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 helps credit risk teams perform credit assessments faster by combining reliable business data, internal information and policy rules. This enables better risk prioritisation, earlier identification of anomalies, and more consistent and well-founded credit decisions.
AI helps real estate organizations analyze lease agreements more efficiently by combining reliable business data with contract information. This makes corporate structures more transparent, enables earlier identification of concentration risks, and supports better-informed real estate decisions.
AI helps detect supplier fraud earlier by combining reliable business data, bank account verification and structured workflows. This enables procurement and finance teams to identify suspicious changes, uncover fake suppliers and make better-informed decisions.
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.
AI-driven lead scoring combines behavioural data with company data to identify the most promising sales leads. By continuously learning from results, AI creates more accurate predictions of conversion opportunities. This enables sales teams to work more effectively, spend less time on low-potential leads, and focus on organisations with the greatest commercial potential.
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.
