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The missing piece of the AI puzzle - process intelligence

Cong Yu Profile picture for user Cong Yu November 7, 2023
By pairing AI with Process Intelligence (PI) businesses tap into an enablement layer that powers enterprise-wide solutions capable of transforming diverse industries for good. Cong Yu of Celonis shares some real-world examples.

Robot filling piece of jigsaw © Kittipong Jirasukhanont  -
(© Kittipong Jirasukhanont -

It took ChatGPT just 5 days to amass one million users. Twitter/X had to wait two years to do the same. The pace of AI adoption has been almost unmatched. The change it’s unleashing cannot be overstated. Businesses, naturally, want their piece of the AI pie for industry transformation.

Less than a year after the emergence of generative AI, one third of organizations were already using it in at least one business function and 40% planned to increase their overall investment in artificial intelligence because of advances in generative AI, according to McKinsey. 

Whether it’s automation of business processes, hyper-personalized customer engagement, or specific industry use cases like ML-backed fraud detection in banking, businesses are betting on AI-backed transformation.

Process intelligence - the enabler for AI 

But there’s a missing piece to the AI puzzle: Process Intelligence. It’s the layer that knows how your business flows, allowing AI to understand how processes interact and impact each other across departments and systems. This enablement layer is created by process mining, which reconstructs the data that business processes leave in systems along the way. To quote Celonis co-founder and co-CEO Alex Rinke, “Process Intelligence is the enabler for AI.”

For example, consider the following three ways Process Intelligence makes generative AI really work for enterprises:

  1. Process Intelligence informs the large language model (LLM) about company business rules so conformance checking can be watertight.
  1. Process Intelligence makes the LLM aware of common causes of process breakdown – so teams know when the next activity in the process will occur; they can predict what is going to happen next; and they can prevent breakdowns happening in the first place.
  1. Pairing Process Intelligence with object-centric process mining lets teams create models for counterfactual situations, build an LLM assistant that rapidly responds to changes, and run analyses of the potential impact of decisions.

Applying AI to IT, life sciences, energy industries

As the above shows, Process Intelligence empowers AI to speak the language of business, which makes the industry use cases near infinite.

Take AI for IT operations (AIOps), for instance. By harnessing AI’s ability to process big data and enhance the effectiveness of IT operations, the Process Intelligence layer helps teams streamline repetitive tasks and optimize data infrastructure. Not to mention improving IT processes and tech stacks. Customers, as a result, benefit from shorter mean time to identify issues and shorter mean time to repair/resolve them – alongside reduced costs and less wasted time for the business.

For years life sciences and healthcare organizations have relied on AI in diagnostics, and in early detection, for example to find patterns in patient data that indicate the presence of a disease. There are also uses for AI in precision medicine, where experts can predict what drug will work best for specific patients, and tailoring treatments accordingly.

AI is also making waves in the energy industry. Thanks to AI’s demand forecasting ability, teams can tap into vast volumes of data to understand what will be needed, where, and when. Armed with this information, enterprises can intelligently allocate resources, optimize operations, streamline supply chains, avoid unnecessary downtime, and maximize profitability. There are also an increasing number of companies using AI in oil and gas exploration, as well as to predict the need for maintenance before cracks appear.

How Vodafone, Ocado put process mining and AI to work

By pairing process mining with AI, enterprises can create the business partner they need to drive truly valuable transformation. Here are two real-world examples.

Vodafone is one of the world’s largest telecommunications companies, with its processes spanning a huge array of countries, systems, and desktops. Vodafone uses emerging technologies, including data-visualization tools and AI, to make its global procurement process more effective. With process mining, Vodafone reconstructs and visualizes its as-is purchase-to-pay process end-to-end from digital traces in SAP systems. It shows the big picture and allows drill-downs, creating the optimal basis for improving process efficiency and quality.

Ocado Group is a technology-led business providing a unique end-to-end solution for online grocery around the world. The Ocado Smart Platform combines robotics, AI, and machine learning to streamline the process from order placed to goods delivered. Using Process Intelligence in conjunction with its own high-tech portfolio, Ocado delivers process optimization, and unlocks cash value — with a projected hundredfold return on investment.

AI must understand processes to unlock its full potential

Feeding Process Intelligence to AI makes it work for enterprises and their industry transformation goals. Whether it’s predictive maintenance or automation of monotonous tasks, AI creates the competitive advantage, for customers and employees alike. AI is far from a bolt-on for business, it’s a necessity and so is Process Intelligence.

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