A new global study highlights that organisations seeing success in AI are significantly increasing investments in core data and analytics capabilities.
According to a survey of 353 data, analytics and AI leaders conducted between November and December 2025, companies with successful AI initiatives invest up to 4x more, as a percentage of revenue, in areas such as data quality, governance, AI-ready talent, and change management compared to those with weaker outcomes.
Despite growing investments, only 39% of technology leaders said they are confident that their current AI spending will positively impact financial performance. The findings suggest that organisations will need to strengthen foundational capabilities through 2030, including trusted data systems, contextual intelligence, and scalable infrastructure.
The report highlights a shift toward AI-first data and analytics, where AI is used to transform business and operating models rather than just improve existing processes. It also points to a redesign of teams, with smaller, more agile groups combining business and technical skills and working alongside AI agents.
Another key focus is the importance of context in AI systems. The study notes that organisations with mature, AI-ready data capabilities are achieving stronger outcomes, including higher revenue growth and better cost optimisation. It emphasises that AI agents need governed and contextual access to data to function effectively.
The report also calls for more integrated engineering practices. It suggests bringing together data, AI, software, and context engineering instead of managing them separately.
Governance remains a major concern. A separate survey of 360 IT leaders in the second quarter of 2025 found that only 23% were very confident in their ability to manage security and governance while deploying generative AI tools. The report states that governance models must evolve to include checks for bias, privacy, and compliance within workflows.
Finally, the study advises organisations to move beyond a narrow focus on return on investment and instead reinvest AI-driven efficiency gains into long-term growth and innovation.
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