chutani, head of digital transformation consulting at Larsen & Toubro Infotech Ltd.

But first, what are the capabilities of RPA and how is it different from AI?

RPA deployments

RPA is the use of software robots – or bots – to perform standardized, repeatable tasks within a business process. These bots perform the same tasks every time and can do them faster and more reliably than human workers. This brings speed and efficiency, reduces costs and errors, and leaves it up to employees to do the more complex, higher-value tasks that only humans can manage.

“It’s easy to implement for processes that are very structured and very action-oriented. It’s easy to take advantage of quickly – it’s an easy win,” added Chutani, noting that many companies have already used RPA to at least perform some of their repetitions tasks.

However, the capabilities and value of RPA have their limits. While the software can perform repetitive tasks at a speed, scalability, and accuracy that is far superior to human workers, RPA cannot deviate from the tasks for which it is programmed.

“You can use RPA for many processes. But at some point you have to make a decision; most of the processes [will] need an intelligence component. There we talk about keeping someone up to date, “said Chutani.

The role of AI in processes

This is where AI comes in: AI can mimic human decisions that RPA is unable to make. In addition, by making these decisions, AI can learn how to improve its work by quickly identifying and analyzing patterns in data at a speed and scale that is impossible for humans.

However, implementing AI is more difficult than deploying RPA. First and foremost, the challenge is to get the data needed to train the AI.

AI takes a lot of data to build these models, and most organizations don’t use their data well to adopt AI quickly or easily,” said Chutani.

Then there is the cost. AI initiatives are more expensive than RPA projects. Training for AI systems takes months or more to get up and running. AI projects also require more expertise to develop, deploy, and maintain, which can be expensive and difficult to find.

In addition, CIOs and other executives often lack a thorough understanding of their business processes and need to tackle them in order to advance their AI programs.

“If I could direct CIOs to do only one thing, it is a complete picture of the process that they are going to automate. They need that end-to-end visibility,” said Cathy Tornbohm, analyst at Gartner.

Experts also found that many executives remain cautious, if not completely against, transferring decision-making power to computers, especially in areas that are under government control or that could endanger their own jobs or those of others.

With all of this in mind, experts said they see a slower adoption rate for AI than RPA.

However, technology remains a top priority for business and IT executives. A 2020 survey by software provider IFS found that AI tops the list of technologies prioritized by executives. 24% of respondents expect it to be a leading technology in the next two years, ahead of other trending technologies such as virtual reality and augmented reality, IoT, blockchain and 5G.

enterprise_ai-5_ai_tech_driving_bus_value-f_mobile Understanding the difference between RPA and AI

Bringing RPA and AI together

Chutani cited invoice processing as an example of how organizations can use both RPA and AI in a business process to maximize results.

Instead of employees manually transferring data from one file or system to another, the RPA software can retrieve the required data from predefined fields on submitted invoice forms and send it to the predefined company systems.

AI is then used to handle the complex tasks along this business process. For example, natural language processing – a type of AI that can understand human language – can identify emails that contain invoices and send them to bots for processing. The AI ​​would then resume the process to determine which invoices meet the requirements for issued payments by sending the approved invoices to payment systems and redirecting the rejected invoices to human managers who would determine next steps.

According to Tornbohm, the use of RPA, in addition to AI, puts companies in the field of hyper-automation, which combines various technologies in order to automate not only tasks within business processes, but as many decision-making aspects within the processes as possible.

“”[Essentially it] means that you will take someone out of the process, “said Tornbohm.

Experts believe that most organizations still have a long way to go to achieve this status, as IT leaders who have turned to hyper-automation are still putting their RPA and AI skills together.

However, the market is changing as some vendors offer both automation and AI capabilities together – a move that, along with increasing interest and pressures for speed and agility, could lead to more implementations.

“All of that,” said Chutani, “should help introduce AI.”

April 15, 2021