{"id":664,"date":"2018-05-02T05:13:58","date_gmt":"2018-05-02T02:13:58","guid":{"rendered":"http:\/\/kusuaks7\/?p=269"},"modified":"2025-06-27T12:05:42","modified_gmt":"2025-06-27T12:05:42","slug":"injecting-artificial-intelligence-into-robotic-process-automation","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/future-of-work\/injecting-artificial-intelligence-into-robotic-process-automation\/","title":{"rendered":"Injecting (Artificial) Intelligence into Robotic Process Automation"},"content":{"rendered":"\t\t
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Ready to learn Machine Learning? Browse courses<\/a>\u00a0like\u00a0Robotics Application Machine Learning<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Visit any website focused on business software and you\u2019ll find robotic process automation (RPA). And for good reason: RPA software has proven to reliably reduce costs by removing manual work from various business workflows and processes.<\/p>\n

But is RPA adoption all enterprises need to automate their business processes? What else does process automation have in store other than RPA? To answer these questions, it helps to understand where RPA technologies came from and at what capabilities they now offer.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Process Automation: Let the Real RPA Stand Up<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Process automation goes back to the early twentieth century, but let\u2019s focus on the 1990s, when commercial software-based automation for human-work processes first emerged. Before that, in the 1980s, most automation was the result of custom-developed, proprietary applications targeting back-end processes\u2014for example, what Sabre Technologies provided in calculating airline fares and fare-matching\/booking automation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\u201cExpert systems\u201d\u2014a form of AI\u2014emerged in the 90s and allowed a person to create encoded rule sets to automate specific tasks. Anyone familiar with Outlook\u2019s autoresponder has encountered work automation using rules. You create a rule based on certain conditions that tells the application when to automatically send a response email. This type of user-based automation emerged as a feature of existing applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Fast forward to the early 2000s when companies began looking to process automation for easier ways to \u201cintegrate\u201d data from one application into another, especially with websites offering more services. Software emerged to \u201cscreen scrape\u201d web-based and other GUI-based data to automate form filling, to aggregate account data from many different sites or applications, and so on. It wasn\u2019t just data integration, though. Other variants using a similar approach included automated software testing (e.g., for websites), such as simulating users executing different commands.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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What separated these new automation solutions from older ones was that they were purpose-built commercial solutions with user-friendly interfaces that didn\u2019t require programming. You simply created a configuration by visually selecting the data from one application GUI and \u201cmapping\u201d it to another.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Enter Robotic Process Automation<\/strong><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Around this same time, software vendors focused on providing an underlying automation platform offering a generic way to apply these data-integration methods with a workflow engine to automate an entire process. These tools gained popularity in the IT world, where efforts involve lots of repetitive rules-based tasks. Take, for instance, provisioning a new employee with a computer desktop and software. Before these automation platforms, an IT staff member would need to manually install the necessary software on a desktop and configure the network, email software and other central systems with the new employee\u2019s information and access levels. Most of these tasks are rote and rule oriented. Using automation tools, such a process requires no manual effort.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Where RPA and AI Intersect<\/strong><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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RPA is founded on an early example of expert systems. This form of AI simply entails encoding the knowledge of a subject-matter expert for a given task into a set of rules that a computer-based system can execute. As such, the more specific and well known the given task, the better RPA is at successfully automating it.<\/p>\n

But RPA software lacks built-in \u201cintelligence.\u201d Most \u201cbots\u201d\u2014a term that identifies a specific automation routine\u2014only work against given rule sets in structured processes. They don\u2019t adapt to new scenarios or learn from mistakes. This realm of AI is called \u201cmachine learning.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The most mature (but still relatively new) RPA application of machine learning is the advance of \u201clearn by example\u201d into the bot-configuration process, where a system can monitor\u2014completely in the background\u2014a given SME\u2019s actions for different situations. This monitoring manifests itself as a \u201clearned\u201d set of rules that can be implemented once complete, and it can be superior and more flexible than traditional rule-based approaches.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Frankly, many RPA deployments lack the ability to dynamically adjust and adapt in an \u201conline\u201d fashion. For example, a process that deviates from its known path is treated as an exception that\u2019s handled manually; often, it lacks any ability to learn about this exception. In essence, the application of machine learning to bot configuration is a sophisticated way to bootstrap the automation of a process, but it still lacks the ongoing ability to add more value to complex, highly-varied tasks and to develop inferences that lead to new insights.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Extending RPA with Machine-Learning-Based Analysis<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The most practical way to apply ongoing machine learning to RPA implementations is to treat RPA as a supplier of a large set of operational data. This data feeds into a \u201clearning system\u201d that identifies patterns and other \u201cdata features,\u201d which can be used to optimize encoded bots. This ongoing learning has value because you can\u2019t ask operational staff to constantly report their every input and output. You\u00a0can,\u00a0<\/em>however, ask a computer system to do it. This ability presents an opportunity to apply machine-learning-based AI to many different processes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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New-Customer RPA<\/strong><\/h3><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Let\u2019s consider a financial institution adding a new customer. This process involves extensive coordination between staff reviewing the necessary documentation and the customer supplying it. It involves many perfunctory tasks that are ideal for RPA:<\/p>\n