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Artificial intelligence has become one of the main drivers of technological transformation in business. Companies are actively investing in generative AI, machine learning, and process automation.
However, an increasing number of companies are not achieving the expected business impact. The reason often lies not in the AI models themselves, but in the foundation on which they operate — data. Without high-quality, consistent, and understandable data, even the most powerful models cannot deliver accurate forecasts or support management decision-making. This is why the focus of the new wave of innovation is on making corporate data accessible, structured, and AI-ready.
Why AI Projects Often Fail to Scale
In most companies, data is distributed across dozens of systems: ERP, CRM, HR platforms, and solutions for logistics, finance, or manufacturing.
These data may be technically accessible, but they often lose their business context. AI models require connected information with coherent relationships. A demand forecasting model that cannot see how procurement conditions interact with suppliers or connect sales with customer payment behavior will generate forecasts that appear mathematically plausible but fail to account for the dynamics that actually affect the business.
As a result, AI models work with an incomplete picture of reality. This is why companies today increasingly focus not only on AI development but also on data governance architecture.
The Next Stage: Corporate Data Platforms
For AI to work effectively, organizations need a unified platform that combines operational and analytical data while preserving their business meaning.
A new approach to data management is emerging — Business Data Fabric. Unlike traditional architectures, where data is extracted and transformed, Business Data Fabric unifies access to information without losing its business context. This approach allows data to be used where it originates, while preserving its relationships.
One example of this approach is SAP Business Data Cloud — a platform that enables the integration of data from SAP systems and external sources into a single structure ready for analytics and AI use. The core idea is to unify diverse data sources, preserve the business context of information, and create a reliable foundation for analytics and AI. This approach helps companies move from fragmented information to a single data working environment.
For example, the entity “Customer” has the same meaning whether it appears in financial planning, supply chain analysis, or workforce modeling.
This may seem like a technical detail, but it has significant operational implications. Take workforce planning as an example. The HR team needs to combine financial data (budget allocation, expenditures), operational data (project timelines, resource needs), and HR data (skills, availability, turnover risk). In a traditional architecture, creating such an analytical view can take weeks or even months of data integration. Even then, the results may remain unstable. When data is unified under a single model, such analytics becomes a standard capability rather than a custom project.
For AI to truly work in business, companies need to move from copying data to a unified, consistent data system. Those who do this early will gain a long-term advantage.
How to Build a Data Architecture for AI
To explore this topic further, SAP is hosting the online event The Fabric of Data and AI, dedicated to modern data architectures and the role of platforms in advancing corporate AI.
When: March 24, 2026, 12:00 Kyiv time
Language: English
Format: Online
Participants will learn how modern technologies help companies integrate data from various sources, implement AI in business processes, and create intelligent digital platforms for enterprise management.
Program Highlights:
Register here:
https://events.sap.com/fabric-of-data-and-ai/en_us/home.html
Useful Resources for Developers and Data Professionals
For those who want to explore the capabilities of modern data platforms in more detail, several resources are available:
