When AI and APIs meet, magic happens for manual, repetitive tasks. When working in sync, this combination has the power to significantly reduce – or even eliminate – manual intervention. After all, manual processes slow down operations and create inefficiencies, all while increasing the risk of human error. And nobody wants that.
While APIs connect systems and enable smooth data flow, AI takes it further by analyzing information, predicting trends, and optimizing workflows without the need for human input.
Here, we explore the state of A(P)Is today and share a forward-looking perspective on what’s next.

AI and Integration: where are we today?

AI for APIs

AI simplifies the development, management, and orchestration of APIs, streamlining integrations to be faster, smarter, and more aligned with business goals. Examples:

1. AI-Assisted API Design and Development

• AI-powered tools like GPTs and Copilot streamline API creation by assisting with brainstorming, design, and developing APIs or integrations.
• Examples:
  • “Generate a JSON schema for an e-commerce API.“
  • “Provide best practices for creating an OpenAPI specification.“
• AI accelerates the development lifecycle by generating documentation, suggesting code snippets, and automating repetitive tasks.
• By suggesting code snippets, optimizing API structures, and automating repetitive tasks, AI empowers developers to build APIs faster and more efficiently.

2. Data Mapping and Transformation

• AI analyzes, maps, and transforms data formats across disparate systems, streamlining ETL (Extract, Transform, Load) processes and reducing manual effort.
• It recognizes patterns in data structures to suggest or automates mappings, ensuring faster, more accurate data transformations for integration.

3. Low-Code/No-Code Integration

• AI-powered integration platforms allow non-technical users to build workflows and automate processes with minimal effort. This reduces dependency on IT teams for everyday integrations.

So, if we had to summarize how AI works for API development and management, the situation today is crystal clear: AI makes integrations faster, smarter, and more efficient than ever before.

APIs for AI

The strong relationship between AI and APIs works both ways: APIs provide AI with access to data and services, acting as its hands and eyes to perform tasks, learn, and adapt effectively.

Examples:

1. Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) models leverage APIs to access external knowledge, enabling AI systems to combine generative capabilities with real-time, domain-specific information.

• Example: A chatbot uses RAG to query APIs for real-time inventory data or customer support history, providing precise and context-aware responses.

2. APIs as Bridge for AI

APIs enable the deployment and integration of AI models as services, making them accessible across platforms and applications.

• Example: An AI-powered recommendation engine, exposed as an API, seamlessly integrates into e-commerce platforms, delivering personalized product suggestions in real time.

At first glance, it may seem like AI and APIs are just now coming together. But don’t be mistaken: they’re already working hand in hand. APIs have long been the backbone that enables AI to retrieve, process, and deliver real-time intelligence.

AI and Integration: what will tomorrow hold?

The future is unpredictable, and this section reflects a personal vision rather than exact science. It’s meant to spark discussion, and we may both be right or wrong in different ways. What truly matters is exploring possibilities, challenge ideas, and shaping the best path forward.

‘Less Focus on Technology, More on Business Value’

• Abstraction of Protocols: Technologies like REST, gRPC and GraphQL will become invisible to users. AI will abstract these layers, allowing developers and business stakeholders to focus on what they want to achieve rather than how it will be achieved. In other words, prioritizing outcomes over implementation details.
  • Example: A business user states “I need real-time customer feedback synced with sales data.” AI then automatically selects the appropriate protocols, manages routing, and connects systems, so users don’t have to worry about the technical details.

    > As we already see today with the open-source Model Context Protocol (MCP), it standardizes the integration between LLMs and external data sources or tools, providing the context they need. It acts as a bridge between LLMs and APIs, enabling smarter, more context-aware interactions.

• Use-Case-Driven Agents: Humans will define the “why” (business use case), and AI agents will handle the “what” and “how.”

‘Business-Centric API Management’

• API Management involvement: API management will evolve from a static HTTP bridge to a dynamic decision-making layer that aligns business intent with technical execution.
  • For years, API Products have aimed to bridge the gap between business needs and technical implementation. However, they were often too abstract for business stakeholders to fully grasp their potential. As API management becomes business-driven, it will serve as a tool for modeling and managing business capabilities, finally making API Products a reality.
  • AI will evaluate API calls at runtime, optimizing decisions such as: Which system should respond (e.g., production vs. backup)? Which data source is most reliable or cost-effective? How to handle API versioning or schema changes without disruptions?
• Autonomous Discovery: Applications will be able to autonomously discover, integrate, and utilize APIs without manual configuration. When an API or data source changes, self-integrating apps will detect and adapt autonomously. This will eliminate brittle and hardcoded API calls for good (fantasic news, right?)

Is the future truly all bright and shiny, or are there shadows we need to prepare for?

The rapid evolution of AI and APIs requires a strategic A(P)I Management approach with robust governance. This is crucial because the powerful synergy between AI and APIs also introduces significant risks:

• Uncontrolled growth: AI systems may evolve faster than expected, adapting in unpredictable or even harmful ways.
• Security threats: Interactions between AI and APIs could accidentally expose sensitive data or amplify existing vulnerabilities.
• Ethical Challenges: Without proper oversight, AI-driven APIs may make decisions that conflict with societal norms or ethical standards.

Conclusion:

But remember: ‘The future is whatever you make it. So make it a good one’ – Back to the Future

The power is in our hands. We know that responsible AI management and governance will be crucial. If we guide this transformation wisely, the future of AI and APIs won’t be something to fear but something to embrace with optimism and excitement.