Why Your AI Agents Shouldn't Be Making Supply Chain Decisions (And What Should Happen Instead)

By Doron Hazan, Director of Data Product & AI

Picture this: Your artificial intelligence (AI) system just ordered 50,000 units of a product because it detected a spike in demand. Sounds efficient, right? But what the AI didn't know was that the spike came from a competitor's recall creating temporary demand, your manufacturing partner is dealing with quality issues, and you're launching a new product variant next quarter that will cannibalize sales of the current version.

This scenario isn’t hypothetical — it’s the risk companies face as they rush to give AI agents full decision-making authority. While I believe AI is absolutely crucial for modern supply chain management, I’ve learned there’s a critical difference between AI that informs decisions and AI that makes them.

The Compelling Appeal of Fully Autonomous AI

The promise of AI agents operating independently in your supply chain is undeniably attractive. Who wouldn't want systems that automatically reorder inventory, reroute shipments around delays, and optimize warehouse operations without human intervention? The efficiency gains seem obvious, and in our fast-paced business environment, the idea of reducing human bottlenecks feels like a competitive necessity.

The convergence of AI and Internet of Things (IoT) technology has made this vision technically feasible. Modern AI systems can process millions of IoT data points simultaneously, identifying patterns and correlations that would be impossible for human operators to detect. They can predict equipment failures, optimize routes, and respond to changes in real time. From a pure data processing perspective, AI systems are becoming remarkably sophisticated.

But here's what I've learned from working in this space: having the capability to make decisions and having the context to make good decisions are two very different things.

AI systems today largely operate in what we might call the digital layer — analyzing structured data, transactions, and historical trends. But the world they’re meant to optimize is physical, dynamic, and full of variables that don’t live in spreadsheets. This is where the concept of Physical AI comes in: intelligence that’s grounded in real-time, sensory data from the physical world — motion, temperature, location, and more. Physical AI connects digital reasoning with the physical reality it’s meant to act upon, creating the bridge between algorithmic precision and operational truth.

The Missing Context Problem

Your supply chain doesn't exist in a vacuum. It's part of a complex business ecosystem influenced by strategic decisions, market dynamics, regulatory requirements, brand considerations, and countless other factors that rarely make it into your IoT data streams or AI training sets.

Consider what AI can see versus what it can't. IoT sensors provide incredible visibility into physical operations — temperature readings, location data, equipment performance, and environmental conditions. AI algorithms excel at identifying patterns in this data and predicting outcomes based on historical trends. But they can't see your board meeting discussions about market expansion, your R&D pipeline that will impact demand patterns, or your CFO's directive to improve cash flow by extending payment terms.

When AI systems make autonomous decisions without this broader context, they optimize for the patterns they can observe while remaining blind to the factors that actually drive business success. They become very sophisticated at solving the wrong problems.

And even when AI is paired with IoT, the challenge isn’t just data collection — it’s data completeness. Without visibility into every layer of the physical flow, AI can misinterpret partial signals. That’s why Physical AI matters: it turns those partial data points into a coherent, continuous understanding of what’s happening in real time, grounding decisions in physical reality instead of digital assumptions.

The 99% Visibility Sweet Spot

This is where I believe the real value lies — and it's exactly the approach we've taken at Wiliot.

Instead of building AI agents that replace human judgment, we empower supply chain leaders with near-complete visibility into what’s actually happening in their supply chain. Our IoT Pixels and Network Infrastructure capture the raw data, and the Wiliot Cloud transforms it into actionable intelligence.

Getting to 99% visibility of assets is transformational. Most companies operate with limited visibility into their supply chain, often discovering problems only after they've already disrupted operations. Those blind spots are where the costly surprises hide — the unexpected delays, quality issues, and inventory discrepancies that derail carefully laid plans.

But it’s important to note what that 99% really means. Wiliot delivers 99% visibility of assets through our cloud and inference models, not an all-knowing view of a customer’s entire business. Critical context — from boardroom discussions to market shifts — lives with the customer. That’s why we provide data applications that let customers incorporate their own information on top of the Wiliot data, filling in the gaps only they can see (more on this to come in a future blog post).

That’s also why Physical AI doesn’t replace human intelligence — it amplifies it. It provides the live, sensory context that AI alone can’t perceive and gives people the confidence to act quickly with the full picture. When machines and humans share the same physical truth, that’s when true intelligence emerges.

Once you have that near-complete visibility of assets, you maintain complete control over what happens next. You can analyze the data through the lens of your business strategy, add the unique insights that only you possess, and make decisions that align with your broader objectives. Human judgement is never replaced; it’s strengthened. No algorithm, no matter how sophisticated, will ever fully understand your business the way you do.

The Human-AI Partnership Model

The most effective approach I've seen combines AI's pattern recognition capabilities with human strategic thinking.

AI excels at processing vast amounts of data, identifying anomalies, predicting trends, and generating insights. Humans excel at understanding context, weighing trade-offs, and making decisions that account for factors beyond historical data patterns.

This partnership model means your AI systems become incredibly powerful decision-support tools rather than autonomous decision-makers. They flag potential issues before they become problems, identify optimization opportunities, and provide detailed analysis of different scenarios. But the final call on actions — whether to increase inventory, switch suppliers, or adjust production schedules — remains with people who understand the full business context.

Think of it as having an incredibly intelligent research assistant who can process information faster and more thoroughly than any human could, but who still needs direction on what questions to ask and how to interpret findings within your business context.

This “human + Physical AI” partnership is where supply chains gain competitive edge. AI offers speed and pattern recognition; IoT provides sensory awareness; and humans contribute strategy and intuition. Together, they form a closed feedback loop that gets smarter with every cycle.

Building vs. Buying Intelligence

One practical consideration that often gets overlooked is the massive investment required to build this kind of AI capability in-house. Developing sophisticated algorithms, managing data infrastructure, and maintaining AI systems requires specialized expertise and ongoing investment that can easily run into millions of dollars.

This is where partnering with specialized providers makes strategic sense. At Wiliot, we've invested heavily in AI for supply chain visibility, so our customers can tap into sophisticated intelligence without becoming AI companies themselves. The goal is not to replace your systems, but to amplify your decision-making power.

The key is choosing partners who complement your existing systems rather than trying to replace them. The goal should be enhancing your decision-making capabilities, not outsourcing them to algorithms.

Think of it this way: you don’t need to hand over control to gain intelligence. By leveraging Physical AI infrastructure, you extend your visibility, enrich your data quality, and empower your AI and human teams alike.

The Path Forward

As AI technology continues advancing, the temptation to give these systems more autonomous authority will only grow. I'd encourage you to resist the allure of fully autonomous AI agents in favor of a more nuanced approach that keeps human intelligence firmly in the loop.

Focus on achieving near-complete visibility into your supply chain operations. Leverage AI to surface insights, predict problems, and analyze scenarios. But maintain control over the decisions that matter most to your business success.

So, when building your AI supply chain strategy, don’t ask: “What can AI decide for me?” Instead, ask: “What visibility and intelligence do I need to make the best decisions myself?”

And increasingly, that visibility and intelligence will come from Physical AI — the fusion of IoT, cloud, and machine learning that brings your supply chain to life. It’s how we move from reactive decision-making to proactive, self-aware operations.

The companies that will thrive in this new era aren't necessarily those with the most autonomous AI systems — they're the ones that most effectively combine artificial intelligence with human judgment to make better decisions faster. That's where the real competitive advantage lies.