As supply chains grow more interconnected and complex, they become increasingly vulnerable to disruption. A blockage in one part of the network can quickly cause difficulties in all directions, affecting production, inventory, costs and customer service.
Conflict in the Gulf region, fluctuating fuel prices, sanctions and unpredictable tariffs are creating even greater volatility. Global businesses know that to achieve competitive advantage, they must innovate and inject greater agility without increasing risk.
In these complex supply chains with many moving parts, making the right decisions quickly and confidently, without real-time, unified and trusted data, can be a formidable task. This ability to adapt quickly, with maximum impact, is now the number-one strategic priority for businesses across all sectors.
Driving this shift toward greater agility are the artificial intelligence toolsets that supply chain organizations can now deploy. AI-driven decision intelligence enables supply chain leaders to surmount disruptions or regulatory changes that would previously have left many organizations floundering.
While traditional tools show what has happened, decision intelligence provides insights into what’s likely to happen next, and offers recommendations about the best course of action for the desired outcome.
In so many organizations, however, fragmented data is a major problem, making it difficult to access these vital new capabilities.
If it’s going to deliver, AI needs clean, trusted and unified data, so that it can provide a single source of truth about the supply chain in real time. With conventional technology, this is extremely difficult, as data is in multiple formats across hundreds of systems, both structured and unstructured. The latter accounts for anything between 70% and 90% of organizational data, according to Gartner.
Structured data can be in logs, inventory counts and transactions in warehouse management, enterprise resource planning, third-party logistics provider and point-of-sale systems. Almost all of it is useful, but in large organizations with multiple overlapping systems and different software versions, it can be full of duplications and inaccuracies that degrade the performance of any AI tools or platforms.
The unstructured data that organizations need for their AI may be buried in supplier emails, meeting notes, call transcripts or customer feedback reviews. It will certainly be distributed across the entire supply chain, which adds to the complications of using it in real time.
To render all this structured and unstructured data fit for AI, supply chain organizations must be fully capable of bringing it together to remove inaccuracies and inconsistencies.
The most effective approach, with quickest implementation and lowest level of internal disruption, is the data-fabric architecture. It connects information across systems and supply chain applications in a unified data-orchestration platform.
Rather than replacing the systems that global organizations already have, this approach sits above them, creating a unified, accurate and trusted view of the supply chain and the data needed to enable AI-driven decision intelligence. It cleans up the data, making it usable and AI-ready.
With access to the right data quality and agentic AI capabilities, decision intelligence enables supply chain leaders to make faster decisions with more confidence, supporting service levels and margins. Generative AI in platforms enables line-of-business managers to ask questions in everyday language and get quick answers, resolving problems or challenges much faster and with greater impact.
These capabilities are vital when an entire supply chain can be knocked off balance by anything from a sudden port disruption or single drone attack to the accelerated imposition of new localization rules or tariffs.
Even in more stable times, it can be difficult for organizations to respond to the kinds of spikes in demand and the problems of availability that afflict enterprise-level supply chains.
With AI supply chain platforms fed by clean, up-to-the-minute data, organizations have true end-to-end visibility, while the speed and accuracy of decision intelligence drive up key performance metrics such as on-time, in full (OTIF).
Optimized real-time demand-sensing also improves forecast accuracy. The result is greater confidence across the chain. This reduces, for example, the likelihood of inventory shortages or overstocking.
Decision intelligence is fast becoming an essential technology, but to make it a reality, organizations must address the challenges of disparate and fragmented supply chain data. By adopting a new approach that connects and unifies the data, they lay the vital foundation for decision intelligence that will deliver major gains in agility rather than incremental changes.
Once these foundations are in place in the shape of a data platform with harmonized and normalized structured and unstructured data with embedded agentic and GenAI capabilities, organizations are better positioned to anticipate and respond to supply chain events or changes in demand.
Forewarned is forearmed. Aware of what’s about to happen and with AI-enabled, optimized decision choices, supply chain leaders will be fully prepared to make the right call. They can turn disruptions into opportunities that drive up revenues and increase customer satisfaction across the globe.
Mark Holmes is global head of supply chain market strategy at InterSystems.
