AI is certainly the buzzword of the moment, especially if you’ve been keeping up with the news over the past few days. However, there’s a danger that logistics businesses can get caught up in the unhelpful hype without knowing all the facts.

The truth is not all AI is the same. There’s a big difference between open-ended generative AI tools and AI embedded within specialist logistics software to perform predefined tasks.

In logistics, the latter has proved to be valuable because it works within a controlled environment, using operational data to solve specific problems, identify patterns and support better decision-making. However, how operators use this technology will very much define the outcomes they get.

One of the biggest challenges with AI for logistics operations at the moment is that it’s only as good as the information – data – it’s working with. If operational data is fragmented across spreadsheets, outdated or inaccurate, the technology won’t suddenly turn that into reliable insight.

Effective AI in logistics requires trusted, structured, real-time data from multiple external sources in order to help plan, execute, comply, verify and analyze activities across the shipment lifecycle. In this way, AI can help solve inter-enterprise logistics problems and help logistics-focused businesses optimize outcomes using insights from all participants.