Organizations ambitious on AI, but struggling with tangible value

Organizations are working hard to realise their AI ambitions, but in practice many remain stuck in the experimental phase or see their scaled-up use cases delivering little tangible value so far. That is according to the Data & AI Monitor by tech consultancy Xebia.

The study, conducted among more than 500 professionals across 10+ industries, explores how organizations are approaching data and AI. On the value of artificial intelligence (AI), leaders are largely in agreement: across sectors, the majority recognize AI’s potential to transform operations, improve decision-making, and generate new value.

Yet despite these ambitions, many still struggle to roll out AI effectively and scale it from a pilot project into an enterprise-wide initiative that is fully embedded into ways of working. As a result, many managers feel that AI is not yet delivering consistent and measurable outcomes.

Missing Data Strategy

The challenges often start with the fundamentals. Fewer than half of organizations have a clear, broadly supported data strategy. A majority (51%) report poor data quality, and almost as many (47%) say they lack a data-driven culture. In half of all cases, there is insufficient budget to get AI experiments off the ground.

“Without these foundations, it is very difficult for companies to truly capitalize on AI,” said Sjoerd Pieksma, Head of Data & AI Strategy at Xebia.

Digital-Native Sectors in the Lead

AI maturity varies significantly across industries, with professional services leading the way. 70% of companies in this sector have well-defined AI plans, and they are also more likely to extract real value from them. Accounting and consulting firms in particular benefit, as they tend to be digital-native and apply AI directly in client projects.

Manufacturing and healthcare, on the other hand, are lagging behind. Manufacturers often operate with outdated systems, while healthcare organizations face strict privacy regulations and fragmented patient data. As a result, both sectors cite data quality and regulatory hurdles as their biggest obstacles.

Use Cases

The study shows that organizations apply data and AI across a wide range of use cases. Dashboards and reporting stand out as near-universal practices, with more than 80% of organizations leveraging data in this area. Other use cases include predictive modelling, data warehouses, and generative AI – though Gen AI remains in the early stages of adoption.

In their AI efforts, researchers highlight a striking finding: only 18% of respondents list Responsible AI as a priority. This is noteworthy given that Responsible AI is a core element of AI success. It ensures algorithms are developed and used in ways that are fair, transparent, and aligned with human values. Responsible AI helps build trust, reduces risks of harm or bias, and supports ethical decision-making within the organization.

AI Agents

The study also gauges expectations around AI agents. This emerging branch of AI could have disruptive effects in certain areas of business, the report notes. “AI agents are not simply algorithms that replace less efficient ones, but autonomous systems that can reshape and rebuild operating models from the ground up.”

However, there are concerns that AI agents may pose serious risks for employees in areas likely to be displaced by the technology. Nearly half (45%) of leaders expect AI agents to reduce the need for staff. Employees themselves echo these concerns, with fears of job loss especially pronounced in manufacturing and healthcare.

“AI agents and assistants can accelerate AI adoption,” said Pieksma, “but they also expose every weakness in an organization’s data foundations. Without a clear AI strategy, robust governance, and high-quality data, these tools may end up doing more harm than good.”

The Xebia consultant stresses the importance of a timely and holistic preparation: “As AI takes over more decision-making, the cost of poor preparation will rise quickly. Moving fast may be tempting, but only a well-thought-out approach ensures lasting advantage.”