Moving beyond data overload

For Niraj Jha, Senior Director of Logistics at Niagara Bottling, one of the biggest challenges facing supply chain leaders today is not a lack of data — but too much of it.

With operations spanning over 50 manufacturing plants across the United States and an international footprint, Niagara Bottling operates in a highly complex environment. Yet, according to Jha, the real issue lies in how organisations interpret the vast number of dashboards and reports available to them.

“Operations leaders are overwhelmed with data,” he explained. “The question is — how do you make sense of it?”

From dashboards to decision engines

Jha’s answer is a fundamental shift in how organisations use data. Rather than relying on static dashboards, he sees the future in AI-powered decision engines that translate data into clear, actionable guidance.

Using the analogy of a GPS, he described the difference: “Imagine if your GPS only gave you coordinates and distance. You wouldn’t know what to do. You need direction.”

In this model, data remains essential — but its role changes. Instead of presenting information, systems will guide decision-making based on context, role, and real-time inputs.

“You are still the driver,” Jha emphasised. “But the system helps you understand what to do next.”

Cutting through the AI noise

Despite the rapid rise of AI, Jha urged leaders not to be distracted by headlines or hype cycles.

From autonomous agents to futuristic predictions, the conversation around AI can feel overwhelming. But for Jha, progress comes from focusing on practical application rather than speculation.

“How do I cut through the noise and ask — what can I do today?” he said.

Start small, scale with confidence

Rather than waiting for large-scale transformation programmes, Jha advocates for a more pragmatic approach: start with a small, clearly defined problem and build from there.

Even without technical expertise, he believes individuals and teams can begin experimenting with AI-driven solutions.

“You don’t need to be a developer,” he noted. “Try to solve a small problem — even if you don’t complete it, you’ll understand the path forward.”

This incremental approach allows organisations to build confidence, capability, and momentum — laying the groundwork for larger transformation over time.

A practical path to transformation

For Jha, the future of logistics is not about replacing human decision-making, but enhancing it. By combining data, analytics, and AI into decision-driven systems, organisations can move faster, act smarter, and reduce operational complexity.

The key is not to wait for the perfect moment — but to take the first step.