Organizations invest heavily in AI tools, hire talented teams, and build ambitious roadmaps. Then they hit an invisible wall: their data systems can't talk to each other.
This is data interoperability—or rather, the lack of it—and it derails more AI initiatives than any technical limitation.
What Is Data Interoperability?
Simply put, it's when your data systems can communicate and work together seamlessly. Different formats, incompatible systems, and isolated databases create barriers that no algorithm can overcome.
Consider a typical enterprise:
- Sales data lives in Salesforce
- Customer support tickets sit in Zendesk
- Financial records are locked in an ERP system
- Marketing analytics flow through a different platform entirely
- Operational data exists in spreadsheets on someone's desktop
Each system was implemented to solve a specific problem. None were designed to work together. And the AI that's supposed to generate insights from "your data" can only see fragments of the picture.
Why This Matters More Than You Think
Delays time-to-value. Every AI project starts with data preparation. When sources are incompatible, teams spend months on cleaning, mapping, and transformation before any actual AI work begins. Projects that should take weeks stretch into quarters.
Obscures critical insights. The most valuable patterns often emerge from connecting disparate data sources. Customer behavior plus support history plus purchase patterns plus market conditions—that's where breakthrough insights live. Disconnected data means disconnected understanding.
Escalates costs relentlessly. Workarounds multiply. Teams build redundant processes. Manual data entry bridges the gaps. Each integration becomes a custom project. The operational expense of fragmented data compounds over time.
Creates brittle AI systems. Models trained on incomplete data make incomplete predictions. When the data pipeline breaks—and it will—the AI breaks with it. Maintenance becomes a full-time job.
The AI Ops Advantage
Here's the counterintuitive insight: the best team to tackle data interoperability isn't IT or data engineering. It's AI Operations.
Why? Because AI Ops understands the problem from the user's perspective:
- How data flows through daily operations — not just where it's stored, but how people actually use it
- Where data lives and in what formats — the documented systems and the shadow IT nobody talks about
- Real business requirements and constraints — what actually matters versus what's theoretically ideal
- User comfort levels and friction points — which changes will be adopted and which will be routed around
Data engineers can build technically perfect integrations that nobody uses. AI Ops builds practical solutions that actually get adopted.
Starting Small, Thinking Big
You don't need a massive data transformation initiative to make progress. Begin with incremental steps that compound over time:
1. Adopt Uniform Naming Conventions
It sounds trivial, but consistent naming across systems eliminates countless integration headaches. "Customer_ID" in one system and "CustID" in another creates friction that multiplies across every project.
2. Implement Tagging Policies
Standardized tags and metadata make data discoverable and connectable. When every team uses the same taxonomy, integration becomes dramatically simpler.
3. Build Bridging Solutions
Where full integration isn't feasible, create bridges:
- APIs that expose data from legacy systems
- Data lakes that aggregate information without replacing source systems
- Middleware that translates between incompatible formats
These aren't permanent solutions, but they unlock value while longer-term transformations proceed.
4. Embed Interoperability in Every New Project
The most important change is cultural: every new data project should include interoperability requirements from day one. Stop adding to the fragmentation problem even as you work to solve existing issues.
The Compounding Returns
Data interoperability isn't glamorous. It doesn't make headlines or demo well in board meetings. But it's the foundation that makes everything else possible.
Organizations that solve this problem unlock AI capabilities that seem almost magical to those still fighting fragmented data. The same tools, the same algorithms, the same talent—but dramatically different results.
The gap between AI leaders and AI laggards often comes down to this invisible infrastructure. The leaders invested in interoperability before they needed it. The laggards are still trying to build AI on a fractured foundation.