AI supply chain systems apply artificial intelligence to sourcing, inventory, production, and logistics decisions. Their role can range from predicting disruptions and comparing responses to taking action within limits set by the business.
When a supply chain crisis hits, teams have to make decisions quickly. They need to identify affected suppliers and components, determine how long existing inventory will last, and check how shipping schedules may change before deciding whether to find alternative suppliers or reroute shipments. As export controls, tariffs, and geopolitical tensions become part of everyday operations, these decisions need to be made faster, even as their consequences spread across the business.
The difficulty is that the information needed to make those decisions is scattered across departments, suppliers, and systems. In a 2026 FedEx report surveying 700 senior business leaders, only 22% said they had access to all the logistics and supply chain data they wanted. Gathering the information is already a substantial task before any analysis begins.
That information gap is one reason companies are turning to AI. If it can organize scattered records, identify orders at risk, and compare possible responses, AI can reduce the burden on supply chain teams. Its role could extend further: Gartner predicted in 2025 that by 2030, half of cross-functional supply chain management solutions would use AI agents to execute decisions autonomously.
Taking an AI recommendation into account, however, is different from letting AI make the decision. In Cleo’s 2026 survey, 97% of business and supply chain leaders said they were comfortable accepting AI recommendations, while 69% wanted human review.
So which decisions should AI handle during a crisis, and when should people intervene?
AI Supply ChainKey Takeaways
AI can trace the business impact of a disruption only when supplier, order, inventory, production, and logistics data are connected.
Routine and reversible decisions may be automated within predefined limits. Decisions with major or unfamiliar consequences should remain subject to human approval.
A practical starting point is one narrowly defined decision with reliable data, clear authority limits, and measurable outcomes.
Why Supply Chain Visibility Still Stops at Tier 1
Before deciding which decisions to delegate, companies need to know how much of the required information they actually have. Predicting a shipment delay is useful, but setting response priorities also requires knowing which components and customer orders will be affected. How far into their supply chains can companies see today?
McKinsey’s 2025 supply chain risk survey found that 95% of respondents had visibility into risks at their direct, or Tier 1, suppliers. Only 42% could see risks at Tier 2 suppliers, which supply those direct vendors, and further down the supplier network. Beyond direct business relationships, the information available to a company drops sharply.
Supplier Visibility by Tier
| Supplier visibility, McKinsey 2025 survey | Share of respondents |
|---|---|
| Visibility into Tier 1 supplier risks | 95% |
| Have mapped their Tier 2 suppliers | 58% |
| Visibility into risks at Tier 2 suppliers and beyond | 42% |
Source: McKinsey, 2025 supply chain risk survey
There has been progress. After years of decline, Tier 2 supplier visibility rose by 22 percentage points from the previous year. Yet fewer than half of the companies that had mapped their Tier 2 suppliers communicated with them directly on a regular basis. Companies may know which suppliers are involved without being able to keep track of their operations.
The difficulty lies partly in how information is requested and shared. Leaders in McKinsey’s survey cited limited staffing and resources, as well as Tier 1 suppliers’ reluctance to connect customers directly with their own suppliers. Some also lacked the technology to monitor risks across multiple tiers, while others said senior management had not made this a priority. Securing supplier cooperation, checking the information received, and keeping it current all require work from the supply chain team.
That work becomes more complex in industries such as semiconductors, electronics, and telecommunications equipment, where materials and components pass through multiple supplier tiers and contract manufacturers. If procurement information is scattered across systems and documents and employees have to assemble it manually, even tracing supplier relationships takes time. News that a factory has shut down may therefore leave a company struggling to determine which materials it supplies, which components use those materials, and which of its own products are exposed.
These gaps also make it difficult to assess how suppliers operate. In EcoVadis’ 2026 sustainable procurement survey, only 12% of companies had visibility into sustainability performance across at least half of their Tier 2 suppliers. Most lacked information about Tier 3 suppliers. Knowing that a company belongs to the supply network is only a starting point; understanding its environmental and labor practices requires further information.
How much of the network companies can see also appears to be related to leaders’ confidence. Blue Yonder surveyed 678 senior supply chain professionals at North American and European companies with annual revenue of at least $500 million in fall 2025. Its report, released in 2026, found that 34% considered their supply chains unprepared for the future, up from 27% in the previous survey.
More confident leaders were more likely to use connected, end-to-end approaches and integrated data platforms. Those with less confidence reported more problems with organizational silos, slow data sharing and integration, and supplier collaboration. Visibility alone cannot explain the difference in confidence, but the findings show that the ability to connect supply chain information and confidence in operations often appear together.
Extending visibility beyond Tier 1 therefore requires supplier cooperation, people to manage the information, and systems that connect it. AI relies on those same connections. Without them, it may miss affected components and orders. Deciding how much authority to give AI starts with identifying what the company knows and where the gaps remain.
Why Supply Chain Decisions Cannot Wait for Full Visibility
Could companies simply wait until those gaps are filled before deciding what to do? Two developments affecting supply chains in 2026 show how little time they may have.
The Strait of Hormuz crisis forced companies to act while the effects were still unfolding. When war involving Iran effectively closed the strait in late February, shipments of crude oil, petrochemical feedstocks, aluminum, and helium were disrupted. As of March 12, more than 44,000 companies across 174 countries had at least one shipment connected to the strait. Analysts warned that the disruption could spread through supply chains after a lag of two to four weeks. They urged companies to assess their Tier 1 and Tier 2 suppliers’ exposure to the Middle East and secure alternative transportation capacity before available options ran out.
By March 22, just over three weeks after the crisis began, 7,716 companies had experienced at least one shipment cancellation. There was little time to wait for a complete assessment before responding. AI
Critical mineral export controls create a similar problem for sourcing decisions. Between August 2023 and September 2024, China progressively introduced export licensing requirements for gallium, germanium, and antimony. In December 2024, it banned exports of those metals and superhard materials to the United States. US importers faced shortages of materials essential to semiconductors and other advanced industries and began looking for supply routes through third countries. The restrictions showed how readily critical raw materials can become bargaining chips and how difficult existing sourcing arrangements can be to adjust when policy changes.
China suspended the ban for one year in November 2025 but kept all three metals on its export control list, so exports to any destination still require Chinese government approval. The suspension is scheduled to expire on November 27, 2026. Although the United States and China announced in late September that their trade truce would run through January 10, 2027, no official notice extending the export-ban suspension had been issued by the end of October 2026. Companies relying on these materials have to plan procurement without knowing the outcome.
Both cases also show how far the consequences of a single decision can extend. Deciding where to buy raw materials and how much to purchase now requires an assessment of international politics. A new product launch also determines which products receive priority for production capacity. Changing a supplier can affect regulatory compliance, carbon emissions, quality assurance, and delivery commitments. Yet companies must make these choices before they can fully assess the long-term consequences.
That uncertainty makes it difficult to keep even a final decision settled. In a survey of 151 supply chain leaders conducted in late 2025, Gartner found that 72% had revisited final decisions on supply chain network investments at least once, causing delays. These included investments in manufacturing and logistics locations and supplier network configurations. More than half reconsidered decisions three or more times, and repeated reviews were associated with lower satisfaction with the outcome. Gartner attributed the problem mainly to persistent, everyday instability rather than major crises.
The same pressure is evident in leaders’ priorities. In the Blue Yonder survey, “faster and better decision-making” rose from seventh place the previous year to second in 2026, behind “improving efficiency and productivity,” the top-ranked priority, selected by 35% of respondents. Investors expect companies to move faster and commit more capital while protecting margins and maintaining supply continuity. But leaders often lack the information needed to anticipate the consequences. As disruptions become more frequent and supply networks more complex, they are being asked to decide faster with less information than they need.
How AI Supply Chain Systems Speed Up Crisis Decisions
During a crisis, teams have to identify the affected suppliers and components, determine what caused the problem, and compare possible responses before deciding what to do. When they have to gather that information manually from disconnected systems, the work leaves less time to respond. AI can shorten this preparation.
AI also changes what teams can do with visibility data. Knowing where shipments and inventory are provides a starting point for examining why a risk has emerged, how it could spread, which business outcomes it threatens, and what action to take before the disruption reaches the company. AI can perform analysis that employees would otherwise have to spend time doing themselves.
What Visibility Provides and What AI Adds
| Category | What visibility provides | What AI adds |
|---|---|---|
| Key question | Where is it now? | What should we do about it? |
| Risk identification | Evidence that a problem has occurred | Possible causes and how the risk could spread |
| Impact assessment | Delayed shipments and affected items | Delivery commitments and business outcomes at risk |
| Response | Information for human decision-making | Comparison of response options and execution within predefined limits |
In practice, this can reduce the time spent on three parts of the response process.
1. Detecting Problems Before Delays Become Visible
Machine learning models can analyze changes in allocations and orders alongside historical variations in supplier delivery performance. This helps identify which customer delivery commitments are likely to be missed before the delays become apparent. Teams can then work with suppliers before the disruption affects an order and give customers a more realistic schedule.
2. Finding the Cause of a Problem Faster
AI planning assistants can summarize why production and supply plans have changed in plain language and identify likely causes within minutes. Employees can use the time they would have spent investigating the change to work on a response instead. Understanding the reason for a change can also improve confidence in the revised plan.
3. Comparing Response Options More Quickly
During the Strait of Hormuz crisis, companies had to consider whether to approach alternative suppliers, secure other transportation options, or increase inventory to withstand the disruption. AI simulations can estimate how each strategy would affect supply, demand, suppliers, and recovery times. Comparing those projected outcomes helps teams prepare a considered response within a limited window.
For these analyses to work, however, the underlying data must be connected. Companies are at different stages of AI adoption, but data preparation has been a common starting point among those achieving results. They define the relationships between suppliers, inventory, orders, and production capacity: Supplier A makes Component B, for example, and Component B goes into Product C. Semantic ontologies and knowledge graphs provide ways to represent those relationships so machine learning and predictive AI can work with them.
Agentic AI can operate on that foundation. It refers to AI that can assess a situation and take action, allowing a system to proceed from detecting a problem to carrying out steps intended to resolve it. Its ability to act brings a further question into focus. AI’s role is to support executive judgment rather than replace it, helping leaders make decisions more accurately, quickly, and consistently. Could companies go further and delegate the decision itself to AI?
Can AI Make Supply Chain Decisions Without Human Approval?
For years, AI’s role in supply chains centered on forecasting and planning, including demand forecasting, inventory management, and production and logistics optimization. It provided analysis and recommendations, with people generally making the final decision. Companies are now extending that role into execution. In its June 2026 supply chain technology trends report, Gartner described a shift in interest from AI-based analysis toward automating everyday supply chain tasks and decisions. Adoption nevertheless remains at an early stage: only 8% of companies in the Blue Yonder survey said they were using agentic AI.
Making that shift requires reliable data, clear limits on AI’s authority, and records that allow people to review the basis for a decision afterward. These requirements help explain why leaders can be comfortable with AI recommendations while still wanting human review. The ability to generate a recommendation does not, by itself, settle whether the system should be allowed to act on it.
Companies working through this issue are treating autonomy as a matter of degree. They are dividing decision-making authority into three levels according to the nature of the decision. How advanced the technology is matters less to that choice than the consequences of the action being delegated.
AI and Human Roles by Decision Type
| Decision type | Example | AI’s role | Human role |
|---|---|---|---|
| Routine, reversible decisions | Inventory replenishment within predefined limits | Execute and record the decision | Review the records |
| Time-sensitive, moderate-risk decisions | Reprioritizing orders expected to be delayed | Execute if no human intervention occurs within a defined period | Intervene when necessary |
| High-risk or unfamiliar decisions | Replacing a supplier or selecting an alternative sourcing partner | Analyze the situation and prepare options | Make the final decision |
These examples are illustrative. Specific decision criteria and authority levels will vary by company.
These examples are illustrative. Specific decision criteria and authority levels will vary by company.
Applied to a disruption like the Strait of Hormuz crisis, the distinction becomes clearer. Reprioritizing orders expected to be delayed may require an urgent response, so AI can act within agreed limits if nobody intervenes during the designated period. Replacing a supplier or selecting an alternative source is harder to reverse and therefore calls for a different arrangement: AI prepares the analysis and options, and a person makes the final decision. During a crisis, authority needs to be assigned decision by decision, rather than handing everything to AI or holding every action for human approval.
Those boundaries also help distinguish genuine AI agents from products that merely use the label. Gartner has warned about “agent washing,” where vendors rebrand existing AI assistants, robotic process automation software, or chatbots without adding meaningful agent capabilities. When evaluating a solution, companies therefore need to ask: Which decisions can the system actually make and execute without human intervention? Defining that authority also helps determine where the first handover should take place.
How to Start an AI Supply Chain Project
Many companies rush to adopt AI because they fear falling behind, but rushing can also increase the risk of a stalled project. Gartner predicts that more than 40% of agentic AI projects across industries will be canceled by the end of 2027 because of rising costs, unclear business value, and inadequate risk controls. Even companies that have already invested struggle to assess the results: two-thirds of supply chain digital investment is allocated to AI, yet 55% of chief supply chain officers lack a clear view of the value those investments have delivered.
The sequence of implementation matters more than the speed of rollout. Rather than handing over every decision on day one, companies should establish that AI performs reliably within a limited scope before expanding its authority.
1. Choose One Narrowly Defined Decision
Start with a clearly bounded problem, such as predicting delivery delays, handling exceptions, or replenishing inventory. Identify who is responsible for the decision and how performance will be measured.
2. Connect the Data Required for That Decision
Supplier, inventory, order, and production capacity data must be reliable and linked before AI receives broader authority. Otherwise, errors in the underlying data can lead to incorrect decisions on a larger scale.
3. Set Authority Limits According to Risk
Define what AI can do, when it must refer a decision to a person, and what records must be retained. Important decisions should require human approval. As the data environment matures, formal governance also needs to develop so decisions can be reviewed and responsibility established.
4. Measure Results Before Expanding
Assess decision accuracy, the number of exceptions AI could not handle, and the resulting business outcomes. Use those findings to decide whether to extend AI into another process or give it more authority.
Expansion also requires realistic expectations about timing. Individual tasks such as supplier communications or invoice validation can sometimes deliver results within three to six months, but those early gains often come from activities with few dependencies on other processes. Changes that connect multiple functions require a longer plan, with a two- to five-year horizon.
With this foundation in place, companies can extend AI’s responsibilities gradually. Agents trained on experienced employees’ decision rules can reconcile data across systems and handle routine decisions. Work previously performed by a small number of specialists can then be carried out across more of the organization.
Control towers can develop along similar lines. A system that has primarily reported problems can begin calculating and executing responses such as rerouting shipments, reallocating inventory, or adjusting order priorities. Connecting plans across departments also allows the company to assess the effects on costs, service levels, risk, and sustainability before a decision is made.
Of these applications, predicting delivery delays and rerouting shipments depend directly on logistics data. For companies moving components or products by sea, shipment location and estimated time of arrival (ETA) are the starting point for delivery decisions. During the Strait of Hormuz crisis, vessels diverted around the Cape of Good Hope faced an additional 10 to 14 days in transit.
If those changes are scattered across carrier systems, teams may discover a delay late or make decisions using different information. AI is limited by the information available to it as well. A solution such as Tradlinx Ocean Visibility brings vessel schedule changes, transshipments, delay events, and ETA updates from multiple carriers into a single, shipment-level view. That gives the team and AI systems using those data a common basis for ocean freight decisions.
AI Supply Chain Decisions: Where Human Judgment Still Matters
How far should companies trust AI during a supply chain crisis? The answer depends on the decision. Routine, reversible decisions can be delegated to AI. Time-sensitive decisions can allow AI to act first while people monitor and intervene when necessary. High-risk or unfamiliar decisions should remain with people, supported by AI analysis. This is why accepting AI recommendations need not mean handing over every decision, and why human review remains important to so many leaders. In every case, reliable data is a prerequisite for trusting the result.
The forces disrupting supply chains are unlikely to disappear soon. As crises recur, companies will be better placed to respond if they detect changes early, understand their exposure, compare options quickly, and act before disruption turns into loss. AI can help supply chain teams identify supplier concentration risk before it becomes a problem. Before production plans are finalized, it can help operations teams assess geopolitical and logistics risks; finance teams can use it to estimate the cost of contingency measures in advance.
For logistics and SCM professionals, this can start without a large AI project. Choose one decision your team makes frequently during disruptions, then check whether the information needed for it arrives on time and is available in one place. Companies will be better prepared to trust AI with crisis decisions when they have already defined what AI can decide and what must remain under human control.
AI Supply Chain Frequently Asked Questions
What Is AI in the Supply Chain?
AI in the supply chain means using artificial intelligence to analyze information from sourcing, inventory, production, orders, and logistics. It can support forecasts and planning, compare responses to disruptions, and carry out actions within assigned limits.
How Does AI Affect Supply Chain Performance?
AI can reduce the time spent detecting delays, investigating causes, and comparing responses. Its value should be assessed through decision accuracy, unresolved exceptions, and business outcomes, rather than the speed of analysis alone.
Will AI Replace Supply Chain Management?
AI can automate parts of supply chain management, including routine analysis and reversible decisions. People still need to oversee high-risk choices, unfamiliar situations, and decisions that involve wider business consequences.
How Is AI Changing Supply Chain Management?
AI is extending supply chain systems from analysis and recommendations into execution. The degree of autonomy depends on the task, with decision rules and human approval requirements defining what the system can do.
How Can AI Help Solve Supply Chain Problems?
AI can identify orders at risk, predict delays, investigate likely causes, and compare response options. Its analysis still depends on the information available, so missing supplier or component data can leave affected orders undetected.
What Data Does an AI Supply Chain System Need?
The required data depends on the decision. Common inputs include supplier relationships, component dependencies, inventory, customer orders, production capacity, shipment status, schedule changes, and estimated arrival times. Those records need to be reliable and connected.
Need help interpreting this disruption or your shipment?
For a quick question, chat with Tradlinx on WhatsApp. For a deeper discussion, book a time below.
Leave a Reply