In 2024, Cloudera set out to understand the state of enterprise AI and data architectures, releasing its first survey report on the subject: The State of Enterprise AI and Modern Data Architectures. The results from that survey painted a picture of an enterprise AI landscape where IT leaders were ready to capitalize on AI but struggled with outdated data architectures.
Now a year later, how are enterprises fairing in their AI journeys? To better understand the current state of AI and data architecture, Cloudera fielded a follow-up survey report: The Evolution of AI: The State of Enterprise AI and Data Architecture.
The survey of 1,574 enterprise IT leaders across the US, EMEA, and APAC, shows that AI is moving from experimentation to deep integration, with a focus on data and current data architecture deployments evolving in lockstep.
Let’s dive into the findings.
The State of Enterprise AI: Maximizing Value
This year’s report reveals that enterprise AI has moved from experimentation to full integration in core processes and workflows:
96% of respondents say that AI is at least somewhat integrated into their core business processes
54% say they have significant AI integration
21% say it’s already fully embedded
These numbers make it clear—AI has become table stakes for enterprise success.
And the benefits of AI aren’t something relegated to the abstract or hypothetical. A growing number of IT leaders are seeing real value generated. In fact, most (52%) report they’re significantly successful in realizing measurable value from AI, while only 1% have yet to see results.
So, what types of AI are these organizations utilizing to generate that success? Cloudera’s survey found enterprise IT leaders are tapping into a broad set of AI forms. This includes generative (60%), deep learning (53%), predictive (50%), supervised learning (43%), classification (41%), agentic (36%), and regression (24%) models.
As AI portfolios diversify, the lesson is clear: leaders aren’t relying on a single “hero model” but building collections tuned to use case, risk, and cost. Likewise, they want visibility and control over all their data, not just a subset, so decisions are smarter and AI more effective.
Enterprises are gearing up for newer forms of AI. Agentic capabilities are crossing from experiments to production. Sixty-seven percent feel more prepared to manage agents than a year ago (26% say much more prepared). Already, 36% run agents as a primary model type, and 83% believe investing in agents is essential to maintaining a competitive edge.
Leading organizations will pair guardrails with clear ownership models for agent actions and data access. The pivot from applications to intelligent agents is underway, and success will depend on unifying policies wherever those agents run.
Examining Today’s Data Attitudes and Architectures
Enterprise culture around data is maturing. Eighty-six percent of leaders describe their organization as at least moderately data driven. Those calling their culture extremely data-driven rose to 24%, up from 17% a year ago. That culture shift is accompanied by a growing level of confidence in enterprise data as well.
Among survey respondents, 24% say they trust their organization’s data much more than they did one year ago, and another 41% say they trust their organization’s data somewhat more.
As enterprise leaders look to enable AI at scale, the foundation of data architecture they choose may vary:
63% of organizations are storing their data in private clouds
52% are storing data in public clouds
38% say they rely on on-premises mainframes
32% note they use on-premises distributed options
With data spread across a mix of storage methods, success with AI hinges on an organization’s ability to bring AI to data anywhere: in clouds, data centers, or at the edge.
As Confidence in Data Rises, the Bottlenecks Still Bite
Even as enterprises grow more confident in their data and embrace a wider range of AI models, many adoption and implementation challenges persist. Asked what the biggest technical limitation of their architecture was, respondents chose data integration (37%) as their top issue. This is followed by storage performance (17%), compute power (17%), lack of automation (17%), and latency (12%).
Then there are challenges that have evolved since last year. Compared to 2024, the cost to access computer capacity for training AI models is on the rise. One year ago, just 8% of surveyed IT leaders noted these costs were too high. Today, that number has increased to 42%—a 34-point jump!
Many respondents also have challenges around accessing and utilizing their organization's data for AI initiatives. While 38% of global respondents note that most of their organization's data was accessible and usable in these instances, just 9% say that all of their data is available. With data inaccessible to AI, these organizations may be missing potential market opportunities or operating with faulty information for decision-making.
Where Are AI and Data Headed Next?
Enterprise leaders are more confident in their data. AI is becoming deeply integrated into core processes, transforming everything from operational efficiency to customer experience. But many still have yet to make all of their data accessible to AI. This gap in access within data architectures poses serious risks from a competitive standpoint but also means AI initiatives may not be as effective as they otherwise could be.
Maximizing the value of AI is critical for the long-term outlook of enterprises, particularly as they seek to scale the technology. Overcoming these challenges starts with understanding internal data needs and prioritizing partners and tools that help bring AI to data anywhere, wherever that data resides.
Read the full report to uncover the current state of AI and data architecture, and learn more about why Cloudera is the only data and AI platform company that large organizations trust to bring AI to their data anywhere it lives.
