Big businesses are throwing everything they’ve got at AI: budgets, teams, tech, and yet many are stuck spinning their wheels. New research from journey management platform TheyDo reveals that while enterprise leaders are more data-reliant than ever, the quality and clarity of that data are dragging their AI ambitions down.
In companies with over 5,000 employees, leaders track an average of eight performance data points, which is far more than smaller businesses. Over half of these are considered “critical.” But instead of clarity, this data flood is creating confusion. Sixty-one percent of enterprise leaders admit they’re overwhelmed, and most say they only make use of about half the insights they collect.
Jochem van der Veer, CEO and founder at TheyDo, said: “Many businesses think they have a data problem – but what they often really have is a context and connection problem. Enterprises today are heavily reliant on data and dashboards to measure success, but at the same time feel overwhelmed by insights and that data quality is holding them back from both being more insights-driven and unlocking the full value of AI.”
A lack of AI-ready data
Poor and inconsistent data quality is the number one reason companies can’t unlock the full value of their information. When it comes to AI, that shortfall gets even more painful. A lack of AI-ready data is now one of the most significant barriers to successful implementation, with nearly four in ten enterprise leaders citing it as a major concern, far above the average.
The sector divide is equally noticeable: financial services are pulling ahead, with 40% already seeing value from AI, but a third of retailers haven’t even started. Across the board, just 28% of businesses say they’re fully capitalising on AI tools.
In terms of generational difference, older leaders are far more likely to distrust AI outputs, and nearly four times more likely to question the accuracy of data dashboards. Where younger teams see potential, seasoned execs see red flags.
Enterprises may be stockpiling data at record pace, but without clean, trusted inputs and clear strategies, their AI dreams could stall before they even start.