How to Use AI Efficiently to Boost Your APIs and Integrations

In a landscape where technical and business departments of large corporations increasingly demand efficiency and assertiveness from AI initiatives, clearly understanding how this resource can leverage integration strategies is a major competitive advantage.

In this article, you will learn more about the impact of AI on API consumption and monetization, the importance of structured AI governance, and how companies are responding to these technological shifts. Insights are provided by Sensedia’s solutions architects: Lucas Xavier, Igor Guimarães, and Marcelo Dias.

Why are so many companies wasting time and money on poorly structured AI initiatives?

In many cases, they start with the technology and forget the foundation: data, integration, and governance. Companies often bet on AI models without ensuring that the underlying data is reliable, accessible, and well-integrated. At the end of the day, AI is only as good as the data it consumes. If integrations are fragile or data arrives incomplete, the model fails to deliver value.

Another critical point is the lack of business alignment. Many AI initiatives are born as isolated experiments without any connection to a real pain point or strategic objective, leading to wasted time and investment. Furthermore, Gartner warns that more than 40% of Agentic AI projects—such as autonomous AI agents and copilots—will be canceled by the end of 2027, precisely due to a lack of structure and clarity regarding the problem they aim to solve.

Ultimately, there is a lack of strategy. AI must be attached to a robust digital ecosystem with adaptive API governance, AI governance, and a more distributed architecture. Only then can it scale safely, efficiently, and, most importantly, bring value to the business.

Related Content: Understand what an AI gateway is and how it solves AI governance

How is AI influencing API consumption in companies?

We are witnessing a radical shift in API consumption. Previously, programmers wrote code based on routes to consume these APIs. Now, with the introduction of new concepts like LLMs receiving information through MCP (Model Context Protocol) Servers to feed AI agents, this entire context is being consumed by non-humans. This significantly increases consumption.

Once we have algorithms that are aware of data repositories, everything becomes more connected, demanding greater investment in security and governance to ensure everything functions perfectly.

According to Gartner, by 2026, more than 30% of the increase in API demand will come from AI applications acting as tools for LLMs. We are talking about accelerated growth driven by AI agents, bots, and automation.

This consumption will generate unpredictability throughout the entire environment. At some point, the context will need to be fed. The bots will not have control over this – there will be demands and needs. This will cause a different impact on this monitoring and on everything we know. From the moment we have applications that consume from a different perspective, a different need, all this control of rate limiting and throttling, for example, must be reviewed.

We will no longer have AI agents or promotions that require API consumption. Consumption will be driven by a need for context updates. Therefore, at this point, we will need to improve the entire policy, observability, and monitoring of these APIs, ensuring security and the quality of the information content. These APIs will require a different approach to versioning because all consumed data will need to be structured before being provided. From this point on, it will be necessary to evaluate which set of information will be part of this visibility spectrum.

‍

Related content: The AI ​​integration plan for AI governance with MCP

What are the financial impacts of AI and how should companies respond to this new operational cost?

Beyond the business issue mentioned initially, regarding the lack of a clear objective in AI projects and the lack of a clear vision of return on investment, there is also a technical aspect related to the increasing consumption of APIs by AI-based solutions. And this consumption is becoming increasingly explosive and unpredictable.

‍

This also generates a proportional increase in costs, mainly in infrastructure, to bring scalability to your environment, maintain performance requirements and adequate latency, cover support costs, and have access to continuous observability of your APIs and increasingly stringent security in this access.

‍

All of this implies costs for improving and sustaining your entire API catalog. Considering this focus on the proportional increase in related costs and the increased consumption of APIs by AI solutions, it is extremely important that your API-based integration strategy includes API monetization strategies.

‍

This can guarantee adequate revenue for you to continue providing a quality service with attractive performance. You can use pricing models based on your API usage, such as pay-as-you-go. It's possible to differentiate access by consumer type: access by humans or access by automated agents, with distinct policies for each type of consumer. Even differentiated SLAs for more premium clients or for AI agents supporting mission-critical processes.

‍

It's important to remember that today there's the role of the developer who will access a Dev Portal to understand documentation and consume this API. In this new vision, with the MCP protocol accelerating this consumption, Dev Portals will need to be prepared for this new need. With this, we are entering a world of AI agents that also need to be governed. Companies end up using resources from traditional LLM platforms, but in the corporate world, a more thorough analysis of this data is often necessary, preventing it from exceeding certain contextual limits.

‍

Sensedia has an offering that addresses precisely this demand: the AI ​​Agents Lab. We understand what our clients will be promoting in terms of AI agents, what the data sources are, and how we will enable governance and consumption of these versioned APIs. So, we close this entire integration cycle based on APIs, but which, ultimately, will promote integrations based on data provided for the AI ​​to work with and automate some activity.

‍

Summary

We discussed how AI is transforming the world of integrations, from automating the creation and maintenance of APIs to the ability to predict failures and adapt usage rules in real time.

We also discussed how solid governance combined with this AI aspect is essential to ensure quality, security, and agility in automated decisions. The truth is, if AI is at the heart of the strategy today, integration, especially the governance and monetization of APIs, also needs to be at the center of your concerns.

This is where Sensedia can help. We have a modern API management platform, now powered by AI capabilities. It allows you to accelerate your digital strategy securely and efficiently. We also offer specialized professional services – a team that deeply understands both the technical and business aspects of integration in the AI ​​age.

‍

Want to learn more about Sensedia's AI solutions? Talk to our team of experts now!

‍