In recent years, robotic process automation<\/a> (RPA) has become mainstream, enabling organizations to free their staff from rote, manual processing. At the same time, machine learning (ML) and artificial intelligence (AI) have become more accessible with the advent of off-the-shelf tools, low-code environments, and simplified frameworks. Today, the term \u201cAI\u201d commonly includes generative AI, which adds the power to create new information based on existing patterns in large datasets.<\/p>\n Each of these technologies is independently reshaping the way businesses operate and compete. Blending them together can have even greater impacts. The resulting capability comprises some of what\u2019s called intelligent process automation (sometimes called hyperautomation<\/a>).<\/p>\n That sounds amazing, but it\u2019s not always clear what intelligent process automation means or how to combine RPA and AI into high-impact, fully automated solutions.<\/strong> In some cases, organizations have launched an RPA program but struggled to incorporate more advanced functionality.<\/p>\n So, how do you get from RPA to AI and beyond?<\/p>\n RPA and AI have a lot in common, like:<\/p>\n That said, they also work in distinct ways to solve different challenges. RPA excels at simple, structured, and objective tasks. AI is meant for subjectivity, decision-making, and unstructured actions. In fact, the beauty of these two tools in concert is how well they complement each other toward a common goal.<\/p>\n But what does this have to do with peanut butter cups? Consider the key ingredients: chocolate and peanut butter. Both are delicious on their own, but for different reasons. The combination is tasty, immensely popular, and preferred by most to either of the individual components. I think you see where this is going.<\/p>\n RPA plus AI<\/a> is the peanut butter cup of process automation. Instead of addressing parts of a process with a particular tool, you combine tools to automate more of the process and get closer to no-touch, end-to-end automation.<\/strong> Not only do more steps get automated, but RPA and AI help enable each other to integrate more seamlessly. In other words, the result is greater than the sum of its parts.<\/p>\n To help demonstrate, let\u2019s consider a hypothetical, real-world process and show how it progresses with automation<\/a>.<\/p>\n Let’s say, our finance department receives invoices from vendors that we need to capture and enter into our enterprise resource planning<\/a> (ERP) system and process. The invoices are always PDF files but don\u2019t have a consistent format or data and may be scanned or handwritten. With so much variation in the data and presentation, traditional integration isn\u2019t possible. We must manually view each file and then hand-key them into our mundane, time-consuming, and inefficient system. We can do better!<\/p>\n RPA is often the easiest and most accessible entry point for automation. The tools are low-code<\/a> and can provide quick wins at low cost for straightforward use cases. Often called bots, these applications can monitor systems, help search engines crawl websites, interact with customers (chatbots), and more.<\/p>\n RPA can also activate AI by, for example, passing data directly to AI services that make decisions and receive structured decisions or outputs to automate further tactical steps.<\/strong> Consider it an ideal starting point and foundation for building towards intelligent process automation.<\/p>\n In our example process, we use RPA to automatically receive vendor emails, download attachments, and associate those files with the right vendor accounts in our system using the sender\u2019s email address. The RPA solution even creates work items and generates notifications for users so they can promptly respond.<\/p>\n Getting started with AI and ML<\/a> is probably easier than you think. Chances are you\u2019re already taking advantage of them. Optical character recognition (OCR), where an AI model can read and digitize scanned or handwritten text, is but one ubiquitous example.<\/p>\n A growing library of prepackaged \u201cskills\u201d is available to add a la carte to your automation. Examples include reading receipts, translating text from one language to another, and determining a message’s sentiment. Besides the obvious benefit of quick implementation, these out-of-the-box services are often proven models that work with a higher degree of accuracy.<\/strong><\/p>\n Incorporating this type of AI, our bot gets two new features \u2013 an intelligent OCR model that digitizes PDF text and an invoice extraction model that reads standard data from known invoice elements (like date, amount and remittance contacts). The RPA<\/a> runs these features on-demand and uses the output for more precise data entry. As a result, the data now populated in our ERP isn\u2019t simply a document to review but structured data auto-populated in certain fields.<\/p>\nThe Peanut Butter Cup Analogy for Intelligent Process Automation<\/h2>\n
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A Practical Example of Intelligent Process Automation<\/h2>\n
RPA: Start with the Basics<\/h3>\n
Off-the-Shelf AI<\/h3>\n
Tailored Models to Fit Your Business<\/h3>\n