Top 5 integration trends for 2025

How is the integration landscape evolving, and what does it mean for organizations? Our lead integration architects Janne Nieminen and Jonas Källström explore five integration trends currently impacting businesses in 2025.

At Digia, we actively monitor the latest developments in the integration space through a mix of research reports from industry analysts and technology providers, as well as insights from our own extensive hands-on experience. With over 40 integration architects working across various technologies, business models, and industries, we believe that we’re well-positioned to share the most relevant trends for the year ahead.

In this blog, we dive into five integration trends that change the way companies approach their integration and data requirements.

Join our webinar 29.4.2025 to hear more about these integration trends (in Finnish) >>>

1. Battle-proven iPaaS

An Integration Platform as a Service (iPaaS) is a cloud-based solution designed to integrate applications, data, and processes both within your organization and across company borders.

When the iPaaS term was first introduced by Gartner in 2011, the iPaaS products were labelled as “lightweight” versions of the traditional Enterprise Service Bus (ESB) platforms, with feature gaps that required more complex integrations still to be implemented using the old technology. Since then, there has been many iterations and improvements on the iPaaS products. Looking at iPaaS now, we can readily say that it has matured into a critical tool for managing integrations, with the market now largely consolidated among key vendors.

Feature-wise, iPaaS platforms have evolved significantly, reaching or even surpassing feature parity with traditional integration platforms, offering robust capabilities for automation, security, and hybrid cloud integration. They usually offer a solid set of API Management capabilities and are a valid choice for implementing APIs on top of the existing systems or data sources. Latest developments introduce Large Language Models (LLMs) into iPaaS products, further enabling AI-supported use that may significantly lower the barrier of entry into the integration development area.

Today, iPaaS is the only integration platform technology you’ll need for your new integrations.

2. Emerging data products

A key trend in data distribution is the rise of the data product approach. Data products are well-governed, easily discoverable, contextualized, reliable, and reusable data assets that save organizations time and can thus reduce costs.

The data product approach enables organizations to manage, distribute, and quickly integrate high-quality data efficiently for different use cases. These can include AI and real-time analytics as well as for running operational processes.

Effective data products require clear ownership, high quality, governance, and efficient distribution, for instance via APIs, SQL-like queries, or real-time streams. Choosing the right model depends on the integration needs and the specific use case.

We see data products being built using cloud data platforms, microservices and integration technologies. They ingest data from source systems through various integration patterns, often relying on iPaaS and ETL technologies. In practice, a unified approach for data and application integration is needed to avoid repeatedly rebuilding the same data flows.

A common challenge that organizations face is how to provide self-service access to data products for their development teams while maintaining effective governance. Organizations are only beginning to adopt data catalogues to help teams discover and utilize data products independently. While they are still in the early stages compared to the more mature API portals, they follow a similar self-service model, but often for a more specialized audience.

Ensuring SLAs for critical data products is also essential, especially when they support operational business processes. Aligning data platforms, integration strategies, and governance increases data value, boosts efficiency, and supports real-time needs.

3. Amplified integration security focus

In an era of increasing cyber threats, integration security and privacy have become a major focus for businesses. This includes addressing platform security, implementation practices, and life cycle models.

With the increasing reliance on APIs to access sensitive business data, protecting these connections has become critical. Integration failures or breaches can lead to significant operational disruptions. As a result, businesses are placing more emphasis on security measures to maintain data integrity, safeguard against threats, and ensure compliance with evolving regulations like NIS2, DORA, and others.

We frequently observe integration owners and security teams asking themselves questions like: Are our integration implementations and integration platforms secure? Do developers adhere to secure practices? Are operations compliant with regulatory requirements?

Despite recognizing these risks, many organizations we collaborate with require assistance to implement systematic methods for securing integrations and assuring compliance. Key areas of concern include

• hardening integration platforms
• ensuring secure development and deployment practices
• setting up proper logs for proactive threat detection
• establishing clear security governance policies.

4. Beneficial AI for integrations

AI is increasingly integrated into integration and API development through the use of large language models (LLMs), traditional machine learning models, and AI agents. The primary use of AI in this context is to enhance the integration development experience through solutions like AI-based virtual assistants for generating configurations and documentation.

While AI is currently best suited to enhance code-based software, many iPaaS vendors have started to include also AI support for low code-based authoring experience and help functions. There is also good potential in using AI in operations to troubleshoot and solve incidents based on the product-specific knowledge that has been provided to the AI model.

While AI presents benefits in tasks such as coding and testing, it also introduces risks related to intellectual property, security, and the potential for biases in generated outputs. The risks can be mitigated by using specific security controls related to your AI model. In the case of LLMs in a custom solution, you should make use of the guardrails and bias detection features of the AI services. For AI features embedded in iPaaS, you should carefully read the service description on how the platform tackles these issues and make decisions based on the quality of evidence.

In the future, AI is expected to further improve development, testing and runtime operations, but skilled integration experts will remain essential for developing comprehensive architectures.  AI will serve as a valuable tool to enhance efficiency and problem-solving.

5. Distributed integration development

Some organizations have been shifting from centralized integration models to a more decentralized approach, giving development teams more control. Traditionally, a centralized Integration Competency Center (ICC) has handled all the integrations, but with the increasing need for speed and flexibility, some businesses are adopting a federated or distributed integration development model. Here development teams take more ownership of the integrations.

A federated model is often supported by an iPaaS (Integration Platform as a Service), which provides common models for security, logging, and access control. Some organizations standardize on a single iPaaS, while others allow teams to choose their own integration tools. As the role of centralized IT evolves, there is often a shift toward supporting multiple technologies, with one strategic integration platform complemented by a few tactical solutions; however, all must adhere to the same target architecture.

The distributed team model for integration development can, however, often lead to technical challenges as teams adopt their own standards instead of enterprise-wide ones. Challenges might also arise with skills management, operations (including 24/7 availability), compliance, and security.

To mitigate these challenges, it has become evident that a smaller central team is still needed to address these challenges. This “Core Integration Team” should provide training, reviews, and guidance while handling complex integrations. An Integration Community of Practice further supports collaboration, ensuring teams share knowledge and refine best practices between the teams. 

 

To summarize, the integration landscape will continue to evolve during 2025. iPaaS platforms get more traction, data products introduce better access to data, and security remains vital. In addition, AI and distributed team models enable faster and more agile development. Organizations that embrace these changes will build a strong integration environment that is ready for the future.