AI in logistics: 10 practical solutions to make your field teams more efficient in 2026

Six in the morning at a parcel depot. A pallet arrives with a torn label, a parcel with no recipient sits in the disputes area, and a customer is contesting yesterday's delivery. In each case, the team needs the same thing: reliable information, at the right moment, without re-keying.

Artificial intelligence is now seen as the main lever for providing it. According to the 2026 MHI Annual Industry Report, produced with Deloitte, 48% of supply chain leaders rate AI's impact as significant or greater, up 25 points in 2025, and 56% of organisations plan to increase their innovation spending. 1 The same report ranks the talent shortage as the sector's second most important trend.

The question is where AI makes a difference. A forecasting model is of little use if delivery status is wrong, and an AI agent can only decide well based on reliable field data. This guide covers 10 AI solutions that directly impact logistics team efficiency, along with the components that belong to other supply chain tools.

Where to start: the flow before the technology

Many AI projects in logistics fail because they start from the tool rather than the flow. The right question is not “which AI should we buy?” but “which logistics flow should we fix first?” Three questions are enough to frame the project:

1. Which repetitive task generates unnecessary data entry, chasing or searching? For example, tracking down a photo of a pallet to contest a penalty.
2. Which error is the most costly today: customer disputes, penalties, lost parcels, equipment not returned?
3. Where does the data originate that would prevent it? In almost every case, in the field, at the moment the task is carried out.

The symptoms are familiar: manual reporting, evidence scattered across drivers' phones, uneven quality from one site to the next, disputes that drag on. If these problems come up every week, the starting point is field execution, not planning.

10 AI solutions with an impact on logistics field operations

The table below summarises the 10 use cases. Each one is then covered in detail, with an example and the corresponding app from the WizyVision catalogue.

1. Identify unidentified parcels with AI vision

A parcel without a legible recipient ends up in the disputes area. Manually listing its contents is slow and, if the parcel cannot be traced, customer service issues a refund. With AI vision, a photo of the opened parcel is enough: the AI detects the objects, reads visible text and brands, and tags the contents automatically. Customer service then searches the database by category, colour or brand, with automatic translation into 15 languages, and a confirmed match triggers re-dispatch.

At Geopost (DPD, Chronopost, SEUR, BRT), the app made content inventory twice as fast, raised the successful re-routing rate from 10% to more than 60% and tripled customer satisfaction when a claim is made.

2. Secure proof of delivery

“You never delivered that pallet.” Without evidence, the dispute often ends with a credit note. A proof-of-delivery app requires a time-stamped, geolocated photo at the point of hand-over, linked to the shipment number, and can block closure until you provide it. A photo of the pallet at the back of the trailer, with the number plate in shot, settles the dispute in seconds.

AI comes in upstream: it reads the shipment number or label automatically and can flag visible damage in the photo.

3. Read labels and non-standard documents

The WMS knows that a pallet has come in, but rarely knows the batch or serial numbers associated with it. Supplier labels, for their part, follow no common format. Intelligent OCR combined with generative AI extracts origin, batch, expiry date or serial number, including from non-standard documents, from a single scan. A custom extractor can be configured to target a specific field, such as a particular supplier's batch numbers.

4. Check quality at goods-in and document supplier anomalies

A crushed box, non-compliant packaging, a missing quantity: at goods-in, the discrepancy is often noted by hand on the delivery note, then lost. An inspection app requires a photo as soon as an anomaly is declared and automatically emails a visual anomaly report to the supplier.

At Findis, France's leading B2B distributor of home equipment, two apps, one at goods-in and one at dispatch, are used to scan product references and photograph packaging compliance. The purchasing department has real-time access to visual evidence to pursue claims, and delivery non-compliance disputes have fallen.

5. Make returns management reliable

The returns flow is the poor relation of traceability: the equipment comes back, but in what condition, and who pays for the repair? Documenting each return with a photo turns a discussion into a verifiable fact.

Altrad Plettac Mefran, a manufacturer of scaffolding equipment, digitised its returns process to track and invoice parts that need cleaning, repairing or replacing. Field teams needed no training, and the photos serve as evidence for invoicing.

6. Track reusable containers

Roll cages, totes, stillages: these containers move between warehouses, stores and carriers, and go missing by the dozen. A tracking app records each movement by scan and photo, and builds a chronological view of each container's life cycle. Discrepancies by site become visible, and so does accountability.

7. Digitise site audits and inspections

Hygiene, safety, ISO compliance, cold chain: depot and warehouse audits are often still done on paper or in spreadsheets. On mobile, AI speeds up data entry (voice dictation, photo analysis), flags missed fields and generates corrective actions automatically.

At Chronopost, where temperature-controlled delivery (Chrono Fresh) requires ISO certification, 25 audit and self-assessment checklists have been in use across 160 depots since July 2024, and the certifications the business depends on are secured.

8. Keep the inventory of high-value equipment up to date

Forklifts, vehicles, dock equipment, IT hardware: the ERP knows the theoretical quantity, but rarely the actual condition or precise location of each item. On mobile, AI extracts the serial number from a photo of the rating plate, the image documents the condition, and any discrepancy with the ERP shows up immediately.

9. Report and deal with safety incidents

A near miss on a dock, reported by instant message, can go unnoticed. A dedicated app lets you report it in seconds with a photo, analyse the root causes, and assign corrective actions. The incident becomes tracked data, comparable across sites.

10. Create new field apps with an AI agent

Agentic AI is arriving in the supply chain. Gartner forecasts that 60% of enterprises using supply chain management software will have adopted agentic AI features by 2030, up from 5% in 2025. 2

In the field, the AI agent has an immediate use: building the app itself. The Wizy AI Agent generates the data entry screens and logic of an app from a description of the physical flow or an existing PDF. An operations manager describes their dock check; the agent produces a first version, and the team adjusts it without any development work. The result: a working proof of concept in 10 days.

What belongs to other supply chain tools

Three families of AI solutions often appear in rankings but act upstream of the field. They complement an execution platform; they do not replace it.

The common thread is clear: these tools optimise the plan, but the quality of their decisions depends on the data coming back from the field. If teams are still copying statuses into Excel, the priority is to make execution reliable before optimising planning.

The data to connect for AI that is useful in logistics

Logistics AI relies on concrete operational data: ERP, WMS, TMS, photo evidence, scans, delivery statuses, incidents, equipment identifiers. The goal isn't to collect everything, but to connect what matters. WizyVision integrates with existing systems via REST API, queries the WMS or ERP by barcode scan, and returns qualified field data.

Quality matters more than volume. The same status, named in three different ways, breaks sorting and analysis. Mandatory fields, controlled lists and standardised evidence matter as much as the algorithm: without this discipline, AI surfaces errors faster instead of correcting them.

The pitfalls that undermine efficiency

A project that is too broad. Trying to cover every flow at once often means covering none. A single flow, properly tracked, such as one delivery round or one goods-in sequence, is enough to prove the value.

An app that is too complex. If operators do not understand the tool the first time they use it, they work around it and go back to paper. On a cold dock at 6 am, with a patchy connection, data entry needs to be guided, quick and able to work offline.

Security dealt with too late. Access control, field data protection, subcontractor identities: these issues block large-scale rollouts if they aren't scoped with IT from the pilot stage onward. For reference, WizyVision is ISO/IEC 27001:2022 certified and holds a SOC 2 Type II report.

How to test a field AI solution in 30 days

Week 1: scope one flow

Choose a single use case (unidentified parcels, proof of delivery, goods-in inspection) and one pilot site. Define three simple indicators: processing time, error or dispute rate, customer response time. Measure the starting position.

Weeks 2 and 3: deploy and use

Configure the app around the real flow, with the operators. With WizyVision, you can deliver a working proof of concept in 10 days, without heavy user training.

Week 4: measure and decide

Compare the same indicators before and after. Check that the evidence is more complete and that incidents are handled faster. Then decide whether to extend to other sites, or to look at another tool if the need is really about network planning.

When WizyVision is not the right solution

WizyVision is not a demand forecasting tool, a route optimiser, an ERP or a WMS. For strategic planning or network analysis, a dedicated supply chain solution is a better fit.

WizyVision is relevant when the challenge lies at the point of execution: multi-site teams that need to capture reliable evidence, handle exceptions, and report usable data without heavy data entry or training.

Conclusion: logistics AI starts at the dock

AI holds great promise for the supply chain, and agentic AI will speed things up. But an agent can only decide well on reliable data, and that data originates in the field: at the dock, in the trailer, at goods-in, at the customer's site. The 10 solutions covered here have one thing in common: they reduce execution variability and turn each task into usable evidence.

A sensible starting point: one flow, one site, three indicators, and an app that teams use from the first week.