Supply chain teams are entering a transitional phase. Many companies are still relying on fragmented legacy systems that limit visibility, make communication clunky, and force planning to remain reactive. At the same time, there’s growing pressure to adopt AI tools – yet many supply chain leaders are still unsure how to apply them in practice.
It’s a known truth that AI cannot deliver without the right foundation. If systems are disconnected and data is incomplete or unstructured, even the smartest tools won’t provide meaningful insights or optimise workflows. In practice, this means that the leap from manual processes to AI-driven supply chains isn’t a straight jump. Instead, it requires deliberate steps to unify systems, improve data quality, and embed technology into everyday workflows.
Once this is achieved, teams can put AI at the heart of their supply chain decisions.
AI depends on connected data
AI-first decision making depends on connected platforms and clean, structured data. Fragmented inputs, where data is only gathered from some systems, will inevitably lead to weak outputs. But if AI can analyse all types of supply chain data – like inventory levels, transportation routes, carrier ETAs and ATAs, customer preferences – it can generate more effective insights and predictions. So, if teams want to get the best out of AI, it needs to have secure access to as much relevant trade data as possible.
Achieving this requires teams to use technology that can integrate data from across internal systems, external partners and IoT devices into one platform. This builds a comprehensive view of the supply chain. With data consolidated, AI insights can then be substantially improved: they can proactively and efficiently outline strategies for tasks such as smart routing, supplier management, risk monitoring and next best actions.
This integration also lays the foundations for newer AI developments like agentic AI, which could revolutionalise how supply chain managers operate. The key difference between AI agents and previous AI models is that they can act independently and perform actions to achieve a desired goal. So, for instance, rather than an AI platform just suggesting the optimum carrier or route to use, an AI agent could automatically confirm these options and then make the arrangements on behalf of the supply chain manager.
But like with AI, agentic AI needs to be able to move between systems easily and access all relevant data in one place.
Moving from fragmented systems to integrated intelligence
So, what are some of the practical steps supply chain teams can take to move from fragmented systems to integrated intelligence?
Supply chain data exists in a whole range of locations. There are internal ERP systems, spreadsheets, email communications, external carrier platforms, online databases, and so on. So, connecting data begins with knowing what data you need to connect and, crucially, understanding where it is. By sketching out what platforms and sources shipping and logistics data lives in today, supply chain managers can then standardise datasets so that they’re shareable across various teams and tools.
This self-evaluation is a crucial first step. But modern supply chains are full of complexity, and manual processes for data entry and sharing don’t have the necessary speed and accuracy to manage real-time disruption. What supply chain teams need are platforms with API technology that can integrate with the range of business-critical systems they identified (and connect with their partners’ data too). But how can teams find the right solution?
When making their assessment, supply chain teams should look for platforms that can automate data entry processes and display their data and shipping documents on a live tracking dashboard. And one of the key features to look out for is whether the dashboards can be shared with various partners and stakeholders – there’s no point collating data if you can’t easily share it too. The most advanced platforms will even be able to tackle more complex operations dilemmas, such as “Which shipments are at risk of detention or demurrage fees in the next seven days?”
It’s also imperative to understand how long the implementation process will take and how any impact to operations can be minimised. Downtime can be incredibly costly, both to entire supply chains and a company’s reputation. Once you’ve implemented a platform that is suited for your needs and connects all of your data, you can then begin to unlock AI-first decision making.
Infrastructure built for interruptions
Disruption is tied into supply chains. From climate events altering routes to tech risks like cyberattacks, there are a variety of factors that are simply uncontrollable. When such interruptions take place, trying to coordinate plans across a global network of carriers and suppliers can be incredibly complex – without the right tools.
Many of the most significant pain points in supply chains, like understanding carrier performance and choosing optimal routes (both in terms of time and cost), can be solved with unified supply chain data. And this is not just about visibility; modern platforms create automated workflows that can streamline operations across regions, partners and suppliers.
AI is now increasingly raising expectations, with users aware they can access key insights with little input. The gap to overcome is practically implementing it – and that comes from connecting data. But the transition isn’t always straightforward. Many digital solutions can promise the world but then underdeliver, so a careful evaluation process is crucial. The perfect implementation approach maintains day-to-day operations while enhancing efficiency and enabling automation.
AI-first decision making won’t stop major interruptions, but it enables supply chain leaders to preempt many of them effectively, or to seamlessly react and find other solutions that cause very little or no disruption at all.
If leaders can harness integrated AI-powered platforms for their supply chains, they can build infrastructure for today that creates resilience and agility for the challenges of tomorrow.
