In just the last few years, artificial intelligence (AI) has exploded across enterprise organizations, with new use cases emerging at a rapid pace. Tools and models like AI agents have introduced new opportunities and innovations that are redefining the marketplace.
Cloudera’s latest report: The Evolution of AI: The State of Enterprise AI and Data Architecture, paints a clear picture. Most organizations have moved beyond experimentation and are integrating AI models into some of the most important facets of their businesses: 96% of IT leaders surveyed say that AI is at least somewhat integrated into core business processes. At the same time, many leaders feel they’ve yet to realize the full potential of AI, and challenges to adoption and secure use of AI persist.
The AI landscape is constantly shifting. So, how does today’s AI environment compare to one year ago? How have attitudes changed? What challenges are enterprise leaders facing when it comes to AI adoption? Let’s dive into some of the biggest shifts.
Confidence in Data is Rising, but with Room to Improve
No matter the industry, maintaining a competitive edge depends on how quickly an organization can make accurate, informed decisions. But going a level deeper, that ability hinges on how an organization can tap into its own data. For AI to be impactful, IT leaders need to ensure they strive to make 100% of their data accessible. Cloudera’s survey reveals a notable gap here as just 9% said that all their data is available and accessible for AI.
Nearly one quarter (24%) of respondents said that they trust their data much more than they did last year, but 41% said they only trusted their data somewhat more. While confidence in data has shown signs of growth, enterprise leaders still hold some security concerns around AI implementation. Of those surveyed, 46% say they’re worried about the security and compliance risks that AI presents. And two of the top concerns relating to AI security are focused on data—50% cite data leakage during model training, and 48% note unauthorized data access as top challenges.
These results are not surprising. Enterprise leaders must maximize value from AI without exposing sensitive data or falling out of compliance. Something that, at a time where new regulations are constantly emerging, can be easier said than done. As organizations strengthen their data architecture and capabilities, governance remains a focal point of any strategy to ensure consistent security.
AI Adoption Challenges Persist
Even as enterprise IT leaders show more trust in their data year over year (YoY), many of the same AI adoption challenges cited in 2024 remain. For example, data integration is still ranked as the top technical limitation in data architectures when supporting AI workloads. Other challenges cited by survey respondents in 2025 included storage performance, compute power, lack of automation, and latency.
While many of the same challenges from 2024 have remained, one of the biggest shifts is the cost to access computer capacity for training models. The number of IT leaders who cite this as a barrier to AI adoption rose from 8% in 2024 to 42% this year—a 34-point jump! As enterprises push for more AI initiatives, with new tools and models, the costs of adoption and operation grow quickly—particularly if the data architecture supporting AI initiatives is not ready to handle more complex systems.
Then there’s the age-old problem of data silos, which have long caused trouble for IT leaders. Breaking down silos is a critical piece of effective AI. When a model is trained on incomplete data, the outputs are vulnerable to inaccuracies that could prove costly. Of the IT leaders surveyed by Cloudera, 61% say that siloed data has at least sometimes negatively impacted their ability to scale AI initiatives, but many are seemingly getting a handle on this problem, with 35% saying this was rarely impacting their own AI initiatives.
What’s Next for AI and Data Architecture
AI is now integrated into some of the most critical business functions across enterprises. As enterprise leaders become more familiar with AI tools and models, the demand for data has accelerated shifts in data architecture. Those shifts have seen organizations become more data-driven culturally, giving leaders more confidence in their organization’s data.
And yet, many of the same challenges surrounding AI adoption and security have remained consistent YoY, while new difficulties around operating costs have emerged.
Wherever an organization finds itself in their AI journey, having the right data architecture and AI infrastructure is critical to establishing long-term success.
Check out the full report and learn more about how Cloudera is helping organizations bring AI to their data, anywhere it resides.
