Logistics and Warehousing
Handle the exceptions before the customer calls about them.
Shipment and document automation, proof-of-delivery processing and exception dashboards that let a small team run a large network.
Operational context
How the sector actually runs.
The work is exception handling. Ninety-five percent of consignments move without intervention, and the entire customer service team exists for the remainder: the missed pickup, the damaged pallet, the address that does not exist and the delivery the receiver disputes.
Proof of delivery arrives as photographs and scrawled signatures. Drivers capture PODs on handheld devices or paper, and the image sits in a separate system from the invoice, so proving a delivery during a chargeback dispute means someone manually hunting for it.
Freight invoices rarely match what was quoted. Carrier invoices arrive with fuel levies, redelivery fees, demurrage and dimensional reweighs applied after the fact, and reconciling them line by line against thousands of consignments is nobody's full-time job but consumes one.
Customers ask the same three questions constantly. Where is it, when will it arrive, and why is it late. Each answer requires someone to check a transport management system, possibly a carrier portal, and sometimes a phone call to a depot.
Integration is brittle and everywhere. A 3PL may hold EDI connections, flat-file drops, carrier APIs and customer portals in a dozen formats, each built at a different time by a different person, and every new customer adds another.
High-value problems
Where the money and the risk actually sit.
My customer service team spends all day answering where is my freight
A grounded assistant answers status questions from the transport management system and carrier feeds, with the consignment reference cited in every answer. It escalates to a person the moment the data is stale, contradictory or the query involves a claim.
We cannot find the POD when a customer disputes a delivery
Proof-of-delivery images are processed on capture, indexed against the consignment and made searchable, so a disputed delivery is answered in seconds rather than after a manual hunt through a device management system.
We are paying carrier invoices we cannot check
Invoice lines are matched automatically to consignments and quoted rates, and only the exceptions surface for review. The team investigates the variances rather than reading every line.
By the time we know about a problem the customer already knows
Exception detection runs continuously against scan events and expected milestones, so a missed pickup or a consignment stalled at a depot raises an alert before the delivery window closes.
Onboarding a new customer integration takes us months
We replace point-to-point integrations with a managed API layer and a documented onboarding pattern, so a new customer connection becomes a configuration exercise rather than a bespoke build.
Evidence from the field
Where this has actually worked, and where it has not.
Published results from other organisations, cited so you can check them. None of these are our clients and none of these numbers are ours.
Where the gains are real
The strongest published numbers in any sector, because logistics measures everything already. Where a business can already count items per hour and dwell time to the minute, an improvement is provable rather than argued.
What the evidence does not support
Almost all headline figures come from operators with capital budgets in the hundreds of millions. The document-handling and exception-detection work transfers to a mid-size operator. The robotics numbers do not.
Amazon
United States, global network
Sequoia, a containerised inventory system combining robotics with computer vision for identification and storage.
Up to 25 per cent faster order processing through a fulfilment centre, and inventory identified and stored up to 75 per cent faster.
What it does not prove: Company-published figures, not independently audited.
Stanford HAI, AI Index
Global
Measured productivity effects in customer support work, which is the shape of a freight service desk answering where a consignment is and why it is late.
Roughly 14 to 15 per cent, with the largest gains going to less experienced staff and the smallest to the most experienced.
What it does not prove: Not measured in logistics specifically. Included because this, rather than the robotics figure above, is the number a mid-size operator should build a business case on.
Rio Tinto
Western Australia, Pilbara
AutoHaul, autonomous heavy-haul freight trains carrying iron ore from 16 mines to four port terminals, supervised remotely from an operations centre in Perth.
More than 1 million kilometres travelled autonomously as at December 2018, across a network of about 200 locomotives and more than 1,700km of track, with an average return journey of about 800km and an average cycle time of about 40 hours including loading and dumping. Rio Tinto puts the programme cost at $940 million.
What it does not prove: Rio Tinto's own release, and it claims potential rather than realised productivity gains: it says the deployment shows significant potential to improve productivity and reduce bottlenecks, without publishing a cycle-time or cost-per-tonne improvement. This is sensing and control automation rather than a learned model, and Rio Tinto stated it expected no redundancies in 2019 from the deployment.
Union Pacific Railroad
United States
Machine Vision Systems photographing rolling stock, with the images processed by machine learning and AI to identify abnormal or defective components, feeding a network of more than 7,000 wayside detection devices.
The wayside detector network generates more than 16 million data points every day. Union Pacific reports total derailments down 21% comparing 2022 with 2019, and track-caused derailments down 55% over the previous 10 years.
What it does not prove: Union Pacific's own reporting. The derailment reductions cover the whole safety and inspection programme, including track maintenance practice and physical detectors, so they cannot be attributed to machine vision alone. No before-and-after figure is published for the machine vision component on its own.
Ocado Group
United Kingdom
On Grid Robotic Pick, robotic arms picking items directly from the storage grid in Ocado Smart Platform customer fulfilment centres, rolled out alongside Automated Frameload.
Overall customer fulfilment centre productivity across the Ocado Smart Platform rose 9.1 per cent in the 2024 financial year, with more than 30 per cent of Luton volumes picked robotically by year end and rollout contracts signed with the majority of partners.
What it does not prove: Company-reported, and Ocado attributes the productivity gain to higher volume utilisation as well as to robotic picking, so the robotics contribution cannot be isolated from the figure. Luton is the most advanced site rather than a typical one.
Australian Bureau of Statistics
Australia
National business survey (Characteristics of Australian Business) measuring whether businesses used artificial intelligence in the workplace, published broken down by industry division.
1% of Australian transport, postal and warehousing businesses reported using AI in 2024-25, the lowest of the 17 industry divisions published, against an all-business rate of 12%.
What it does not prove: Adoption is not outcome, and the figure covers all transport, postal and warehousing businesses including very small operators, so it is dominated by owner-drivers and small fleets rather than by the large logistics operators most likely to have deployed something. Read it as a base rate for the sector, not as evidence that AI does not work in logistics.
Australian Bureau of Statistics, Characteristics of Australian Business, 2026
Sector figures are useful for deciding what to try first, and almost useless for forecasting your own result. The organisations above had the data, the systems access and the governance in place before they measured anything. The full global picture covers 13 industries, sets out what separated the organisations that got a result from the ones that got nothing, and carries the randomised trials that found no gain at all.
Use cases
What we build in this sector.
Each is tagged with the disciplines involved, because most useful work crosses more than one.
Consignment status assistant
A grounded assistant answering status and ETA questions from live transport data, citing the consignment record and escalating anything uncertain.
- AI
- Automation
Proof-of-delivery processing
Extract and index signatures, timestamps, exception notes and pallet counts from POD captures so disputes are answerable immediately.
- AI
- Data
Freight invoice reconciliation
Match carrier invoice lines to consignments and agreed rates, surfacing only variances and accessorial charges for review.
- Automation
- Data
Exception dashboards
Continuous monitoring of scan events against expected milestones, ranked by customer impact rather than raw age.
- Data
Consignment note and manifest extraction
Read consignment notes, manifests and customs paperwork arriving as PDF or scan, and structure them for downstream systems.
- AI
- Automation
API and EDI modernisation
Consolidate point-to-point integrations behind a managed, monitored API layer with a repeatable customer onboarding pattern.
- Cloud
- Security
Warehouse throughput analytics
Pick rates, dock utilisation, dwell time and labour against volume, at a granularity that supports shift decisions.
- Data
Human oversight
Where a person still decides.
Automation proposes. A named person approves. These are the points we design the workflow to stop at.
- A customer service officer approves any communication sent to a customer about a delayed or damaged consignment. The assistant drafts, a person sends.
- A finance officer approves every invoice variance before a carrier charge is disputed or paid.
- An operations supervisor approves any automated re-book, re-route or redelivery that incurs cost.
- A claims officer reviews every POD-based dispute outcome before it is communicated externally.
- An account manager approves any new customer integration going live, after a parallel run against the previous method.
Delivery options
On Azure, on AWS, and afterwards.
The platform is chosen for the workload, not for us. Both paths end in the same place: someone still owns it after go-live.
- 01
Delivered on Microsoft Azure
Suits operators with a Microsoft back office, where customer service already works in Teams and reporting is expected in Power BI.
- Azure AI Document Intelligence processes consignment notes, PODs and carrier invoices at volume.
- Azure AI Search grounds the status assistant against live consignment and exception data.
- Microsoft Copilot Studio delivers the assistant into Teams for the customer service desk.
- Azure API Management fronts carrier and customer integrations with consistent authentication, throttling and logging.
- Azure Functions and Azure Event Hubs handle scan-event ingestion and exception detection.
- Microsoft Fabric and Power BI provide throughput, exception and cost-to-serve reporting.
- 02
Delivered on AWS
Suits operators running custom transport platforms or high-volume event processing, and those already using AWS for their customer-facing systems.
- Amazon Textract extracts data from PODs, consignment notes and carrier invoices.
- Amazon Bedrock powers the status assistant, with Bedrock Guardrails preventing answers beyond the retrieved consignment data.
- Amazon API Gateway and AWS Lambda provide the integration layer for carrier and customer connections.
- Amazon EventBridge and Amazon Kinesis process scan events and drive exception detection in near real time.
- Amazon S3 stores POD imagery with lifecycle policies matched to the dispute window.
- Amazon Redshift and Amazon Quick deliver throughput and cost-to-serve analytics.
- 03
Operated after handover
Carrier formats change without notice and volume spikes are seasonal. This is the sector where unattended automation degrades fastest.
- Document extraction accuracy monitored per carrier and per document type, with new formats onboarded as they appear.
- Assistant answer quality sampled and reviewed, with escalation rates tracked as the primary health signal.
- Integration endpoint monitoring, including carrier API changes and failed EDI transmissions.
- Event pipeline monitoring with alerting on ingestion gaps that would silently suppress exception detection.
- Peak-period capacity planning ahead of known seasonal volume.
- Monthly reporting on exception volumes, resolution times and reconciliation recoveries.
Related services
The capabilities behind this work.
Artificial Intelligence
AI that is chosen for a reason, costed before it is built and governed once it is live.
Cloud Modernisation
Ageing systems become a cloud platform your team can change safely, recover predictably and account for line by line.
Data and Analytics
Reporting your executives trust, produced once, with a modelled layer underneath it that the next AI or forecasting project can stand on.
Managed Services
Your platform keeps earning its business case after go-live, with cost, security posture, reliability and adoption reviewed on an agreed cycle rather than left to drift.
Free discovery workshop
Start with one logistics challenge.
Bring a process that costs more than it should. We will map the opportunity, the readiness gaps and a recommended next step.