The rapid deployment of AI tech made data a critical resource for companies looking for an edge on their competition, however, new research has found a troubling gap between business leaders’ goals and their ability to use data to deliver technological transformation.

Software development firm SoftServe’s latest report, The Great Data Divide, found that while almost all (98%) business and tech leaders believe that a data-strategy update is required to gain the full benefits of AI, 73% admitted their company has allocated funds or talent to genAI trends at the expense of more valuable data and analytics initiatives.

More than half (58%) said that their firm made strategic business decisions based on inaccurate or inconsistent data most of the time, while even more (60%) said it was a real challenge to get access to the data they need, when they need it.

SoftServe’s report, based on a survey of 750 business leaders, as well as technology and data executives, found that a staggering 65% of firms said no one in their organisation has a comprehensive understanding of the data collected or how to access it.

An overwhelming majority of business leaders (98%) confessed that they were facing some considerable challenges in improving their firm’s data strategies, with the main barriers being the use of legacy or varied tech by different units (73%) and data ownership not being consolidated (75%).

Added to that, 76% of firms needing a data-overhaul said that their leadership does not fully grasp how to generate value from the data the company collects, a problem compounded by a disconnect at C-suite level with 79% of VPs believing that no one in their company fully understands the data they collect compared to only 45% of other C-suite executives.

Underdeveloped data strategies are having a real impact on businesses’ AI goals. Just 42% said that they are capable of training genAI models on their own data, while 89% said they struggle to incorporate business data in using AI.

Among the biggest challenges deterring organisations from moving forward with AI is the risk of data exposure (39%), an inability to capture or access the right data to build models (39%), and issues around compliance (36%), highlighting problems across the board in AI governance as well as skills shortages.

Many organisations are also facing a misalignment between AI initiatives and core business objectives, with 64% of business leaders admitting their companies frequently rollout AI solutions without establishing a viable use case, while even more (74%) agreed their firms often jump into AI pilots without any clear plans to scale.

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However, the good news is that organisations with greater data maturity were found to deploy AI initiatives less aimlessly. While 83% of companies that require a data strategy overhaul often or always deploy AI solutions without identifying use cases, the figure falls to 60% for data-mature companies. 

“GenAI and data have a mutually beneficial relationship,” wrote Iurii Milovanov, director of AI and data science at SoftServe.   

“Stronger data results in more robust genAI implementations, while business-aligned genAI applications convert unused data into valuable assets. The key is finding the balance to create a virtuous cycle.”