Shekar Natarajan is the founder and CEO of Orchestro.AI.
In every supply chain, there comes a moment when someone has to decide what matters most. Which shipment gets prioritized when there’s not enough capacity? Who gets the product first when there’s a shortage? How do you weigh cost against resilience, or speed against equity?
These aren’t abstract questions. In critical supply chains, they can determine whether a hospital receives lifesaving medication or whether a rural community gets left behind. That’s why cognitive bias isn’t just a philosophical issue for supply chain leaders. It’s a real and present danger.
Cognitive bias refers to the unconscious mental shortcuts and assumptions that distort how we perceive information and make decisions. In supply chains, these biases can skew judgment—prioritizing cost over ethics, speed over equity or familiarity over facts—especially under pressure. When embedded in data or AI models, these distortions don’t disappear; they scale.
Even as we adopt AI to improve efficiency, the human flaws embedded in our decision-making don’t just disappear. In fact, they can multiply. When bias is baked into the data or the model’s objectives, automation doesn’t remove risk. It accelerates it.
So, how do we build AI systems that reflect our best thinking, not our worst instincts? Here are three steps every supply chain leader should consider.
1. Create Tension Between Competing Models
Most AI systems are trained to optimize a single outcome: reduce costs, minimize delays, increase fill rate. But real-world tradeoffs don’t live in a vacuum. When we frame every decision around one objective, we miss the bigger picture.
That’s why we need more friction.
Supply chain leaders should deploy multiple models trained on different data sets and priorities. One might optimize for cost, another for ethical sourcing, a third for resilience. The goal isn’t to crown a winner—it’s to expose blind spots. When models disagree, they force human operators to confront the assumptions behind each one. That tension produces better judgment.
For example, consider a critical healthcare supply chain delivering temperature-sensitive vaccines to remote areas:
• Model A optimizes for speed of delivery
• Model B prioritizes equitable access, routing doses first to underserved or high-risk communities
• Model C balances cost with temperature control, ensuring efficacy in regions with limited cold-chain infrastructure
None of these models are “right” on their own. But together, they surface the tradeoffs and help leaders make more deliberate, values-driven choices.
It’s like a healthy boardroom. Disagreement isn’t dysfunction. It’s a safeguard against groupthink.
2. Use A Mixture Of Experts
Once you’ve built a set of diverse models, the next challenge is judgment. Who decides which one to trust?
This is where a Mixture of Experts architecture comes in. Think of it as a panel of specialists: one model trained to forecast disruption, another to monitor emissions, another to handle regional logistics. A central gating system—essentially the moderator—decides which expert to listen to based on context, performance history and real-time conditions.
Take demand forecasting as an example:
• Expert 1 predicts demand spikes based on historical order data
• Expert 2 monitors real-time social media chatter to detect early indicators of consumer interest
• Expert 3 analyzes warehouse capacity and logistics constraints to assess fulfillment feasibility
The gating system dynamically weighs their inputs depending on the scenario—holiday season, regional event or logistics bottleneck.
The advantage isn’t just accuracy. It’s auditability. Instead of relying on a monolithic model that can’t explain its reasoning, you get a transparent system that routes decisions based on domain-specific logic. It’s the difference between a black box and a smart boardroom.
3. Course-Correct With Human Feedback
Bias doesn’t vanish with better architecture. It creeps in slowly, through misaligned incentives, flawed KPIs and overconfidence in automation. That’s why the final step is creating an ongoing feedback loop.
AI systems must be teachable. That means reinforcement learning with real-world oversight. Don’t just reward speed or cost savings. Measure ethical impact, transparency and long-term resilience. Create feedback loops that include not just system performance, but human input from operators, partners and end-users.
When models receive that kind of feedback, they don’t just get smarter. They get wiser.
Beyond The Models: Building A Culture Of Cognitive Awareness
Even the best-designed AI can fail if the people around it aren’t trained to recognize their own biases. Technical safeguards must be paired with organizational awareness. Supply chain leaders should invest in:
• Bias Awareness Workshops: Equip teams with tools to recognize confirmation bias, sunk-cost fallacies and other mental shortcuts that quietly distort decisions
• Cross-Functional Collaboration: Invite finance, ethics, logistics and community representatives to weigh in on key decisions, ensuring diverse perspectives are accounted for
• Scenario Planning: Use tabletop simulations of crises—natural disasters, supply shocks, geopolitical shifts—to reveal hidden assumptions and test how the system responds under stress
Building Trust At Scale
Ultimately, AI is not a magic fix. It’s a reflection of the choices we make—what we optimize for, what tradeoffs we accept, what values we embed in the system.
For supply chain leaders, the goal is not to eliminate human judgment. It’s to elevate it. That means:
• Training teams to recognize bias and ask better questions
• Encouraging model diversity to expose tradeoffs
• Investing in decision-routing systems that reflect real-world complexity
• Measuring what matters: equity, resilience and transparency
• Keeping humans in the loop—especially when the stakes are high
The best AI doesn’t just make decisions, it challenges them. It creates space for reflection, debate and course correction. In high-stakes supply chains, that’s not just smart tech. That’s good leadership at scale.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Read the full article here
