Seeking efficiencies and streamlining operations, businesses have been turning to AI-based automation. As they pursue this, they need a way to look beyond their assumed processes to improve their actual processes. To achieve this goal, they are finding process mining a key strategy.

In its simplest form, automation may take the form of robotic process automation (RPA), a technology that has seen stunning growth. Another approach now gaining attention is hyperautomation, which Gartner describes as a business-driven, disciplined way to rapidly identify, vet and automate as many business and IT processes as possible.

But identifying which business processes to automate is not easy. Factors such as cognitive biases, inaccurate assumptions and a lack of detailed knowledge of ground operations can cloud decision-making and create obstacles to innovation.

What’s needed is a precise understanding of the performance of existing processes and how they operate. 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 involved 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. Through automated process discovery and mapping, organizations can achieve greatly improved optimization of workflows.

“Process mining plays a fundamental role in creating visibility and understanding before you automate, and it lays the groundwork for business operations resilience, which helps you alter operations in the face of changing business conditions,” Marc Kerremans, VP analyst at Gartner, told VentureBeat. 

He added that process mining is not only a fundamental part of creating visibility and understanding before you automate. Through its monitoring capabilities it also visualizes how 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 integrating various tools and technologies to enhance an organization’s ability to automate work. Process mining plays a critical role.

While RPA is the foundation of hyperautomation, its full potential can only be realized by pairing it with complementary solutions such as process mining, AI, analytics and other advanced tools. Enterprises achieve optimized efficiency when they automate more processes to extract insights useful to everyone involved in an organization’s digital transformation efforts.

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

Initially, it is used to identify processes suitable for automation and 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 emerged as a valuable driver of successful RPA initiatives. Its versatility in addressing the multiple stages of RPA implementation has proven particularly beneficial. Using process mining, organizations can identify potential areas for automation within their business processes and prioritize them based on their ROI potential.

Gartner sees an evolution whereby advanced techniques such as root-cause analysis, predictive analysis and even prescriptive analysis incorporate AI for more detailed and extended insights into how processes behave and will behave.

“These advanced techniques also support operational decisions such as what cases to prioritize, what additional resources should be involved, and what tasks could be expedited,” said Kerremans. “The other way around, process mining creates a process model context to put the results of AI in a wider context that is more understandable for decision-makers in an organization.”

With process mining, enterprises can optimize both tech-driven and human-driven business processes, boosting operational efficiency and reducing costs. Process mining solutions can also enhance employee experience by streamlining resource allocation. Although process mining tools may be unfamiliar to some potential users, these tools are rapidly evolving and gaining increased functionality to cater to organizations with growing automation needs.

Best practices for efficient process automation

For automation to succeed, underlying organizational challenges must be addressed, according to Jaclyn Rice Nelson, cofounder and CEO of data and AI consulting firm TribeAI. Process automation requires effective change management, she said.

“Change management has proved more challenging than the technical work required to automate the processes. The difference between companies that can navigate this change and companies that struggle comes down to the CEO and their commitment to automation,” Nelson told VentureBeat. “Leadership buy-in and alignment of team incentives to support automation are critical to the success of process mining and automation efforts. Without [buy-in and alignment], investments in process automation are doomed to fail.”

AI is essential to hyperautomation, as it empowers bots to undertake tasks that require reading, comprehending and processing data with greater intelligence. Furthermore, by incorporating cognitive technologies like machine learning (ML), natural language processing (NLP), optical character recognition (OCR) and AI into RPA through process mining, organizations can significantly enhance process efficiency and accuracy.

“Despite the sci-fi-like advances in AI like ChatGPT, I believe some of the most valuable business applications from large language models will be from the next wave of process automation (RPA),” Nelson said. That’s because process mining often requires a massive lift on data aggregation, standardization and cleaning.

With new AI tools parsing masses of unstructured data, opportunities will open for companies to automate their highly manual workflows. Nelson says these process improvements can greatly reduce costs, enabling companies to redeploy resources to higher-value business areas. 

“A food distribution company was losing market share due to a highly manual and siloed bid development process. Through our partnership at Tribe AI, we built an ML-driven system that mined and automated manual tasks and increased data utilization,” said Nelson. “Further, this upfront data work required for the process improvement meant they finally had access to their data in a centralized view that could be leveraged for business intelligence and high-value tasks like demand forecasting and price negotiation.” 

To measure the success of their process mining initiatives and what metrics they should be tracking, Nelson suggested CTOs and CIOs focus on time reduction, cost reduction and revenue lift.

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

“Process mining can help organizations extract more value from hyperautomation by providing the insights they need to optimize their processes, streamline their operations and achieve greater efficiency and effectiveness,” Alshikh told VentureBeat. “Therefore, process mining initiatives should be carried out in collaboration with stakeholders from various 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 being used to inform decisions and investments related to their digital transformation goals.”

What’s next for process mining for automation? 

Gartner’s Kerremans predicts that other critical use cases in an organization, such as process discovery and analysis and process comparisons for compliance, auditing, sustainability, business architecture and composability, will ultimately be automated through process mining. 

“Processes never live in isolation; they are interrelated. So to connect to a digital or business transformation, it is essential to take a step back from processes and have a business operations perspective. Therefore it is essential for business operations to combine processes, interactions and activities that result in products, services and information, and ultimately provide value to customers and stakeholders of the organization,” said Kerremans. “Process mining and action/automation are very strongly connected — process mining without action is a daydream, action without process mining is a nightmare.”

Likewise, Tribe AI’s Nelson says that the next step in process mining is to enable models to access APIs and eliminate repetitive enterprise workflows while enhancing output prowess through automation.

“Automation applied to the identification and even resolution of automation opportunities is the ultimate action-driven AI future,” she added. 

For his part, Writer’s Alshikh believes that process mining will continue to evolve as hyperautomation technology improves. Companies will be able to get even more data-driven because of hyperautomation, as improvements in process mining better ensure that the data collected is accurate and complete.