Context-aware AI is about to change how supply chains run
Senior leaders at Celonis expect operations teams to rethink how they use AI by 2026. The focus shifts from one-off automations to systems that understand process context, coordinate outcomes end-to-end, and plug into open, interoperable platforms.
The takeaway for Ops: advantage will come from AI that sees how work actually flows, can act across functions, and isn't boxed in by closed tech stacks.
AI needs operational context
Dan Brown, Chief Product Officer at Celonis, said companies will move past early AI experiments and give AI a detailed view of operations through a "living digital twin." That model lets AI sense and reason about processes and connect decisions to action in a responsible way.
"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," said Brown.
He expects decision-making to move from isolated steps to full-flow visibility. Systems will predict bottlenecks, flag the exceptions that matter, and coordinate recovery plans based on financial and service-level impact - closing the gap between planning and execution.
"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," Brown added. For many teams, that means building a digital twin that reflects current operations, not just ideal process maps.
From task automation to autonomous operations
Peter Budweiser, GM of Supply Chain at Celonis, expects a pivot from automating individual tasks to orchestrating outcomes across workflows. "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.
He recommends treating enterprise AI as a discipline for redesigning end-to-end processes - not as add-on bots over broken steps. Success should be measured by continuous improvement of the whole process, not the speed of a single activity.
- Dynamic execution: reroute shipments, rebalance inventory, and adjust production plans in response to signals.
- Constraint management: surface capacity limits early and resolve them with planners and suppliers in the same loop.
- Exception handling: auto-triage by financial and service impact, then assign owners and actions with due dates.
- Closed-loop KPIs: tie every intervention to OTIF, backlog burn-down, inventory turns, and cost-to-serve.
Open ecosystems over walled platforms
Vanessa Candela, Chief Legal & Trust Officer at Celonis, expects companies to question closed platforms and vendor lock-in. "In 2026, enterprise value will shift to open, interoperable, system-agnostic ecosystems where processes are no longer constrained by the systems they run on," she said.
Her guidance: ask partners for interoperability and data portability so you can redesign processes around business needs. Open architectures reduce switching risk as AI becomes mission-critical.
She also expects broader cross-company visibility of operational data - from capacity signals and supplier risk to emissions, credit blocks, and advance shipping notice quality. "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," said Candela.
What operations leaders should do next
- Build a living digital twin: connect order-to-cash, plan-to-produce, and procure-to-pay data for real-time process visibility.
- Instrument decision telemetry: log why AI made a choice, who approved it, and what changed in KPIs - make every action explainable.
- Start with outcomes: define target metrics (OTIF, inventory turns, expedite rate, cost-to-serve) before automating steps.
- Pilot high-friction lanes/SKUs: stand up closed-loop control on a few flows; expand as exception rates fall.
- Operationalize governance: human-in-the-loop for high-impact decisions; policy-as-code for compliance and audit trails.
- Standards-first integrations: require open APIs and data portability in vendor contracts to avoid new lock-ins.
- Blend planning and execution: push plan updates directly into execution systems and verify impact within hours, not weeks.
The signal is clear: context, orchestration, and openness will separate tomorrow's supply chains from yesterday's. Teams that invest here over the next 12-24 months will be ready for AI at operating scale.
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