Ontdek hoe AI-gestuurde inkoop, betrouwbare leveranciersdata en de samenwerking tussen Ivalua en Dun & Bradstreet CPO’s helpen betere beslissingen te nemen.
Master Data Management is evolving towards continuous data quality, clear ownership and reliable business information for processes and AI. These six trends show how organisations can future-proof their master data for automation, integration and Agentic AI.
SAP Clean Core helps organisations keep their SAP landscape manageable and future-proof by developing innovations alongside the ERP core using SAP BTP. Combined with reliable Business Partner data, this creates a strong foundation for S/4HANA, e-invoicing, AI and further automation.
The SAP deadline has been extended to 2030, but that does not mean organisations can afford to wait. Without data cleansing, duplicate, outdated and incorrect customer and supplier data will be carried over to S/4HANA. Investing in reliable Business Partner data now creates a stronger foundation for a successful migration, automation and AI.
Many organisations now have a data platform such as Databricks, Snowflake or Microsoft Fabric, but effective AI requires more than technology alone. By enriching your data with reliable business context, such as corporate structures and business relationships, you can make your data AI-ready and unlock more value from both your data platform and AI applications.
Agentic AI places higher demands on data than traditional AI. Agents must not only understand information, but also independently access systems and perform actions. Four building blocks are essential for this: semantically rich data, real-time context, an action-oriented architecture, and governance at the source. This makes data reliable, up to date, and usable for safe, controllable AI actions.
AI is increasingly delivering measurable returns, and organizations are scaling applications more quickly. Yet the readiness of the underlying data is lagging behind: only 6% say they are fully prepared to support AI at scale. To sustain returns, organizations need a reliable data foundation in which business information is kept up to date, connected, and identified consistently.
AI only delivers reliable results when it has access to up-to-date and verified business data. The experiment shows that an AI model without reliable sources can hallucinate, while APIs and D&B MCP provide more accurate and better-supported answers. Ultimately, the quality of AI is determined by the quality, availability, and context of the data it uses.
Data providers are not losing their value because of AI; instead, they are taking on a new role as the trusted context layer for AI systems. Current, validated, and traceable business data is essential to prevent AI from drawing convincing but incorrect conclusions. With solutions such as D&B MCP, organizations can connect AI to trusted data at the source, enabling more accurate insights and better business decisions.
AI is increasingly delivering measurable returns: 60% of organisations are already seeing results. As a result, the focus is shifting from experimentation to embedding AI in business processes where it adds real value. Successful scaling requires reliable data, clear objectives and effective oversight of outcomes.
RevOps often fails because sales, marketing, and customer success operate with fragmented and inconsistent data. As a result, reliable insights are lacking, leading to poor forecasts, inefficient targeting, and internal confusion. Without a single unified data layer, teams cannot collaborate effectively or steer growth. A central data layer is therefore an essential foundation for successful RevOps.
AI can sound convincing, but that does not mean it is correct. Generative AI works based on probability rather than verified facts. In a business context, this is risky, especially in areas such as ownership structures and compliance. That is why it is important to see AI as an interface rather than a source of truth. Real value emerges when AI is combined with reliable and verifiable data.
