Why AI Still Stops Short Of Decisions In Supply Chain Planning

As supply chain organizations pour investment into artificial intelligence, many are discovering that better forecasts and smarter dashboards do not automatically translate into better decisions. Despite advances in models and compute, planning teams often remain stuck in manual judgment, slow consensus cycles, and brittle execution when disruptions hit.

The gap matters now because volatility has become structural. Demand swings, supplier fragility, and geopolitical shocks require faster, more confident decisions under uncertainty. Yet in many companies, AI outputs are treated as advisory signals rather than operational levers, limiting their real impact on cost, service, and resilience.

The Real Bottleneck Is Organizational, Not Technical

In comments shared with The Supply Chainer, Ramakrishna Garine, founder of ResilienceXAI, said the core obstacles to AI adoption are no longer technical. “Most of the technical barriers in AI adoption are largely solved. The real obstacle lies in organizational adaptation,” he said.

According to Garine, many AI initiatives stop at recommendations because last-mile decisions are still shaped by informal constraints that models struggle to encode. “Factors like customer relationships, suppliers, informal capacity, flexibility, and regulations play a very important role in decision-making,” he noted, adding that explainability is critical if planners are expected to trust and act on model outputs rather than use them only as a cross-check.

Data quality presents a second, deeper challenge. While data silos are well known, Garine emphasized the problem of data decay, where lead times, capacities, and bills of materials drift over time without being systematically refreshed. “Although their algorithm works perfectly fine, it can be too late for them to realize they are solving yesterday’s problem, not tomorrow’s,” he said.

Talent gaps compound the issue. Garine described a disconnect between advanced data science and operational reasoning, where models are mathematically sound but poorly understood by planners, or planners grasp the business logic but not the model assumptions. The result is limited adoption at the point where decisions should actually change.

Durable Use Cases Versus Expensive Illusions

Garine pointed to several AI planning applications that are proving durable. Demand sensing stands out, particularly where AI can incorporate external signals such as weather, sentiment, and real-time indicators that planners historically understood intuitively but could not process systematically. Probabilistic scenario simulation is another area gaining traction, allowing organizations to stress-test networks across thousands of disruption scenarios instead of assuming a single future.

AI is also improving anomaly detection in execution, identifying subtle drifts in supplier quality, transit times, or demand patterns before thresholds are breached. Garine stressed that these capabilities enhance human attention rather than replace judgment, which is a key factor in adoption.

By contrast, some widely promoted concepts remain immature. Fully autonomous replenishment still breaks down at the edges, where roughly 30% of SKUs require human oversight because the economics and risk of errors outweigh the benefits of automation. End-to-end control towers, he said, often devolve into “an expensive dashboard” that highlights problems without triggering replanning actions. Multi-enterprise optimization faces similar limits, as true network-wide optimization depends on data sharing across organizational boundaries that most industries have yet to achieve.

Ramakrishna Garine, Founder of ResilienceXAI

How Leaders Should Measure Real AI Value

To separate value from narrative, Garine outlined four metrics operators should use to evaluate AI-enabled planning. The first is decision velocity, measuring not just how fast recommendations are generated, but how quickly they lead to executed decisions. The second is confidence calibration, ensuring systems surface uncertainty rather than overconfident point estimates.

Planner override rates offer a third lens. Garine suggested that acceptance rates between 50% and 70% often signal healthy human-AI collaboration, while very low or very high rates point to distrust or blind reliance. The fourth, and most difficult, is counterfactual measurement, comparing AI-driven outcomes against credible baselines from previous processes. Without this discipline, he warned, “AI value attribution will just become another form of storytelling.”

For supply chain leaders, the implication is sobering but actionable. AI in planning delivers value not by replacing judgment, but by reshaping how decisions are framed, timed, and measured. Organizations that focus on decision design, data freshness, and measurable outcomes are more likely to turn AI from an analytical asset into an operational one.