Artificial intelligence in logistics is no longer a futuristic buzzword; it increasingly transforms warehouse operations and route planning. It is worth focusing on concrete applications that can deliver measurable savings in a typical logistics company within 6-12 months.
One of the most mature AI applications is the optimization of picking routes in the warehouse. FM Logistic Polska was the first logistics operator in the world to deploy Google AlphaEvolve technology to optimize e-commerce order picking algorithms, achieving over a 10% improvement compared to the previous solution and reducing the annual distance traveled by operators by more than 15,000 kilometers, without additional infrastructure investments.
The system learns real behaviors, zone loads and SKU characteristics, then generates optimal pick sequences and walking routes between locations. In practice, this means faster picking, fewer errors and better use of existing space and equipment.
Modern AI modules in WMS also create intelligent batches for single- and two-stage picking strategies, shortening worker routes and increasing throughput without rebuilding the warehouse layout.
A key trend for 2026 is adaptive picking-the use of AI in WMS to continuously reorganize routes and prioritize orders based on live data about workload, customer SLAs and resource availability.
Instead of static rules, the system analyzes incoming orders, shipment schedules, availability of staff and forklifts, then proposes the optimal order sequence, storage locations and walking routes, reducing bottlenecks during operational peaks.
Early implementations show that this approach improves process transparency, accelerates operational decisions and measurably increases picking and shipping efficiency. For companies, this means lower risk of delays, better adherence to carrier time windows and more consistent service quality under variable demand.
On the transport side, AI is entering increasingly complex planning scenarios. Machine-learning-based solutions can plan routes taking into account specific road conditions, fuel prices, customer delivery windows and driver hours regulations, then automatically update plans when fuel prices or traffic conditions change.
Industry experts note that in Poland the number of companies using AI to plan routes, refueling and driver rest stops is growing; systems can compute hundreds of variants faster than a human and find more economical solutions while remaining compliant with regulations.
AI-based TMS products are appearing on the market, and specialist firms (for example, providers of digital twins for warehouse and fleet operations) combine route optimization with demand forecasting and inventory management. This enables shipping decisions not only based on current orders but also on forecasted needs and warehouse load.
Combining AI in intralogistics (picking, storage, resource planning) with AI in transport planning creates synergy: shorter internal routes, better use of loading slots, more economical transit routes and more stable handling of demand peaks.
Practically, this means lower per-order handling costs, more predictable delivery times and better control of margin at the level of individual shipments and routes. Importantly, most of the described solutions can be implemented incrementally, starting with AI modules in existing WMS/TMS systems without a full infrastructure replacement.
- 1. Where to start implementing AI in warehouse operations? The usual first step is analyzing data from the current WMS and piloting AI modules in the order-picking area, where time and distance improvements are seen fastest.
- 2. Does AI for route planning require a complete TMS replacement? Not necessarily-many vendors offer optimization modules and AI engines that can be integrated with an existing TMS or run as a layer on top of current planning.
- 3. How to measure the effects of AI implementation in a warehouse? Key metrics include picking time, error rates, distance traveled by workers, space utilization and on-time shipping performance during peaks.
- 4. Are AI solutions cost-effective for mid-size companies, not only global operators? Yes, more WMS/TMS vendors provide AI features via subscription models, lowering the entry barrier and enabling mid-size firms to use algorithms similar to large operators.