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Across global supply chains, from manufacturing to aerospace and beyond, many leaders are hesitating on adopting artificial intelligence. Not because AI lacks potential, but because they fear it will fail.
High-profile pilots have underdelivered, and the narrative of “AI failure” has taken hold. Some organizations are waiting for others to prove the path before committing. Others are moving cautiously, confining AI to low-risk, low-impact use cases that minimize exposure but also limit results. Meanwhile, competitors are moving ahead, learning faster, improving decision speed and capturing real operational value.
The real question is not whether AI can work, but how to invest wisely, start meaningfully and de-risk the journey from the very first pilot. In today’s supply chains, the greatest risk is not AI itself. It’s hesitation.
Experimenting with AI in low-risk, low-value areas might feel safe, but it creates an illusion of progress. Value quietly leaks away when teams spend time automating what already works instead of tackling the work that matters. The “wait-and-see” approach widens blind spots, slows decision cycles and wastes opportunity.
Every quarter spent waiting is a quarter when competitors gather data, refine models and learn from smaller failures. Starting small is wise, but it must start with something meaningful. One way to prioritize is to map opportunities on a simple two-by-two grid: value on one axis and complexity or risk on the other. The goal is to find the high-value, manageable-risk zone, where results are visible and measurable. That’s where momentum begins.
AI isn’t about bots or job loss. It’s about elevating the human role by freeing people from repetitive, time-consuming tasks so they can focus on critical thinking and creativity.
Many say “human in the middle,” but a stronger position is that AI should serve the human. It’s not an all-knowing being. It’s a set of tools that can analyze at incredible speed, revealing patterns humans can’t see alone. Even so, people are still in charge. The challenge is learning to use AI effectively, and ensuring that it serves human intelligence rather than replaces it.
When applied correctly, AI clears the noise and gives people more space to think. For example, your company can use automation to handle data collection and standard exceptions, then route complex cases to experts. The result is sharper focus, faster analysis and better decisions.
Most companies stop at automation because it feels tangible. They automate what they already do. That can make processes faster but not necessarily smarter. The real opportunity is to use AI to determine what work should be done at all.
Before scaling automation, leaders must fix the fundamentals. Organizational processes, focus and data quality still determine success. Automating broken processes or poor data only multiplies inefficiency. The principle of “garbage in, garbage out” still applies. Enterprises are too complex to be replaced by automation alone.
Once the foundation is solid, AI can move from performing tasks to orchestrating decisions. That means connecting data, context and human insight so teams can decide not only how to act, but whether action is needed in the first place. True orchestration doesn’t just make processes faster. It also determines when they should run, or whether they should run at all.
That’s the real power of AI. But it’s a progression that requires readiness checkpoints at every step. Fix what’s broken, automate what’s stable, then orchestrate across teams to guide decisions dynamically.
The best way to start is with one workflow that matters. Good candidates include demand forecasting, throughput from quote to ship, or supplier risk management. These areas are measurable, data-rich and visible across functions.
Design pilots that measure real outcomes such as speed to insight, cash impact and inventory release. Use those results to build confidence among teams. When people see that AI helps them make faster, more informed decisions, trust grows. That trust is what allows AI to scale responsibly.
The key is to start where value and visibility intersect. A well-chosen pilot shows that AI adoption isn’t a leap of faith. It’s a disciplined process that builds understanding and improves collaboration between humans and technology.
AI won’t eliminate uncertainty. What it can do is help organizations respond faster and with more confidence. Companies that move now will de-risk AI by building understanding, process maturity and trust between humans and intelligent systems.
Waiting for others to fail first is not a strategy. Fix the workflows that matter most, and AI will naturally integrate into them. As those workflows improve, decision orchestration across teams becomes possible, which is the real end state.
The goal isn’t automation for its own sake. It’s orchestration that connects people, processes and data, so that decisions happen with precision and purpose. That’s how companies de-risk AI, protect competitiveness and turn technology into a genuine performance advantage.
Seema Phull is chief executive officer of ForeOptics.
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