Gartner Says Chief Supply Chain Officers Can Scale AI With Data Fabric Architecture

Data Fabrics Can Accelerate Decision Making Without Requiring an Overhaul of Existing Systems

Chief supply chain officers (CSCOs) seeking to scale advanced analytics and AI across complex, multi-system environments can leverage a data fabric architecture to create an AI‑ready data foundation, according to Gartner, Inc., a business and technology insights company.

A data fabric architecture is a modern approach to data architecture that enables organizations to manage and utilize data seamlessly across diverse systems, locations, and partners. Rather than centralizing data into a single platform, data fabrics establish a unified data layer that connects information wherever it resides, spanning both cloud and on-premise supply chain applications.

By leveraging active metadata, semantic models, and AI, data fabrics automate data discovery, integration, and delivery, allowing CSCOs to access consistent, trusted data in real time without the need to overhaul or physically move existing systems.

“For CSCOs, data fabrics offer reduced costs and time for data integration efforts, as well as improved decision making through the ability to operationalize AI across supply chain activities,” said Vas Plessas, Director Analyst in Gartner's Supply Chain practice. “As supply chains face continued uncertainty and complexity in their operating environments, data fabrics enable integrated, scalable intelligence across expanding data models.”

Key benefits of implementing a data fabric include:

1. Unified data access across silos: Data fabrics connect information from cloud platforms and legacy or modern supply chain applications into a single, coherent view. This allows teams to analyze data directly and bypass IT-led consolidation efforts.

2. Real-time insights for AI and analytics: Continuous access to data supports optimal decision making, providing AI models with timely, high-quality data for accurate forecasts and recommendations.

3. Composable architecture: Flexible and modular by design, data fabrics evolve with business needs, supporting new use cases, integrating diverse data sources, and scaling across functions and regions.

4. Automated data management: Leveraging metadata, semantics, and machine learning, data fabrics automate manual tasks like cataloging, mapping, and quality checks, reducing IT burden and accelerating deployment.

Early Adoption Challenges

While the vision for data fabrics is compelling, realizing its full potential is complex and requires careful planning. Many organizations remain in early adoption stages and face several practical challenges, including that there is not a single off-the-shelf solution available today.

“Data fabrics are an architectural approach, requiring the integration of multiple technologies into a unified system,” noted Plessas. “Vendor solutions are still maturing, and buyers must understand both the current solutions’ limitations and have a strong grasp on their own supply chain architecture. Laying the right data foundation today is what turns the vision of AI-enabled supply chains into reality tomorrow.”

To realize the benefits of data fabrics, CSCOs should take a deliberate, staged approach aligned with business priorities via the following actions:

1. Evaluate data architecture for AI readiness: Establish clear data governance roles to maintain data quality and ensure technology architecture can support real-time data ingestion and transport across diverse systems.

2. Partner with data and analytics leaders: Collaborate closely on governance, metadata strategy, and data ownership. Supply chain leaders set business context and priorities, while IT teams design and operationalize the infrastructure.

3. Engage business unit leaders early: Identify high-impact, feasible use cases—such as sales and operations planning (S&OP) and sourcing—that benefit from unified data access.

4. Assess internal skills and capabilities: Evaluate the organization’s data management, integration, and modeling maturity. Metadata management is central to enabling data fabrics; address skill gaps through upskilling or targeted hiring.

5. Start small, scale fast: Launch focused pilots, such as real-time inventory visibility, and expand as capabilities mature.