AI investment is soaring, businesses are starting to see real value being generated, and industries are being completely reshaped. As enterprises aim to operationalize AI at scale, the need to bring AI to data anywhere—from cloud to data centers or the edge—it lives becomes critical. But even as AI projects advance from small pilot projects to significant investment, it may not be as deeply integrated as it might seem. In fact, a Cloudera survey of IT leaders found that just one-in-five (21%) had fully integrated AI into their core business processes.

So, in spite of the growing investment and emphasis on AI, what’s keeping organizations from reaching full integration? For many, the answer comes down to two factors: costs and data access.

Soaring costs are holding enterprises back

Even as investment climbs, the push for integration at scale has meant organizations need to devote even more resources to AI projects that stretch much further than initial pilots. One of the most significant shifts has been with AI model training. Forty-two percent of Cloudera survey respondents noted the rising cost of accessing computer capacity for training AI. That number just one year ago? Eight percent.

That big of a jump is jarring and a result of a handful of factors, ranging from the types of AI models being trained, the complexity of those models, but the most significant shift comes down to scale. Organizations aren’t just dedicating a small budget to a one-off pilot project that is being driven by a group of a few data engineers. Enterprise leaders see the value that AI can generate and are seeking to capitalize. What we’re seeing now is enterprise-scale and happening in every corner of a business, inside systems that are essential to overall operations. At that scale, training becomes more complex, demands more data, and the storage space needed for those models grows exponentially.

Tackling the data dilemma: Incomplete access, incomplete intelligence

Whatever an organization aims to achieve with AI, there’s no way around the fact that success hinges on data. Specifically, can they access and utilize the totality of their data to train AI models? And just as importantly, can that be done securely?

For many enterprises, AI projects don’t have the full picture. Cloudera’s survey found that just 9% of IT leaders said all of their data was available for AI initiatives. Similarly, only 38% said that most of their organization’s data was accessible and usable.

And failing to close the gap in limited data access is often what separates successful AI from failed projects. Fragmented, siloed information that is spread across cloud, data centers, and edge environments limits model accuracy, relevance, and trustworthiness. To deliver real business value, enterprises must be able to connect to and analyze 100% of their data, regardless of where it resides.

The path to full integration starts with a strong foundation 

Overcoming these roadblocks is where a partner like Cloudera becomes critical. Cloudera is trusted by large organizations to bring AI to their data anywhere it lives. The company’s platform empowers enterprises to securely access and analyze 100% of their data, whether it’s structured or unstructured, on-premises or in a cloud environment.

By bringing AI to data wherever it resides, Cloudera helps organizations overcome both cost and access challenges. With Cloudera, businesses can train and deploy AI models closer to where their data lives, which helps bring down training costs and quickly generate value while maintaining governance and compliance.

Learn more about how Cloudera helps enterprises overcome these hurdles to make the most of all their data, no matter where it’s stored.