When people talk about artificial intelligence in logistics, the image that comes to mind is often fully automated warehouses or delivery drones. But the most practical and accessible AI application in the industry is much closer to daily reality: intelligent delivery route planning.
What AI actually does in route planning
An AI-based optimization engine isn’t a black box. It’s a system that takes your actual delivery data as input and produces the best possible route plan, simultaneously considering dozens of variables:
- Delivery addresses and their time windows
- Capacity and type of each vehicle in the fleet
- Industry-specific constraints (cold chain, hazmat, restricted zones)
- Expected traffic conditions by time slot
- Delivery history to predict stop times and bottlenecks
The result is a plan that minimizes distance traveled, respects all constraints and balances workload across drivers. All in a few minutes, versus the hours needed for manual planning.
The difference from traditional planning
Manual planning — whether on Excel, on paper or in the logistics manager’s head — works by approximation. The operator knows the areas, knows which customers are hard to reach and builds routes based on experience.
This approach has two structural limitations:
- It doesn’t scale. It works with 20 deliveries, becomes impossible with 200.
- It’s not optimal. The human brain can’t simultaneously evaluate all possible combinations of sequence, vehicle assignment and constraints.
AI doesn’t replace the logistics manager’s experience. It amplifies it, handling the computational complexity and leaving strategic decisions and exceptions to the operator.
You don’t need to change how you work
One of the main barriers to adopting new technology is the fear of having to overhaul existing processes. But a good optimization platform adapts to your current workflow, not the other way around.
Here are the most common ways to work with a route optimization system:
- Excel import: keep using your spreadsheet and upload it to the platform. The AI recognizes data even if the format isn’t perfect.
- API integration: if you have a management system (ERP, WMS, TMS), connect it directly. Data flows automatically.
- CSV export: download data from your system, upload to the platform, optimize and re-export.
- Direct platform use: work directly in the software throughout the day, leveraging all real-time features.
The choice depends on the company’s digital maturity. What matters is that you can start immediately, even with the simplest method, and evolve progressively.
What results to expect
Numbers vary based on industry, delivery volume and starting situation. But average results from companies adopting route optimization solutions are consistent:
| Metric | Average improvement |
|---|---|
| Distance traveled | -10/20% |
| Fuel costs | -10/15% |
| Planning time | From hours to minutes |
| Vehicles needed | -5/10% |
Investment payback is typically measured in 3-9 months. This isn’t a long-term project: benefits are visible from the first weeks of use.
When does it make sense to start
There’s no perfect time to optimize logistics. But there are situations where the cost of not doing it becomes obvious:
- The fleet is growing and manual planning can’t keep up
- Margins are shrinking and you need to cut operating costs
- Customers demand precision on delivery times and tracking
- Sustainability matters and you need to reduce km, emissions and waste
AI in last mile logistics isn’t future technology. It’s a tool available today, accessible even for small-to-medium fleets, and one that delivers measurable results from day one.