What is enterprise application integration?

Business systems—including customer relationship management (CRM), business process management (BPM), enterprise resource planning (ERP), database management and supply chain management software—ordinarily struggle to communicate with each other. They might use different programming languages, operating systems and data formats and exist in separate environments or architectural layers.

EAIs help these systems exchange critical data points, overcoming incompatibilities that would otherwise hamper business operations. Integration solutions also allow organizations to use legacy systems, preserving critical historical data and eliminating the need to rebuild applications each time developers introduce new services.

Finally, EAIs enable systems to share automations, accelerating and simplifying workflows across departments. In an e-commerce context, for example, organizations can use an integration platform to automatically process payments, update inventory and create shipping labels each time a customer places an order—even when these processes take place across different systems or environments.

EAI architectures can help support distributed networks, where applications and services are loosely coupled and operate independently. Traditional EAI platforms were often on-premises, server-based middleware, such as enterprise service buses, that the organization’s IT team installed and operated internally. Today, many organizations also use integration platform as a service (iPaaS) solutions to facilitate and manage integrations.

iPaaS provides a similar service, and is a type of enterprise application integration solution, though its delivery and operating model are different—iPaaS is hosted externally and delivered through the cloud. In practice, many organizations, particularly larger organizations, use both: legacy EAI for core on‑premises systems and external iPaaS for cloud and SaaS integrations.​

EAIs often use message-oriented middleware (MOM) to facilitate and manage connections between services. MOMs receive and transport data packets called messages, and support asynchronous data sharing, where incoming messages are temporarily stored in a queue or buffer until the receiving service (the consumer) is ready to process them.

For example, if the consumer experiences downtime, the queue can preserve messages until the receiving service is back online. A message broker is responsible for managing the queue and routing messages to the correct services. Brokers can also prioritize high-priority messages over less urgent ones. MOM-based systems enable services to share information even without knowing the identity of specific consumers, simplifying data flows.

Asynchronous integration is often best for backend tasks that aren’t dependent on real-time data—and where short delays are acceptable. One common use case is managing non-time-sensitive system integrations, such as data exchanges between ERP and CRM systems.

While the CRM might continuously send customer updates, demand forecasts and other data to the ERP, the ERP can wait to process this data during off-peak hours. This strategy improves system performance and resource optimization. Meanwhile, asynchronous approaches might not be ideal for front end applications, where customers expect immediate access to services.

Other EAI platforms use synchronous data flows, where an application makes an API call or request to a service and waits for a reply. This process is more immediate and direct, in part because there is no queue to slow down requests.

But synchronous processing can be prone to latency in high-volume scenarios because tasks must be completed in order. Services are also tightly coupled, reducing their independence. Synchronous approaches are often used for front end services and real-time business applications, especially services that require an immediate response (such as checking inventory management software before fulfilling an order).

Many modern EAI platforms incorporate both synchronous and asynchronous data flows to fulfill different integration needs.

EAI architecture is a blueprint that defines how applications and services communicate within an integrated ecosystem, including which models, components and protocols are used to facilitate connections. EAI patterns generally describe more granular design approaches, including specific routing, endpoint and message constructions.

A 2003 book by software architects Gregor Hohpe and Bobby Woolf identified 65 integration patterns, giving developers a shared language for what types of integrations are possible and how to implement them. However, because many of these patterns have been abstracted away in modern EAI platforms, this overview will instead focus on broader architectural styles. 

Businesses often incorporate multiple architectural approaches, each supporting different layers or functions within the system. Common integration architectures include:

Point-to-point integration connects two or more apps, often by using an API, middleware or custom code, so that they can exchange data directly without a centralized management plane. This approach works well for systems that contain only a few services because it’s relatively simple to set up and maintain.

But on a larger scale, point-to-point connections can become tangled and overly complex, a phenomenon known as spaghetti integration. With no intermediary to manage data exchanges, performance bottlenecks are difficult to identify and troubleshoot. And with limited oversight over each connection, point-to-point approaches are vulnerable to security and optimization issues.

Finally, rollouts become a challenge, as they must be configured separately for each integration in the system. Organizations might start with point-to-point integration but evolve to more mature integration approaches as they scale up their operations.

In hub and spoke models, multiple systems or services (the spokes) connect to a central hub. The hub manages connections between services so that they do not have to interact with each other directly.

Often, the central hub takes the form of an enterprise service bus (ESB), a higher-level middleware solution that directs and manages data exchanges. The hub’s responsibilities might include routing, governance, authentication, monitoring and data conversion. While ESBs handle management tasks, integrated MOMs typically transfer data by using a protocol such as JMS or MQTT. Alternatively, hub and spoke approaches can use API gateways for API orchestration (with the gateway as the hub and APIs as the spokes), enabling synchronous communication, which often uses HTTP as the transport mechanism. 

Hub and spoke approaches are often more efficient and resilient compared to point-to-point ones, especially in complex deployments that feature dozens or hundreds of services. These systems can also be easier to maintain and govern because every interaction takes place through a shared management plane. Finally, new applications can be added without affecting integrated services.

A major downside though, is that because every service relies on the centralized management plane, an error in the central hub can affect the entire system.

In service-oriented architecture (SOA), services are aligned around shared policies and standards, but remain loosely coupled and self-contained, promoting reusability and interoperability. Services share their functions and capabilities through contracts without exposing the internal code and data needed to run them, improving discoverability.

For example, an organization’s payment-processing service can be added to new applications without developers needing to rebuild the service from scratch. Downsides include high cost of implementation and maintenance and added system complexity, which can strain performance and create security vulnerabilities.

As a platform-agnostic design philosophy, SOA can be used with any number of architectures. For example, when applied to a hub and spoke model, the central hub continues to manage interactions, but services can describe their business functions so that developers can seamlessly combine and reuse them without prior knowledge of what they’re capable of.

Microservices build on SOA’s core principles, but adopt newer, cloud-native features. SOA requires every service to share strictly defined standards, making the system less flexible and more prone to slowdowns. 

Microservices, meanwhile, prioritize lightweight transport (often through the use of APIs), with the endpoints themselves implementing business logic and processing requests. This approach gives each service more autonomy, allowing individual teams to dictate internal governance, deployment and storage approaches for the services they manage. The two approaches also differ in scope: SOA typically handles enterprise-level applications, while microservices are often more granular, breaking individual services into smaller components.

Finally, while SOAs frequently use ESBs to facilitate service-to-service communication, microservices more often rely on API gateways or service meshes. Microservices are quickly becoming dominant: 74% of businesses currently use microservice architecture, while an additional 23% say they plan to in the future, according to a 2023 Gartner report.

While messages might contain actions or requests, events are static indications that a notable action has taken place. Event-driven architecture enables services to efficiently and securely exchange event notifications.

Typically, applications send events to an event broker, which is responsible for distributing them to the appropriate services. Consumers can choose which events they’d like to subscribe to, so they receive only records that are relevant to their own functions or business needs.

For example, an e-commerce company might use events to notify an email service each time a customer makes a purchase. When the email service receives the event notification indicating a sale, it can automatically send the purchaser an order confirmation. Meanwhile, an analytics database might subscribe to events related to downtime or performance to collect relevant datapoints.

One benefit of event-driven frameworks is that services do not need to understand how their events are being used, or which consumers are using them—they need to only know how to report events to the event broker. Event-driven approaches are also simpler to scale because developers can duplicate or remove services without interfering with event reporting mechanisms.

But without proper management, event-driven platforms can over-report or unintentionally send duplicates of events, making it more difficult for consumers to make sense of them. Also, as organizations scale up, they often add more consumer instances to improve performance. But this proliferation of services can make it more difficult for developers to isolate and troubleshoot errors.

Finally, because event-driven platforms can experience lag, they aren’t ideal for real-time data exchanges.

Broadly speaking, iPaaS sits under the EAI umbrella; it’s a newer, cloud-based model for integrating enterprise applications. Integration platform as a service (iPaaS) refers to cloud-based integration tools, which an external vendor typically manages. Examples include IBM’s webMethods Hybrid Integration, Salesforce’s MuleSoft and Microsoft’s Azure Integration Services.

iPaaS platforms often use generative capabilities, pre-built connectors, low or no-code development tools, Internet of Things (IoT) and other modern innovations. Often running on serverless or containerized architectures, iPaaS platforms tend to be flexible and lightweight because they don’t rely on on-premises ESBs (which can be bulky and prone to misalignments) to facilitate connections.

One main draw is that organizations do not need to spend time and resources building custom connections and can instead rely on the overlying infrastructure provided by the iPaaS platform. iPaaS might sometimes be packaged alongside other SaaS products, such as ERP or CRM software.

EAI is an older, traditional approach, which is most often managed on-premises or through a hybrid architecture. One main advantage of EAI is that companies maintain complete control over their integrations. This approach might be preferred in highly regulated industries, such as law or healthcare, where IT teams need a deeper level of customization and oversight than is available through third-party iPaaS platforms.

Despite iPaaS’s growing popularity, 80% of businesses still build at least some of their integrations in-house, according to a 2024 Fortune Business Insights report. In larger organizations, EAI and iPaaS are often used in conjunction to automate different orchestration layers.

While EAI platforms primarily help enterprises share data internally, electronic data interchange (EDI) standardizes and facilitates the transfer of information—such as invoices, transcripts or shipping notices—between organizations, replacing physical paperwork. EDI transactions date back to the 1960s, when governments and companies began automating data exchanges, reducing their reliance on manual data entry.

EDI uses specialized protocols to help companies maintain compliance with regulations and international standards. For example, HIPAA requires that organizations exchange American healthcare data through the security-oriented X12 protocol, while international business transactions are often conducted through the EDIFACT global standard.

Enterprise resource planning brings human resources, product lifecycle management, finance and other business processes together through a centralized, shared database to improve connectivity and data consistency between internal systems. ERP platforms are often made up of multiple enterprise modules, each representing a different business function. These modules can perform distinct tasks while working together to achieve shared business goals.

While EAI and ERP both support integration, they operate at different levels of an organization’s technology stack. EAI acts as a bridge or connector between individual applications, while ERP provides a unified interface where organizations can access multiple business functions.

Updating or replacing an ERP system can be operationally challenging and costly because each enterprise module is tightly bound to a centralized application suite. EAI, meanwhile, can be implemented gradually and in phases because it relies on middleware or APIs, which can often be reconfigured without interrupting data flows.

Organizations often use EAI and ERP platforms in conjunction, with ERP systems managing core business functions and EAI platforms handling connections between the ERP platform and other components, such as analytics platforms and CRMs.

Security vulnerabilities, data silos and incompatibilities can arise when business systems are isolated and unable to communicate. Without an EAI strategy, organizations might need to rely on custom coding and manual data entry to maintain connections, potentially leading to brittle integrations. EAI systems can help overcome these barriers by:

While EAI can streamline business functions, it can also introduce system complexity and operational obstacles. Common challenges include:

Although EAI is a decades-old concept, today’s EAI platforms increasingly incorporate modern innovations to improve interoperability, performance and network resilience. Teams now use generative AI to automatically flag misalignments and failures—or proactively fix them before they disrupt communication flows. Machine learning can also orchestrate complex automation pipelines, reducing workloads and misalignments.

EAI is becoming more accessible as a discipline, too, giving teams the ability to design integrations with low-code, pre-built connectors. Serverless systems give organizations the flexibility to weave between cloud, hybrid and on-premises environments. And API and microservice-oriented architectures improve discoverability and reusability.

The emergence and popularity of iPaaS solutions means that organizations can subscribe to only the integration services they need, reducing costs and freeing them up from time-consuming management tasks.