How process mining can unlock value from hyperautomation

Seeking efficiencies and streamlining operations, companies have been turning to AI-based automation. As they pursue this, they want a approach to look past their assumed processes to enhance their precise processes. To obtain this aim, they’re discovering process mining a key technique.

In its easiest kind, automation could take the type of robotic process automation (RPA), a expertise that has seen beautiful progress. Another method now gaining consideration is hyperautomation, which Gartner describes as a business-driven, disciplined approach to quickly establish, vet and automate as many enterprise and IT processes as attainable.

But figuring out which enterprise processes to automate isn’t straightforward. Factors corresponding to cognitive biases, inaccurate assumptions and an absence of detailed data of floor operations can cloud decision-making and create obstacles to innovation.

What’s wanted is a exact understanding of the efficiency of present processes and the way they function. Process mining can present this.

Process mining: An important precursor to automation

Process mining is a strategy that makes use of occasion logs — digital information created by data techniques and accrued over time — to extract priceless data and actionable insights. By filtering, processing and organizing this knowledge, it’s attainable to precisely seize every step of the processes concerned and detect any deviations from their supposed paths.

This permits organizations to exactly visualize enterprise processes and their variations, and monitor them in actual time. Through automated process discovery and mapping, organizations can obtain tremendously 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, advised VentureBeat. 

He added that process mining isn’t solely a basic a part of creating visibility and understanding earlier than you automate. Through its monitoring capabilities it additionally visualizes how completely different islands of automation are linked, and the way they can be improved.

From automation to hyperautomation

Hyperautomation is a complete method to process automation. It entails integrating numerous instruments and applied sciences to reinforce a corporation’s capacity to automate work. Process mining performs a important position.

While RPA is the inspiration of hyperautomation, its full potential can solely be realized by pairing it with complementary options corresponding to process mining, AI, analytics and different superior instruments. Enterprises obtain optimized effectivity once they automate extra processes to extract insights helpful to everybody concerned in a corporation’s digital transformation efforts.

With efficient process mining, analyzed knowledge can additional be mixed with AI/ML to generate data-driven analytics that helps organizations uncover the present state of their enterprise processes and establish new alternatives for optimization and automation. Furthermore, process mining is an integral a part of the a number of levels of the RPA lifecycle.

Initially, it’s used to establish processes appropriate for automation and analyze the extent to which RPA can be carried out in legacy processes and techniques. Later within the process, it displays and analyzes RPA efficiency to facilitate steady enchancment.

Process mining has emerged as a priceless driver of profitable RPA initiatives. Its versatility in addressing the a number of levels of RPA implementation has confirmed notably helpful. Using process mining, organizations can establish potential areas for automation inside their enterprise processes and prioritize them primarily based on their ROI potential.

Gartner sees an evolution whereby superior methods corresponding to root-cause evaluation, predictive evaluation and even prescriptive evaluation incorporate AI for extra detailed and prolonged insights into how processes behave and can 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,” stated 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 each tech-driven and human-driven enterprise processes, boosting operational effectivity and decreasing prices. Process mining options can additionally improve worker expertise by streamlining useful resource allocation. Although process mining instruments could also be unfamiliar to some potential customers, these instruments are quickly evolving and gaining elevated performance to cater to organizations with rising automation wants.

Best practices for environment friendly process automation

For automation to succeed, underlying organizational challenges should be addressed, in response to Jaclyn Rice Nelson, cofounder and CEO of knowledge and AI consulting agency Tribe AI. Process automation requires efficient change administration, she stated.

“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 advised 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 important to hyperautomation, because it empowers bots to undertake duties that require studying, comprehending and processing knowledge with better intelligence. Furthermore, by incorporating cognitive applied sciences like machine studying (ML), pure language processing (NLP), optical character recognition (OCR) and AI into RPA via process mining, organizations can considerably improve process effectivity 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 stated. That’s as a result of process mining typically requires an enormous raise on knowledge aggregation, standardization and cleansing.

With new AI instruments parsing plenty of unstructured knowledge, alternatives will open for firms to automate their extremely handbook workflows. Nelson says these process enhancements can tremendously cut back prices, enabling firms to redeploy sources to higher-value enterprise 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,” stated 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 need to be monitoring, Nelson advised CTOs and CIOs focus on time discount, price discount and income raise.

Likewise, Waseem Alshikh, co-founder and CTO of generative AI platform Writer, believes that organizations could face a problem integrating numerous knowledge sources right into a single repository to seize the suitable knowledge for process mining. They can overcome this by using a knowledge lake that can simply combine knowledge from numerous sources right 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 advised VentureBeat. “Therefore, process mining initiatives ought to be carried out in collaboration with stakeholders from numerous departments, and their efforts ought to be built-in into the general 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 subsequent for process mining for automation? 

Gartner’s Kerremans predicts that different important use instances in a corporation, corresponding to process discovery and evaluation and process comparisons for compliance, auditing, sustainability, enterprise structure and composability, will in the end be automated via 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,” stated 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 following step in process mining is to allow fashions to entry APIs and remove repetitive enterprise workflows whereas enhancing output prowess via automation.

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

For his half, Writer’s Alshikh believes that process mining will proceed to evolve as hyperautomation expertise improves. Companies will be capable of get much more data-driven due to hyperautomation, as enhancements in process mining higher be sure that the info collected is correct and full.

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