Consumer artificial intelligence (AI), most notably ChatGPT, has become a constant topic of discussion. At the same time, AI is making significant strides in the business world, with companies across various industries harnessing its power to streamline their operations, enhance decision-making processes, and unlock new opportunities for growth and innovation.

However, to capitalize on the potential of AI in the business world, companies must address the challenge of deriving meaningful insights from their data. Extracting real value from data requires context. Without context, data cannot tell a story, and without a compelling story, the data remains just that data. While there is no shortage of raw data in the corporate sector, most of which is stored in enterprise resource planning (ERP) platforms, many businesses struggle to make sense of their vast amounts of data. 

This is where process intelligence comes in. Process intelligence provides the missing context businesses need to convert their raw data into actionable AI insights. 

Finding Context: The Role of a Digital Twin

Gartner defines a digital twin as “a digital representation of a real-world entity or system.” In the context of business processes, a digital twin is a virtual model that mirrors the real process, allowing for analysis, simulations, and predictive modeling that were previously unattainable. This technology can revolutionize the way organizations manage and optimize their processes, particularly when combined with the capabilities of ERP solutions.

Today’s ERP solutions perform a critical role for large organizations. While they are widely used for managing business processes, they have limitations. ERP solutions can record, process, and retain vast amounts of transactions; however, they are designed to manage that data and not to provide insights or optimize an organization’s processes. Simply put, they lack context.                

This context is crucial to unlocking the next level of performance, known as process intelligence. Before a process can be analyzed and optimized, it must be understood why it exists, the roles and responsibilities of employees involved, critical dependencies, and interactions with other processes.

Cong Yu, Vice President of Engineering at Celonis, sees AI as helping businesses establish the context to streamline and improve their operations. “With generative AI combined with process mining, I think there's a lot more potential because generative AI can provide a much easier way of defining analysis and workflows.”

He also sees a shift in how companies approach their inner workings. “I think a lot of companies are getting smarter in terms of understanding their processes. Digitization enables all these data and events to be logged in their systems, which they can then apply process mining techniques,” says Yu. Therefore, a critical step in establishing context is creating a digital twin of a process.

Maximizing Efficiency with Digital Twins and Process Intelligence

With a digital twin in place, businesses can more easily analyze, improve, and monitor their processes. Celonis recently launched the Process Intelligence Graph, which is core to the Celonis platform and powered by AI. It allows organizations to analyze processes in minute detail, apply sophisticated analytics, and build simulations of optimized digital processes.

The Process Intelligence Graph employs transactions captured in an ERP, adds context, and uses out-of-the-box KPIs to detect improvement opportunities. Utilizing the approach can bring context to any aspect of an organization’s operations captured in its ERP. For example, companies can ask questions like, “How can I reduce late deliveries in North America, which of my suppliers delivers goods on time most often, or how often do we make on-time payments?”

Leveraging the digital twin allows businesses to utilize process mining for comprehensive modeling, analysis, and optimization of their processes. This includes analyzing interdependencies and testing the use of automation.

Yu also sees an often-overlooked problem that process intelligence solutions can address. “There’s knowledge accumulated over time in an organization. You can encode this institutional knowledge instead of having it reside in people's heads, which is obviously very difficult to extract and codify. Now, for the first time, it's quite possible to incorporate such knowledge that would normally be lost.”

Empowering AI Integrations with the Process Intelligence Graph

The Process Intelligence Graph allows companies to directly embed AI in ready-to-use solutions for fast insights and value. Companies can enable AI anywhere in their tech stack by feeding the insights from the Process Intelligence Graph into their existing models.

“Inside the Process Intelligence Graph, we have two layers. The first layer is where we take your raw ERP data. We also have the Knowledge layer, which captures, for the first time, a lot of the inherent business knowledge as part of the processing,” says Yu. “This includes the formulas for the KPIs that you care about.”

Part of Celonis’ ready-to-use solutions includes its Copilot, a chatbot to answer process questions; and AI-powered apps such as the Duplicate Checker app. Customers can also build with AI directly in the ML workbench and enable AI with the Intelligence API.    

Yu provides an example of the chatbot in action. “For example, if you have an invoice for IT support, which department should it be assigned to pay? You want the AI agent to tell you which department to assign the invoice to.”

Companies can ask questions using the Copilot to find the correct department to charge. “Where's this invoice from? Who was the customer? Who was the vendor? What about the last five invoices, and where did they go?” By using the process intelligence-enabled agent, Yu says companies immediately get more context than the invoice provides. This context allows for accurate transaction handling.

Conclusion

For businesses to work the way they’re meant to, they must understand how they operate and the processes that underpin them. While many companies embed ERPs to support their operations, these platforms do not make extracting process intelligence and conducting process mining easy.

From Yu’s perspective, using AI to establish context may signal the beginning of a new era. “Eventually, we may have autonomous enterprises, where many of the operations currently running will be semi-autonomous, with humans in the loop to do the final confirmation, and the agents will automatically carry out a lot of the mundane and tedious work.”

While consumer AI has taken hold of the public’s consciousness, generative AI is quietly transforming how businesses run their operations. When coupled with process intelligence and mining solutions, AI can establish context for every process, which allows companies to explore new ways to generate cost savings, improve operational performance, and comply with regulations.