AI is everywhere in logistics conversations: route optimisation, demand forecasting, exception management, autonomous replenishment. The results so far are sobering. Boston Consulting Group's 2025 study, The Widening AI Value Gap, found that only about 5% of companies are getting real value from AI at scale.
That is not a reason to avoid AI. It is a reason to understand why most businesses are not yet seeing the results they hoped for. In logistics, the cause is almost always the foundation the AI was built on, not the tool, the vendor or the ambition of the project.
The Problem Is Not the AI
The pattern is familiar. A business identifies a genuine operational problem and deploys an AI tool to solve it. Six months later, the outputs are being ignored, overridden or are wrong often enough that nobody trusts them, and the team is back on spreadsheets.
The tool rarely fails on its own terms. It fails because the data it depends on is inconsistent, incomplete or scattered across systems. AI that makes decisions or predictions cannot compensate for poor inputs.
Logistics is particularly exposed. Stock levels, lead times and order patterns change constantly, and the data often sits across a warehouse system, an ERP, several spreadsheets and people's inboxes.
What Good Data Foundations Look Like in a Logistics Business
A sound foundation for AI in a logistics business has four parts:
- One system of record for stock, purchasing, sales orders and finance
- Stock movements recorded as they happen, not at the end of the day
- Purchase orders that carry an agreed delivery date, with the actual receipt date recorded, so lead time and on-time performance data builds up automatically
- Orders that flow from placement through picking, despatch and invoicing without being re-keyed
None of this is exotic. It is what Dynamics 365 Business Central is designed to deliver for SME logistics businesses. Once it is in place, the tools that sit on top, such as Copilot, Power Automate and Power BI, have reliable data to work with.
Where AI in Logistics Actually Delivers Value
With those foundations in place, three use cases consistently earn their keep:
- Demand forecasting, built on clean order history
- Replenishment automation, driven by accurate stock and lead time data
- Exception management, flagging orders at risk of missing their service level agreement and purchase orders running late
Automated document processing is the exception to the rule that data has to be right first. Extracting data from purchase invoices and delivery confirmations works on each document as it arrives, so it needs far less historical data. It delivers quick, measurable time savings, which makes it a good first project while the wider foundations are being fixed.
What generally does not work at SME scale is standalone AI layered over a fragmented set of systems. The AI cannot connect data that the underlying systems have never connected.
The Right Order of Operations
The logistics businesses that get value from AI tend to follow the same sequence:
- Consolidate onto a modern, integrated ERP that provides clean, current data.
- Improve reporting and decision-making with Power BI.
- Add AI capabilities such as Copilot, Power Automate and more advanced forecasting or optimisation.
This is not caution for its own sake. Each step makes the next one cheaper and more likely to work. Skipping straight to AI before the foundations are right is the most common reason businesses end up disappointed with the results.
Talk to Our Logistics Team
If you want an honest view of where AI could make a realistic difference to your operation, and what needs to be in place first, we are happy to talk it through.
Contact Advantage today or call 020 3004 4600 to speak to our team.
Related Resources
- Dynamics 365 Business Central
- Microsoft Copilot
- Everyday AI That Works for You
- Advantage Transformation Sprint™
- Microsoft Copilot for Logistics: AI Demand Forecasting Guide
- Real-Time Stock Visibility for Logistics and Distribution
Frequently Asked Questions: AI in Logistics
Why do so many AI projects in logistics fail to deliver results?
Usually because the AI is deployed on data that is fragmented, inconsistent or incomplete. The tool itself is rarely the problem. Fixing the data foundation first is what separates the projects that deliver from those that stall.
What data does a logistics business need in order before investing in AI?
You need one system of record for stock, purchasing, orders and finance; stock movements recorded as they happen; purchase orders with agreed delivery dates matched against actual receipts; and orders that flow through fulfilment without re-keying. Dynamics 365 Business Central is designed to provide exactly this for SME logistics businesses.
What AI use cases work best in an SME logistics context?
The strongest are demand forecasting and replenishment where clean order history exists, automated processing of purchase invoices and delivery confirmations, alerts for orders or deliveries at risk, and plain-English questions about operational data through Microsoft Copilot. Fully autonomous supply chain orchestration needs more data maturity than most SMEs have today.
Is Microsoft Copilot useful for logistics businesses?
Yes, mainly as a productivity tool: drafting supplier emails, answering questions about stock and orders, summarising exception reports and handling routine documents. Copilot in Business Central is included with your Business Central licence, while Microsoft 365 Copilot is licensed per user on top of Microsoft 365. Neither needs new infrastructure if you already run on Microsoft.
