By Erik de Vos
In today’s dynamic world, supply chain planning is more relevant than ever. What is changing, however, is the way planning needs to be organized. Traditional planning approaches, built around fixed cycles, manual effort, and disconnected data, are no longer sufficient in an environment where volatility, complexity, and speed of decision-making continue to increase. Organizations need to work on improving their decision intelligence to avoid product shortages, inventory imbalances and network disruptions when the next big thing hits them.
This is exactly where digital transformation is a driving force. And with the growing need to apply AI in supply chain planning, the urgency is only increasing. In our experience, successful transformations require a re-design of processes into digital setups and three vital foundational enablers: organizing enterprise-grade data, augmenting roles toward decision-making, and connecting it all through smart-touch digital planning ecosystems.
In this blog we focus on organizing enterprise-grade data through a common data model. We explain what it is, why it is pivotal for digital transformation, and why its importance only grows once AI comes into play.
A common data model is the foundation for scalable digital planning
A common data model creates one harmonized, enterprise-wide data foundation. It centralizes data collection, enforces standardized definitions, and organizes data processing for scalability. In doing so, it becomes the single source of truth that can serve all planning use cases and AI applications consistently, reliably, and at scale.
“A common data model is a single source of truth that can serve all planning use cases and AI applications consistently.”
However, a common data model is often misunderstood as merely a data centralization exercise. In reality, it is much more than that. Centralizing data without harmonizing its structure, improving its quality, and translating it into a usable language will not create the foundation needed for digital planning or AI-enabled decision-making.
When we speak about a common data model, at least three elements are essential:
- Data structure and central data: data form all sources should be organized in a uniform way that makes it accessible and reusable across applications, functions, and planning horizons
- Data quality: data gaps, inconsistencies, and errors should be actively managed and resolved to avoid incorrect planning outcomes
- Data language: data should be embedded in a specific context. So that both humans and AI can interpret and use the data efficiently
This is why the common data model sits at the core of digital transformation. It does not only help connect tools and processes, but also determines whether AI can be applied effectively. Without a solid foundation, AI will simply scale confusion faster. With the right common data model in place, organizations can unlock a structured, trusted, and intelligent basis for planning and decision-making.
Bringing structure: how AI can help unlock planning data
A major challenge in many organizations is that relevant planning data does exist, but not yet in a format that allows it to be used efficiently. This is where a common data model adds value by bringing structure to fragmented data sources, and where AI can increasingly become part of the solution.
A typical example is contract data. Contracts often contain valuable information on volumes, commercial agreements, timing, and customer commitments. Yet this information is frequently locked in documents, emails, or non-standard formats. In the best case, effort is made to manually transfer these elements into a system. In practice, however, this often leads to delays, incomplete visibility, and mistakes. The challenge becomes even bigger when agreements change over time and manual updates cannot keep pace. With the right setup, AI agents can help unlock this data by retrieving and completing predefined data elements automatically. In that setup, the human role shifts away from repetitive processing toward supervision, exception handling, and filling in the blanks where needed.
“A common data model adds value by bringing structure to fragmented data sources, where AI can increasingly become part of the solution.”
A second common example can be found in customer service inboxes. In many organizations, customer orders still arrive by email and require reading, validation, and manual processing into the ERP system. This delays their visibility for demand and supply planning and introduces unnecessary friction in the process. Now imagine a setup in which AI agents automatically convert incoming emails into structured order information in ERP, ready for human approval before release. The AI unburdens the planners doing the processing and on top of that process’s orders faster. In such cases, AI accelerates the path towards a common data model by helping organizations extract, standardize, and structure data that would otherwise remain underused.
Safeguarding data quality: overcome data flaws
Structure alone is not enough. You can have a well-organized data foundation that technically unlocks data across your organization, but if the data itself contains flaws, it will either not be used or, worse, it will be used in ways that drive the wrong planning decisions. That is why safeguarding data quality is a second critical element of a common data model. This has always mattered in digital transformation, but it becomes even more substantial once AI starts relying on that same data for planning purposes.
“You can have a well-organized data foundation that unlocks data across your organization, but if the data itself contains flaws, it will not be used.”
Again, AI can also be part of the solution. Imagine agentic AI collaborating to detect missing values, inconsistent master data, unusual patterns, or conflicting definitions across systems. Where confidence is high, it can help resolve issues automatically. Where uncertainty remains, it can direct human attention exactly to the gaps that matter most. In that sense, AI can help secure the quality of the very data that other AI applications will later use to improve planning.
Creating a language both humans and AI can work with
The final element is common data language. This is often where organizations underestimate the true complexity of AI readiness. Supply chain data typically comes from highly diverse systems, each with its own field names, logic, and definitions. ERP environments are full of technical names, overlapping date fields, and inconsistent terminology. Simply redirecting such data into one central storage layer does not solve that issue.
“A common data model is what translates raw system data into a business language that connects human questions to data without confusion.”
Imagine a planner asking where the biggest sales gaps in the last period were. For AI to answer correctly, it must understand which fields represent sales, what should be compared to what, and at what time aggregation the organization works. A common data model is what translates raw system data into a business language that connects human questions to data without confusion. In the strongest setups, it also adds context by embedding KPI logic, metric definitions, and planning assumptions directly into the model.
Ready to strengthen the foundation of your digital planning transformation?
A common data model is not just a technical data layer. It is the foundation that connects processes, roles, planning tools, and AI into one scalable digital setup. Organizations that get this right do more than centralize data. They create the structure, quality, and language needed to plan smarter and decide with greater intelligence.
You can also read about this topic in our 2026 Digital Transformation report, which was written by Bart Paridaen and Ieke le Blanc.