Based on the Gartner whitepaper “Pivot your data engineering discipline to effectively support AI use cases” https://www.gartner.com/document-reader/document/6509371
AI is not just another technology trend. It’s a revolution, not an evolution.
The enterprise AI race just entered a new phase.
Salesforce’s acquisition of Informatica and ServiceNow’s acquisition of data.world are not just bold M&A plays—they’re signals. Signals that the world’s largest enterprise platforms are no longer treating data as a back-office function. They’re making it central to their AI strategies.
These moves validate a hard truth: you can’t deliver meaningful AI without a semantic, governed, and real-time data foundation. Enterprises are waking up to the fact that LLMs and AI agents are only as good as the data feeding them—and most enterprise data is still fragmented, cryptic, and context-free.
“AI can’t thrive on siloed databases, brittle pipelines, or manual data stitching. To move from experimentation to impact, enterprises need a new kind of architecture—one that embeds semantic understanding directly into the data layer.”
This is the real story behind these acquisitions: Salesforce and ServiceNow aren’t just buying data integration tools. They’re buying semantic leverage. They know that to win the AI game, especially in the age of agentic AI and generative intelligence, they must ground their systems in meaning, not just in data.
And they’re not alone. Gartner’s May 2025 report, “Pivot Your Data Engineering Discipline to Efficiently Support AI Use Cases,” echoes this shift: the semantic layer isn’t optional anymore—it’s foundational. Without it, enterprises risk feeding AI engines with noise, bias, and blind spots.
In this blog, we’ll explore why semantic modeling, DataOps, and converged platforms are now non-negotiable—and how Stratio’s Generative AI Data Fabric delivers exactly what this new era of AI demands.
AI is reshaping how businesses operate, how decisions are made, and how value is created.
But despite the hype and enthusiasm, most enterprises are struggling to translate AI into meaningful outcomes.
The root cause? Legacy thinking and legacy architecture.
While data and AI teams are under intense pressure to deliver fast—business value, efficiency, competitive edge—the reality on the ground looks very different:
- Data is fragmented across outdated IT systems, shadow clouds, duplicated sources, and vendor silos.
- Most of it is technical and context-free, offering no shared meaning between business and machine.
- Teams spend enormous amounts of manual effort discovering, cleaning, stitching, and aligning data before it can even be used.
- Data governance is scattered, pipeline failures are common, and BI dashboards are still being used once a month—if that.
- Meanwhile, demand for GenAI, AI agents, and automated decision-making is exploding—and data leaders don’t have the infrastructure to support it.
Here’s the hard truth: AI can’t succeed on yesterday’s data foundation.
We’re at a point where making incremental improvements—tweaking pipelines, optimizing lakehouses, or adding more dashboards—won’t cut it. AI requires a fundamental rethinking of how data is managed, governed, modeled, and delivered.
But most business and data leaders are not yet equipped to drive that kind of transformation. Many still see AI as an extension of analytics, not a fundamentally new paradigm.
Now is the time to rethink your architecture and here is why
Traditional data warehouses and lakes were built for storing and reporting on the past—not for supporting real-time, AI-assisted decision-making at scale. Even lakehouses, with their hybrid promise, were designed for big data analytics—not for intelligent, contextual, always-on AI.
To enable AI—especially GenAI and agentic use cases—you need:
- Real-time pipelines, not batch refreshes
- Semantic understanding, not just schema management
- Federated governance and lineage, not siloed controls
- End-to-end automation, not manual workarounds
- Business-aligned meaning in the data, not raw tables and cryptic field names
And all of it must work across domains, teams, and technologies, securely and scalably.
This is why Gartner’s May 2025 report, “Pivot Your Data Engineering Discipline to Efficiently Support AI Use Cases,” is so important. It doesn’t just call for tools; it calls for a complete reset in how organizations architect for AI.
At Stratio, we’ve built our platform around that reset.
Our Generative AI Data Fabric is built to solve this exact problem: delivering real-time, business-context-rich, AI-ready data with full governance and automation—at scale.
Key takeaways from the Gartner report “Pivot your data engineering discipline to effectively support AI use cases”
The semantic layer is non-negotiable
Gartner confirms what data leaders already feel: “AI-ready data” is the top investment priority. But most organizations are missing the key ingredient—semantic modeling.
Without semantics, AI models hallucinate or misinterpret enterprise data.
Stratio’s Generative AI Data Fabric addresses this head-on, embedding a semantic layer with business meaning that transforms raw, fragmented data into accurate, understandable, and machine-readable intelligence. This not only improves AI performance—it enables real business users to interact with data naturally and confidently.
DataOps unlocks scalable, trusted AI pipelines
Gartner emphasizes the need to move beyond ad-hoc AI pilots. Organizations must adopt DataOps practices that:
- Automate pipeline delivery and validation
- Integrate governance into every step
- Break down silos between data engineers and AI teams
Stratio enables DataOps out of the box—automating everything from lineage and compliance checks to metadata-driven pipeline orchestration.
Converged platforms reduce risk and complexity
According to Gartner, the era of fragmented tools is ending. The future belongs to converged data management platforms that unify integration, metadata, observability, and AI enablement.
Stratio’s Generative AI Data Fabric is built to meet this need. It consolidates:
- Semantic modeling and metadata management
- Real-time data delivery
- AI integration with LLMs from AWS, OpenAI, Anthropic, and more
All in one platform. All accessible in real time. All governed and auditable.
What enterprises must do now
Gartner’s guidance aligns with what Stratio has been building for years. Here’s how to future-proof your AI initiatives:
Adopt a semantic-first approach
Equip your teams to build knowledge graphs and ontologies that embed business meaning into your data. This reduces AI hallucination, improves explainability, and enables self-service across business units.
Embed DataOps into every AI project
Organize teams around AI data products, automate governance, and manage pipelines with agility. Think of your AI use cases as products, not projects.
Unify your data stack with a converged platform
Choose a solution like Stratio’s Generative AI Data Fabric that connects ingestion, transformation, metadata, governance, and AI enablement in one unified experience.
Why this matters: beyond AI hype to automated business decisions
The endgame isn’t just better dashboards. It’s AI that can assist—and eventually automate—enterprise decisions. But that will never happen if your data lacks structure, meaning, or trust.
With a semantic foundation, DataOps practices, and a converged architecture, Stratio helps you:
- Deliver AI-ready data in real time
- Reduce integration debt
- Empower business users
- Build AI systems that don’t just generate content—but drive real business outcomes
Ready to make your enterprise data truly AI-ready? Let’s talk.
