FEATURE Industrial AI
THE GATEWAY TO ACHIEVING DATA THAT IS READY FOR AI
THE GATEWAY TO ACHIEVING DATA THAT IS READY FOR AI
Mark Holmes, Global Head of Supply Chain Market Strategy, InterSystems, says building fast reactions will overcome major supply chain disruptions
W
hen the Francis Scott Key Bridge collapsed in 2024 blocking the major port of Baltimore in the US, it
was tragic, totally unexpected and highly disruptive to trade. Yet it is precisely this kind of unforeseen
disruption that supply chain leaders today must be capable of rapidly surmounting. Global logistics is immensely complex and volatile but it is a world where data seldom lives in one place or one format. Fragmentation produces a time-lapse between insight and decision-making at critical moments.
In global transport networks where
disruptions are frequent and costly, competitive advantage. This requires more than connected IT systems – it demands that supply chain managers can rely on for better decisions.
from where they can see what is happening, to a powerful position where they have AI-ready data for true decision intelligence. Once they are in the right place with their data, organisation’s supply chain leaders can model scenarios, anticipate disruption, and act pre-emptively. Organisations must pull supply chain
data from diverse systems. In many enterprises, however, the sheer number of data sources makes a single view of the in data architecture. A typical enterprise-level supply chain
organisation needs to unify data from the platforms of multiple suppliers and partners, internal CRM, ERP systems, and warehouse or transportation management systems. It’s a long list.
Other logistics operations may need data
from networks of IoT devices, and from news, weather, and business data feeds supplying live market information. An organisation must make sense of all this data very quickly if decision-making is to be truly
22 January 2026 | Automation
The problem is this information is duplication. A large enterprise may have 30 ERP systems that give multiple IDs for a single SKU, for example. All this harmonised so supply chain managers are safe to use it when resolving a bottleneck or other challenge. Organisations need a new architecture that is simpler and brings data together into a single, consistent view they can trust. Not by ripping out every existing system, but by adding a unifying layer to provide one clean, reliable source.
“Having data that is ready for AI is the route to real-time
decision intelligence, greater agility and bigger revenues”
This new data gateway architecture technologies that are brought to the data, rather than the other way round, as with conventional approaches. managers can see right along the supply chain, spotting costly bottlenecks to supply chain planner, for example, gains accelerated access to customer orders and transport schedules in a single place. Accuracy of forecasting and demand- management increases by many factors applications managers use.
In a market like fast-moving consumer
goods, for example, where demand can change overnight, logistics managers have the real-time insight they need for quick decisions – reducing lead times and costs while improving customer satisfaction. With a data gateway in place, organisations low-code or no-code access to siloed data. Supply chain professionals can use generative AI to solve their business problems using natural language rather than computer code. gateway could use generative AI to work out almost immediately which customers would example, allowing fast action to reduce the business impact. With AI and machine learning up and
running, predictive analytics alert teams to potential problems, allowing them to reroute shipments or adjust production before small setbacks cause major bottlenecks. Simultaneously, prescriptive analytics guide teams towards solutions that have the highest chance of success. This approach reduces human error, supports better forecasting, and speeds up decision-making. Organisations that implement a data gateway waste time making sense of information from poorly integrated systems. Managers will see what the problem is and make the right decision to resolve it right away, assisted in their decision- making by advanced analytics. Having data that is ready for AI is the route to real-time decision intelligence, greater agility and bigger revenues. architecture.
InterSystems
www.intersystems.com
automationmagazine.co.uk
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