Code-Free Focus Unlocks ‘Citizen Process Mining’

Marlon Dumas, co-founder and chief product officer at Apromore, met with Acceleration Economy to discuss the top five buyer questions developed by our practice analysts representing the modern executive purchasing committee, as part of the Q&A portion of the Process Mining Battleground.

Founded in 2009, Apromore has been committed from the start to developing an easy-to-use platform for democratic, no-code process mining. While no-code functionality is a key differentiator of Apromore’s technology, Dumas explains how artificial intelligence (AI) integration and other innovations enable the company to offer robust process mining functionality to customers. Below are the biggest takeaways from our discussion.

Top customer use cases and industries

Dumas opened the discussion by highlighting the role and value of Apromore process mining in customer-facing business functions. “It’s really about customer-centric processes,” says Dumas. “All the use cases that we’re driving adoption are around that.”

For example, in the banking industry, Apromore helps managers improve customer onboarding processes and reduce the time it takes to make decisions, such as borrowing and loan processing. In the insurance industry, Apromore helps one of the largest insurers in the United States manage policies, reducing the time it takes to process claims. (The insurance example is insightful and powerful because policy management is one of the most fundamental functions in that industry.)

In the telecom and utilities sectors, Apromore helps customers optimize their customer-facing processes, such as field service management.

Dumas explains how Apromore enables companies to manage touchpoints in customer-facing processes that, if not managed effectively, can negatively impact key performance indicators (KPIs), increase service level agreement (SLA) violations, or increase customer churn. By identifying these touchpoints, organizations have the knowledge to address them.

How Apromore deals with shifting priorities and macroeconomic conditions

The big priority among customers in 2023 was time to value. Specifically, customers are looking to their process mining systems, and analytics in general, to capture value in weeks rather than months. The question, according to Dumas, is how to empower business teams to quickly identify the friction points in their customer touchpoints so they can address them in a timely manner.

Because of that need to deliver value in a timely manner, Apromore doubles down on codeless process mining, providing a point-and-click interface that allows business users to quickly analyze and identify improvement opportunities. This functionality speeds up time-to-value.

Dumas summarized the company’s approach to time to value and how it enables clients to respond to the current macroeconomic turbulence. “How do we enable a business team to identify the friction points in their customer touchpoints tomorrow, next week, next week,” he says, “and not start a big project with 10 data engineers and 10 data scientists, and hope that in six to 12 months we’ll find out what’s going on?”

Top Award Winners

Dumas identified Apromore’s key differentiators as no-code functionality, digital process twins, and AI-powered predictive process monitoring. Below you will find more information about each of these.

Dumas describes Apromore’s no-code features as its biggest differentiator, and this focus has been in place since the company’s inception. “It’s the citizen approach to process mining,” says Dumas. “Everything we offer is packaged in a way that can be used by business users, by the business teams themselves.” Today, Apromore is enhancing its AI-enabled software to further reduce the time to identify issues and accelerate time to value.

“We’re not looking at code generation [with generative AI]. It doesn’t make sense to us because we’re a no-code solution. We look at generative AI as a vehicle for automated process improvement.”

Marlon Dumas, co-founder, Apromore

Apromore analyzes data to drive change – not after the fact – using digital process twin technology. This includes using AI to build simulation models that capture how employees prioritize and manage their work. These simulations help business users ask important questions about processes and how they need to change to become more efficient. “Business users can literally start asking questions like, ‘What changes can I make to . . . my time to decide in the context of lending?

Dumas explains how predictive process monitoring enables organizations to use historical data to train machine learning models that can be used to make accurate, process-oriented predictions.

Making it easymore to Apply Process Mining

Like many technology companies, Apromore had to respond to customer budget constraints due to the economic downturn. Apromore used the downtime caused by the COVID-19 pandemic to tailor its offerings to customer needs.

During that time, Apromore’s development team redesigned the entire platform so that users can scale up and down directly in the cloud. This meets the fluctuating budgets of many organizations and allows them to analyze as many processes as they need. “We completely rebuilt it on top of a technology called Elastic Map Reduce. That’s very technical, but what it means is that we’re in the cloud and we can automatically scale in both directions, up and down, instantly.”

One of North America’s largest insurers has benefited from this development. The insurer started with a small team of five to six analysts who work on one process and then go through other processes. As more teams see value, they tend to purchase additional licenses.

The role of AI in the product roadmap

Apromore uses generative AI to investigate processes discovered from data. The AI ​​algorithm in turn makes suggestions to improve the process.

“We do things a little differently when it comes to generative AI,” says Dumas. “We don’t look at generating code anyway. It doesn’t make sense to us because we’re a no-code solution. We look at generative AI as a vehicle for automated process improvement.”

The process improvement recommendations from generative AI are fed into the process mining system. “We then bring in the subject matter expert to tell us which changes are feasible, which changes are not feasible, and which don’t make business sense.” These suggestions are refined into a shortlist. Collectively, Apromore calls these steps automated process improvement.

Dumas expressed some skepticism about using generative AI outputs when explaining the company’s approach: “Generative AI tells as many truths as it does falsehoods.”

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