ROBOTIC PROCESS AUTOMATION (RPA)
is the Path to AI
1ST QUARTER 2020
Lenore Keerigan UIPath’s Country Director for South Africa, talks exclusively to Synapse Magazine about the path to AI using RPA
SYNAPSE
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WHAT ARE some of the main things that African or African-based businesses looking to introduce artificial intelligence (AI) should consider? We are seeing the biggest implementation of AI in business processes related to Customer Experience e.g. processing of claims, complaints, call centres, applications, etc. Improved customer experience is one of the highest focus areas for CEOs in a competitive and economically constrained market. There is plenty of hype around AI, but a word of warning – many current AI initiatives work in isolation and have shown limited success. So, business leaders are still wondering where to start. This is the beauty of Robotic Process Automation (RPA) - companies can leverage the automation platforms whose software robots are already using AI and Machine Learning algorithms, making RPA the enabler for organisations to introduce AI into the standard business processes. RPA is the path to AI. Which industries / sectors stand to benefit the most from introducing RPA or AI to their business processes and which ones should definitely be thinking about implementing this technology? While the banking and financial services have pioneered the field of automation, at UiPath we have seen deployments in every industry, from manufacturing to retail, to aviation and the hotel industry, to the legal profession and the public sector. Functions like human resources, finance and accounting, contact
centres, accounts payable, claims processing which are common across industries abound in repetitive, rules-based tasks that can and should be automated as a means to not only improve customer service, but also boost employee engagement and satisfaction. UiPath is promoting an Automation First mindset to organisations of all sizes, meaning that we offer them an end-to-end hyper-automation platform to practically eradicate the mundane and boring tasks from the job descriptions of the human employees allowing them to get involved in the more valuable and creative aspects of their jobs. Let the robots count the beans, and the humans analyse the data brought forth by the robots. What could go wrong when AI is not implemented well, can you give some real-world examples? In the area of Machine Learning (ML) there have been some high-profile cases of undesirable outcomes. The first was when a leading software company let a chatbot out ‘in the wild’ and left it to learn based on conversations people had with the robot. It took less than 24 hours for users on Twitter to corrupt the robot resulting in it spewing out offensive content. Another MLrelated high-profile case was a CV screening algorithm that a leading Cloud Platform company implemented being scrapped immediately after it showed a bias against women. Both these examples show that you need to control the data that is being fed to the algorithms for their
