More than RPA — the synergy between RPA and AI

According to a report by the Economist (2019), 61% of US companies executives already use Intelligent Automation and Hyper Automation in their business processes extensively. We previously explored how Conversational A.I. Office Assistants could help to improve engagement with staff in the workplace and improve on overall efficiency and productivity. In this article, we will dive deeper into both Robotics Process Automation (RPA) and AI as the technologies driving the automation landscape.

RPA — an Introduction

Challenges of RPA

So first, let’s understand some limitations of RPA. RPA, in its current form, is designed and suitable for automating repetitive and fixed processes like managing payroll data, but generally speaking they are not suitable for tasks related to knowledge and cognition such as writing an article, or where the process itself is subject to frequent change like handling customer service enquiries. In short, RPA may be able to speed up a process, but it is not able to fix the process itself, therefore design and implementation must be clearly defined to have a reasonable chance of project success. Unfortunately, many companies do not have a clearly defined working processes or they are frequently changing with new administration personnel — such as adding new approval procedures to existing processes — and consequently maintaining RPA systems can become complicated.

Furthermore, while up to 80% of business processes can be automated without great difficulty, the final 20% of tasks relating to knowledge and cognition can prove to be a costly challenge. The rule-based logic framework used within RPA simply makes the technology unsuitable for tasks requiring complex logic or where the data is not in structured formats. Attempting to automate these types of tasks, the final 20% of processes, is likely to result in complicated designs and the project cost would increase as much as five times on average. It is also debatable that setting up such a complex system would ultimately increase the post-deployment management and maintenance cost, quickly eating into the cost saving benefit of adopting such a system in the first place. According to a study in 2019 by UiPath about RPA employee experience, 93% of the respondents said that they already struggle to understand the different RPA deployment options without any added complications.

How can A.I. help?

Conversational AI

It is also worth noting that one of the key emphasis of Conversational AI is to create a better understanding, and to humanise, the relationship between humans and computers. Conversational AI makes it possible for software robots to understand human languages, which ultimately can be leveraged to create much more natural interfaces and user experiences for human use. By making these software robots easier to interact with and to communicate our intents and requests, it is possible to reduce the training time and cost for using the system, and in some cases, to eliminate them altogether.



Boulton, C. (2019, June 12). RPA is poised for a big business break-out. CIO Magazine.

Deloitte. (2018). Robotic and Cognitive Automation — The fusion of digital with operational excellence. (PDF Version)

Dilmegani, C. (2021, January 9). 21 RPA Pitfalls/ Challenges & A Checklist to Tackle Them in 2021. AIMultiple.

Gartner. (2020, September 21). Gartner Says Worldwide Robotic Process Automation Software Revenue to Reach Nearly $2 Billion in 2021.

I.T. Explained. Cognitive Automation Explained.

The Economist. (2019, May). The advance of automation Business hopes, fears and realities.

UiPath. (2019, March). The Impact Of RPA On Employee Experience. (PDF version downloaded on 19 January 2021).

Intelligent Machine Conversation