Discover top fintech news and events!
Subscribe to FinTech Weekly's newsletter
Read by executives at JP Morgan, Coinbase, Blackrock, Klarna and more
Enterprise AI Gains Traction, but Scaling Issues Persist, New DataIQ Survey Finds
AI is becoming more embedded in enterprise workflows, but large-scale adoption continues to face familiar roadblocks. That’s the picture emerging from a new report by DataIQ and Blend, which surveyed senior data and analytics professionals across industries, including members of the DataIQ 100 list.
The study captures how AI tools are being deployed across businesses—and where they continue to fall short of expectations.
Over half of the organizations surveyed reported having at least 12 AI applications in use, often deployed in isolated proofs-of-concept. Yet 28% still report using only 3–5, suggesting difficulties in expanding from initial testing to broader implementation. These figures underscore an uneven trajectory in how enterprises are moving beyond experimentation to embed AI into operational systems.
While interest in AI integration is growing—appetite for enterprise-wide adoption is up 25% compared to 2023—investment in foundational elements remains limited. Only a third of respondents said their organizations are prioritizing training or change management for AI tools, pointing to a potential misalignment between strategic ambition and implementation readiness.
The report also reflects a shift in how generative AI is being used within enterprise environments. Usage in data engineering has more than doubled over the past year, with 65% of respondents now applying generative AI to support backend data functions. In 2023, that figure stood at just 28%.
Beyond implementation rates, the report also explores the role of leadership and organizational culture in shaping AI outcomes. Companies with mature data strategies appear better positioned to integrate AI more systematically, while those relying more on intuition-based decision-making show slower adoption trajectories.
Trust and governance also continue to shape the pace and effectiveness of AI deployment. As organizations navigate regulatory scrutiny and internal risk concerns, formal structures for oversight and accountability are increasingly seen as necessary to scale responsibly.
The findings suggest that while AI is becoming a standard feature in enterprise planning, the ability to operationalize it remains mixed. Many businesses still face a disconnect between ambition and execution—particularly when it comes to enabling the workforce, ensuring transparency, and integrating AI into complex legacy environments.
