Celonis predicts context-aware AI to reshape supply chains

By Pascal Coubard (pictured), VP Sales APAC for Celonis

Last month, Celonis held its highly anticipated flagship event, Celosphere, in Munich – an amazing gathering of more than 3,000 changemakers including customers, partners and employees.

One particular moment stopped everyone in their tracks – this year’s Nobel Peace Prize winner María Corina Machado – shared a powerful reminder that transformation, in business or in society, always starts with people brave enough to lead it.

During the event, I asked several of our leaders what they thought 2026 was going to look like.

Dan Brown, our Chief Product Officer, was very pragmatic. He said that after years of experimentation, companies will realise that AI can’t improve what it doesn’t understand.

Dan said that in 2026, competitive advantage will shift to organisations that give AI the operational context it needs – a living digital twin that shows how the business actually runs. As Dan said, this is how AI learns to sense, reason, act, and improve responsibly.

His advice is to build trust in AI through transparency, not control. “Your digital twin becomes the unbiased source of truth that makes every AI action traceable, explainable and continually improvable. When teams can see why AI made a decision, they can refine it – turning AI into a true partner,” he said.

In addition, context-aware AI will reshape supply chain decision-making. “Instead of optimising isolated steps, AI will understand the full flow, predicting bottlenecks before they occur, identifying exceptions that matter, and orchestrating recovery plans grounded in financial and service-level impact. This closes the gap between planning and execution.” I thought that was pretty powerful.

Dan’s final words were: “AI can’t drive business value without understanding how your business flows. When you give it that context — the real-time visibility into how work gets done — the trust comes naturally. You see why it made a decision and how to make it better. That’s when AI becomes enterprise ready.”

Peter Budweiser, our GM of Supply Chain, focussed on the race to autonomous operations. He said enterprises have spent a decade automating tasks but in the agentic future, the differentiator won’t be how many tasks you automate, it will be how well you orchestrate outcomes.

“In 2026, leaders will shift from fragmented automation to coordinating AI, people and systems across the entire workflow. This is the only way to transform business processes into truly autonomous operations,” he said.

His advice is to treat Enterprise AI as the strategic discipline of redesigning end-to-end processes, not layering automation on top of broken workflows. “Success should be measured by the continuous improvement of the whole process, not the speed of a single task,” he said.

Furthermore, supply chains will become the proving ground for orchestration. “AI will dynamically reroute shipments, rebalance inventory, surface capacity constraints, and coordinate suppliers and planners in the same loop – turning fragile networks into intelligent, adaptive ecosystems that are able to respond instantly to tariffs, disruptions and volatility.”

I also got the views of Vanessa Candela, our Chief Legal & Trust Officer, about open ecosystems. Vanessa’s belief is that the era of walled platforms and vendor lock-in is ending.

“In 2026, enterprise value will shift to open, interoperable, system-agnostic ecosystems where processes are no longer constrained by the systems they run on. This is how organisations will improve continuously, adapt instantly, and innovate freely,” she said.

Her advice is to push back against closed systems. “Insist that your technology partners support interoperable solutions and data freedom so you can redesign your processes around business needs, not system constraints. Open architectures future-proof your operations and prevent new forms of vendor lock-in as AI becomes mission-critical.”

She said open ecosystems will enable cross-company visibility — capacity signals, supplier risk, emissions data, credit blocks, ASN quality — creating a shared operational picture. “AI agents across companies will co-decide on allocations, routing, buffers, and lead-time risk, shifting the focus from internal efficiency to network-wide competitive advantage.”