Insights · Logistics
AI readiness assessment for UK logistics companies
Logistics is data-rich but AI-poor — the data lives in TMS, WMS, ERP, and telematics silos that have not yet been brought together. This is the four-minute readiness check that maps the gap.
Walk into the operations centre of a UK logistics business and you will find more real-time operational data than almost any other sector outside financial services. You will also find that almost none of it is currently usable by AI tooling — because it sits in five or six separate systems that have never been integrated, normalised, or jointly queried.
Logistics has the highest unrealised AI value of any UK sector we work with, and the highest barrier to capturing it: the data foundations work that has to happen first.
Why logistics is data-rich but AI-poor
A typical logistics business runs at least five operational systems:
TMS — transport management. Routing, dispatch, driver assignment, vehicle utilisation.
WMS — warehouse management. Inbound, putaway, pick, pack, dispatch.
ERP. Customer master, billing, finance, sometimes inventory.
Telematics. Vehicle telemetry, driver behaviour, fuel use, location history.
Customer-facing systems. Portal, tracking, customer service.
Each was procured at a different time, against a different brief, often from a different vendor. Each is good at the job it was bought to do. None speaks fluently to the others without an integration layer that does not yet exist.
The result: a logistics AI proof of concept that wants to predict ETAs needs to read from at least three of the five systems above, plus weather, plus traffic, plus historical performance. Even the best AI tooling cannot bridge that gap if the data does not flow.
The integration question is the AI question
For logistics, the "are we AI ready?" question and the "do our systems talk to each other?" question are almost the same question.
A logistics business that has invested in operational data integration over the last few years is, almost by definition, well placed for AI. A business that has not is, almost by definition, blocked from AI value until the integration work catches up.
Five readiness dimensions applied — heavy weight on data and infrastructure
Governance. Lighter weight here than in legal or financial sectors, but not zero. Logistics businesses processing personal data (driver, customer, recipient) still need an AI usage policy and approval framework. The framework can be lighter touch than in regulated sectors but should exist.
Data. The big one. Can you query operational data across systems? Can you connect AI tooling to a clean dataset without it pulling in inconsistent records, duplicates, or stale data? Have you classified data by sensitivity (commercial-sensitive routing, customer PII, driver PII)?
Infrastructure. The other big one. Integration layer between systems. API access where modern systems allow, scheduled-batch where legacy systems force it. Storage capacity for the historical data AI needs. Compute capacity for the workloads.
Security. Network segmentation between operational systems and AI infrastructure. Access controls on the integration layer. Vendor security for AI tooling. Driver and customer data residency.
Use case. The use cases that pay off first in logistics are typically: ETA prediction, routing optimisation against constraints (delivery windows, vehicle types, driver hours), customer enquiry handling, and anomaly detection on operational data. Each requires the data and infrastructure work first.
Use cases that pay off first
Routing optimisation with constraints. AI-augmented routing that handles delivery windows, vehicle constraints, driver hours, and historical traffic patterns simultaneously. Typically improves fleet utilisation 8–15% in the first six months on businesses with clean data.
ETA prediction at customer-facing detail. AI prediction of customer-visible ETAs, refreshed in real-time, fed back into the customer portal and proactive notification systems. Improves customer satisfaction; reduces inbound enquiry volume.
Customer enquiry handling. AI-assisted handling of common customer enquiries ("where is my delivery", "can you change my delivery slot") with seamless escalation to human agents for the cases that need them. Typical headcount efficiency: 25–35% on the customer service team within nine months.
Anomaly detection on operational data. AI surveillance of operational data streams, surfacing anomalies (unusual fuel consumption, route deviations, late-running services) for proactive investigation. Reduces operational surprise.
Each is achievable. Each requires the data and infrastructure foundations to be in place first.
Frequently asked
What is an AI readiness assessment for a UK logistics company?
A structured review of whether the operational data and infrastructure foundations are in place for AI to deliver actual value — routing, ETA prediction, customer enquiry handling, anomaly detection. For logistics, the readiness conversation is mostly a data-integration conversation. The Arx Certa scorecard is the 4-minute free starting point.
Why is data integration the AI readiness question for logistics?
Most logistics businesses run five separate operational systems — TMS, WMS, ERP, telematics, customer-facing portal — each procured at a different time, often from different vendors. AI tooling needs to query across them. Until the integration layer exists, even the best AI cannot deliver.
Which AI use cases pay off first in UK logistics?
Four, in order of typical ROI: routing optimisation with constraints (8–15% fleet utilisation gain on businesses with clean data), ETA prediction at customer-facing detail (reduces inbound enquiry volume), AI-assisted customer enquiry handling (25–35% headcount efficiency on customer service over 9 months), and anomaly detection on operational data (reduces operational surprise).
How does AI readiness affect our customer-facing systems?
Significantly. Most logistics customer-experience improvements (real-time ETAs, proactive notifications, self-service enquiry handling) depend on AI infrastructure that draws from internal operational systems. The scorecard tells you whether the foundations make those improvements achievable in the next 12 months.