While Process Discovery Remains Popular, Expectations Don’t Always Match Actual Benefits

Do a quick online search on the term “process discovery” and literally thousands of articles will pop up touting the benefits of process discovery tools and the number of companies which are turning to process discovery to drive digital transformation.

As it turns out, those articles describing how many companies are now using process discovery are right on the mark. A survey recently conducted by Blueprint Software Systems indicates that more that 56% of the companies polled are currently using process discovery tools, while 38% of those that have yet to implement this technology are planning to do so.

Although the survey confirms the popularity of process discovery, it also sheds light on the fact that many of the benefits organizations anticipated before deployment don’t necessarily match the benefits they actually realized. For example, delivering task automation and eliminating waste were identified by most survey participants prior to implementation as the highest anticipated benefits of process discovery. After implementation, however, increased quality and performance of task execution, enhanced compliance, and improved task visibility were the benefits most survey participants said they actually achieved.

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Similarly, only one in five companies had concerns about privacy and security before deployment took place. That number, however, rose to more than 30 percent following implementation, again suggesting a mismatch between expectations and reality.

While every organization operates somewhat differently, the discrepancy between expected and actual benefits generally appears to come down to two key factors:

• Most companies fail to realize how much time will actually be needed to manage and monitor the mountains of data that process and task mining tools produce. Because these tools cast a wide net, identifying variations for how each process or task is executed and merging all of that information back together through the use of machine intelligence, they require someone capable of scrubbing all of that data in order to create an accurate picture of what’s actually happening at the company. This miscalculation results in a significantly longer time-to-value for these organizations to realize improved task efficiency and automation.
• Many organizations also run into pushback from their own employees, who resent the intrusiveness of process and task mining tools. These tools work by using recorders which are installed on each employee’s computer and record each step (i.e., mouse clicks, hotkeys, keyboard interactions, etc.) the user performs. They then use artificial intelligence like computer vision to infer what the user is doing and document that task. All of this is a little too much for some employees, who feel that the tools represent an invasion of privacy which negatively impacts their performance and work experience.

These factors are leading many companies to forego a process discovery methodology anchored exclusively by technology in favor of a more human-driven approach. Rather than relying on process or task mining tools, some organizations are beginning their process discovery efforts by turning to those employees who already know how the business operates and are capable of readily providing the insights needed to identify and map out the higher-level processes which the company currently uses.

With these higher-level processes in hand, companies can move more rapidly to identification of the low-level, detailed task information (i.e., the actions, parameters, screenshots, inputs, value metrics, and applications with which each task interacts) which underlie the services being used, the specific activities being undertaken, and the specific boundaries of those activities. This low-level task information allows organizations to more accurately assess whether a specific task represents a viable candidate for automation. If so, it will also help to speed the development process because it is these low-level details that need to be coded in any automation.

To identify the details of these low-level tasks, organizations are turning to more accessible and functional human-driven task capture solutions. While similar to task mining tools in that they both discover and document tasks the company currently is performing, task capture solutions don’t rely on machine-based technology that drops agents onto employees’ computers and produces an overwhelming amount of data. Instead, each employee must manually trigger task capture tools when they execute and want to record a task.

Once a task capture solution is triggered, it functions largely like a task mining tool, recording each interaction, taking screenshots at each step, and then mapping all of that information into a process editor where it can be further modified and optimized to determine whether the task can then be automated. The difference is primarily in the control these solutions offer their human users.

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Task capture solutions also allow organizations to identify critical dependencies, the various applications, business rules, regulatory and compliance constraints, and security protocols that are connected to, and in some way impact, the company’s processes and tasks. These critical dependencies provide the context needed to understand what is currently occurring and why tasks are executed in a certain way. This, in turn, improves both the quality of automation initiatives undertaken and the company’s ability to remain compliant with regulatory changes.

As more organizations recognize the ability of these human-driven, task capture tools to collect the details of each task needed for their digital transformation, they are also realizing that process discovery doesn’t have to rely exclusively on a technological solution. Internal process owners already understand how their business works and can easily define and model higher-level processes. Rather, it is the low-level details of each task, collected in a way that isn’t intrusive and doesn’t produce an overwhelming amount of data that are needed most. And that is precisely the point where technology in the form of task capture solutions can make a difference.

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