The biggest barrier to AI in supply chains isn’t technology

Most companies investing heavily in artificial intelligence (AI) for their supply chains are skipping the most important step. Before asking what AI can do, they need to ask whether their supply chain is ready for it. AI is not a transformation strategy. It is a force multiplier. It amplifies whatever system it inherits. Point it at a clean, disciplined operation, and it delivers extraordinary results. Point it at a fragmented operation filled with complexity and inconsistency, and it simply scales those problems faster.

That distinction matters more than ever. Leaders see the potential of autonomous planning, procurement and logistics—yet results consistently fall short. The issue is rarely the technology itself. More often, organizations are trying to automate complexity rather than eliminate it.

3 steps to success

Before deploying AI, supply chain leaders should focus on three fundamentals: Simplify. Standardize. Automate. Each step depends on the one before it. Skip a step and AI magnifies the inefficiency rather than creating value.

1. Simplify before you scale

Many organizations view AI as a way to untangle the complexity their supply chains have accumulated over time. AI does not dissolve complexity. It encodes it.

Simplification starts by questioning not just how work gets done but why. Most organizations have built up layers of approvals, reviews, handoffs and reporting requirements that were created for a specific purpose but never removed once they no longer apply.

The symptoms are familiar. Checkers checking the checkers. Reports produced, reviewed and never acted on. Forecasts updated weekly because they always have been, even when less frequent updates would deliver the same accuracy. Documents created for one meeting, converted into presentations for a review meeting, then translated back into actions.

AI does not benefit from any of this. It absorbs all of it.

The goal is to redesign workflows with a clean-sheet mindset. Remove organizational barriers. Eliminate activities that do not create value. Reduce unnecessary approvals. Connect AI directly to the underlying data sources and decision points rather than routing information through layers of spreadsheets, presentations and reviews.

The highest-return automation project is often the process you stop doing entirely. Organizations will need to simplify first, creating the conditions for AI to operate effectively. Those that don’t simply automate bureaucracy.

2. Standardize for reliability

Once you remove complexity, standardization makes AI trustworthy. AI excels at repeatable, consistent work. It struggles when every team follows different rules, definitions and decision criteria.

Supplier performance management is a clear example. In many organizations, procurement teams evaluate suppliers differently across commodities, regions and business units. Metrics vary. Weightings differ. Scoring methods evolve independently. One team prioritizes cost, another quality, a third delivery performance—each using a different measurement framework.

Without standardization, AI cannot reliably identify performance patterns, benchmark suppliers, predict risk or recommend actions across the enterprise. The data may exist, but it lacks a common language.

Standardization does not mean eliminating judgment; it means creating a common framework for how work is performed, measured and evaluated. When teams operate consistently, AI can learn from larger datasets, generate more reliable recommendations and scale best practices across the organization.

This is as much a cultural challenge as a technical one. Success requires organizations to align on common workflows, metrics and decision frameworks before introducing automation.

3. Automate to compound the gains

With complexity removed and standards in place, automation creates lasting value, dramatically reducing the time between identifying a problem and taking action.

Consider supply-and-demand balancing. Teams often spend significant time gathering inputs, validating assumptions, building spreadsheets and creating supply-and-demand views for future planning cycles. The work is repetitive, sequential and resource-intensive. AI changes the economics of that process. Rather than building one supply-and-demand balance at a time, AI can simultaneously generate scenarios across multiple commodities, suppliers and regions for the next three to six months. What once required hours or days of manual effort can be completed in minutes. More importantly, AI can initiate workflows, assign actions, track follow-ups and escalate exceptions automatically.

This is where agentic AI becomes powerful. It does not simply generate recommendations. It orchestrates execution.

As these agentic workflows enter production, most will keep humans in the loop. Over time, organizations will build confidence by validating decisions and gradually expanding autonomy.

Build a supply chain worth amplifying with AI

The companies that win with AI will not necessarily be the first to deploy it. They will be the ones that make it easiest for AI to operate effectively. They will simplify workflows, standardize decision-making and eliminate unnecessary complexity before introducing automation.

AI will transform supply chains. The question is not whether that transformation will happen. The question is whether your organization is building the foundation required to benefit from it. AI amplifies whatever system it inherits. Leaders who create disciplined, resilient and scalable operations today will gain more than efficiency tomorrow. They will create a competitive advantage that compounds over time.

About the author

Kevin Brown is Executive Vice President of Global Operations and Chief Supply Chain Officer for Dell Technologies. Brown has more than 30 years of leadership experience across operations, technology and procurement. During his time at Dell, he has played a central role in shaping one of the world’s most efficient, sustainable and innovative supply chains, earning global recognition across industries.

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