As AI becomes increasingly integrated into business operations and decision-making, organisations need to strike the right balance between technological innovation, operational efficiency, risk control and long-term value creation. Data quality, governance frameworks, AI agents and human expertise are emerging as critical pillars for companies looking to transform and stay competitive in the years ahead.
Transforming early AI initiatives into lasting business value
From everyday tasks to complex strategic decisions, artificial intelligence has clearly moved beyond the testing phase. For organisations today, the question is no longer whether AI should be adopted, but how it can be deployed at scale in a reliable, secure and sustainable way. Despite widespread enthusiasm around its capabilities, a significant gap remains between AI’s perceived potential and the measurable results achieved within companies. While AI features prominently in corporate strategies, it is still far less embedded in actual decision-making processes.
The latest edition of the Data & AI Barometer 2026 by Les Echos Etudes, conducted on behalf of Coface among 900 business leaders, highlights major differences in AI maturity based on company size, with larger organisations demonstrating stronger integration of AI into business decisions. Today, one-third of medium-sized and large companies consider themselves advanced in AI adoption, compared with only one in ten SMEs. Although two-thirds of decision-makers express confidence in their data, one-third believe it does not adequately support business requirements, risk management needs or decision-making tools.
Data quality is, more than ever, the key factor in the successful industrialisation of AI within a company, enabling it to move faster and achieve efficiency gains.
The resources, skills and capacity to structure Data & AI projects seem to favour large organisations. This carries the risk that the gap will become increasingly difficult for small businesses to bridge as AI applications become more structured,
outlines Nesrin Gonin, Business Information Director - Coface Western Europe & Africa.
Without data governance, AI cannot deliver its full potential
Regardless of industry or company size, the scalability and effectiveness of AI depend heavily on the quality of the underlying data. Even the most advanced AI technology cannot consistently overcome outdated, fragmented or poorly structured information. Yet 60% of organisations surveyed by Coface continue to use AI without a formal governance framework. As AI adoption accelerates, effective management and oversight have become critical priorities. However, most organisations still lack a fully established data governance strategy, creating limitations that often lead to underwhelming outcomes for both businesses and their teams.
Strong data governance relies on accurate, real-time information aligned with business objectives and supported by a shared semantic framework. In other words, organisations need common definitions, consistent indicators and a unified understanding of how data should be used. In practice, the same dataset may represent entirely different realities depending on whether it is viewed by Risk, Finance, Procurement or Sales teams.
AI acts as a catalyst: it amplifies both the quality of the data and its weaknesses. As it becomes more widespread throughout the organisation, establishing clear rules on data, usage and responsibilities is becoming essential.
Data governance is no longer a peripheral issue: it is a prerequisite for creating sustainable value with AI,
states Samia Boujatioui, Group Head of Data Management – Coface Data Office.
Business alignment and technology innovation: enabling smarter, faster decisions
The next major step in enterprise AI adoption may come from AI agents connected directly to corporate data ecosystems. Their capabilities are already becoming clear:
instantly analyse customer portfolios,
detect businesses with deteriorating risk profiles,
explain market trends and uncover comparable growth opportunities.
This evolution addresses a practical business requirement. Almost one in two decision-makers wants AI to deliver clear, actionable insights that can support immediate decision-making, without completely handing control over to technology. Finance teams are increasingly focused on improving fraud prevention and anticipating customer risks, while sales departments are leveraging AI to identify untapped markets and new revenue opportunities.
In the future, competitive advantage will not necessarily belong to the organisation that gathers the most data, but to the one that successfully combines business needs with secure, high-performing technology while maintaining consistent governance across the entire organisation.
What will set apart a company that has successfully completed its Data & AI transformation is its ability to turn its data into concrete, faster and more effective decisions to generate a genuine business advantage.
says Nesrin Gonin.
Stay ahead of change and make decisions with confidence
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