In this age of digital transformation, companies face the need to automate their business processes as they scale. Automation helps free your team members to focus on what they do best: cognitively complex, dynamic work. High-volume, tedious, and predictable tasks make great candidates for automation. When you start doing research, process automation, robotic process automation (RPA), artificial intelligence (AI), and low-code technologies are all bound to come up. A modern digital transformation strategy applies the strengths of these technologies to the tasks they are best suited for: matching the right tool to the right job.
So, how do you identify the right jobs for each of these technologies? It’s an important question that rarely receives a satisfactory answer. This is because technology vendors will often play to their strengths. When all you have is a hammer, everything starts to look like a nail. So if you’re a vendor with a strong solution for robotic process automation, why not automate everything with a bot? Well, for the same reason you wouldn’t bring a hammer to screw in a nail. It’s not the best tool for the job.
Understanding the strengths and weaknesses of the different tools you can bring to the table empowers you to build a better and more holistic automation strategy. Let’s take a closer look at RPA, AI, and low-code.
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RPA imitates the way humans interact with a computer. It’s best suited to simple, high-volume, and repetitive tasks. A robot can click around a user interface, browse the web and collect data, login to a desktop, and even enter inputs on a keyboard. This makes RPA incredibly valuable: it can take over the kind of tasks that many humans would call drudge work.
It’s easy to imagine when this could be useful, but an example couldn’t hurt, right? Let’s say your company receives thousands of orders a month, each needing to be manually entered into your ERP system by a human. Writing an RPA bot to perform the data entry action will have impactful benefits:
The RPA bot can execute these actions much faster than a human can.
RPA is less error prone, as it is programmatically driven.
The person who was previously doing this task can focus on non-tedious work.
So, what’s the catch with RPA? Well, writing a robot to mimic human interactions is not always the most efficient way to automate. For example, let’s say this same ERP was more modern and had an application programming interface (API) that allowed you to send the order data digitally via an integration. This would be much easier to set up than RPA, more performant, and would also not be at risk of breaking if the ERP user interface changes as a result of an update. Not so for RPA.
Knowing when RPA is not needed or not ideal reinforces its value when it is needed. If there is no API, which is the case for a lot of legacy systems, mainframes, and websites, then RPA becomes a necessary technology for automating that workflow. Your best bet at achieving a high ROI with RPA is picking high-volume tasks with repetitive steps that cannot be solved via a direct computer-to-computer integration.
A helpful way of thinking of AI is as cognitive automation, or automation of human thought. AI can be used for classifying incoming emails to be routed to different support groups, predicting fraud with insurance claims, or even suggesting clauses for contract awards. If RPA imitates what a person does, AI imitates how a person thinks.
In some cases, an AI tool has its own set of eyes via computer vision technology, which helps it make sense of things visually like a human can. This is helpful when dealing with unstructured data or information that hasn’t been stored in a database or made usable to computer applications. One thing we took for granted in our RPA order entry story was the fact that the order data was already structured and ready for use by the robot or the API integration. Very often order data can come in an unstructured format, such as a PDF or even a handwritten document. These are not yet ready for use by the robot or the integration.
Adding AI into the mix, machine learning (ML) models can be trained to understand the structure and key value pairings of data on the order form, and extract the data from the document into a structured format that the robot or integration can use to enter into the ERP. You can also set up logic to send the document straight through if the AI was confident about the extraction with no human interaction needed.
Machine learning (ML), by the way, refers to how a digital system “learns” from real data that it examines using patterns or rules. ML is used to solve problems and analyze data sets.
One challenge with AI often boils down to the toil of building and maintaining machine learning models to execute your predictions and data extractions. A more specialized model can perform better but will apply to fewer use cases, whereas a more generalized model can perform less optimally but apply to more use cases. As with most automations, you will achieve a better ROI picking high-volume workflows.
A commonly misunderstood difference between AI and RPA is that while many AI tools use technology like machine learning and neural networks to get smarter over time, using data and experience, RPA tools do not. RPA tools just maintain consistent software bots for performing unchanging tasks.
Up until this point we have focused on what kinds of automations you can build, but we haven’t talked about how you build these automations. It’s easy to gloss over the fact that both AI and RPA are sophisticated technologies that span many specialized technical concepts, like data engineering and machine learning as well as browser automation and operating system integration. Recruiting and retaining talented people for AI and other kinds of automation work remains a pain point for many IT organizations. If you don’t happen to be sitting on a pile of highly skilled software engineers looking for work to fill their day, you may be wondering how exactly you will build any of these automations for your business?
Low-code provides a compelling solution for this problem. It strikes a balance between traditional (or high-code) and no-code technology. It doesn’t require highly specialized AI software developers to build the automations, and it also doesn’t remove the extensibility that code can provide to make an automation fit your business needs.
[ Read our related article: Why RPA and Low-Code Go Hand-in-Hand. ]
More importantly, low-code is much faster and more agile than high-code software development, which is imperative when adjusting to constant changes in markets, processes, and external regulations. That’s why agility represents a top goal for many digital transformation projects.
Making low-code part of your automation strategy, which may also include RPA and AI, ensures you are aligning on an approach that will promote rapid, agile development and maximize your ability to quickly make changes over time. An enterprise low-code platform that brings together all these technologies offers additional benefits for speed, agility, and maintenance.
I hope it’s clear now that in the same way you aren’t going to build a house with just a hammer, you aren’t going to build a successful automation strategy with just RPA. An enterprise automation strategy will necessitate a combination of different technologies, leveraging the right tool for each job. A thoughtful approach to business process automation (BPA) helps you make workflows more efficient and productive across an entire organization, not just automate in corners where an RPA bot can help. So, what other BPA tools should you be considering? Some key technologies include workflow automation, RPA, AI, business process management, and intelligent document processing (IDP), among others. (To learn more about BPA strategy and how to choose the right automation tools, read our related articles: What is BPA? and BPA: 6 Key Benefits.)
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