Generative AI State of Data Report 2024

85% of organizations expect to have implemented Generative AI tools by year’s end

Hakkoda’s Generative AI State of Data report surveyed 500 data leaders from large organizations across major industries. Respondents hold director-level or higher positions in IT and analytics roles at US companies with 1,000 or more employees in the following sectors: Distribution/Supply Chain, Education, Entertainment, Financial Services, Government, Healthcare, Manufacturing, Retail, and Telecommunications. Those leaders shared their plans, challenges, and goals for generative AI, so you can put a finger to the pulse of your industry through 2027.

Findings from the Generative AI State of Data Report 2024 report include:

  • Gen AI Is a Top Driver of Modernization in 2024 — 94% of companies said they need to modernize their data stack. Only 29% of organizations report they have transitioned to a centralized data cloud platform and just 42% indicate they are already using Generative AI tools. As organizations look to close that gap in 2024, 45% of companies plan to begin using a primary cloud platform this year, with another 23% predicted to join them in 2025. Only 3% of organizations have no plan to make the transition. Of those who had already made the jump to a centralized cloud platform in 2023, 48% said enabling Gen AI capabilities was a top reason for transitioning.
  • Top Gen AI Performers Are Large Enough to Invest, but Small Enough to Stay Agile — $10B organizations demonstrate higher AI usage rates than other orgs, but also report higher rates of dependence on legacy technology. Organizations with less than $500 million in revenue surprisingly reported higher usage rates than much larger companies. Organizations with 10,000 to 24,999 employees represent the sweet spot of AI deployment, outpacing larger and smaller organizations alike with a 49% usage rate. These organizations were 33% more likely to be deploying Gen AI than their larger peers, leading all organizations of any size in that capacity.
  • Organizational Dissonance is the Biggest Threat to Gen AI Success — Executive and C-level team members were 32% more likely to report that they felt “extremely confident” in their internal data teams’ ability to build necessary Gen AI capabilities than lower levels of leadership. Executive and C-level team members were much more likely than lower-level leadership to report that their data teams frequently developed creative ideas for automating processes—a crucial factor to ensure successful implementation of Generative AI tools. Only 4 in 10 organizations strongly agreed that they have the skills and expertise to support the use of Gen AI.
  • Gen AI Tools Can Build Upon Consistent ROI on Investments — More than half of the most data-mature organizations had already begun using AI tools in 2023, and this year 84% of all orgs are set to begin deploying them. Organizations across industries reported an average ROI of 126% on data tech investments in 2023—a number that jumped to 164% among data-mature orgs. While 50% of orgs are already using AI for automation, far fewer are tapping into the potential of more sophisticated functions like AI copilots, ETL/ELT, and schema matching and integration. While companies are more likely to be using AI for automation, applying AI to automate decision-making was a top challenge for 45% of organizations in 2023, trailing only data quality and governance.

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“The Generative AI State of Data 2024 report shows that the true winners of generative AI will be organizations that take a strategic approach to implementation, investing in both the modernization and the long-term data strategy that will allow their business to radically alter day-to-day operations. A data strategy that aligns everyone from the C-suite to the most granular engineering functions around a clear set of goals, investment requirements, and well-defined obstacles is essential to continuing to grow and expand with generative AI in the years ahead.”

 - Erik Duffield, CEO & co-founder of Hakkoda