Navigating the future of logistics with AI and machine learning

By Dr. Ashvini Jakhar, Founder and CEO, Prozo

The Changing Face of Logistics

A decade ago, logistics was all about scale. The bigger your operations, the better your cost efficiencies. Today, scale alone is no longer a competitive advantage. Logistics is evolving—from a linear, infrastructure-heavy function to a dynamic, intelligence-driven ecosystem where speed, adaptability, and precision are the new differentiators. At the heart of this transformation lies Artificial Intelligence (AI) and Machine Learning (ML).

We’re entering an era where supply chains no longer react—they anticipate. Where demand is sensed and responded to in real-time. Where logistics is not just a cost center, but a strategic edge. An era where the question is: Are businesses ready to embrace this shift?

From Visibility to Intelligence

Traditional logistics technology focused on visibility, track-and-trace tools, basic automation, and ERP efficiencies. While these systems are foundational, they fall short in addressing the unpredictability that defines modern supply chains—be it weather disruptions, geopolitical tensions, fuel volatility, or demand surges.

AI changes the game. It doesn’t just report what’s happening; it learns and adapts. AI-powered demand forecasting, for instance, can reduce forecast errors by up to 50% by analysing real-time consumer behavior, market signals, and external variables. The result? Smarter inventory decisions and improved working capital utilisation.

At the operational level, real-time technologies—RFID, barcode scanning, automated control towers—now offer end-to-end traceability of goods. CCTV integrations, role-based access controls, and shelf-life tracking enhance accuracy, accountability, and inventory health across warehouse operations, raising any pre-emptive alerts that would need managerial attention

Hyper-Responsive Supply Chains

Consider the complexity of a global supply chain with thousands of SKUs, multiple fulfillment nodes, and ever-evolving consumer demand. Traditionally, optimising such networks required months of static planning. Today, AI can run millions of real-time simulations to dynamically adjust procurement, routing, and stocking strategies on the fly.

Warehouses have become intelligent fulfillment centers. AI enables tailored picking strategies—wave-based, SKU-level, or batch picking—based on operational priorities. These intelligent workflows optimise manpower, cut down order processing time, and allow businesses to seamlessly fulfill B2B, D2C, and marketplace orders from the same node.

In the last mile—one of the most complex and expensive parts of logistics—AI-powered route optimisation is proving transformative. Accounting for variables like weather, traffic, and delivery density, it reduces fuel costs, shortens delivery times, and improves customer experience.

Crucially, AI doesn’t plateau. ML algorithms continuously improve with new data, making the supply chain not only faster but also smarter over time.

Supply Chain as a Strategic Advantage

Historically, the supply chain has been viewed as a cost function—an area to streamline but not necessarily innovate. That mindset must change. The next generation of supply chain leaders will be those who embrace AI not just for efficiency but for differentiation.

Demand-sensing AI can flag impending stockouts before they occur. Smart warehouses can shrink fulfillment times from days to hours. Dynamic pricing models, guided by real-time supply chain data, can protect profitability in volatile markets.

Further innovations include predictive maintenance through AI-powered warehouse execution systems (WES), which reduce downtime by anticipating equipment failures. Digital twins—virtual, AI-powered replicas of physical warehouses—allow operations to be tested and optimised in a simulation before being implemented live.

Beyond productivity, AI is enabling greener operations. Smart energy usage, space optimisation, and efficient material handling are helping reduce environmental footprints while maintaining high service levels.

The Real Barriers to AI Adoption

Despite the promise of AI, adoption isn’t seamless. Many businesses are locked into legacy systems that resist integration with modern, AI-first platforms. Data fragmentation across systems, vendors, and geographies continues to be a major hurdle.

Workforce adaptation is another challenge. AI-driven supply chains require collaboration between operations, data science, and engineering teams that often speak different languages. Change management, upskilling, and cultural readiness become as important as the technology itself.

Cost is also a concern, especially for mid-sized businesses and D2C brands. However, the rise of flexible, pay-as-you-go warehousing and fulfillment models is lowering the barrier to entry. These models provide access to cloud-first platforms, automation, and AI-based insights without massive upfront capital investments.

The Road Ahead

Despite the challenges, the future of logistics is intelligent, predictive, and autonomous. AI will empower self-optimising supply chains capable of responding to disruptions and market changes in real time. Warehouses will no longer be static stockrooms—they’ll be smart, adaptive hubs driving business growth.

We are witnessing the convergence of technology, data, and physical infrastructure in ways that were unimaginable a few years ago. As AI continues to mature, businesses that scale these capabilities early will set the benchmarks for speed, efficiency, and customer delight.

The question is no longer whether AI will reshape logistics, but how fast businesses can transform to keep up.