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Ai and Data glossary
Interoperability

What is Interoperability?

Interoperability is the ability of different systems, devices, or software applications to communicate, exchange data, and use the information that has been exchanged accurately and consistently. While often confused with “integration,” the distinction is critical for enterprise strategy:

  • Integration is the process of building a specific “bridge” between two systems (often via custom code or middleware).

  • Interoperability is the state where systems speak a “common language” (standards and protocols), allowing them to connect and collaborate without bespoke development.

In 2026, interoperability has shifted from a technical “nice-to-have” to a mandatory pillar of Sovereign AI strategy. As enterprises move toward multi-agent ecosystems, interoperability ensures that an AI agent built on one platform (e.g., OpenAI) can seamlessly execute a task using data or tools housed in another (e.g., AWS or a proprietary legacy database).

The Four Layers of Interoperability

To compete with “Walled Garden” ecosystems, modern enterprise architecture must achieve interoperability across four distinct layers:

  1. Syntactic Interoperability (The Envelope): Ensuring systems can exchange data packets using common formats like JSON, XML, or Parquet and protocols like REST or gRPC.

  2. Semantic Interoperability (The Meaning): Ensuring the meaning of the data is preserved. This requires shared ontologies and Data Contract Enforcement so that “Customer_ID” in a CRM is interpreted identically by an AI-driven billing agent.

  3. Operational Interoperability (The Workflow): Aligning business processes and security protocols (like OAuth2 or OpenID Connect) so that workflows can span across different organizational units or platforms safely.

  4. Agentic Interoperability (The Intent): The 2026 frontier. This allows autonomous agents to negotiate capabilities and share context using emerging standards like the Model Context Protocol (MCP) or the Agent-to-Agent (A2A) protocol.

The 2026 Breakthrough: Model Context Protocol (MCP)

Often described as the “USB-C for AI,” the Model Context Protocol (MCP) is an open standard that has unified how AI models interact with data sources. Before MCP, every new data source required a custom-built connector. Now, any MCP-compliant AI application can “plug into” any MCP-compliant server—whether it’s a SQL database, a Slack channel, or a specialized Industrial IoT hub.

Key benefits of MCP-driven interoperability:

  • Zero-Code Integration: AI assistants gain real-time access to project context (code, docs, chat) without manual API mapping.
  • Vendor Agnosticism: Swap underlying LLMs (e.g., moving from GPT-4 to a specialized Small Language Model) without rebuilding your entire data infrastructure.
  • Dynamic Context: Agents can “query” the latest enterprise data on-demand, reducing hallucinations and improving ROI in document processing.

Industry-Specific Applications

AdTech and CTV

In the highly fragmented AdTech landscape, interoperability is the only way to achieve unified measurement. 2026 standards like AdCP (Agentic Advertising Communication Protocol) allow buying-side agents to negotiate directly with selling-side platforms, automating bid enrichment and real-time decisioning across disconnected MarTech tools.

Industrial AI (Manufacturing)

Physical AI requires “Operational Technology (OT) to Information Technology (IT)” interoperability. By using Unified Namespaces (UNS) and protocol translation (e.g., Modbus to MQTT), manufacturers can create a Digital Twin that remains interoperable even as they add new robotics or sensor arrays to the factory floor.

Strategic Value: Why Interoperability Wins

  • Linear Scalability: Interoperable systems support horizontal scaling because new nodes or services can be added to the cluster without re-architecting the entire communication flow.
  • Future-Proofing: It prevents “technical debt” by ensuring that 15-year-old legacy systems can still provide data to modern agentic workflows.
  • Reduced TCO: Companies save millions by eliminating the need for “integration hell”—the constant maintenance of custom, brittle API connections.

Related Concepts

Back to AI and Data Glossary

FAQ

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What does interoperability mean in healthcare?

In healthcare, interoperability means that electronic health records (EHR) and other healthcare systems can securely exchange and interpret patient data, regardless of the software vendor or healthcare organization. This ensures that healthcare professionals have comprehensive access to a patient’s medical history, leading to more informed decision-making and, ultimately, better patient outcomes. Interoperability in healthcare benefits challenges and resolutions are central to improving patient care.

Interoperability in healthcare examples demonstrate both successes and ongoing challenges. Differences in data standards, privacy regulations, and system designs often create barriers. One of the most pressing issues in EHR interoperability is the inconsistency of data formats and the reluctance of certain vendors to adopt open standards, which prevents seamless sharing of patient information across healthcare providers

What best describes interoperability issues in the EHR?

Interoperability issues in EHR systems are often described as challenges related to data consistency, standardization, and compatibility. A lack of standardized data formats means that information stored in one system may not be readable or usable by another. Additionally, privacy concerns and varying regulatory requirements can complicate data sharing, creating a fragmented ecosystem where crucial patient information may not be available when and where it’s needed. These interoperability challenges in healthcare can hinder effective data sharing.

Healthcare organizations frequently face difficulties in achieving true interoperability due to proprietary technologies and differences in how systems store and process information. These challenges can lead to incomplete patient records, reduced care coordination, and a higher likelihood of medical errors. Health information exchange and interoperability are essential to overcoming these barriers.

What are the interoperability weaknesses in cloud computing?

In cloud computing, interoperability can sometimes be seen as a weakness, particularly when services from different vendors struggle to work seamlessly together. Cloud providers may use proprietary standards and protocols, which makes it difficult to transfer workloads or data between platforms without significant reconfiguration. This “vendor lock-in” issue limits flexibility, creating barriers to optimal system design and increasing costs for businesses that need to adapt or change their cloud environments. Interoperability blockchain projects are emerging as a potential solution to address some of these issues.

To mitigate these weaknesses, organizations often push for adherence to open standards and multi-cloud strategies that reduce dependence on a single provider. By doing so, they can enhance their ability to switch providers or use a combination of cloud services without worrying about compatibility issues. Interoperability blockchain and interoperability crypto are also being explored as innovative approaches to improving data exchange across platforms.

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