Generative AI State of Data Report



Even in its relative nascency, it is hard to overstate the far-reaching impact Generative AI has already had on every major industry. But the AI paradigm shift has only started to unfold, and many organizations are just now beginning to seriously put a Gen AI strategy in place. By every indication, the next two years will be a critical moment for organizations looking to harness these powerful new technologies.

On top of the pile (for now), the manufacturing sector is consistently outperforming other industries when it comes to Gen AI deployments and their confidence in their internal data teams to implement concrete AI use cases. Industries like government, education, and healthcare, meanwhile, have begun to trail behind in defining and applying AI.

Correlations are also beginning to emerge between organization size, revenue, and rates of AI deployment—though the largest and highest-grossing organizations aren’t necessarily the most successful early AI adopters. Rather, the right blend of raw investment power and an agile data strategy seems to be the single greatest predictor of success.

The demand for generative AI is also spurring organizations on in their intentions to modernize other aspects of their data strategies, with as much as 74% of all orgs indicating their intention to  implement a centralized cloud platform this year, and another 79% of organizations indicating the need for a “moderate” to “large” amount of outside help to get there.

A survey of 500 Director to CEO level data leaders across eight major industries reveals a digital landscape on the brink of colossal change as early winners and late bloomers contend to come out on top of emerging tools and technologies. While AI-driven interventions and innovations continue to change and evolve almost daily, findings from this survey nevertheless illuminate major marketplace trends and cast a forward glance at the top opportunities and investments we’re likely to see by 2027.


Executive Summary

Rates of data modernization and Gen AI deployment are both expected to skyrocket this year.

Just 29% of all organizations report that they have already migrated  to a centralized data cloud platform, while just 42% indicate they are already using Gen AI tools. These numbers are expected to change drastically this year, as 74% of organizations indicate the intention of centralizing in the cloud this year. 85% of organizations also expect to have implemented Gen AI tools by year’s end.


Gen AI Is among the top drivers of data modernization in 2024.

Among organizations 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. Other top motivations for modernizing included the need for more agile and flexible data architecture (50%) and for enhanced security (49%). Unsurprisingly, the most data mature organizations, which have the highest rates of cloud adoption, also had the highest rates of overall AI deployment.


Organizational dissonance is the single biggest threat to Gen AI success.

Within organizations in every industry, assessments of data needs, teams, and outlooks differ radically across different levels of leadership. Executive and C-level leaders, for instance, were 32% more likely to report that they felt “extremely confident” in the ability of their organizations to build necessary Gen AI capabilities internally than lower levels of leadership. They were also more optimistic about their organizations’ internal data literacy, data-driven decision making practices, and alignment between data teams.

Even the most innovative organizations aren’t getting the most out of their Gen AI tools.

Differences also emerge with regard to which applications of Gen AI  are being leveraged. For example, 50% of orgs are already using AI for automation, but  sophisticated use cases like AI copilots, ETL/ELT, and schema matching and integration are significantly less common. Equally telling, the use of AI to automate decision-making was identified as a top challenge for 45% of organizations in 2023.


Consistent returns on data investments aren’t necessarily an indicator of Gen AI success.

With the most data-mature organizations reporting  between 137% and 164% returns on average on their data technology investments, it is hardly surprising that less mature organizations are eager to learn from their successes. Surprisingly enough, however, only 56% of the most data-mature organizations have implemented Gen AI use cases, leaving plenty of room for growth even among innovation-stage organizations.


Top performers in the AI arms race are large enough to invest but small enough to stay agile.

While some correlation exists between revenue and AI success, organizations with $500 million in revenue actually reported higher usage rates than much larger companies. Organizations with 10,000 to 24,999 employees, meanwhile, outpaced both larger and smaller organizations alike with a 49% Gen AI deployment rate—an 11 point edge on organizations with more than 25,000 employees, who came in at 38%.


A handful of industries are coming out on top in the Gen AI arms race.

Manufacturing orgs ranked first  in nearly every Gen AI application, with high confidence levels in their use cases and internal capabilities to match. With an overall Gen AI deployment rate of  50%, they were also the most likely to be using Gen AI tools for data cleaning and processing (71%) and for cataloging (59%). Education & Government and Healthcare, meanwhile, came in behind every other industry in their Gen AI deployments and reported the lowest rates of confidence in having implementable Gen AI use cases.


Gen AI Is a Top Driver of Modernization in 2024

The notion that the “wave” of data modernization has already come and gone is a persistent myth. In reality, modernization is still very much a work in progress for the vast majority of organizations.

Across industries, 2024 will be the year of modernization, as 94% of companies said they would need to modernize their data stack in 2024, with 48% reporting they would need to modernize “a great deal.”


The day-to-day reality for most organizations also supports this trend, with only 29% of orgs reporting they have transitioned to a centralized data cloud platform and just 42% indicating they are already using Gen AI tools.

But those figures are set to rise dramatically in 2024, with 74% of organizations projected to use a centralized cloud platform this year and 85% of orgs expecting to implement Gen AI tools.


Unsurprisingly, the most innovative organizations were ahead of modernization curve in 2023. These orgs were significantly more likely than their peers to have already migrated their data to a centralized cloud platform and to have begun using Gen AI tools for automation and data cleaning purposes.

But even among these industry leaders, many organizations have yet to harness the potential of these game-changing data tools, including 51% of Innovation organizations who have yet to transition to a centralized cloud platform and 37% who are not yet using Gen AI for automation purposes.


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.

Tellingly, 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. Those who plan to join them in 2024 will be looking to tap into that same potential.

The data is clear: modernization is happening now, and the power of Generative AI is a driving force. Organizations looking to keep up with the competition and build an efficient, sustainable data stack know they’ll need the power and convenience of Gen AI in 2024 and beyond.


Organizational Dissonance is the Biggest Threat to Gen AI Success

In order to successfully modernize their data stack and tap into the transformative power of Gen AI, organizations need a clear and precise understanding of their data strategies and goals. But for many companies, organizational dissonance is a major obstacle to data modernization, with different levels of leadership reporting contradictory outlooks on their org’s data needs.

In order to properly harness Gen AI tools, many organizations will find it necessary to deeply assess their internal data teams’ abilities and resources when it comes to building the Gen AI capabilities their org needs.

But 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. Meanwhile, they were 52% less likely to report low levels of confidence in their internal data team.

Despite their belief that in-house teams hold the keys to success, executive leaders were still quick to report an intention to utilize outside help, with many of them indicating they would require a “large” amount of outside help to modernize their data stack in 2024.


In sum, higher-level leadership expresses both great confidence in their internal data teams while also pointing to a need for significant external support. Meanwhile, lower-level data titles expressed the exact opposite sentiment.

This incongruity reveals profound organizational misalignment when it comes data needs and strategies. Despite ambitious goals to modernize this year, organizations will find that transforming their outdated data stacks and harnessing Gen AI tools will prove difficult without a consistent vision for their businesses’ data outlook.


But the dissonance doesn’t stop there. Executive and C-level team members expressed significantly more optimism across the board when it came to their organization’s data approach, including factors like internal data literacy, data-driven decision making practices, and collaborative alignment between data teams. Meanwhile, lower-ranking team members were consistently more pessimistic when asked about these same factors.


Similarly, 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 Gen AI tools.

These discrepancies point to a deeper disunity when it comes to how organizational leadership views their data needs. Until this dissonance is resolved, organizations will find it difficult to modernize their data stack, which will require a unified vision of what data modernization will entail to succeed.


Despite upper-level leadership’s confidence in their data teams and approach, that optimism simply isn’t reflected on a broader level, as only 4 in 10 organizations strongly agreed that their have the skills and expertise to support the use of Gen AI.


Even The Most Innovative Organizations Know They Still Aren’t Getting the Most Out of Their Gen AI Tools, Despite Consistent ROIs on Data Investments

It goes without saying that Gen AI is rapidly becoming a fixture of the modern data stack. More than half of Innovation organizations had already begun using AI tools in 2023, and this year 84% of all orgs are set to begin deploying them.


But it’s not just the allure of cutting-edge technology that’s drawing these companies to Gen AI. Organizations across industries reported an average ROI of 126% on data tech investments in 2023—a number that jumped to 164% among Innovation orgs.


With data tech delivering such robust and consistent revenue returns, it’s no surprise that modernization is set to explode this year, bringing Gen AI into the fold for the vast majority of organizations across industries.

Yet there’s more to the story than whether or not an organization is using Gen AI tools. It’s just as important to ask what AI functionalities are orgs using, and how effectively are they making use of the tools they are deploying?


There are major inconsistencies when it comes to the specific Gen AI capabilities organizations are currently deploying.

While 50% of orgs are already using AI for automation, far fewer are tapping into the potential of functions  like AI copilots, ETL/ELT, and schema matching and integration.


And 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.

Yet, although more than half of organizations have yet to begin using Gen AI as of 2023, 97% of companies plan to be using not just one or two but all nine Gen AI capabilities detailed in the table above by 2025. That number accurately reflects the 95% of organizations who said they believe Gen AI would be “moderately” to “critically” important to their success by 2027.


But with so many tools and functions to integrate into their operations and with limited faith in their internal data teams, organizations might be facing an uphill battle when it comes to fully embracing Gen AI.

If they intend to reach their goal of broad implementation of AI tools, they’ll need to streamline their data stack for maximum efficiency and likely rely on external data support to overcome time, talent, and resource constraints. Unsurprisingly, 79% of organizations said they believe they’ll need a “moderate” to “large” amount of outside help to achieve their modernization goals in 2024.


Top Performers in the AI Arms Race Are Large Enough to Invest but Small Enough to Stay Agile — A Handful of Industries are Also Coming Out on Top

Successfully leveraging the power of Gen AI looks different for every organization—so who were the biggest movers and shakers when it came to Gen AI deployment in 2023?

Unsurprisingly, there was some correlation between revenue size and Gen AI usage in 2023, with $10B organizations demonstrating higher AI usage rates than other orgs. But that correlation wasn’t consistent across the board, as organizations with less than $500 million in revenue orgs surprisingly reported higher usage rates than much larger companies.

The relationship between number of employees and Gen AI usage was perhaps more telling, with organizations with 10,000 to 24,999 employees representing the sweet spot of AI deployment, outpacing larger and smaller organizations alike with a 49% usage rate, compared to a more meager 38% usage rate among organizations with more than 25,000 employees.

significant but are not the most important determinant of successful Gen AI deployment. The relationship between revenue and data innovation is further complicated by the fact that, while $10B organizations led the competition in Gen AI deployment, they also reported higher rates of dependence on legacy technology.

While these larger orgs likely have an abundance of tools and resources in keeping with their revenue size, they aren’t necessarily deploying these tools and resources like Gen AI efficiently. These larger, more established orgs may lack the agility and adaptability to effectively modernize their data stack and deploy new technologies like Gen AI  as efficiently as their smaller, less deeply entrenched peers.

Gen AI usage trends were much more consistent across industries, with Manufacturing orgs reportedly ahead of the curve in nearly every category. These organizations led the competition when it came to Gen AI deployment rates in general at  50%, as well as in using Gen AI tools for data cleaning and processing (71%) and cataloging (59%). They were ahead of the curve when it came to their current use rates of nearly every Gen AI capability.


Unsurprisingly, manufacturing organizations also reported the highest confidence in their defined and implementable Gen AI use cases, as well as in their internal data team’s ability to 
build the Gen AI capabilities their org needed.

However, these organizations were also the most dependent on legacy technology, suggesting that even manufacturing orgs haven’t cracked the code when it comes to Gen AI deployment. Tellingly, their usage rates for AI automation were much closer to average than their other rates, and they were among the most likely industries to site Gen AI automated decision-making as a major challenge. They also trailed all other industries when it came to using AI copilots.

Entertainment and Retail organizations led all industries when it came to deploying Gen AI automation, with both industries among the leaders when it came to using Gen AI for data cleaning.

Meanwhile, Education & Government and Healthcare trailed all other industries in Gen AI usage rates. They also reported the lowest rates of confidence in having implementable Gen AI use cases.



Generative AI may be the term at the tip of everyone’s tongue in 2024, but most organizations are far from realizing its potential. While early adopters are working to implement discrete tools and perhaps even exciting their investor boards with shiny new offerings, 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. As with many disruptive shifts in our respective industries, outcomes are likely to boil down to the strength of our data leaders’ vision.

Still looking to get ahead and align your business around a plan that can take you surging past competitors? You’re not alone. As the entirety of the data driven world struggles to articulate what exactly generative AI can and should mean, data leaders are fighting this same battle inside their organizations. A data strategy that aligns everyone from the C-level to the most granular engineering functions around a clear set of goals, investment requirements, and well-defined “obstacles to be removed” is essential to continuing to grow and expand with generative AI in the years ahead.

If the road to true generative AI feels daunting, that’s because it is. The good news? Almost everyone is starting out with a long road to run. That leaves even those with the slowest start plenty of time to catch up—if they invest wisely and quickly. When the tools are this good, there’s little that an aligned leadership team, a smart plan for external resources, and a multi-year data strategy can’t accomplish.