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 missing, leading to poor forecasts, inefficient targeting, and internal confusion. Without a single unified data layer, teams cannot collaborate effectively or drive growth. A central data layer is therefore an essential foundation for successful RevOps.
AI can sound convincing, but that doesn’t mean it is correct. Generative AI works based on probability, not verified facts. In a business context, that is risky, especially on topics such as ownership structures and compliance. That is why it is important to view AI as an interface rather than a source of truth. Real value emerges when AI is combined with reliable and verifiable data.
Master data excellence does not require tooling, but ownership, focus, and a phased approach to achieve First Time Right and real process value.
AI offers enormous opportunities, but without reliable data and clear processes, it remains nothing more than a promising prospect. Many organizations invest in AI without the proper preparation. In this blog, you can read why AI Readiness is the key to moving from experimentation to real impact and how a robust database is where success begins.
Agentic AI is changing the way we work. In the Altares webinar, experts shared how AI agents not only perform tasks but take over entire processes. What does this mean for companies, data, and employees? Read the key insights here and discover how to take the first step toward an AI-ready organization.
Typeahead is no longer just a UX tool but a strategic part of data-driven AI processes. By converting vague input into structured company data, it helps AI agents make autonomous decisions. Combined with no-code platforms, you can quickly build reliable, automated workflows for lead generation, KYC, risk assessment, and more, without major IT investments.
When is your organization ready for a master data management strategy? In this blog you will read which signals indicate this (such as fragmented data, data problems or growth plans) and how to set up an MDM strategy in 9 clear steps. From setting goals to securing data quality: this roadmap will help you get a grip on your data and be ready for further growth and digitization.
AI can take sales and marketing to the next level, but only if the underlying data is correct. Without reliable, up-to-date, and integrated customer data, AI has no impact and leads to inefficiency and wrong decisions. Master Data Management (MDM) is crucial for this reason: it provides a complete customer view and ensures data cleanliness, integration, and insight. With the right data infrastructure, AI becomes truly valuable, for example through smart lead scoring.
In the information age, it is difficult to distinguish truth from noise. The DIKW hierarchy (data, information, knowledge, wisdom) helps companies turn data into strategic insights. It starts with filtering noise: unfounded opinions and misleading figures. Next comes data verification and context, leading to knowledge that drives better decisions. Organizations that consciously go through these steps gain a competitive edge in the market. How well is data in your organization transformed into true wisdom?
