Over the last few years, the enterprise AI mandate has revolved around one question: “What can AI do?” That curiosity sparked a wave of pilots – creative, varied, and sometimes downright whimsical. Each offered lessons that deepened understanding of AI’s potential and limits. Now, the question has changed: “How do we make AI profitable?”
Nearly every enterprise has implemented AI to some degree: 96% of IT leaders say it’s at least somewhat integrated into their core business processes. Yet, few say they’ve totally integrated the technology. As time passes and costs mount, the pressure is on for AI to deliver measurable returns. Across those early pilots, one truth has become clear: AI is only as valuable as the data it can access. Let’s talk about what that means and how to solve for it.
Key early learnings: The missing link between AI and ROI
Today, only 9% of organizations report that all their data is available and usable for AI. For the other 91%, data integration remains the top technical challenge (37%), calling attention to the reality that enterprise data is scattered across public and private clouds, data centers, mainframes, and even the edge. This fragmentation locks information behind inconsistent architectures and governance rules, often compounded by latency and duplication issues. Pulling it all together feels like a difficult task at best, or an unacceptable risk at worst.
As such, AI models are often trained on and deployed using incomplete or outdated data. But when inputs are partial, so are the decisions they inform. That gap can ripple into everything – from customer targeting to risk management – diluting AI usefulness as a value driver. The result is that AI offers only a partial view of reality and can’t be fully relied on as a strategic decision-making tool. This underscores the key point that if data lineage and quality aren’t assured, AI outputs can’t be trusted or used meaningfully. Even the most sophisticated model can’t compensate for inaccessible or unreliable data.
Consider healthcare, where AI models supporting patient billing or clinical recommendations must be traceable from output back to the original file, entrant ID, note, date, and timestamp. The same principle applies across both regulated and higher-risk sectors like finance, government, insurance, and education.
AI’s ability to drive value depends less on what it can imagine and more on what it can see. IT leaders who make their data fully visible, trusted, and actionable will be the ones who lead the charge, turning AI into a growth engine instead of a cost drain.
Reframing the CIO mandate: Bringing AI to the data, wherever it resides
Enterprises that have unified data access report faster model deployment, reduced duplication of training data, and clearer audit trails – all of which translate directly into faster ROI realization. But for most, data duplication or movement isn’t an operationally sustainable option. Overcoming this challenge starts with flipping that idea on its head and applying intelligence where the data resides, rather than moving data to the model.
By bringing AI to the data, CIOs can create a unified data and AI architecture that spans clouds, data centers, and the edge. This approach:
Enables consistent governance and policy enforcement
Lowers latency and compute costs
Secures access to sensitive or regulated data
Reduces redundant cloud storage expenses
Real business value comes from creating a single source of truth, embedding AI at the data layer rather than bolting on isolated tools. And to reach that point, enterprise leaders need to embrace a data platform that is not only capable of bringing AI directly to data, but bringing it to data, regardless of format—whether that’s structured, unstructured, or semi-structured. The result is faster insights, fewer blind spots, and greater trust in outputs.
Learn more about Cloudera’s State of Enterprise AI report, which explores how leading organizations are achieving that shift, and why full data access is becoming the ultimate driver of AI ROI.