{"id":53982,"date":"2026-05-28T11:41:04","date_gmt":"2026-05-28T09:41:04","guid":{"rendered":"https:\/\/www.altares.be\/?p=53982"},"modified":"2026-05-28T15:29:58","modified_gmt":"2026-05-28T13:29:58","slug":"the-next-phase-of-ai-from-experiment-to-return-on-investment","status":"publish","type":"post","link":"https:\/\/www.altares.be\/en\/blog\/master-data-management\/de-volgende-fase-van-ai-van-experiment-naar-rendement\/","title":{"rendered":"The next phase of AI: from experimentation to ROI"},"content":{"rendered":"

AI is increasingly delivering measurable business results. New global research from Dun & Bradstreet, conducted among 10,000 companies across 32 countries, shows that 60% of organizations are now achieving at least some measurable return on investment (ROI) from AI. At the same time, only 24% report broad or significant results, while 56% plan to increase their AI investments. <\/p>\n\n\n\n

These findings suggest that AI is entering a new phase. The focus is shifting from experimenting with possibilities to identifying where AI genuinely creates value. In this article, we explore what the D&B research reveals about AI ROI, why scaling requires deliberate choices, and the role data plays as AI becomes embedded in business processes. <\/p>\n\n\n\n

AI Is moving from experiment to business process <\/h2>\n\n\n\n

For many organizations, AI is no longer limited to a pilot project or a single-team initiative. The research shows that 97% of organizations have active AI initiatives. In addition, 30% are now scaling AI into production environments, while 26% are operationally deploying AI across multiple core business processes.  <\/p>\n\n\n\n

As a result, the role of AI is changing. What once served primarily as a tool for exploring new possibilities is increasingly becoming part of processes where information is handled daily and decisions are prepared. Examples include analyzing customer developments, gathering supplier intelligence, or identifying changes that require attention. <\/p>\n\n\n\n

At this stage, technical functionality alone is no longer enough. AI must fit seamlessly into the processes where employees use it. A fast result only becomes valuable when users understand what it means and how they should act on it.<\/p>\n\n\n\n

Interesting read:<\/strong> Agentic AI: from hype to practical reality<\/a> <\/p>\n\n\n\n

Not every successful AI application should be scaled <\/h2>\n\n\n\n

The fact that organizations are already seeing returns from AI is an important development. However, the gap between the 60% reporting some measurable ROI and the 24% achieving broad or significant results highlights that success does not automatically translate across the organization. <\/p>\n\n\n\n

An application that helps employees process documents more efficiently may deliver immediate time savings. However, AI systems used for customer selection, supplier monitoring, or risk assessment require a different level of reliability. In these cases, outcomes must not only be delivered quickly but also be accurate, relevant, and trustworthy enough to support decision-making. <\/p>\n\n\n\n

As organizations increase their AI investments, it becomes increasingly important to define for each application: <\/pre>\n\n\n\n