RPA and AI: Living up to the Hype?

Within the last year, we have seen customers voice their desire for RPA solutions that extend beyond individual task automation, integrating AI components to enable broader use cases and more sophisticated task variation. This is just the beginning but begs the question as to why there is such low adoption in the industry?

58% of those surveyed state the inability to fully comprehend and communicate the business potential of AI in their business processes as a key barrier to success. As with similar challenges we see with RPA adoption, 48% also state the difficulty in building in-house expertise to build machine learning or AI applications from the ground up.

Areas of future growth in AI are generally consistent with where they are most used;

AI for analysis and RPA for processing.

The survey results also tell us that:

• Automation levels are particularly low and data is not being used for decision-making purposes
• RPA is being used to some extent, with positive outcomes but organizations need to upskill their workforce to reduce the number of bot failures
• A minority are using AI, with many organizations having plans to leverage this capability further to empower analytics. However, it seems there is still a lack of knowledge and use cases to demonstrate where and how finance teams can leverage AI to its full potential.

A 2020 survey by Deloitte states that 53% of their respondents started to implement some form of RPA, and this percentage will become 72% by 2022. We know that RPA isn’t just noise, but it’s clear organizations need to be realistic and engage with experts to help them identify and deliver the most value to their organization.

1. Take a more holistic approach to process management. Businesses should connect with IT and client-facing teams to embrace new process initiatives to help drive change. Resisting new tools and processes in favor of old ways of working will slow down transformation efforts.
2. Digital transformation is not just about technology. Equally important factors are people and process. Recognize the value of upskilling and leveraging the right data and technology to aid decision-making. Set up continuous improvement programs to ensure skill sets are at the right level to achieve an outstanding, sustainable customer experience.
3. Leverage a fully integrated O2C integrated receivables platform.  Leveraging a single platform to automate financial processes and manage the entire o2c cycle increases productivity, enhances cash flow and makes it easier for clients to do business with you.
4. Select a partner based on their RPA and AI references. To communicate the value of AI to stakeholders, organizations should work with a partner that is dedicated to customer success. When evaluating providers, ensure you speak to their customers about how they are leveraging RPA and AI – do these results provide deductive reasoning to come to an outcome, such as predicting invoice disputes or payment delays for example.