A bulk spices consignment clears exchange documentation on Wednesday morning. By Friday, it is flagged at destination. Moisture ingress. Grade compromised. Contract disputed.
At the origin warehouse, logs show nothing unusual. Cleaning followed schedule. The humidity sensor had no alert threshold. The Warehouse Management System recorded entries, but it was disconnected from building infrastructure data. No one was negligent. The system simply was not built to detect the issue. Variations of this scenario, including metal oxidation, pest ingress and unnoticed cold chain breaks, occur across India’s commodities warehousing network. They are quietly absorbed as the cost of doing business.
India’s commodities markets have evolved rapidly. Agricultural products, metals, energy, and industrial raw materials now trade on digitised exchanges with real-time pricing, tighter settlements, and increasing institutional participation. Warehouse receipts back lending. Regulators audit compliance more frequently. Insurers price risk based on storage conditions.
3 parallel realities
Warehouses are expected to function as precision instruments. Yet many still operate on clipboards, static rosters and disconnected systems. In most facilities, three parallel realities exist: a siloed Warehouse Management System, manual operational processes, and building infrastructure data that rarely integrate. Together, they quietly erode value every day.
This fragmentation is precisely what AI-driven facility management is designed to address.
AI-driven facility management does not replace the people running these facilities. It equips them with something they have never had before: a continuously updated, unified view of everything happening across the building, and the ability to intervene before problems escalate.
When a WMS logs a high-value agricultural consignment in Bay 7, cooling and ventilation can automatically adjust to required thresholds. Housekeeping can be alerted for a pre-arrival hygiene check. If temperature or humidity begins to drift outside defined parameters, AI flags the deviation and recommends corrective action before cargo is affected.
Planned maintenance
This is the long-awaited shift from reactive to predictive operations.
Equipment that might otherwise fail during a peak dispatch window can be identified weeks in advance through continuous performance monitoring. Maintenance becomes planned rather than disruptive.
Cleaning and housekeeping resources can be deployed based on real-time occupancy and activity data rather than fixed schedules. When inbound activity surges in one section, resources respond dynamically. When an area is idle, they redeploy accordingly.
The AI is not making dramatic decisions. It is making hundreds of small, accurate interventions, continuously and without fatigue.
Theory model no longer relevant
When a consignment approaches its inspection window, audit-ready documentation, including storage conditions, access logs and cleaning records, is already compiled. The system has been recording and organising this information in real time, eliminating last-minute scrambles and compliance risk.
This model is no longer theoretical.
Live implementations have demonstrated up to a 20 per cent reduction in utilities expenditure while handling significant increases in throughput in legacy assets, without requiring wholesale infrastructure replacement. These are based on experiences over 3,000 sites across India on a next-generation, tech-enabled platform.
Institutional capital is entering India’s warehousing sector at scale for the first time. Infrastructure funds, logistics REITs, and development finance institutions are underwriting these assets much like commercial real estate, where operational performance, yield predictability, and ESG compliance directly influence valuation.
An asset with continuous environmental monitoring, AI-driven compliance reporting, and responsive service operations is inherently more financeable. An asset that cannot provide this transparency carries risks that are increasingly difficult to price.
Modernisation does not necessarily require heavy upfront capital. In many cases, upgrades can be structured against verified operational savings, allowing recovered running costs to fund the investment over time.
The era of the passive storage shed is ending.
What replaces it is a connected facility where AI works quietly across systems, monitoring, aligning, predicting and protecting the value of what the building holds. And demonstrating that performance, continuously, to every stakeholder in the chain.
The author is Executive Director, SILA
Published on March 7, 2026
