Gartner: Why CDAOs Struggle to Measure AI Investment Impact
Global spending on AI systems is at an all time high, yet organisations continue to struggle with measuring returns on these investments.
This challenge comes as boards demand greater accountability for technology spending amid economic pressures and increased scrutiny of digital transformation initiatives.
The role of chief data and analytics officer (CDAO), which emerged over the past decade as organisations sought to derive value from growing data volumes, now faces its greatest test, as these executives must demonstrate concrete returns on significant investments in data infrastructure, AI and analytics capabilities.
Against this backdrop, Gartner’s study, Chief Data & Analytics Officer Agenda Survey, gathered responses from 504 data and analytics executives globally between September and November 2024 – and found challenges in how organisations track and measure the impact of their data initiatives, as chief data and analytics officers face hurdles in demonstrating the value of their AI and data initiatives.
Measuring the impact of data projects remains elusive
Gartner reveals that 30% of data chiefs cite their primary challenge as the inability to measure how their data, analytics and AI programmes affect business outcomes.
“Organisations are increasingly prioritising the development of a robust D&As strategy to enhance decision making and operational efficiency and this falls to D&A leaders,” says Michael Gabbard, Senior Director Analyst at Gartner.
"There is a massive value vibe around data, where many organisations talk about the value of data, desire to be data-driven, etc., but there are few who can substantiate it.”
Strategic planning outpaces operational execution
Gartner also found a widening gap between ambition and implementation in corporate data strategies.
While 91% of chief data officers report that creating an actionable data and analytics strategy is their primary responsibility, only 22% of organisations have established and tracked business impact metrics for their data initiatives – and this disconnect becomes more pronounced when examining operational models.
Additionally, more than one-third of data chiefs do not consider establishing and evolving their operating model as a core responsibility, despite its critical role in executing data strategy.
- 30% of (CDAOs) cite the inability to measure data, analytics and AI impact on business outcomes as their top challenge
- Only 22% of surveyed organisations have defined, tracked and communicated business impact metrics for the bulk of their D&A use cases
- 91% of CDAOs said that creating an actionable D&A strategy is a primary responsibility for them
This means that the shift towards value-focused responsibilities has become prominent over the past 18 months, with more than 90% of data leaders reporting that demonstrating business outcomes has become their main focus – reflecting broader changes in how organisations view the role of data leadership.
Value measurement challenges persist across sectors
Gartner further highlights persistent challenges in quantifying the return on investment from data and analytics initiatives as organisations across sectors report difficulties in establishing clear links between their data programmes and business performance metrics.
For data leaders, this measurement challenge creates obstacles in securing future investment and maintaining support for long-term data transformation projects – creating the inability to demonstrate concrete value threatens to undermine the strategic position of data and analytics within organisations.
Michael says: “Often, organisations that regularly align to data themes outperform their peers.”
However, without standardised measurement frameworks, these performance claims remain difficult to verify across different sectors and organisations.
Operational context shapes strategic decisions
The pressure on data leaders to demonstrate value comes as organisations increase their investment in data and analytics capabilities.
Yet the gap between strategic planning and practical execution remains a significant hurdle for many enterprises.
The research suggests that successful data leaders are those who can bridge the divide between strategic vision and operational reality and this requires building robust frameworks for measuring impact while maintaining focus on long-term strategic objectives.
Measurement frameworks lag behind investment
The absence of robust measurement frameworks presents particular challenges for organisations investing in AI and advanced analytics.
Without clear metrics, companies struggle to justify continued investment in data infrastructure and talent.
The survey further reveals that organisations face multiple challenges in developing effective measurement frameworks and these include difficulties in attributing business outcomes directly to data initiatives, challenges in measuring indirect benefits and complications in tracking long-term value creation.
Data leaders also report that traditional return on investment calculations often fail to capture the full value of data and analytics initiatives, particularly in areas such as improved decision making and operational efficiency.
"CDAOs must accelerate data-driven and decision-centric vision by spotting important trends early to make proactive, informed choices about D&A strategy and building operational context around these drivers," says Michael.
"There is a perennial gap between planning and execution for D&A leaders."
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