AI is all the rage.

Everyone is being told that AI will transform logistics with smarter forecasts, faster reporting, and millions in savings.

The potential is real. McKinsey’s Technology Trends Outlook (May 2025) found that logistics leaders using AI successfully can reduce costs by 5–20% and improve forecast accuracy by 20–50%. Those are game-changing numbers. But here’s the problem: most companies never see those results.

Studies show that nearly 70% of logistics AI projects fail. Not because the technology is weak, but because of the data that underpins the numbers.

The Hype vs. Reality

‍The promise of AI is attractive: accurate demand forecasts, emissions tracking in real time, instant dashboards for finance and ESG teams. But pilots often break down after the first demo.

Why? The data that feeds these systems is fragmented and unreliable.

• Carriers deliver files in different formats.
  
• Invoices arrive late and don’t match shipment records.
  
• Emissions factors are incomplete or inconsistent.
  
  

Instead of learning from reality, AI ends up working with guesses, averages, and gaps. Forecasts look impressive in a presentation, but fail when it comes to daily operations, procurement, or compliance.

This disconnect explains why so many pilots never scale. Without reliable inputs, the outputs cannot be trusted.

The Foundation That Makes AI Work

‍The companies that succeed with AI in logistics are not the ones chasing every new algorithm. They are the ones fixing the foundation first: their data.

This means creating a single, trusted transport data model that brings together every carrier feed, ERP extract, and invoice. It means removing duplicates, correcting errors, and aligning everything into one source of truth.

When this foundation is in place, AI moves from hype to ROI.

• Finance teams see accurate costs as they happen, not weeks later.
  
• ESG teams push audit-ready Scope 3 numbers straight into reports.
  
• Procurement makes decisions based on verified CO₂ and €/km rates.
  

Without this step, AI is simply guessing. With it, AI becomes a driver of competitive advantage.

The Business Case

The upside is too large to ignore. With clean transport data as the foundation, AI can support:

• 5–20% cost savings through smarter procurement and better capacity planning
  
• 20–50% higher forecast accuracy, reducing both stockouts and excess inventory
  
• Faster and more reliable reporting for ESG disclosures, customer invoices, and internal KPIs

The difference between success and failure is not the AI layer itself, but the readiness of the data beneath it.

The Road Ahead

AI in logistics isn’t slowing down. If anything, adoption will accelerate as regulations tighten and margins come under pressure. The winners will be those who treat transport data as critical infrastructure, not an afterthought.

By 2027, companies with unified data models will be running predictive procurement, dynamic emissions benchmarking, and automated accruals as standard. Those still patching together spreadsheets will not only lag behind competitors, they’ll struggle to even meet basic compliance demands.

Conclusion

‍AI in logistics is not about being the first to deploy a shiny new model. It is about a clear use case supported by the cleanest, most reliable data.

Build on messy data, and AI projects fail, wasting time and budget. Build on clean, unified transport data, and AI delivers what leaders are hoping for: real savings, accurate forecasts, and reports that both customers and regulators can trust.

AI is powerful. But it is only as strong as the data that powers it.

Want to learn how Kinver can get your team ready? Reach out