In 2019, a McKinsey report found that only 55 percent of organizations<\/a> found success with their automation program. Robotic process automation (RPA) promised a big ROI, but for some companies, capturing that value proved elusive. Fast-forward to today, and the meshing of RPA with AI technology \u2014 hyperautomation, or the marriage of multiple automation capabilities \u2014 has opened a whole new world of automation.<\/p>\n Much of the pre-AI trouble with RPA comes down to the complexity or variability of seemingly simple tasks.<\/p>\n For example, say you want a bot to analyze invoices and send them to the right contacts at your organization. Seems simple enough, right? Think again. Before the bot can handle the task, you\u2019d need to standardize every invoice, or you\u2019d have to program the bot to know how to handle dozens of different document variations. Suddenly, this \u201csimple\u201d RPA initiative became headachingly complex and expensive.<\/p>\n AI contributes a layer of intelligence to RPA that wasn\u2019t previously possible. RPA on its own is limited to straightforward, structured and objective tasks. AI expands the possibilities to include capabilities with nuanced, subjective or unstructured data.<\/strong> By combining RPA and AI tools<\/a>, you can automate a bigger chunk of your processes. For example, hyperautomation<\/a> can:<\/p>\n Organizations can train an AI model to accurately read and sort complex documents, eliminating the need to manually program bots to know how to handle varying document formats. AI can even interpret handwriting, so organizations no longer have to manually enter handwritten documents into a digital format.<\/p>\n For instance, my company, Centric Consulting, recently partnered with UiPath<\/a>, the world\u2019s largest RPA vendor, to help World Wide Technology<\/a> (WWT) apply hyperautomation to better manage the company\u2019s high volume of purchase orders. Those purchase orders come in numerous formats and languages.<\/p>\n Without a standard template, RPA alone could not \u201cread\u201d and sort the documents without a separate setup for every format. With AI, however, it was another story.<\/strong><\/p>\n We trained an AI tool on over 100 purchase orders from one of WWT\u2019s vendors. By combining RPA with AI, WWT has successfully automated a process that previously required significant back-end work. This has been a strategic part of WWT\u2019s hyperautomation journey.<\/p>\n \u201cWWT is creating the capabilities to harness the power of GenAI and RPA to transform its business,\u201d says Dave O\u2019Toole, senior director of portfolio and product management at WWT. \u201cBy applying GenAI and RPA to its software development, sales engagement, customer service and operations, WWT aims to achieve higher quality, efficiency and productivity and better customer satisfaction and loyalty.\u201d<\/p>\n AI can read and understand free-form text, chats, emails and other unstructured data. Say you have a team of people tasked with reading and sorting customer emails. Now, you can use an AI-powered bot<\/a> to read those emails and send them along to the right team within your organization, freeing up employees to work on higher value-add tasks. And because AI models continue to learn, the accuracy improves over time.<\/p>\n With AI, an RPA tool can analyze images and flag discrepancies or errors. For example, a food and beverage customer of UiPath needed to ensure the accuracy of product labels. The customer used an AI-powered automation<\/a> to inspect and analyze design drafts, note discrepancies, and notify the appropriate design agencies.<\/strong> Before, this process was error-prone and required lots of time from lots of employees to complete. Now, each inspection takes a few minutes and is more accurate, saving the company from reputational risk and regulatory fines.<\/p>\n Large organizations spend countless hours pulling, formatting and producing reports. An AI-enabled RPA tool makes this tedious backend work unnecessary. To share another UiPath client example, a global oil and gas company processed millions of pages of production operation reports each year.<\/p>\n Because these reports lacked a uniform layout and data structure, employees were forced to extract data manually, a painstaking process. With the help of hyperautomation, the company saved its employees countless hours, freeing people up to focus on higher-value work and speeding up the company\u2019s decision-making process.<\/strong><\/p>\n An Australian mutual bank<\/a> needed to modernize how it investigated living expenses for assessing loans \u2014 the manual process was too time-consuming and error-prone. The bank first implemented a bot that could classify about half of a loan applicant\u2019s transactions.<\/p>\n This improved the fully manual process but still left a lot of hands-on work. By adding in a custom machine learning model<\/a>, the bank can now automate about 90 percent of the work involved in generating living expense reports, improving the speed and accuracy of the loan application process.<\/p>\n The examples above merely scratch the surface of the high-impact possibilities of hyperautomation.<\/p>\nAccurately read and sort documents.<\/h2>\n
Interpret free-form text.<\/h2>\n
Analyze images.<\/h2>\n
Produce and analyze complex reports.<\/h2>\n
Classify complex information.<\/h2>\n
Ready, Set, Automate<\/h2>\n