While the process industries were among the earliest adapters of robotic process automation (RPA), roughly half of all users are reporting that RPA is failing to provide the lower costs, higher productivity and improved customer service it was supposed to deliver. This has led them to blame everything from the inability of RPA to scale to the constant break-fix cycles some automations experience.
The real problem, however, may be more obvious. At its heart, RPA is most effective when it is applied to those business processes that showcase its strengths. These tend to be processes which occur regularly, employ structured and readable input, rely on predictive business rules and, perhaps most importantly, affect customer satisfaction.
The good news for processing companies is that there are a number of process discovery technologies available to help, such as process mining and task capture software. But despite the strong returns these machine-based tools generate, many processors find themselves struggling to make sense of the overwhelming amount of data they produce.
That is, perhaps, why processors should not be overly reliant on process mining or task capture solutions to discover the current processes being employed. The truth is, most processing companies already have subject matter experts and/or process owners on staff who know how the business operates and what processes are being used. Equipped with the right set of tools, these “citizen designers” should be able to initiate a structured approach to process discovery by mapping out higher-level processes in a user-friendly and intuitive process editor.
Once these processes have been identified, processing companies can turn to capturing the low-level, detailed information of each task needed to execute each higher-level process. This involves collecting all of the actions, parameters, screenshots, inputs, value metrics and applications with which each specific task interacts.
Capturing contextual, low-level task details such as the services that are being used, the specific activities being undertaken and their specific parameters is essential if the processing company is being used to drive RPA candidate identification, prioritization and automation design and delivery at scale. Ultimately, this type of detailed task information accelerates automation pipelines and delivery in two distinct ways:
- First, automation feasibility can be quickly assessed when all of the applications, services used, actions and parameters within a task are explicit and defined. With that key information available, it is now possible to quickly evaluate whether a specific task is too complex for automation or a viable RPA candidate which can be turned into a bot for increased efficiency and quality of task execution.
- Identifying detailed task information also expedites the RPA development phase because it is precisely those applications, services, actions and parameters that need to be coded in any automation. When this kind of critical information can be provided directly to the company’s RPA Center of Excellence (CoE), developers will be empowered to: get a head start on development; complete their work faster; and generate higher-quality bots that experience fewer outages due to the rich and exact guidance this information offers.
Task capture software represents the best process discovery technology for obtaining the low-level details of the tasks which comprise each process. A more cost-effective and non-invasive alternative to process mining software — which while capable of comprehensively scrutinizing processes across the entire enterprise also produce an overwhelming amount of data — task capture software enables processors to discover, understand and analyze all of the individual tasks needed to execute each specific step in a business process.
At their core, task capture solutions are local or browser-based recorders. An employee manually triggers the recording feature of a task capture tool when they execute or want to record a task. The solution then records every mouse click, hotkey and keyboard interaction, taking screenshots at each step of the way. It then maps everything into a process editor where it can be further modified and optimized.
Ideally (since all task capture software is not created equal), the solution will also collect the essential low-level details needed for every single step. Because all of the essential information is available, the company’s RPA CoE will be in a much better position to assess the task to determine if it is a viable automation candidate. A good task capture solution will also package all of this information into a format that is compatible with any RPA platform being used to accelerate and improve both development and deployment.
Defining the high-level process and gathering the low-level task details provides a processing business with the “what” of any process being used. To successfully drive digital transformation objectives forward — regardless of whether they are geared at process improvement, process standardization or automation — the company also needs the “why” of every process step. Why does someone from Accounts Payable, for example, open a certain spreadsheet and delete a certain cell?
This is precisely why it is important to add the critical dependencies of each process to this structured approach to process discovery. Critical dependencies refer to all of the applications, business rules, regulatory and compliance constraints, security protocols, etc., that are connected to — and in some way impact — the processes and tasks a processing company is using. For example, the task of extracting information from an invoice that is attached to an email and entering that information in an Enterprise Resource Planner (ERP) like SAP will have dependencies on the email provider like Microsoft Outlook and the ERP, which in this case might be SAP.
At its most basic, adding critical dependencies to processes provides the context needed to understand a processor’s current state and why tasks are executed the way in which they are. For example, detailing that an Excel spreadsheet is opened and a particular cell is deleted due to a specific data privacy regulation is critically important. This practice is also a crucial component to change management. If that regulation ever changes, it becomes a relatively easy proposition to pinpoint which process and process step need to be reviewed to ensure compliance.
At a more granular level, adding critical dependencies dramatically improves RPA initiatives. One of RPA’s biggest challenges impacting all adopters is quality and change management.
When change happens, which it inevitably does, there is a massive reactive effort and a scramble to identify which automated processes are impacted and where. Bots have to be pulled out of production and business value is left sitting idly, eating away at all of the returns RPA promised to deliver. Adding in critical dependencies enables a processor’s change management strategy to be proactive, minimizing business value lost and optimizing returns on the investment.
Again, task capture software represents the easiest way to add critical dependencies to a company’s processes. Most task capture tools, in fact, provide a canvas that can be edited with text boxes in which critical dependencies can be defined.
Bottom line, task capture software plays a critical role as the future of work — which includes the interaction and synergy of machines and humans — is being defined. By enabling processing companies to identify, optimize and automate those tasks which will drive operational efficiency, their employees will be in a significantly better position to focus on more meaningful, creative tasks that offer a higher, value-add to the organization.
Further, embracing and fostering this kind of innovation represents a vital means for ensuring the processor’s continued growth, protecting it from falling behind to digital disruptors. Digital transformation should be at the forefront of every company and task capture can — and should — lead the way.
Tony Higgins is the Chief Product Officer at Blueprint Software Systems and is responsible for the vision and evolution of Blueprint’s Enterprise Automation Suite, a powerful digital process discovery, design and management solution that enables enterprise organizations to capture, identify, design and manage high-value automations with speed and precision in order to scale the scope and impact of their RPA initiatives. Tony has a broad base of software delivery skills and experience ranging from start-ups to global enterprises, and is passionate about building technology that helps teams to rapidly optimize, automate and digitally transform their organizations. For more information, visit https://www.blueprintsys.com/
