Supply chain leaders aren’t asking whether AI can create value at a high level anymore. Instead, they’re asking whether AI-generated recommendations are trustworthy, how to measure AI’s ROI, and how to scale the benefits.
A new IDC InfoBrief shows just how much the conversation has shifted: only 2% of supply chain organizations have no AI capabilities, yet only 12% consider themselves leaders. This suggests that even as the market grows more discerning about what it expects from AI, myths about what AI can deliver for global supply chains are still holding companies back from gaining benefits sooner.
In this article, we pull back the curtain on four common AI myths and explore the realities of using AI in supply chain planning.
Myth #1: AI will take humans out of the supply chain planning loop
Reality: The highest value AI doesn’t remove people from supply chain decisions. It helps them make faster, more consistent decisions.
It encourages human oversight where judgment, context, and accountability matter most. The IDC InfoBrief reinforces this point: 79% of supply chain leaders see AI as opportunity over threat.
A good example is in the context of risk intelligence. AI can help planners detect problems or identify potential issues faster, and understand root causes more efficiently. This gives planners the best information and recommendations possible, while allowing them to remain in control of taking mitigating actions.
Supply chain leaders who are embracing human-in-the-loop principles—where people review, approve, and adjust AI-generated recommendations—are the leaders who are poised to get more ROI from their AI.
Myth #2: AI can’t be trusted for real supply chain decisions
Reality: In supply chain planning, complexity is the norm, and smooth operations depend on precision.
Reflecting this, the IDC InfoBrief reports that 52% of organizations surveyed cite trust in AI-driven decisions as a top barrier to faster adoption.
The truth is, however, that AI becomes trustworthy when it’s grounded in the right context. Current operational data, real-world supply chain constraints, explainable reasoning, and governed decision flows are integral for supply chain AI to be reliable.
AI planning solutions only work when they have operational context that reflects the real-world physics of enterprise operations—the real constraints of operating in a physical world—including navigating real demand, supply, capacity, and inventory constraints.
Another major part of the trust equation is data quality: the IDC InfoBrief found that 62% of leaders said better data quality and integration would accelerate AI investments. Other barriers to trust are incorrect decisions and lack of transparency. This means AI must be able to explain the logic, systems, decisions, and predictions in terms non-technical experts can understand, so that humans may ultimately verify AI’s recommendations.
Trustworthy AI is accountable AI. That means clear oversight that determines when AI recommends, when it acts, when a human approves, and when exceptions escalate. Accountable AI can also prove its impact on the business by demonstrating better decisions, faster responses, and stronger performance.
Myth #3: AI’s only real value is in automation
Reality: Automation follows rules. AI helps when there aren’t clear rules.
Automation without AI can handle many straightforward, deterministic workflows, i.e. if this occurs, then do that. For example, when an inbound shipment is delayed, an automated workflow can identify affected production orders, check approved options like alternate inventory, inter-site transfer, etc., and act optimally based on predefined business rules.
AI shines in complexity, when you need flexibility and reasoning to work through challenging situations, like sorting through thousands of data points to find the best way to reorganize your network to avoid supply bottlenecks.
The key is making sure your team has the supply chain domain knowledge and experience to know which tool fits each situation best. For example, an automated workflow can handle the late inbound shipment described above, but AI could also help assess broader tradeoffs, surface patterns, or recommend where human judgment is needed. That’s how you can move from insights into coordinated decisions that meaningfully change business outcomes.
This matters because the IDC InfoBrief found that most organizations want to automate more tasks across the supply chain: while 6% of organizations surveyed have autonomous AI at scale today, 41% expect to reach it within one to two years. We believe the opportunity here is not just to automate more tasks, but to orchestrate better decisions across complexity, constraints, and change.
Myth #4: Governance slows AI down
Reality: Governance speeds up scaling AI safely.
Governance creates the guardrails, decision rights, transparency, and escalation paths you need to scale with confidence. AI that’s bolted on or vibe-coded without proper governance can introduce unnecessary fragmentation and risk. Nothing will slow your organization down faster than a data breach from someone’s hacked-together tool.
A workspace that integrates AI directly into your planning tool prevents you from having to flip back and forth between separate tools, ensures integration and accountability, and helps your operations run more smoothly. Nearly 67% of supply chain leaders believe accountability for AI outcomes requires the most governance change, according to the IDC InfoBrief.
Both the enthusiasm and caution around AI have their merits. The best course of action is to truly understand what AI is capable of and what it needs to succeed. We hope that by debunking some of these common AI myths, supply chain leaders can start to set up their organizations to gain the most value from AI as quickly as possible. Real ROI is out there as long as you’re pairing enthusiasm with the right knowhow, talent, and supply chain expertise.
For more info on recent AI sentiments in supply chain, check out the IDC InfoBrief, sponsored by Kinaxis.
