Editor’s note: This story originally appeared in the Optimization issue of Workflow Quarterly.
Many of us are spending so much time on digital transformation that it can be easy to forget the underlying purpose. At its core, digital transformation aims to create a better experience for our customers and employees. Automation is a key part of the story. To deliver better products and services, businesses are automating manual, repeatable processes. This frees up employees to perform more creative tasks while making it possible to achieve business goals even faster. It’s better for customers and employees.
If we focus on the goal of digital transformation, it becomes easier to see the next logical step, which is increasing the quality and the efficiency of our digital experiences. In a word, the next step is hyperautomation.
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Hyperautomation brings together cutting-edge technologies like artificial intelligence, machine learning, robotic process automation, and process mining to automate as many business processes as possible. In the years to come, you’re going to be hearing a lot more about all of these technologies, but I want to focus on process mining.
Process mining is a uniquely powerful tool that can reveal bottlenecks and redundancies that slow down business processes. A business process is any set of related, structured activities that lead to a specific outcome, like when a customer submits a support ticket that must be routed to the correct team. Here’s how process mining works and why I think it’s so important.
An X-ray for business processes
How does process mining work? It starts with a fundamental part of modern IT infrastructure: event logs. Every time someone performs an IT function—a customer engages with a chatbot, or an employee addresses a support ticket—this action generates event log data. Event logs record interactions between humans and bots.
Process mining analyzes those event logs to generate snapshots of how systems are functioning in real time. Think of it as an X-ray. If you suspect that you’ve broken your knee, you can’t know for sure until you get a picture of what’s going on beneath your skin. Only when you have that picture can you think about how to repair the bone. In the same way, process mining exposes redundancies, bottlenecks, and other problems that might negatively impact a process.
The reason why I’m so excited about process mining is that it’s an invaluable tool for digital transformation and hyperautomation. When you start thinking about how to digitize and automate business processes, it can be easy to get carried away. In fact, many teams do make the initial mistake of automating too much or indiscriminately. Process mining gives you the information you need to prioritize digitization and automation. Once you know where the problems are, you know where to start.
Applying process mining
I know process mining can seem rather abstract, so it’s helpful to see a real-world example. Let’s take the loan approval process. Here’s how we might apply hyperautomation—and specifically process mining—to improve this cumbersome, complex process.
When someone first applies for a loan, we can use virtual agent technology like a chatbot to guide that initial conversation. The chatbot can help the loan applicant determine which loan they should apply for. Using simple orchestration, we can then route that application to the appropriate loan officer. An AI system can aid the loan officer in deciding which loan would be the best fit for the applicant.
Now, this is where process mining comes in. Remember, each of these steps generates an entry in an event log. Process mining gathers data from these event logs to give us visibility into the entire loan approval process. We can then audit each step to figure out where we can reduce errors, roadblocks, and redundancies.
While hyperautomation can improve each step, we won’t know exactly how to apply the technology—or which technology to apply—without process mining.
The future of business
Loan approval is just one example we can finetune using process mining. But don’t let the scale of this example fool you; process mining can have a huge impact on any organization as a whole.
Instead of blindly trying to automate a company’s entire IT infrastructure, process mining allows the company to understand how processes interact with each other, identify inefficiencies, and make smart decisions to improve them. In this way, we can use process mining to improve enterprise supply chain management, resource planning, IT infrastructure, and more.
Process mining gives you the information you need to prioritize digitization and automation.
I have a hard time imagining a future where process mining doesn’t play a crucial role. At the moment, the suite of technologies involved in hyperautomation work in tandem. Thinking back to the loan approval process, for example, virtual agent technology, orchestration, and AI each work unilaterally to automate the process, and process mining provides auditability independent from those technologies. But in the future, that will change.
As our technology improves, hyperautomation will make it possible for these technologies to affect each other. We’ll be able to run process mining, for instance, and then have AI look at the output to identify bottlenecks. When hyperautomation begins to work on this meta level—automating the automation, if you will—we’ll be able to create experiences and products faster than ever.