What is AI in supply chain management?

AI isn’t a future add-on—it’s already reshaping how supply chains plan, sense, and respond in real time, powering everything from forecasting and orchestration to risk management and real-time decisions.

Technologies like machine learning, optimization, predictive, generative, and agentic AI unlock new levels of speed, scale, and intelligence across planning and execution.

AI in supply chains combines predictive, generative, and agentic capabilities to help organizations sense disruptions, predict outcomes, prescribe actions, and execute—faster and with greater confidence. The result: smarter decisions, stronger performance, and greater resilience in the face of volatility.

What does AI do in the supply chain?

Across functions, AI helps teams:

• Detect exceptions using machine learning and pattern recognition
• Predict changes in demand, supply, and lead time
• Recommend or automate actions across sourcing, production, inventory, and logistics

The broad potential of AI in supply chains is clear. What’s challenging is that there is no one-size-fits-all model. While many vendors promise sweeping impact, supply chain teams have to focus on where AI drives value and supports their unique needs.

To realize that value, your supply chain must have a strategic need and clearly defined outcomes.

Analysts point to a clear shift: AI is moving from experimental pilots to embedded infrastructure. It started with predictive analytics, moved into real-time decision-making, and now pushes forward with intelligent agents and autonomous action.

Capgemini Research (2025) found that AI adoption in supply chains reduced fulfillment costs by 23% on average and improved forecast accuracy by up to 85%, helping to lower excess inventory and carrying costs by up to 15%.

These findings reinforce earlier research by McKinsey (2021), which also found AI in supply chains consistently delivered positive outcomes:

• Reduced logistics costs by 15%
• Cut inventory by 35%
• Improved service levels by 65% compared to slower-moving peers

With results like these, it’s no surprise that AI is reshaping how supply chain teams operate, from executives to frontline planners.

AI is becoming foundational to modern supply chains. Leading companies rely on platforms that embed AI across systems, connecting ERP, WMS, TMS, and internal models to enable:

• Real-time dashboards for risk and opportunity detection
• Seamless ingestion and interpretation of internal and external data, from ERPs to supplier signals to market shifts
• Automated planning that flags discrepancies, adjusts plans, and guides fulfillment in real time

What is Generative AI (GenAI) and how is it integrated into supply chain management?

Powered by large language models (LLMs), generative AI (GenAI) introduces a conversational interface to planning and execution. It helps users to query systems and summarizes insights, sharing recommendations in natural language.

According to Gartner (2024), half of supply chain leaders plan to implement GenAI within the year, and 14% already have. Use cases include:

• Accelerating configuration and low-code automation
• Surfacing insights from data without building dashboards
• Guiding planners through assistant-style interfaces

In Kinaxis Maestro™, generative AI acts as an interface; agentic AI drives the logic and orchestration behind the scenes, working like digital co-pilots.

Agents simulate trade-offs, recommend actions, and coordinate decisions using the same models, logic, and data sources as human planners—but grounded in your planning rules and constraints. Working within staff-defined guardrails and business constraints, agents can automate responses while keeping humans in control through transparent, explainable logic.

Say you’re evaluating a supplier delay: is it worth expediting a shipment?

Determining this typically involves pulling reports, cross-checking dashboards, tracking down root causes, and coordinating across teams. It’s manual, time-consuming, and often reactive.

With AI agents, users can ask a question and get a scenario-backed answer in seconds. Even better: the response doesn’t just come as text. Agents can generate a visual summary—like a side-by-side simulation of trade-offs or fulfillment options—so cross-functional teams can align quickly and take action.

However, agents only become intelligent when orchestrated within a real-time planning environment. It’s the fusion of predictive signals, embedded logic, and cross-functional data that gives agents the context to guide autonomous action aligned with business objectives.

Supply chain orchestration (SCO) is about unifying planning and execution in real time, so disruptions can be sensed, evaluated, and acted on fast. But this requires a fusion of AI and conventional capabilities working together in a continuous loop.

Within Kinaxis Maestro, orchestration is powered by a unique blend of predictive and generative AI, advanced optimization, and planning heuristics. These are layered over the Kinaxis Supply Chain Data Fabric, a shared planning foundation that connects and synchronizes data from different sources. This ensures rules, constraints, and exceptions remain current and accessible across the enterprise.

Predictive AI powers embedded intelligence in supply chain orchestration, surfacing early warnings like demand shifts, late shipments, and lead time variability.

Once a disruption is detected—whether it's a breached threshold, a forecast deviation, or another system signal—the platform raises an alert and activates the appropriate teams, systems, and workflows across planning and execution.

Intelligent agents play a key role here in both planning and execution:

• Planning: Agents simulate trade-offs, evaluate scenarios, and recommend next-best actions based on your current constraints and objectives.
• Execution: They can also trigger and coordinate downstream actions such as reallocating stock, adjusting logistics, or notifying suppliers once a decision is made.

Traditional systems surface alerts or highlight KPIs but leave planners to stitch together a response. Intelligent agents do a bit more: they interpret what’s happening, simulate trade-offs across functions, and suggest the next best action based on your actual planning rules and constraints.

Agentic AI operating within an end-to-end orchestration platform that spans planning and execution means agents don’t just solve problems locally; they evaluate how decisions in one area ripple across the entire supply chain.

For example, if a shipping delay crosses a risk threshold, an intelligent agent might:

• Evaluate alternate fulfillment options
• Simulate labor and cost impacts across regions
• Flag bottlenecks or risks to service levels under different scenarios (such as missed delivery windows, penalties, or cascading conflicts across the network)

This gives planners confidence that every recommendation aligns with service, cost, and margin objectives. Thus, intelligent agents help you stay ahead of problems—and avoid surprises—by proactively recommending decisions that align with your broader objectives.

This shift—from reactive workflows to AI-supported orchestration—is already reshaping how supply chain teams operate. Here’s how the benefits show up across different roles:

Chief Supply Chain Officers (CSCOs)

AI gives CSCOs faster visibility and better control across the global network. Instead of sifting through reports, they can ask a question and get a scenario-backed answer, helping them act quickly, reduce risk, and steer transformation with confidence.

VPs / SVPs of planning, operations, and logistics

With AI agents monitoring signals and surfacing key issues, hands-on leaders spend less time chasing down exceptions and more time improving cross-functional execution. AI also helps them pilot innovation safely, without breaking what’s already working.

Digital transformation leads and heads of innovation

Instead of managing dashboards and data pipelines, these leaders get a clearer path from idea to impact. GenAI makes experimentation faster, and agentic AI helps move from use cases to operational value, without needing to replatform.

Planners and analysts

AI simplifies their day-to-day. They can query the system in plain language, explore scenarios, and trust that recommendations are grounded in real-world planning logic. Instead of reacting to problems, planners get early warnings and suggested actions. That frees them to focus on higher-value decisions.

Some teams are moving fast, driven by urgency. Others hesitate, unsure where to begin. Fear of missing out (FOMO) is fueling exploration. But fear of messing up (FOMU) is just as strong. Without a clear roadmap, AI can feel risky: high costs, uncertain outcomes, and plenty of hype muddy the waters. That’s why success depends not just on technology, but on trust, alignment, and readiness.

Here are a few roadblocks in the way of AI adoption:

1. Data quality and integration remain major blockers

AI is powerful. But it needs reliable data and an integrated ecosystem to succeed.

Many organizations struggle with siloed data and fragmented systems, which limit the reach and value of AI. Without a clear integration strategy, teams risk building patchworks of disconnected tools that are fast to launch, slow to scale, and limit optimization.

Similarly, if your data is noisy or messy, it’s like grabbing a handful of Jelly Belly jelly beans—popcorn, pear, marshmallow, and licorice—and popping them all into your mouth, when you really just want to taste one at a time. The result isn’t clarity; it’s confusion.

Likewise, AI ends up blending conflicting inputs instead of surfacing clear signals, making its output harder to trust, interpret, or act on.

2. Change management and team readiness

New technology is only part of the challenge, because adoption hinges on preparing people. Planners need training, support, and a clear understanding of how AI complements existing workflows. Without a human‑centered approach to AI, even the most powerful solutions stall in pilot mode, especially when teams don’t see how new capabilities support their goals or responsibilities.

3. Trust, explainability, and the fear of getting it wrong

Trust remains a critical barrier to adoption, especially when AI recommendations can’t be easily explained. Some AI agents operate as black boxes, making decisions without showing how they arrived at them. Without visibility into the logic, users often spend time retracing steps.

Generative AI adds another layer of complexity. Because it relies on probabilistic language models, it can sometimes “hallucinate”: it will confidently surface inaccurate or illogical answers.

That’s why explainability is essential. Planners rightly need visibility into what data was used, which constraints were applied, and how trade-offs between KPIs, such as cost, labor, and service were evaluated.

A human-centred approach builds trust gradually, such as:

• Human-in-the-loop: Planners review and approve every AI-generated recommendation
• Human-on-the-loop: As trust grows, AI takes the first step, such as executing workflows, while humans monitor and validate outcomes

Both models depend on transparent, explainable AI, where every recommendation can be traced, audited, and aligned with your planning logic.

4. Security, privacy, and compliance risks

As AI platforms access more systems and ingest external data, companies must address security and compliance concerns. Governance, data privacy, and control over sensitive IP or supplier data become critical considerations, such as when working across regions or partners.

5. Organizational alignment and culture

Even with ready-to-use AI agents, successful adoption depends on more than technical integration. Teams need clarity on what AI can do, how it supports decision-making, and where it drives measurable impact for them. Embedding AI into workflows is thus just part of the equation. Executive alignment and a culture of trust are what turn potential into results.

Teams that succeed with AI plan for it. Without a formal strategy, many organizations end up tacking AI onto legacy systems and risk what Gartner calls “Frankenstack” environments. These disconnected pilots may show short-term promise but have difficulty scaling and delivering long-term value.

Before adopting AI, consider the following:

• Assess your current operations and system architecture. Identify friction points across planning and execution, including siloed systems, spreadsheets, or manual handoffs.
• Evaluate your data readiness. AI thrives on complete, connected, and contextual data. A unified data model across supply, demand, capacity, and constraints is key to enabling automation.
• Align AI efforts to business priorities. Focus on measurable outcomes—such as reduced lead times, improved service levels, or fewer expedites—rather than generic automation.
• Choose tools and partners that support embedded AI. Look for platforms that integrate AI into planning workflows, with flexibility, transparency, and a track record of time-to-value.
• Prepare your team. Planners need to trust AI recommendations and that AI is there to help them, not replace them. Invest in training that emphasizes explainability, scenario modeling, and how AI augments human expertise.
• Build trust over time. Adoption improves when teams start with clear use cases and grow confidence in AI recommendations through real-world application.

So where does all of this lead?

For companies that get it right, embedded AI delivers measurable ROI. A 2024 Capgemini survey of 1,607 business leaders across industries reported an average 1.7x return on AI investments in business operations, including supply chain, procurement, and finance.

Confidence in AI is growing alongside adoption. In particular, embedded AI and agentic systems are delivering value through faster insights, improved forecasting, and more responsive planning.

Examples of the advantages AI confers include:

• Faster time-to-insight and decision-making, as embedded AI surfaces issues early and guides action
• More accurate forecasting and inventory management, reducing costly overstocks and stockouts
• Higher service levels, enabled by smarter trade-offs and improved delivery commitments
• Greater agility, with faster responses to sudden shifts in demand, supply, or policy

But ROI doesn’t come from technology alone. Ultimately, it’s tied to how teams perceive and use AI. When planners trust the AI and understand how to apply its recommendations, organizations see real, scalable value. But that requires systems that are:

• Explainable and auditable, so users understand how recommendations are made
• Grounded in planning logic and flexible enough to reflect real-world constraints
• Embedded in enterprise systems, so insights scale across functions, enable coordinated action, and deliver value fast