Celonis executives predict that by 2026, companies will rethink their artificial intelligence, automation, and technology ecosystems with a focus on operational context, autonomous processes, and open platforms.
A senior leader from a process mining and execution management software company outlined his views on how AI and supply chain will evolve over the next two years.
They expect competitive advantage to depend less on isolated automation projects. They expect an increased focus on how AI systems interact with real-world business operations.
AI and context
Dan Brown, chief product officer at Celonis, said companies will move beyond early AI experiments.
He said many organizations will realize that AI systems require a detailed view of their operations.
Brown said that in 2026, competitive advantage will shift to organizations that provide operational context to AI through “living digital twins” that reflect how their business operates.
He said this type of model allows AI systems to sense and reason about processes. He said it also supports acting in a responsible manner and improving.
Brown predicts that trust in AI will depend on clear visibility into how decisions are made, rather than strict controls.
“Digital twins provide an unbiased source of truth that makes every AI action traceable, explainable, and continuously improved. Once a team understands why the AI made a decision, it can refine it and turn it into a true partner,” said Dan Brown, Chief Product Officer at Celonis.
He said context-aware AI will change supply chain decision-making.
Brown expects the system to monitor the entire flow, rather than specific steps in the process.
He said AI predicts bottlenecks in advance. He said they will identify significant exceptions and adjust recovery plans based on financial and service level impacts.
He said this approach narrows the gap between planning and execution in complex supply chains.
Brown said organizations need AI systems that understand how processes actually work.
He expects real-time visibility into how work is done will become essential to AI-based decision-making.
“AI can't drive business value without understanding how the business flows. When you give AI that context, real-time visibility into how the work is being done, trust naturally builds. You know why the AI made the decisions it made and how to improve them. That's when AI becomes enterprise-ready,” said Brown.
autonomous operation
Peter Budweiser, general manager of supply chain at Celonis, foresees a shift from automating tasks to orchestrating results.
He said many companies have spent years automating individual activities.
He expects the next phase will focus on how companies better align AI, people, and systems across processes.
“In 2026, leaders will move from piecemeal automation to orchestrating AI, people, and systems across workflows. This is the only way to transform business processes into truly autonomous operations,” said Peter Budweiser, general manager of supply chain at Celonis.
Budweiser said organizations should treat enterprise AI as a discipline for redesigning end-to-end processes.
He said that adding automation on top of inefficient workflows should be avoided.
“Success should be measured by continuous improvement of the entire process, not the speed of a single task,” Budweiser said.
He expects the supply chain to serve as a test bed for this more integrated approach.
Budweiser said its AI system will reroute shipments and rebalance inventory as conditions change.
He said these systems would highlight capacity constraints and coordinate suppliers and planners in the same loop.
This will transform fragile supply networks into more adaptive ecosystems that can quickly respond to tariffs, disruptions and fluctuations, he said.
open ecosystem
Vanessa Candela, chief legal and trust officer at Celonis, predicts that by 2026, more organizations will be questioning closed technology platforms.
She said the days of walled systems and vendor lock-in are over.
“In 2026, enterprise value will shift to open, interoperable, system-agnostic ecosystems where processes are no longer bound by the systems they run on, giving organizations the freedom to continually improve, adapt instantly and innovate freely,” said Vanessa Candela, Chief Legal and Trust Officer at Celonis.
Candela said companies should resist new forms of dependence on a single vendor.
He said organizations need to look to their technology partners for interoperable solutions and data portability.
“Insist that your technology partners support interoperable solutions and data freedom so you can redesign processes based on business needs, not system constraints. Open architecture future-proofs operations and prevents new forms of vendor lock-in as AI becomes mission-critical,” said Candela.
She expects a more open ecosystem to support cross-company visibility of key operational data.
He said this includes capacity signals, supplier risk, emissions data, credit blocks, and quality of advance shipment notices.
He said companies will begin to share a more consistent view of how their entire network operates.
“AI agents across the enterprise will co-determine allocation, routing, buffers, and lead-time risks, shifting the focus from internal efficiency to network-wide competitive advantage,” said Candela.
Celonis executives say that organizations that invest in operational context, integrated orchestration, and open platforms over the next two years will be well-positioned for an AI-driven business environment in 2026.
