AI vs human decision-making in logistics: Collaboration, not competition
By Kamal Kishore Kumawat, Co-founder & CTO, Edgistify
Every few years, a new technology arrives in logistics, and the same question surfaces: Will machines replace the people who run supply chains?
It happened with ERPs in the 1990s. With GPS tracking in the 2000s. With warehouse automation in the 2010s. Each time, the answer was the same: the technology did not replace human judgement. It changed what humans needed to judge.
AI is no different. The question is not whether it will displace logistics professionals. The question is how fast organisations can redesign their decision-making to make the most of what AI can genuinely do and preserve the irreplaceable things that humans bring.
I have spent the better part of a decade building supply chain technology for India’s D2C ecosystem. The answer I keep arriving at is the same: AI and humans are not in competition. They are in sequence.
The scale problem that humans cannot solve alone
India’s e-commerce logistics market stood at $18.55 billion in 2024 and is projected to reach $106.67 billion by 2032 – a 24% compounded annual growth rate. Beneath that number is an operational reality that is difficult to describe without having lived it.
A mid-size D2C brand processing 15,000 orders a day across eight cities is generating demand signals from hundreds of thousands of data points simultaneously. Pin-code level RTO rates. SKU velocity by region. Courier performance by delivery zone. Customer return patterns by product category. Seasonal demand shifts that play out differently in Jaipur, Coimbatore, and Patna.
No human team, however experienced, can process that in real time and convert it into correct decisions at the speed the market now demands. The SLA that took 5 days a decade ago became 48 hours, then same-day, and quick commerce is now pushing 10 minutes in metros. The decision cycle has compressed by orders of magnitude. Human cognition has not.
This is where AI’s contribution is not incremental; it is structural. Delhivery’s RTO Predictor, trained on 2.7 billion shipments across 18,500 pin codes, scores every COD order before dispatch and flags high-risk deliveries in real time. Over 4,800 D2C brands are already using it, reducing RTOs by up to 20%. Flipkart’s Central Planning Platform runs demand forecasting at the pin-code level, guiding inventory placement and fleet allocation across every supply chain node. McKinsey’s research puts numbers to the outcome: AI-driven forecasting reduces errors by 20 to 50 per cent, cuts lost sales from stockouts by up to 65 per cent, and lowers warehousing costs by 5 to 10 per cent.
These are not projections. These deployments are already live in India today.
Where human judgement remains irreplaceable
AI systems are powerful precisely because they are narrow. They optimise for what they are trained on. They cannot navigate what they have not seen before, and they cannot weigh competing priorities the way a person with context can.
Consider what happens when a brand’s marquee product goes viral on Instagram on a Tuesday evening. Within four hours, demand has spiked 300 per cent in Delhi NCR and the southern metros. The AI system sees the order surge. It can flag the risk of stockout and recommend reallocation. What it cannot do is call the manufacturing partner, negotiate a 48-hour production cycle, decide whether to delay launch in Tier-2 markets to protect metro availability, and communicate all of that to the customer service team in a coherent message.
That sequence of judgement calls, strategic, relational, and organisational, belongs to people. The AI surfaces the signal. The human decides what to do with it.
The same is true for vendor relationships, for crisis management when a key courier partner goes down, and for the call on whether to enter a new geography with unproven logistics infrastructure. These decisions carry context that no training dataset fully captures. Experienced logistics professionals bring years of pattern recognition, stakeholder trust, and situational judgement. That is not being replaced. It is being freed up.
The real shift: From decision-making to decision architecture
The most dangerous misconception about AI in logistics is that it is a tool you plug in and step back from. Organisations that treat it this way, deploying AI systems on top of fragmented operations without redesigning the decision flows around them, consistently underperform.
The real transformation happens when humans stop making every operational decision individually and start architecting the systems that make decisions. An ops leader in 2025 is not asking, “Which courier should this order go to?” That is an AI call. They are asking: “What courier allocation rules should govern orders in Tier-3 markets during festive seasons, and what exceptions should require human review?”
This shift from decision-maker to decision-architect requires a fundamentally different skill set. The supply chain professionals who thrive in an AI-augmented environment are those who understand data well enough to interrogate AI recommendations, who know the business deeply enough to identify when the model is wrong, and who can translate AI outputs into operational action.
One of the companies that are in courier aggregation launched their AI Copilot directly in the seller panel in late 2025, and GoKwik, which has processed over $2 billion in GMV while building RTO intelligence and checkout optimisation, is not selling AI as a replacement for operations teams. They are building tools that make operations teams more effective by an order of magnitude.
The integration imperative
One pattern I observe consistently across growth-stage brands is a disconnect between the AI layer and the execution layer. A brand will invest in a sophisticated demand forecasting tool, but the warehouse team is still working off a spreadsheet. The AI generates the right inventory recommendation; nobody acts on it in time.
Technology and operations must be fused, not layered. India’s logistics cost as a share of GDP is 13 to 14 per cent, one of the highest among comparable economies. The government’s National Logistics Policy targets bringing this closer to 8 per cent. Closing that gap is not a technology problem or an operations problem in isolation. It is an integration problem.
Brands that are winning on this are treating their WMS, TMS, OMS, and AI forecasting tools as one connected system rather than separate point solutions. The AI output flows directly into execution. The execution generates data that feeds back into the model. The loop closes, and the system gets smarter over time.
What this means for logistics teams
The honest answer is that some logistics roles will change significantly. Roles built entirely around data collection, manual reporting, and rule-based routing decisions will shrink. Roles that require contextual judgement, strategic thinking, and cross-functional coordination will grow.
The supply chain professional of 2030 will need to be fluent in data, able to read model outputs, identify anomalies, and interrogate AI recommendations with the same rigour applied to any other business metric. They will need to be system thinkers, not just process managers.
This is not a threat to the profession. It is an upgrade. The most experienced logistics leaders I have worked with do not spend most of their time on routing decisions and inventory reorders. They are thinking about network design, vendor strategy, risk management, and customer experience. AI gives more people the space to operate at that level.
Conclusion
The framing of AI versus human decision-making in logistics misses the point entirely. The real question is how quickly organisations can redesign themselves to use both well.
AI makes supply chains faster. Humans make them smarter. The organisations that understand this and build their operations accordingly will not just survive the current wave of transformation. They will define what Indian logistics looks like on the other side of it.
The competitive moat in logistics is no longer built on relationships and experience alone. It is built on who can fuse human intelligence and artificial intelligence into a single, high-performance system and then keep improving it.