The revelation comes from Alteryx, an AI platform for enterprise analytics, which has unveiled significant challenges hindering organisations’ ability to leverage generative AI effectively. 

The report, titled Data Stack Evolution: Legacy Challenges and AI Opportunities, draws insights from a survey of 3,100 global IT leaders, shedding light on pervasive barriers across data management, technological strategy, and organisational culture.

Despite a prevailing sense of confidence among IT leaders regarding their data maturity, with 54% rating it as “good” or “advanced,” the research underscores underlying issues plaguing data stacks worldwide. Notably, only a mere 10% of respondents reported having a “modern” data stack capable of supporting successful generative AI rollout.

While 76% of respondents express trust in their data, challenges such as data bias (22%) and data quality (20%) persist. These issues underscore the inadequacies of current data stacks, potentially impeding the effective deployment of generative AI.

Despite efforts to modernise systems, inflexible tech strategies pose a significant obstacle to innovation. Over half (54%) of IT leaders indicate that once budgets are allocated, adjustments for other priorities or emerging projects become challenging, limiting the agility required for impactful innovation in the rapidly evolving AI landscape.

A striking 41% of global IT leaders report a lack of centralised data or analytics functions within their organisations, leading to data silos where information remains confined within individual departments. Additionally, ambiguity persists regarding the role of data ownership within organisations, with varied responses highlighting a lack of consensus on this critical aspect.

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Jason Janicke, senior vice president for EMEA at Alteryx, emphasised the pivotal role of data readiness in navigating the era of automated, data-driven intelligence. He stated, “With generative AI now reaching the peak of the hype cycle, business leaders and IT teams across the UK must realise that one clear differentiating element can make or break a business: data.”

“To succeed in this era of automated data-driven intelligence, modern data stacks and a data-skilled workforce should be brought together to take full advantage of available data, compute and automation resources. 

“The modern data stack must accelerate the data journey while empowering everyone to solve business challenges and deliver decision intelligence, not just the tech workers. The best measure of success of such a stack is the number of teams it empowers to make data-driven decisions faster and more effectively, ahead of the competition.”