As headlines continue to spout new AI models, tools, and even agentic capabilities, I’m seeing a subtle but important shift. While the media and startups often focus on AI as the standalone product, enterprises aren’t looking for an end in itself—they want solutions that make their teams faster, more cost-efficient, and more accurate. In other words, what companies truly need is AI as a feature—an enabler integrated into their existing workflows and systems.
The hype vs. the reality of AI
For the past few years, the industry narrative has revolved around “new” AI models and flashy generative AI tools. Headlines celebrate breakthroughs like agentic AI and low-cost models that are rapidly commoditizing the underlying technology. For instance, as reported in Barrons, leaders like Palantir’s CTO highlighted that while AI models are improving, they are also becoming more similar and cheaper—almost a commodity now. Similarly, Business Insider noted that lower-cost AI is turning these tools into productivity enhancers rather than job replacers.
Yet, a Wall Street Journal analysis reminds us that AI’s real promise lies in improving the products and services we already use rather than reinventing them from scratch. This perspective is echoed in various industry discussions, which explore the difference between building AI as a product versus embedding it as a core feature.
Enterprise AI integration requires real use cases, not just models
Despite the excitement around new AI models, enterprise leaders have a different priority. What they really need is a solution that solves real-world problems—one that enhances workflow, drives ROI, and fits seamlessly into their operations. A recent PwC survey, discussed by Dan Priest in an AP interview, showed that while many Fortune 1000 companies have AI in their workflows, nearly half are only now looking to scale these solutions into products and services.
There is also this growing recognition that many startups are creating “micro-SaaS” applications to address niche challenges in verticals such as healthcare, finance, and manufacturing. These bespoke solutions often solve specific problems but leave the hard part—the “last mile” of integrating and operationalizing the solution—up to the enterprise. This gap means that companies must either invest heavily in training scarce AI talent or rely on third-party platforms to plug these gaps, a model that is simply not scalable in the long run.
Bridging the “last mile” with Cohesity
At Cohesity, we’re reimagining what a data platform can be. Rather than being seen solely as a data protection solution, our platform, Cohesity Data Cloud, is evolving into a scalable data fabric—one that already supports critical workloads like Cohesity DataProtect and data security, and is poised to drive a whole host of new applications. Our vision is to extend this fabric further by bringing AI directly to the data, leveraging not just the raw data but also its rich, extended metadata. This ties back to one of the earliest Cohesity beliefs of hyperconvergence and bringing compute to the data.
The first step in this evolution was the release of Cohesity Gaia, our conversational assistant that uses retrieval augmented generation (RAG) on top of our unified data fabric to unlock deeper insights and drive AI-powered workloads. By integrating AI with our data fabric, Cohesity Gaia transforms how enterprises derive value from their data—moving beyond traditional protection to enable predictive analytics, automated decision-making, and more agile operational intelligence.
Looking ahead, as we continue to harness more of the platform’s rich metadata, we’ll unlock even more innovative workloads that further drive business value. Our goal is to provide a platform where enterprises can secure, manage, and actively use their data to power next-generation applications.
I’m also committed to sharing this journey. In the coming months, I’ll update you and write blogs with real-world use cases and success stories that demonstrate how these innovations are applied. My aim is to drive enterprise adoption by showing that AI isn’t just a standalone product—it’s a powerful feature embedded within a scalable, secure, and versatile data fabric that transforms everyday business operations.
AI as a practical implementation to solve business challenges
This year is poised to be a turning point. The industry is finally shifting from celebrating AI as the end product to embracing it as an integral feature that drives innovation. As businesses increasingly demand solutions that deliver speed, accuracy, and cost savings, we expect to see a consolidation of AI efforts—where hyperbolic claims give way to practical implementations that solve real business challenges.
Companies that can seamlessly integrate AI features into their workflows, much like what Cohesity does for enterprise data, will lead the charge. They won’t be chasing the next flashy AI model—instead, they will be delivering tools that empower every employee to work smarter and more efficiently.
Watch a demo of Cohesity Gaia, below:
