Artificial intelligence (AI) does not operate independently of data. Its value is realised when inputs are accurate, consistent, and properly governed. Where data integrity is weak, insights become unreliable, and decision-making reverts to reactive execution. Where it is strong, organisations unlock predictive capability, improved planning, and more disciplined operational control.  

Petrus Gerber, Supply Chains Solutions Manager at Bidvest International Logistics (BIL), explains that this is already evident in how AI is applied to operational datasets, noting that “we can apply AI algorithms to historical and transactional data to generate predictive insights that enhance both logistics planning and execution.” He adds that these insights help identify seasonal patterns, order behaviours, and operational peaks and troughs, enabling more precise planning and execution.  

From a commercial perspective, Tebogo Mpanyane, BIL’s Head of Africa, says, "Data is central to how we operate and make decisions when entering African markets, where structured intelligence aligns operational performance with broader trade developments such as AfCFTA. This lets teams identify priority markets and support client expansion more effectively."  

This connection between data and execution is embedded in daily operations. Structured data inputs ensure alignment between planning, capacity, and client requirements, improving coordination and consistency across the supply chain.

At the client level, platforms such as CargoWise rely on accurate, continuously updated data to enable visibility, control of documentation, and communication among stakeholders. Without this, visibility becomes fragmented and loses operational value.  

The real constraint is no longer access to data, but the fragmentation of data across systems, functions, and markets. As organisations scale AI and analytics, disconnected data environments erode visibility, weaken decision quality, and limit the ability to execute consistently at scale. Centralised, well-governed data structures are therefore critical, but technology alone is not sufficient.

Data integrity ultimately depends on disciplined systems, consistent user behaviour, and strong governance frameworks, particularly as automation increases reliance on accurate inputs while still requiring human oversight.  

This places greater emphasis on compliance and cybersecurity. As Gerber notes, standards such as ISO and TISAX are increasingly becoming a “ticket to entry” within global supply chains, reinforcing the need for robust data governance and risk management.

In increasingly complex trade environments, organisations that treat data integrity as a strategic capability rather than a technical requirement will be better positioned to lead, scale, and compete with confidence. The future of logistics execution will belong to businesses that can trust their data as much as their strategy.  

By Petrus Gerber, Supply Chains Solutions Manager & Tebogo Mpanyane, Head of Africa, BIL.