Presented by Celonis
The State of Oklahoma had a $3 billion problem: In 2022, its Legislative Office of Fiscal Transparency found that a full quarter of the state’s $12 billion budget was spent without oversight, posing serious financial and legal risks. Its processes were hopelessly broken. But they found a solution that was not only 200 times more efficient but slashed potential costs by $11.4 million: process intelligence. It’s a technology that is transforming business operations – and it’s proving crucial to successful generative AI as well as the rapidly approaching agentic AI future.
“Every organization in every industry runs on a collection of interacting processes – finance, supply chain, sales, marketing – and all have to work well, and they have to work well together, and that’s not easy, since we’re talking about multiple systems and departments and multiple languages,” says Alex Rinke, co-CEO and co-founder of Celonis. “Process intelligence platforms give you full visibility into how these processes are operating, where they’re getting stuck, where you have your bottlenecks, where you have your deviations, where you have process issues, and then remediates those issues.”
For instance, in a matter of months, process intelligence further helped the State of Oklahoma pivot to reviewing state purchases in real-time, so staff are able to serve their state and be transparent with taxpayer dollars. And across the pond, the NHS (National Health Service) in the U.K. used process intelligence to eliminate 1,800 appointment cancellations each week just by shifting when the appointment reminder goes out, uncovering ways to reduce the waiting list by around 5,300 patients in eight weeks by optimizing the patient journey, and realized an estimated savings of £2.8M a year along the way. In other words, instead of the business equivalent of throwing spaghetti at the wall and hoping something sticks, process intelligence revealed where process changes or AI solutions could offer profound results.
“Process intelligence provides business context – a true understanding of where, in any end-to-end process, we need to apply a change, and identifies the places AI can have the biggest impact for our customers, for their bottom line, for their green line, for their people and their productivity,” Rinke adds. “Without visibility into a process, you’re tossing AI at a problem just because you want to use AI. You’re not actually moving the needle. Process intelligence is the only way to achieve ROAI – return on AI investment.”
Why process intelligence is the key to AI
To understand the challenges of enterprise AI, consider how it differs from consumer AI. Both rely on a wealth of data to operate correctly. However, consumer AI not only has the whole internet of data at its proverbial fingertips, that data also includes resources like Wikipedia, which offer crucial context for how all those individual data points are connected, and why.
“Consumer AI models are very good at cases where they’ve seen a lot of examples on the internet. They’ve seen millions of example bar exams or code so they can pass the bar exam or code a website,” Rinke says. “But enterprise AI doesn’t get trained with examples of a company’s unique processes – how it makes products, pays suppliers, makes contracts with customers. That information is scattered across all these different systems, with no central repository of rules, desired processes and who’s responsible for what. All that is implicit in the organization.”
The Celonis Process Intelligence Platform makes that knowledge explicit, and pulls together all that enterprise data sitting in IT systems such as ERP and CRM across the organization in many different form factors. The Celonis solution in particular gives that raw enterprise data what amounts to the Wikapediaesque cognate it needs to ground AI in business and process context. It provides the connective tissue that gives organizations the insight they need to identify powerful AI use cases and feeds AI with the process insights it needs to be useful, scalable and reliable.
For instance, integrating process intelligence with generative AI means that answers to gen AI prompts are furnished using real-time process data and knowledge. And process intelligence can unlock the major benefits of AI agents, the next evolutionary step for AI, that are able to independently perform a series of interlinked tasks and make autonomous decisions along the way. Eventually networks of agents will be able to talk to one another to complete entire processes – for instance, getting a marketing deliverable reviewed and approved by legal, then releasing it to a customer channel, monitoring metrics and delivering a report.
But that’s a lot of moving parts, with a lot of potential points of failure when organizations leap into agentic AI with their eyes closed. Process intelligence helps organizations identify the kinds of clearly defined and narrowly scoped problems AI agents are best at solving. That helps eliminate inconsistent responses or hallucinations, and the number of potential and actual dropped steps is slashed significantly when a process intelligence platform can monitor, track and flag agent decisions.
AI and the process intelligence platform
At the center of the Celonis Process Intelligence platform is the Process Intelligence Graph (PI Graph). Using process mining, it extracts process data from transactional systems (e.g., ERP, CRM, HCM) and brings them together into a data layer—a living digital twin of the business processes. The PI Graph combines this digital twin with a knowledge layer—the context mentioned above (i.e., what makes something “good” or “bad” for the organization) defined by KPIs, benchmarks, process models and so on. In short, it knows how processes run across the entire enterprise and shows people how they can run better.
For example, in order management, a user can dig into an order process in progress, see how it’s related to the returns process, how it impacts the invoicing process, how it informs the sales process and so on. And to manage it all, the platform offers capabilities like dashboarding, app building, real-time monitoring, workflow automation, orchestration, alerts, root cause analysis and process optimization. In other words, it explains how and why things happen, and also what steps to take to unlock process improvement opportunities.
“When you bring the data layer and the knowledge layer together, you have tremendous input for generative and agentic AI – for example to give someone in customer service at a credit agency all the right information to decide whether a customer should be approved for a limit increase,” Rinke says. “And that’s just scratching the surface. It’s an opportunity to break out of the status quo and find new solutions to ongoing problems – or problems you didn’t even realize you needed to address.”
For instance, a global energy company found out that they paid over $100 million worth of invoices twice, and never noticed. Other customers have missed credits from their suppliers, or make the wrong planning assumptions and supply chain issues emerge. Across industries, public and private, the impact of addressing issues like this is massive.
“We call it the ‘aha’ moment, when an organization gets started with process intelligence and what’s really going on under the hood is revealed,” he says. “Organizations have so many things going on that if you don’t have that data that screams at you and says, ‘here’s how you could change your process, here’s how you can make it better,’ then you don’t do it. And for organizations like the NHS and the State of Oklahoma, it doesn’t just make these organizations more efficient. It makes society better.”
Celonis will unveil new features of its Process Intelligence platform at this year’s Celosphere 2024, taking place Oct 23-24 in Munich. Watch for more news to come!
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