Celonis process mining boosts enterprise AI impact with real-world industrial gains

At Celosphere and in customer briefings, Celonis argued that process mining furnishes the contextual foundation enterprise AI needs to deliver measurable value , and several customers presented practical results that back the claim.

According to the original report, Celonis co‑founder and co‑CEO Alexander Rinke cited an IDC finding that only 11 per cent of companies capture the full value of their AI investments. “Isn’t that strange?” he asked, using the observation to set out three prerequisites for effective enterprise AI: AI must understand how the business actually operates (context); it must be deployed in the right strategic areas; and it must interoperate with existing systems. The company says its process‑mining platform builds that context by ingesting system logs, documents and transaction data and enriching them with business rules, KPIs and company‑specific information to create a digital twin , what Celonis terms a “process intelligence graph”.

Industry examples shown at the event illustrate how that graph is being applied in practice. According to the original report, Mercedes‑Benz first used Celonis during the chip shortage to link disparate datasets and surface true bottlenecks. Jörg Burzer, chief technology officer at Mercedes‑Benz, said the effort reduced delivery delays, lowered production errors and improved parts logistics, and added: “This approach not only brings about changes in processes, but also changes in our corporate culture.”

Other customer cases highlight different operational priorities that resonate with industrial decarbonisation and supply‑chain resilience. The machine builder Andritz has deployed an app that automatically estimates a machine’s CO2 footprint by linking bills of materials to purchase, production and transport transactions; Marco Mansoner, data scientist at Andritz, said the tool lets sales and engineering teams estimate emissions before and after production using up‑to‑date supplier data. Thyssenkrupp Rasselstein used process intelligence to synchronise production schedules with its Duisburg steelworks, improving visibility over inbound deliveries and enabling early shortage prediction; site executives reported the platform presents insights in natural language using generative AI so planners can reprioritise upstream systems. Vinmar, a chemical distributor, has applied AI to carrier selection and is automating aspects of order‑to‑cash to shift staff effort toward exception handling and customer experience.

Celonis and its customers frame these outcomes as evidence that process data , organised, enriched and made queryable , materially increases the ROI of AI projects. The company’s recent product messaging and partner announcements extend this premise: Celonis has emphasised integrations with data lakes, APIs to deliver process intelligence into BI and workflow tools, and tooling to build agentic AI grounded in process context. The vendor says these advances let organisations orchestrate end‑to‑end processes, supply AI agents with the right inputs, and scale interventions across departments.

Industry data and independent observers, however, suggest caution. Broad analyses of AI adoption indicate many initiatives fail to move beyond pilots because of poor data quality, unclear use cases and weak change management. For industrial operators the technical task of integrating transactional and operational systems remains non‑trivial, and deriving reliable emissions estimates requires careful treatment of scope boundaries and supplier data quality. Accordingly, the company’s claims that process intelligence alone will unlock AI value should be read as a proposed route rather than a universal proof.

For practitioners in industrial decarbonisation, the practical takeaway is pragmatic: start by mapping the processes that drive material emissions and supply‑chain risk, invest in cleaning and linking the relevant data sources, and focus AI on high‑impact, well‑defined workflows where the process graph reveals bottlenecks or recurrent exceptions. According to the original report, firms that follow that sequence , build context, select targeted AI interventions, then integrate with enterprise systems , are the ones reporting tangible improvements in delivery performance, emissions visibility and operational agility.

The company said its platform is open for customers to design and deploy bespoke AI solutions; independent results from adopters at Celosphere show promising use cases, but operators should combine platform capabilities with domain governance, supplier engagement and outcome‑oriented KPIs to convert pilot wins into sustained, auditable value.