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 agents help B2B companies qualify leads faster, identify prospects, and recognize commercial opportunities. Combined with reliable business data, they operate more accurately and deliver more relevant insights. This makes sales and marketing processes more efficient and enables companies to find high-potential customers more quickly.
KYC is still often based on onboarding and periodic reviews, even though customer data and risk profiles are constantly evolving. This creates the need for an up-to-date, consistent customer view that immediately reflects changes in ownership, management, or corporate structure. Perpetual KYC shifts this to a continuous, event-driven process that detects risks earlier and provides a stronger foundation for risk-based decision-making.
AI can accelerate compliance processes when supported by reliable, up-to-date data. With D&B Risk Analytics, the D&B Commercial Graph, and the D-U-N-S® Number, organizations gain the context needed for KYC/KYB, screening, due diligence, and continuous monitoring. This enables compliance processes to become faster, better informed, and more risk-focused.
Market analysis only becomes valuable when it helps translate commercial opportunities into actionable insights. By combining company data, customer relationships, and market intelligence, organizations can better identify where the greatest growth potential lies and which prospects deserve priority. This leads to more effective sales and marketing efforts and a more focused use of time and resources.
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.
Data provenance shows where business data comes from and how it has changed over time. This helps organizations build greater trust in their data and make better-informed decisions. Insight into the origin of data is becoming increasingly important, especially for compliance and risk analysis.
Many organizations are evolving credit risk management from a reactive process into an integrated, data-driven approach. By combining customer and risk data, they gain better insight into credit risks, including internationally. This makes it possible to identify risks earlier and intervene in a more targeted way. As a result, credit risk management is shifting toward a proactive and strategic approach.
In four steps to a Risk Intelligence Hub: from a single, consistent partner view with Master Data Management, through combining customer and supplier data with Know Your Partner, to linking risk signals to context and group structures. This shifts risk management from reactive to predictive, with greater control over risk, cash flow, and continuity.
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.
Many organizations still focus their credit management on the moment an invoice becomes due. The emphasis is on reminders, follow-up and collections, while the causes of late payments often arise earlier in the process.
