Generative AI, driven by the rise of LLMs in the general public, makes it possible to make warehouses more intelligent, conversational, and to formulate more precise predictions about the future.
What if tomorrow, warehouses became hubs for predictive and conversational intelligence? Driven by the dazzling advances in large language models (LLM), generative artificial intelligence is emerging as a strategic lever for the digital transformation of logistics. Challenge: moving from reactive management to intelligent, proactive and autonomous orchestration of flows, resources and processes.
A direct impact for logistics professions
Concretely, generative AI opens up new perspectives for supply chain players. Logistics departments can better anticipate hazards, reduce disruption costs and optimize the use of resources. For example, a warehouse manager can automatically receive alternative scenarios in the event of supplier delays, or trigger a stock rebalancing between sites without going through a series of manual validations. Supply chain managers have tools capable of cross-referencing order histories, weather and transport availability to adjust planning in real time and secure deliveries. As for operational teams, they gain autonomy thanks to digital assistants integrated into mobile terminals: reporting an incident, generating a transfer voucher or finding the missing part only takes a few seconds. Rather than a replacement, it is a real increase in human expertise that is taking shape.
A technological breakthrough with high operational value
The emergence of GPT, Gemini or Claude type language models in the industry is no longer a matter of foresight: it is a reality already at work in many organizations. In the logistics field, this revolution is not limited to the simple automation of repetitive tasks. It paves the way for augmented logistics, capable of handling the growing complexity of modern supply chains, streamlining human-system exchanges and strengthening operational resilience.
The real contribution of LLMs lies in their ability to reason about heterogeneous data, to generate intelligible text, and to interact in natural language with users. Applied to the warehouse world, these models can play a key role in predictive inventory management, anticipation of shortages, automatic generation of reports or even contextual assistance to field operators. Unlike traditional algorithms, LLMs can aggregate logistics, financial, HR or customer data to produce high value-added analyses, without requiring the development of complex rules.
These capabilities are part of a context where competitive pressure requires greater operational agility, a reduction in human errors and continuous optimization of available resources.
Use case: towards a man-machine symbiosis in the field
Consider the example of a logistics operator facing an unexpected disruption in the supply chain. Rather than successively requesting several applications (WMS, ERP, TMS), he could communicate with an AI integrated into his mobile terminal to obtain in real time a diagnosis, a recommendation for action and the automatic generation of a transfer order or an incident note. This approach reduces operational friction and frees up valuable time for higher-value tasks.
Another illustration: certain platforms coupled with generative AI assistants already make it possible to anticipate congestion in warehouses, dynamically reallocate resources and generate alternative picking or delivery scenarios. By combining order histories, weather hazards, seasonal peaks and HR constraints, these tools transform logistics planning into an intelligent and iterative process. This marks a shift towards warehouses capable of self-regulating and constantly adjusting their operations.
Quantified gains already measurable
The economic impact is notable: according to a study published by McKinsey in 2024, generative AI could generate between $60 billion and $110 billion in annual value in the global supply chain sector by 2030. And according to Gartner’s 2025 report, more than 30% of mid-sized warehouses will adopt an AI-powered conversational assistant by 2026, compared to less than 2% in 2022. These figures reflect a dynamic of rapid appropriation, but also a change in uses and logistics professions.
These are also indirect gains: reduction in picking errors, improvement in customer satisfaction, limitation of shortages and delays, acceleration of audits and quality controls.
Limits that can still be crossed
Despite these promises, the implementation of LLM in industrial environments raises several challenges: data governance, cybersecurity, control of algorithmic hallucinations, explainability of recommendations. The models must be trained on corpora adapted to the logistics field, integrated into robust infrastructures (Edge + Cloud) and supervised by business experts.
Furthermore, dependence on proprietary SaaS solutions can represent a strategic obstacle. It becomes crucial to think of these tools as interoperable bricks, capable of communicating with SAP, Oracle, Manhattan Associates or in-house WMS, via API or specialized agents. The role of integrators becomes central to guaranteeing this compatibility and securing production.
Rethinking logistics as a cognitive ecosystem
The introduction of LLMs in logistics cannot be limited to technological modernization. It involves a redefinition of processes, roles and interfaces, serving a smarter, more adaptive and more resilient supply chain. In this context, integrators and publishers like HRC have a key role to play: supporting this human-machine hybridization by ensuring coherence between technology, organization and strategy.
The logistics of the future will not only be automated. It will be contextual, conversational and cognitive.
