As artificial intelligence spreads across manufacturing and logistics, many companies are discovering that better visibility alone is not delivering the operational breakthroughs they expected.
According to Niraj Jha, a supply chain executive leading AI transformation initiatives within one of the world's largest private label beverage manufacturing networks, the biggest misconception in industrial AI today is that dashboards equal progress.
"Most AI systems stop at insight," Jha said. "They tell you what's happening. But operational performance changes only when systems influence what happens next."
Jha has been involved in applying AI and real-time decision frameworks across complex, high-volume manufacturing and distribution environments where production, logistics, and energy constraints intersect daily. His experience suggests that the gap between prediction and execution is where much of AI's promised value is currently being lost.
The "Dashboard Trap" in Industrial AI
Across operations, organizations now have access to more real-time data than ever before. From production performance and inventory levels to transportation flows and demand forecasts, leaders can see disruptions forming earlier than in the past. Yet many still rely on manual interpretation and layered decision-making processes to respond.
"An alert fires, someone reviews it, another system gets checked, emails start, meetings happen," Jha explained. "By the time action is taken, the window to prevent disruption may already be closing."
In fast-moving industrial environments, he argues, decision latency—the time between insight and action—can be more damaging than imperfect data.
From Prediction to Decision
Rather than focusing solely on improving dashboards and analytics tools, Jha believes manufacturers must build what he describes as decision engines—systems designed to translate signals directly into operational moves within defined constraints.
These systems do not replace human oversight. Instead, they operate within guardrails that reflect physical limits, safety rules, labor constraints, and cost tolerances, while enabling faster structured responses.
"Instead of asking, 'What is happening?' the system should be asking, 'Given what is happening, what should we do right now?'" Jha said.
Examples include adjusting production sequences when supply risk crosses thresholds, reallocating inventory dynamically when demand patterns shift, or triggering earlier maintenance interventions before a performance dip becomes a failure.
Why Model Sophistication Isn't the Bottleneck
While much industry discussion centers on algorithmic improvements, Jha believes operational structure is the more decisive factor.
"If a model predicts risk with high accuracy but nothing changes operationally, its practical value is limited," he said. "A slightly less precise system that consistently triggers earlier, structured action often delivers far greater impact."
In large-scale manufacturing, he notes, decisions are constrained by more than data—including equipment capacity, workforce availability, safety standards, and increasingly, energy limits. Embedding those realities into decision frameworks is what shifts AI from an information layer to an operational control layer.
Redesigning How Decisions Happen
Jha's perspective reflects a broader shift among industrial leaders who see AI's future value not in eliminating humans, but in redesigning how decisions are made.
"The goal isn't removing people," he said. "It's reducing the time between signal and action, while keeping humans guiding the system instead of manually stitching every response together."
As AI programs mature, he believes the dividing line between leaders and laggards will be less about who has the most data and more about who has redesigned their operating model so that intelligence flows directly into execution.
"Dashboards help people understand," Jha said. "Decision engines help systems act. That's where AI starts to change outcomes."
About Niraj Jha
Niraj Jha is a supply chain and manufacturing executive focused on applying AI and real-time decision frameworks to improve operational resilience, productivity, and competitiveness in complex industrial systems. He has led large-scale AI-driven transformation initiatives in energy-intensive manufacturing and distribution environments.
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