During the last couple of year Artificial intelligence (AI) and generative AI (GenAI) models are rapidly becoming commodities, with costs plummeting and accessibility rising. However, even as algorithms grow cheaper, data remains the most critical asset for AI success. This creates a data interoperability challenge, as most organizations struggle with fragmented, heterogeneous data trapped in silos, which lead to poor model performance, biased outcomes, wasted resources, and AI agents that cannot collaborate as required for solving complex problems and producing sophisticated outcomes.
The Data Dilemma: Heterogeneity and Chaos
AI systems depend on high-quality, structured data to function effectively. However, 90% of corporate data is unstructured (e.g., text, images, spreadsheets), which makes it unusable for AI without costly preprocessing. At the same time, poor data quality costs lead companies to allocate significant efforts and costs as they need to cope with errors that can cause inaccurate predictions, flawed automation, and reputational damage. Moreover, data heterogeneity-divergent formats prevent systems from sharing insights, which creates “islands” of unusable information. Overall, without interoperability, it is impossible to achieve the ever-important seamless exchange and interpretation of data across AI systems. Furthermore, models trained on incomplete or inconsistent data produce unreliable results, which autonomous AI programs like AI agents struggle to collaborate.
Interoperability: Fixing the Broken AI Pipeline
Interoperability bridges gaps between disparate data sources, tools, and AI agents. Some of the most prominent solutions that address the above-listed gaps and challenges include:
- Common Data Models: Standardized frameworks like conceptual, logical, and physical data models ensure consistency across systems. For example, there are conceptual models that define shared terminology (e.g., “customer” vs. “user”), while logical models map relationships (e.g., linking sales data to inventory systems). Finally, physical models end-up implementing structures in databases or APIs. AI systems using common models for terminology, relationships and implementation can perceive and interpret a data-driven application in the very same way. Likewise, they can seamlessly and uniformly apply data analytics functions.
- Semantic Layers and Ontologies: Semantic models enrich raw data with context, while transforming unstructured text into machine-readable formats. For instance: an AI system for healthcare can use ontologies to link “myocardial infarction” to “heart attack” across clinical notes. This is usually done using standards-based metadata that enhance data with context-specific meaning. AI systems that share the same standards and semantics can share, exchange and combine data from diverse data sources, while being able to perceive these data in the same way. In essence, semantic models are a form of common data models that are enhanced with additional semantics about the industrial domain at hand. Semantic models help AI developers and deployers to build graphs of knowledge, which are usually called “Semantic Knowledge Graphs” (SKGs). There are also query languages and tools (e.g., SPARQL), which are specialized for interconnected knowledge graphs towards uncovering hidden data patterns.
- Agentic Protocols (e.g., MCP): In 2025 AI interoperability is largely about enabling independent and autonomous AI agents to collaborate in order to solve complex problems. In this direction, emerging standards like Anthropic’s Model Context Protocol (MCP) act as universal connectors for AI agents. MCP enables secure access to external tools and APIs, while at the same time facilitating structured and interoperable two-way communication between models, databases, and services. As such MCP can support the development and deployment of modular workflows where agents delegate tasks (e.g., booking flights) while adhering to governance rules.
Best Practices for Achieving Interoperability
Despite the availability of technologies, service and tools for seamless data exchange across AI systems, there are still persistent AI interoperability challenges. Organizations had better remedy these challenges and avoid AI project failures by adopting the following strategies and best practices:
- Prioritize Data Structuring: Organizations must ensure that the data they collect and manage are properly structured and suitable for use alongside AI and GenAI models. To this end, organization should migrate to cloud platforms as a means of centralizing and standardizing data. It is also recommended to use NLP (Natural Language Processing) and AI-driven tools to auto-label unstructured datasets, as this could increase their value and utility.
- Adopt Open Standards: Interoperability is largely about systems that speak the same language. It is therefore recommended that enterprises adopt and implement proven standards for data exchange and services invocation. For instance, they should implement REST APIs, JSON/XML formats, and open table formats (e.g., Apache Iceberg) for cross-system compatibility. Moreover, they should consider leveraging frameworks like MCP or Google’s A2A for agents’ interaction and collaboration.
- Invest in Data Governance: Organizations should establish and following data governance principles. For instance, they can classify data by sensitivity towards restricting interoperability for Personal Identifiable Information (PII) or other types of confidential information. As another governance measure, they can establish semantic vocabularies and validation pipelines to maintain quality.
- Embrace Modular Design: Enterprises dealing with agentic infrastructures and projects must build AI systems with plug-and-play components. This is key for avoiding vendor lock-in and ensuring responsive pipelines. Moreover, it is suggested to use AI testing frameworks to ensure backward compatibility during updates.
The Future: Interoperability as Competitive Advantage
As AI commoditization accelerates, organizations with interoperable data ecosystems will outperform rivals. The lack of data and systems interoperability remains one of the most common AI project failures causes. On the other hand, AI interoperability can lead to improved outcomes, while translate to effective business processes and better decisions that can set an organization apart from its competitors. In this context, structured data, semantic clarity, and agent collaboration aren’t just technical goals-they’re business imperatives. Hence, companies have no other option than to understand and plan on how to best structure their data for scalable, future-proof AI innovations.
The message is clear: “Fix your interoperability gaps now in order to unlock AI’s full potential in the near future, while avoiding the considerable cost of bad data”. It’s probably already time to reflect on this article and ask yourself: “Are you Ready to transform your AI strategy”?
