AI Adoption in Supply Chains Set to Nearly Double by 2028

New research shows companies are betting big on artificial intelligence to solve persistent disruption challenges, but legacy systems remain a major obstacle.

Supply chain leaders have talked about digital transformation for years. Now they are putting real money behind it. Nearly half of all companies implementing AI in their supply chains report cost reductions of at least 10%. The technology is moving from pilot projects to production at scale.

A recent IDC report surveyed 488 supply chain professionals and found that AI adoption is expected to grow from 50% to 86% within three years. The research also revealed that 80% of companies now consider AI either important or very important across all areas of the supply chain. This marks a clear shift from experimentation to operationalization.

Legacy Systems Block the Path Forward

The biggest obstacle to AI adoption is not strategy or budget. It is old technology.

IDC found that 46% of companies cited legacy systems as a trigger for upgrading their supply chain management applications. These older on-premises systems lack the flexibility and scalability that modern AI requires. Another 41% pointed to poor integration between new applications and legacy implementations as a major pain point.

The problem runs deep. Supply chain organizations report that legacy IT continues to drag down their responsiveness. When disruptions hit, slow systems translate to slow decisions. Cost increases, transportation delays, unpredictable deliveries, and volatile demand patterns all persist. Companies know they need to respond faster. Their technology cannot keep up.

This creates a vicious cycle. Organizations focused on cost efficiency at the expense of resiliency (35% admitted this) now find themselves unable to adapt quickly. They lack visibility into their supply chains. They cannot see where and how to respond effectively.

Three Types of AI Are Reshaping Operations

The report distinguishes between three categories of AI now entering supply chains: traditional AI and machine learning, generative AI, and agentic AI.

Traditional AI leads adoption today. About 74% of companies already use it. Another 26% plan to implement it within 18 months. These systems handle tasks like demand forecasting, inventory optimization, and predictive maintenance.

Generative AI follows close behind. Currently 41% of companies use it, with 59% planning adoption in the next 12 to 18 months. IDC projects generative AI adoption in supply chains will grow from 25% to 37% within three years. Companies apply it to process automation, real-time decision support, and exception management.

Agentic AI represents the newest frontier. Only 31% of companies use it today, but 69% plan to adopt it soon. This technology enables autonomous decision-making in specific domains. Most companies (30%) believe AI agents should make decisions in most areas with human oversight for critical issues. Another 29% want all decisions approved by humans.

The research emphasizes that benefits are maximized when supply chains combine all three types. Traditional AI provides the analytical foundation. Generative AI accelerates human productivity. Agentic AI enables faster autonomous responses. Together they create a more capable and responsive operation.

The Cloud Connection

AI requires modern infrastructure. IDC found that over 80% of respondents say modernizing their applications in the cloud is important to fully benefit from AI innovations.

The numbers tell the story. Today, 52% of companies deploy traditional AI in the cloud. That figure rises to 62% within 24 months. For generative AI, current cloud deployment stands at 65%, climbing to 77% in two years. Agentic AI shows similar patterns: 64% cloud-deployed today, 73% in 24 months.

Cloud platforms provide the computing power and data accessibility that AI demands. On-premises systems struggle to deliver the speed and scale needed for real-time supply chain decisions. Companies that delay cloud migration also delay their AI capabilities.

Where Companies See the Biggest Returns

Supply chain planning leads the list of realized benefits. Process automation for increased efficiency tops the chart at 32%. Improved predictive analysis follows at 21%. Real-time decision-making comes in at 17%.

In fulfillment and logistics, cost reduction leads at 25%. People productivity improvements reach 19%. Reduced delivery lead times and transportation route optimization each deliver meaningful gains.

The aggregate impact is substantial. IDC found that 48% of companies implementing AI report at least a 10% reduction in supply chain costs. Another 40% show a 10% improvement in productivity. And 35% demonstrate a 10% improvement in innovation delivery.

These are not marginal improvements. A 10% cost reduction across a global supply chain can mean tens or hundreds of millions of dollars in savings. Productivity gains compound over time. Innovation improvements create competitive advantages that persist.

Investment Levels Are Rising

Companies are backing these benefits with significant capital. About 23% plan to spend between $1 million and $9.9 million on AI-powered supply chain initiatives in the next 12 to 18 months. Another 9% plan investments of $10 million to $50 million.

Looking further out, spending accelerates. In the 18 to 36 month window, 31% of companies plan to invest $1 million to $9.9 million. Another 12% target the $10 million to $50 million range.

Most organizations (63%) are willing to spend as much as 20% of the total cost of replacing their supply chain management systems on AI capabilities. Only 7% expect AI functionality at no added cost. Companies recognize they must pay for these capabilities.

Key Takeaways

First, AI is no longer optional for supply chain competitiveness. Companies implementing these technologies report significant cost reductions, productivity gains, and faster innovation. Those who delay risk falling behind.

Second, legacy systems are the primary barrier. Organizations cannot unlock AI benefits while running outdated technology. Cloud migration is a prerequisite for advanced AI deployment.

Third, success requires a holistic approach. Traditional AI, generative AI, and agentic AI each contribute different capabilities. The greatest benefits come from combining all three within an integrated platform.

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What is your organization’s biggest obstacle to AI adoption in supply chain? Is it legacy systems, budget constraints, talent gaps, or something else? Share your experience in the comments.

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