How process mining can unlock value from hyperautomation

In search of efficiencies and streamlined operations, companies are turning to AI-powered automation. In pursuit of this, they need a way to look beyond their own supposed their improvement processes actual processes. To achieve this goal, they consider process mining to be a key strategy.

In its simplest form, automation can take the form of robotic process automation (RPA), a technology that has seen staggering growth. Another approach currently receiving attention is hyper-automation, which Gartner describes as a business-oriented, disciplined way to quickly identify, verify and automate as many business and IT processes as possible.

However, determining which business processes need to be automated is not easy. Factors such as cognitive biases, inaccurate assumptions, and a lack of detailed knowledge of ground operations can hamper decision-making and create obstacles to innovation.

A thorough understanding of the performance of existing processes and how they work is needed. Process mining can provide this.

Process mining: an essential precursor to automation

Process mining is a methodology that uses event logs – digital records created by information systems and accumulated over time – to extract valuable information and actionable insights. By filtering, processing and organizing this data, it is possible to accurately capture each step of the processes and detect any deviations from their intended paths.

This allows organizations to precisely visualize business processes and their variations and monitor them in real time. With automated process discovery and mapping, organizations can achieve significantly better workflow optimization.

“Process mining plays a fundamental role in creating visibility and understanding prior to automation, and lays the foundation for business operations resiliency, which helps change operations in the face of changing business conditions,” said VentureBeat Marc Kerremans, vice president of analyst at Gartner.

He added that process mining is not just a fundamental part of creating visibility and understanding before automation. With its monitoring features, it also visualizes how the different islands of automation are connected and how they can be improved.

From automation to hyperautomation

Hyperautomation is a comprehensive approach to process automation. It involves the integration of various tools and technologies to increase the organization’s ability to automate work. Process mining plays a key role.

Although RPA is the foundation of hyperautomation, its full potential can only be realized by combining it with complementary solutions such as process mining, artificial intelligence, analytics and other advanced tools. Enterprises achieve optimal performance when they automate more processes to generate insights that are useful to everyone involved in an organization’s digital transformation.

Through effective process mining, the analyzed data can be further combined with AI/ML to generate data-driven analytics that help organizations discover the current state of their business processes and identify new opportunities for optimization and automation. Moreover, process mining is an integral part of many stages of the RPA lifecycle.

Initially, it is used to identify processes suitable for automation and to analyze the extent to which RPA can be implemented in legacy processes and systems. Later in the process, it monitors and analyzes RPA performance to facilitate continuous improvement.

Process mining has proven to be a valuable driver of successful RPA initiatives. His versatility in solving the many stages of RPA implementation proved particularly beneficial. Using process mining, organizations can identify potential areas for automation in their business processes and prioritize them based on their ROI potential.

Gartner sees an evolution whereby advanced techniques such as root cause analysis, predictive analytics, and even prescriptive analytics use AI to gain more granular and augmented insights into behavior and process behavior.

“These advanced techniques also help with operational decisions such as prioritizing cases, committing additional resources, and tasks that can be accelerated,” said Kerremans. “Conversely, process mining creates context for the process model to place AI results in a broader context that is more understandable to decision makers in the organization.”

Through process mining, enterprises can optimize both technology-driven and people-driven business processes, increasing operational efficiency and reducing costs. Process mining solutions can also improve the employee experience by streamlining resource allocation. While process mining tools may be unfamiliar to some potential users, these tools are rapidly evolving and gaining more and more functionality to meet the needs of organizations with growing automation needs.

Best practices for effective process automation

According to Jaclyn Rice Nelson, co-founder and CEO of data and AI consulting firm Tribe AI, for automation to succeed, fundamental organizational challenges must be addressed. Process automation requires effective change management, she said.

“Managing the change turned out to be more of a challenge than the technical work required to automate the processes. The difference between companies that can handle this change and companies that struggle comes down to the CEO and his commitment to automation,” Nelson told VentureBeat. “Leadership commitment and alignment of team incentives to support automation are critical to the success of process exploration and automation efforts. Without [buy-in and alignment]investments in process automation are doomed to failure.”

AI is essential for hyper-automation as it enables bots to undertake tasks that require reading, understanding, and processing data with greater intelligence. In addition, by incorporating cognitive technologies such as machine learning (ML), natural language processing (NLP), optical character recognition (OCR) and artificial intelligence into RPA through process mining, organizations can significantly increase process efficiency and accuracy.

“Despite advances in sci-fi-style AI like ChatGPT, I believe some of the most valuable business applications from large language models will come from the next wave of process automation (RPA),” said Nelson. This is because process mining often requires huge investments in data aggregation, standardization and cleaning.

New AI tools that analyze masses of unstructured data will open up opportunities for companies to automate their highly manual workflows. Nelson says these process improvements can significantly reduce costs, allowing companies to shift resources to higher-value business areas.

“A food distribution company was losing market share due to a highly manual and isolated tendering process. Through our partnership with Tribe AI, we built a machine learning-based system that explored and automated manual tasks and increased data usage,” said Nelson. “In addition, this initial data work required to streamline the process meant that they finally had access to their data in a centralized view that could be used for business intelligence and high-value tasks such as demand forecasting and price negotiation.”

Nelson suggested that CTOs and CIOs focus on reducing time, reducing costs, and increasing revenue to measure the success of their process mining initiatives and what metrics they should track.

Similarly, Waseem Alshikh, co-founder and CTO of the generative AI platform Writer, believes that organizations may face the challenge of integrating different data sources into a single repository to capture relevant data for process mining. They can overcome this by using a data lake that can easily integrate data from different sources into one repository.

“Process mining can help organizations get more value from hyperautomation by providing the insight they need to optimize processes, streamline operations, and achieve greater efficiency and effectiveness,” Alshikh told VentureBeat. “Therefore, process mining initiatives should be implemented in collaboration with stakeholders from different departments, and their efforts should be integrated into the overall digital transformation plan.

“They should also ensure that the results of their process mining are used to inform decisions and investments related to digital transformation goals.”

What next for process mining for automation?

Gartner’s Kerremans anticipates that other critical use cases within the organization, such as process discovery and analysis, and process comparison for compliance, auditing, sustainability, business architecture, and composability, will eventually be automated through process mining.

“Processes never live in isolation; are interrelated. So, to connect with digital or business transformation, it is necessary to take a step back from processes and take a business operations perspective. That is why it is so important for business operations to connect processes, interactions and activities that result in products, services and information, and ultimately deliver value to the organization’s customers and stakeholders,” said Kerremans. “Process Mining and Operation/Automation are very closely related – process mining without action is a dream, action without process mining is a nightmare.”

Similarly, Tribe AI’s Nelson says the next step in process mining is to allow models to access APIs and eliminate repetitive enterprise workflows while increasing output efficiency through automation.

“Automation used to identify and even solve automation opportunities is the ultimate future of action-driven AI,” she added.

For its part, Writer’s Alshikh believes that process mining will continue to evolve as hyper-automation technology develops. Companies will be able to become even more data-driven through hyper-automation as process mining improvements better ensure that the data they collect is accurate and complete.