AI in logistics and warehousing means handing the forecasting, sequencing, recognition and reading work of a depot or transport operation to data-driven models. It predicts volume, suggests where stock should sit, orders picks and drops, spots damaged cartons on camera and reads delivery notes. People still make the calls; AI takes the guesswork off their plate.
Where does the time go on a warehouse and dispatch desk?
In a typical distribution warehouse the morning order wave sets the pace. A planner decides in spreadsheets which order goes on which vehicle, and the supervisor assigns pickers to aisles by experience. At goods-in, delivery notes are keyed in by hand, and when a crushed carton turns up the photo usually stays on someone's phone.
That works while volumes are steady. When a promotion lands or the experienced planner is on holiday, the operation leans on the memory of two or three people. The problem is rarely one broken process; it is that forecasting, sequencing and checking are all manual.
This article is an overview: each use case links to its own detailed guide, and the aim here is to show which jobs suit AI and where to begin.
AI in logistics by the numbers: reported results and wasted capacity
Logistics is one of the functions where companies report AI outcomes. According to the Stanford HAI AI Index Report 2025 (McKinsey survey data), 43% of respondents using AI in supply chain and inventory management reported cost savings, and 63% of those using it in supply chain management reported revenue gains. The same report is candid that most savings were below 10% and the most common revenue uplift was under 5%.
Wasted capacity is visible too. Eurostat's road freight statistics show that in 2025, 21.8% of the distance travelled by road freight vehicles in the EU was driven empty. For companies operating in Türkiye the point carries extra weight:
Road accounted for 90.6% of domestic freight transport in Türkiye in 2023. (Turkish Ministry of Environment, Urbanisation and Climate Change — Environmental Indicators)
Starting unprepared has a price too. In a press release of 29 July 2024, Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, unclear business value and escalating costs. In logistics, the equivalent is building a model on top of stock records that do not match the shelves.
Eight AI use cases in logistics and warehousing
The use cases below follow the flow of goods from receiving to delivery; each can run as a pilot on its own.
- Volume and demand forecasting. Models trained on order history, seasonality and promotions predict the coming weeks' workload, so shifts and vehicles are planned ahead. We cover method choice in our guide to AI demand forecasting.
- Document reading at goods-in. Delivery notes, invoices, bills of lading and customs paperwork are read with OCR, matched against the purchase order, and only the mismatched lines go to a person. The mechanics are explained in OCR invoice processing.
- Damage and label checks by camera. A camera at the receiving or dispatch dock flags crushed cartons, torn stretch wrap, unreadable barcodes and wrong labels. It is the warehouse version of computer vision quality inspection on a production line.
- Smart slotting. Fast movers and items ordered together are proposed for locations near the dispatch area. The model needs a sound warehouse bin location system underneath it first.
- Pick sequencing and wave planning. Orders are split among pickers by walking distance and cut-off time. We compare hands-free options in our guide to voice picking in warehouses.
- Load and route planning. Orders are matched to vehicles and stops are sequenced within capacity, weight, delivery window and traffic constraints. How those constraints are modelled is covered in route optimisation.
- Exception and anomaly alerts. Unusually long stops, odd fuel consumption, stock-count variances and recurring returns are caught early with anomaly detection techniques.
- Operational correspondence. "Where is my shipment?" queries, arrival-time notices and routing customer emails to the right team through AI email classification are sped up by language models, while the answer itself still comes from the tracking system. Tools that suggest replies and summarise cases for your agents are covered in agent assist for customer service.
Public examples show the scale these ideas can reach. According to a 2020 UPS press release, its ORION routing system, which uses advanced algorithms, AI and machine learning, has saved the company about 100 million miles and 10 million gallons of fuel a year since it was first deployed. In Türkiye, an AWS case study reports that Turkish quick-commerce company Getir cut forecasting model training time by 90% and improved forecast accuracy by 10%. Both figures are the companies' own statements; only your pilot will show what your operation can expect.
Which approach fits which logistics job?
Not every logistics task needs a large language model; some suit classic optimisation. The table summarises the technique and data each job needs.
| Logistics job | Suitable approach | Data needed | Prerequisite |
|---|---|---|---|
| Volume and demand forecasting | Time series / machine learning | One to two years of order history | Promotions and returns recorded separately |
| Document reading | OCR plus field extraction | Samples of delivery notes, invoices, bills of lading | A list of document types |
| Damage and label checks | Computer vision | Labelled photo set | Fixed camera angle and lighting |
| Slotting and pick sequencing | Optimisation plus order analysis | Stock by location, order lines | Accurate locations and stock in the WMS |
| Route and load planning | Combinatorial optimisation | Customer coordinates, vehicle capacity, delivery windows | Clean customer address data |
| Exception alerts | Anomaly detection | Vehicle, stock-count and returns history | A named owner for each alert |
| Customer correspondence | Language model plus tracking data | Email archive, shipment status | One source of truth for answers |
The last column matters most. Slotting fails in a warehouse whose locations are wrong, and route planning fails on a customer list with missing coordinates. That is why many projects start by fixing stock accuracy in the warehouse management system and tightening up handheld terminal stock counts.
Not every job needs a model either. Threshold alerts or routing documents by type are often cheaper and more predictable with fixed rules; we explain how to draw that line in AI vs rule-based automation.
How to choose a first AI pilot in logistics
The best pilot is a job that is high-volume, easy to measure and cheap to get wrong. Document reading at goods-in and volume forecasting are common choices for that reason; fully automated route dispatch is a risky first step.
Four questions narrow the choice:
- How often does the job happen each week, and how many person-hours does it take today?
- Does the data sit in a system, or on paper and in spreadsheets?
- If the model gets it wrong, how costly is the error and how quickly would anyone notice?
- Can the result appear on a screen the team already uses?
Measure the baseline before you start: processing time per delivery note at goods-in, picking time per order, kilometres per vehicle per day. Our guide to measuring AI project ROI shows how to calculate the return against those figures. For the wider question of which department to start with, see AI in business: where to start.
In Türkiye, camera footage that shows employees falls under KVKK, the Turkish personal data protection law, and electronic dispatch documents should be planned alongside the move to e-waybills in Türkiye, the electronic delivery note run by the Turkish Revenue Administration (GİB) and mandatory for taxpayers within its scope.
How Digital Bridge builds AI for logistics operations
We do not sell off-the-shelf packages; every project starts with a needs analysis. With your warehouse and operations teams we map the flow from goods-in to delivery, find the two or three most time-consuming steps and where their data lives. You then receive a written proposal setting out scope, phases and cost.
The pilot focuses on a single job. If paperwork is the bottleneck, we set up the document OCR layer using your real delivery notes and invoices and measure accuracy on them. If the goal is damage and label checks at the dock, we design camera, lighting and model together under our computer vision service, starting with your existing IP cameras where they are suitable and, if needed, installing cameras, lighting and processing units on site.
Results must appear where people already work, so we connect the model to your warehouse and inventory management (WMS) screens or to logistics software covering dispatch planning, vehicle tracking and mobile proof of delivery. Volume forecasting is handled through demand forecasting analytics, and links to your existing ERP and WMS fall under AI integration within the same project.
Next step
Pick one job from last month: goods-in, picking or the daily vehicle plan. Note how long it takes and how often it needs correcting. Then get in touch with us and we will help you choose the right logistics task for a first pilot. For use cases in other departments, visit our Artificial Intelligence topic page.