This report examines the operating conditions supply-chain leaders should establish before AI agents are given meaningful responsibility across planning, procurement, logistics, inventory, fulfilment, and operations. It is a qualitative executive synthesis and decision framework, not proprietary survey research. It does not infer adoption rates, financial impact, campaign performance, or production readiness where verified evidence is unavailable.
EXECUTIVE SUMMARY
The next phase of supply-chain AI is moving from isolated assistance toward operational participation. AI agents can increasingly monitor events, assemble context, recommend actions, coordinate workflows, and—in bounded situations—execute decisions. The opportunity is significant, but so is the operating challenge. Supply chains are interconnected systems in which a local decision can create downstream consequences across production, inventory, transport, suppliers, customers, and working capital.
The central readiness question is therefore not “Do we have AI?” It is “Can our processes provide enough trusted context, explicit authority, human accountability, and observable evidence for AI to participate safely in operational decisions?”
Our synthesis identifies five priorities: process context, decision-grade data, explicit agent authority, human-agent orchestration, and evidence-led scaling. These priorities are mutually dependent. Strong models cannot compensate for fragmented context. Good data cannot compensate for unclear authority. Automation cannot compensate for poor escalation. And impressive demos cannot substitute for operating evidence.
PRIORITY 1: BUILD PROCESS CONTEXT BEFORE AGENT AUTONOMY
AI agents need more than access to records. They need an understanding of how the relevant process works, how the current situation emerged, what dependencies exist, and which constraints matter.
A supply shortage illustrates the difference. A system may know that inventory is below target, but a useful operational decision may also require inbound supply, production schedules, supplier commitments, substitute materials, customer priorities, logistics options, and financial trade-offs. Without those relationships, an agent may optimise the wrong variable.
Readiness implication: define the minimum operational context required for each priority decision. Map the event, process history, dependencies, constraints, policies, owners, and downstream actions before increasing autonomy.
PRIORITY 2: MAKE DATA DECISION-GRADE, NOT MERELY AVAILABLE
Supply-chain data is often distributed across ERP, planning, procurement, warehouse, transportation, manufacturing, supplier, and analytics environments. Availability alone does not make that data suitable for agentic decision-making.
Decision-grade data has sufficient quality, timeliness, meaning, lineage, and business relevance for the specific workflow. Teams should know which fields are authoritative, how frequently they change, where conflicting records occur, and what an agent should do when required information is missing.
The practical test is not whether an agent can retrieve a value. It is whether the organisation can explain why that value should be trusted for the decision being made.
Readiness implication: establish source ownership, freshness expectations, exception handling, and semantic clarity for the data elements that materially influence the workflow.
PRIORITY 3: DEFINE AGENT AUTHORITY EXPLICITLY
Technical capability is not business authority. An AI agent may be capable of changing an order, initiating an expedite, reallocating inventory, contacting a supplier, or triggering a workflow without being authorised to do so.
A practical authority taxonomy includes four levels:
Observe — monitor, retrieve, summarise, classify, or detect.
Recommend — propose a response or action for human approval.
Execute — perform a predefined action inside approved conditions.
Escalate — transfer responsibility when uncertainty, impact, or policy requires judgment.
Authority should be assigned at the workflow step, not at the platform level. A single agent can have different authority across different decisions. Low-impact, reversible actions may support more autonomy than decisions involving strategic suppliers, large financial commitments, customer service risk, or production changes.
Readiness implication: document permitted actions, prohibited actions, thresholds, escalation conditions, and accountable owners before moving a use case from recommendation to execution.
PRIORITY 4: DESIGN HUMAN + AI ORCHESTRATION AS ONE OPERATING SYSTEM
Human oversight should not be treated as a generic fallback. The strongest operating models define how people and agents collaborate throughout the workflow.
AI is well suited to continuous monitoring, pattern recognition, context assembly, prioritisation, summarisation, and rapid evaluation of alternatives. Humans contribute commercial judgment, negotiation, relationship awareness, accountability, and the ability to resolve ambiguous or novel exceptions.
The handoff is where many workflows succeed or fail. When an agent escalates, the human should receive the relevant event history, process context, evidence, actions already taken, options considered, and reason for escalation. A handoff that forces the person to reconstruct the case eliminates much of the operational benefit.
The reverse handoff also matters. Human overrides and exception decisions should become part of the learning and governance record where appropriate. Repeated overrides can reveal a data defect, an incomplete rule, an authority problem, or a process design issue.
Readiness implication: define escalation triggers, owners, context-transfer requirements, response expectations, and feedback loops as part of the workflow design.
PRIORITY 5: SCALE FROM OPERATING EVIDENCE
Agentic operations should be expanded from evidence, not enthusiasm. The organisation needs visibility into process outcomes, decision quality, and control behaviour.
Process evidence can include cycle time, exception backlog, rework, delay, throughput, service exposure, or other workflow-specific measures. Decision evidence can include recommendation acceptance, override patterns, exception resolution, or outcome variance. Control evidence should show whether the agent stayed within its permissions, escalated appropriately, and left a reconstructable decision trail.
No single metric proves readiness. Higher automation is not inherently better. Lower escalation is not inherently better. Faster execution is not inherently better if downstream consequences become harder to see.
Readiness implication: define the evidence threshold for expansion before the pilot begins. Make the next increase in scope or authority a deliberate decision gate.
THE AGENTIC OPERATIONS READINESS MODEL
Stage 1 — Context discovery. The organisation maps the workflow, required data, dependencies, constraints, and ownership.
Stage 2 — Assisted intelligence. AI monitors, retrieves, summarises, detects, and recommends while people retain execution authority.
Stage 3 — Governed execution. Selected actions can be executed automatically within explicit thresholds and permissions.
Stage 4 — Orchestrated operations. Multiple workflows, people, systems, and agents coordinate through shared context and observable controls.
Stage 5 — Evidence-led expansion. Scope and autonomy increase only when operating evidence demonstrates that the current level is dependable.
This is a planning model, not a ranking of organisations. Different workflows inside the same enterprise can legitimately sit at different stages.
EXECUTIVE READINESS QUESTIONS
1. Which operational decision are we trying to improve?
2. What context changes the quality of that decision?
3. Which data sources are authoritative for the workflow?
4. What may the AI observe, recommend, execute, or escalate?
5. Which actions remain human-owned?
6. What conditions force escalation?
7. What evidence will show whether the decision and process improved?
8. Can an authorised reviewer reconstruct why an action occurred?
9. What must be true before authority or scope increases?
10. Who owns correction when the workflow behaves unexpectedly?
FUNCTIONAL IMPLICATIONS
Supply-chain leaders should define the operational outcomes and priority decisions. Planning teams should identify constraints and decision dependencies. Procurement leaders should define supplier and commercial boundaries. Logistics teams should map transport exceptions and recovery actions. Operations teams should define execution realities and production consequences. IT and data teams should establish integration, observability, and trusted data foundations. Security and governance stakeholders should define access, permissions, and review requirements. Finance should help evaluate material trade-offs where the workflow affects cost, inventory, or working capital.
The executive task is coordination. Agentic operations cross organisational boundaries, and fragmented ownership can become a larger barrier than model capability.
WHAT THE EVIDENCE DOES NOT PROVE
This report does not establish a universal adoption rate, ROI threshold, autonomy target, or deployment timeline for agentic supply-chain operations. Those outcomes depend on the workflow, data, process maturity, integration, controls, operating environment, and baseline performance of each organisation.
Publication and sales messaging should therefore avoid universal claims such as “fully autonomous supply chains,” “guaranteed ROI,” or “industry-standard autonomy” unless a specific authoritative source directly supports the statement.
READINESS PRIORITY INTERDEPENDENCIES
The five priorities should not be implemented as independent workstreams. They form an operating system. Process context defines what the decision means. Decision-grade data provides the trusted inputs. Authority determines what the AI may do. Human-agent orchestration defines how responsibility moves through the workflow. Evidence-led scaling determines whether the operating model should expand.
A weakness in one priority can limit the others. Strong data cannot compensate for an unstable process whose exceptions are poorly understood. A well-mapped process cannot support safe execution if the agent’s permissions are ambiguous. Clear authority does not create value if human escalation loses the context already assembled. Good outcomes during a pilot do not justify scale if the organisation cannot determine why those outcomes occurred.
Leaders should therefore review readiness as a set of dependencies rather than a checklist of completed technology tasks.
READINESS BY WORKFLOW, NOT BY ENTERPRISE LABEL
Organisations often ask whether the enterprise is “ready for agentic AI.” That framing is too broad for operational decisions. Readiness can differ significantly across workflows inside the same company.
A stable, repetitive inventory workflow with clear data and reversible actions may support governed execution. A supplier negotiation can remain human-owned because the decision depends on relationships and commercial judgment. A logistics exception process may be ready for AI recommendations but not execution. A planning workflow may benefit first from context assembly and scenario support.
This variation is expected. It reflects the fact that process stability, data quality, decision complexity, action impact, and ownership differ across the operating portfolio.
The practical implication is to assess readiness at the level of the decision and workflow. Enterprise capabilities such as data platforms, integration, security, and governance create enabling conditions, but they do not automatically determine the appropriate authority for every use case.
THE READINESS EVIDENCE PACKAGE
Before a workflow moves into a more agentic operating model, leaders should be able to review a concise evidence package. It should describe the current process, decision objective, required context, authoritative data sources, known exceptions, initial AI role, human owner, escalation conditions, and measures used to evaluate the workflow.
For recommendation use cases, the evidence should show whether the agent consistently assembles the required context and whether human reviewers find the recommendations decision-useful. Override patterns should be visible. For bounded execution, the evidence should additionally show whether actions remained inside approved conditions, whether exceptions were detected, and whether outcomes were observable.
The package does not need to become a large governance document. Its purpose is to make the operating basis for the decision visible enough that leaders can approve, challenge, or narrow the next step.
DATA READINESS: RESOLVING CONFLICT, FRESHNESS, AND OWNERSHIP
Decision-grade data requires more than integration. Supply-chain workflows frequently encounter records that are stale, incomplete, duplicated, or inconsistent across systems.
For each material data element, teams should identify the authoritative source, expected freshness, acceptable tolerance, and behaviour when the value is missing or conflicting. An agent should not silently choose between contradictory records when the difference can materially change the action.
Ownership is equally important. When a data issue blocks a decision, the workflow should know who is responsible for correction or interpretation. Without ownership, data quality becomes a recurring operational exception rather than a managed condition.
Readiness therefore includes the ability to handle imperfect information deliberately. The goal is not perfect data everywhere. It is sufficient reliability and explicit exception handling for the specific decision.
PROCESS READINESS: STABILITY BEFORE AUTONOMY
A process can be documented and still be unsuitable for greater autonomy. The relevant question is whether the workflow behaves consistently enough that the organisation understands its normal path, material variants, and recurring exceptions.
Frequent workarounds are an important signal. If teams regularly bypass the formal process because the documented path does not reflect operational reality, an agent built on the formal process can reinforce the wrong behaviour. Repeated manual interventions can indicate missing context, unresolved policy, or unclear ownership.
Process intelligence can help reveal these patterns by showing how work actually flows across systems. Leaders can then decide whether the immediate priority is AI participation or process improvement.
Readiness is stronger when the workflow has a clear trigger, understandable decision points, known exception types, defined ownership, and observable outcomes.
AUTHORITY READINESS: PERMISSION MUST BE TESTABLE
Authority should be written in conditions that can be understood and, where appropriate, tested. “The agent can act on low-risk cases” is not sufficiently precise unless low risk is defined for the workflow.
A stronger authority definition identifies the action, required context, permitted population, thresholds, prohibitions, and escalation triggers. It should also reflect reversibility. A technically simple action can require approval if the consequence is material or difficult to unwind.
Authority readiness also requires a human owner. Someone must be accountable for defining the boundary, reviewing exceptions, and deciding when the boundary should change. Without ownership, permissions can expand through operational habit rather than deliberate decision.
HUMAN-AGENT ORCHESTRATION READINESS
A workflow is not ready simply because a human approval step exists. The handoff must be designed so that the person can act efficiently.
The human should receive the event, process history, relevant data, constraints, options considered, actions already taken, and reason for escalation. The reviewer should understand what the agent is asking them to decide. When the human responds, the outcome should return to the workflow in a form that can support future review.
Different human roles should be distinguished. Approval, exception handling, supervisory review, and strategic judgment are not interchangeable. A workflow may need one person to approve a material action and another owner to review recurring override patterns.
Readiness is therefore a property of the combined human-and-AI workflow, not the AI component alone.
SCALING READINESS: SEPARATE VOLUME, SCOPE, AND AUTHORITY
Scaling decisions should be decomposed. Increasing transaction volume, expanding to new process variants, connecting additional systems, adding more users, and granting new execution permissions are different changes.
A workflow can demonstrate readiness for more volume without demonstrating readiness for greater authority. It can perform well for one supplier group without proving that the same context and exception patterns apply elsewhere. It can support a new recommendation while keeping execution human-owned.
Separating these dimensions makes the evidence more interpretable. If performance changes after expansion, leaders can identify which change is most likely responsible.
This also prevents a successful pilot from becoming an uncontrolled bundle of scope and permission increases.
A CROSS-FUNCTIONAL READINESS WORKSHOP
A practical readiness review should bring together the people who understand the decision from different angles. Supply-chain and operations leaders define the business objective and process consequences. Planning, procurement, logistics, or fulfilment experts identify constraints and exception patterns. IT and data teams clarify system access, integration, source ownership, and observability. Security and governance stakeholders define permission and review requirements. Finance contributes where decisions have material cost, inventory, or working-capital implications.
The workshop should focus on one bounded workflow. The output should be a shared definition of the decision, context, AI role, human role, evidence, and next gate. The purpose is not to create a large committee. It is to resolve cross-functional ambiguity before the workflow is given operational responsibility.
READINESS FAILURE MODES
Several failure modes recur in agentic-AI programs.
Technology-first selection occurs when teams begin with an agent capability and search for a process to apply it to. This can create demonstrations without a clear operating outcome.
Data-access optimism occurs when integration is mistaken for decision readiness. The agent can retrieve information, but the organisation has not defined which sources should be trusted or what to do when they conflict.
Permission ambiguity occurs when technical access quietly becomes operating authority. The system can act, but no explicit business decision has granted that responsibility.
Human-fallback design occurs when escalation simply sends the problem to a person without transferring context. The organisation retains human oversight but loses the speed benefit.
Pilot-success overreach occurs when positive evidence from one workflow, population, or authority level is generalised beyond what was actually tested.
Recognising these failure modes helps leaders protect the distinction between capability and readiness.
THE EXECUTIVE READINESS SCORECARD
A useful executive scorecard can organise review around five dimensions without turning them into an unsupported universal maturity score.
Process context: Is the real workflow sufficiently understood, including dependencies and exceptions?
Decision-grade data: Are the material inputs trusted, current enough, and owned?
Authority: Are permitted actions, prohibitions, thresholds, and escalation conditions explicit?
Orchestration: Are human roles, handoffs, and feedback loops designed?
Evidence: Can the organisation observe outcomes and determine whether the current role should expand?
Each answer should be supported by workflow-specific evidence. Where evidence is missing, the appropriate status is unknown rather than assumed readiness.
IMPLEMENTATION ROADMAP FOR A BOUNDED USE CASE
A practical implementation begins by selecting one recurring decision where improved context or coordination can create operational value. Map the workflow and establish the baseline problem. Identify the minimum context required and resolve the most material source-ownership questions.
Assign an initial AI role that matches the current evidence. For complex or high-impact decisions, observation or recommendation can create learning while preserving human authority. Define escalation triggers and owners before operation begins.
Instrument the workflow so process outcomes, recommendation behaviour, overrides, escalations, and control performance can be reviewed. After a bounded period of operation, assess whether the evidence supports more volume, broader scope, increased authority, or no change.
The roadmap should remain adaptable. If the process changes or context quality deteriorates, the organisation should be able to narrow the role while the issue is resolved.
CONCLUSION
Operational AI readiness is the demonstrated ability to give AI a useful role inside a real supply-chain process while preserving context, authority, accountability, and evidence.
The strongest organisations will not simply deploy more agents. They will build better operating conditions for agents and people to work together. They will connect data to process context, separate capability from permission, design escalation intentionally, and scale from observed outcomes.
That is the path from AI experimentation to agentic operations that can be trusted in the flow of work.
Join the September 17 webinar, “Operational AI in the Supply Chain: How Context Empowers Agents and Humans to Operate Side by Side,” to explore how operational context can help organisations move from AI potential toward dependable human-and-agent execution.
REFERENCES
1. Celonis — Supply Chain Transformation: https://www.celonis.com/solutions/supply-chain-transformation/
2. Celonis — Context Model: https://www.celonis.com/platform/context-model
3. Celonis — Enterprise AI: https://www.celonis.com/solutions/ai
4. NIST — Artificial Intelligence Risk Management Framework (AI RMF 1.0): https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10
5. NIST — Generative AI Profile (NIST AI 600-1): https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
