Beyond Middleware: The New Role of Enterprise Integration in a Cloud-First World

Apr 03, 2025
Enterprise Integration | 5 min READ
    
AI and data virtualization are redefining application integration. Here’s how to stay ahead.
As the landscape of technology evolves, so does the role of application integration. Now and in the near future, application engineers working on integration projects will be facing unique challenges and opportunities shaped by advancements in cloud computing, AI, and modern business demands. Let’s explore what integration looks like today and how engineers can excel in this domain.
Before the advent of cloud computing, identifying the scope for integration was very simple. Systems of record were always developed in silos and whenever there was a need to connect to support a business process, integration platforms used to come to the rescue.
Paresh Kokate
Paresh Kokate

Senior Integration Architect

Birlasoft

 
Siloed systems spoke different languages and integration solutions mediated the communication like a message exchange.
If you wanted to future-proof your integration solution, you created a service bus and a new integration use case could be on-boarded with minimum change.
Building service bus was an organization-wide initiative and needed a significant commitment from customers, both time-wise and money-wise.
If you needed a low-key future-proofing solution, you could go with a simple pub-sub queuing approach.
After cloud computing, you must've noticed that things are not that straightforward anymore, systems of record are reinvented as SaaS services that have baked-in standard messaging interfaces which are very easy to consume and do not require mediation.
Even queuing systems are now cloud-hosted and come with a standard easy-to-use REST interface. All these SaaS services, now, can talk to each other without an integration platform and custom integration solution.
So, is this the end of application integration?
Not quite! Application integration is pretty much alive; the tricky part is to know when to use it.
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Don’t Integrate for the Sake of Integration
Integration platforms and solutions should never be applied indiscriminately. For instance, if Salesforce needs to call an address validation SaaS service, let it do so directly. Adding an integration layer in such cases adds unnecessary complexity, cost, and latency.
Here is how you know when it is your time to shine
A Simple Analogy – A House and its Water Supply
Think of your customer's organization as a house that has many rooms like a kitchen, bathroom, laundry room, etc.
Now, think of each room as a system of record (either SaaS or homegrown). You need a water supply to some of these rooms for example sink in the kitchen, a shower in the bathroom, washing machine in the laundry room.
The water supply pipeline connecting from external sources, outside of the house, to various water fixtures is your integration pipeline and here you do need an application integration solution and integration platform
However, appliances such as ice makers and coffee machines where water flows through the appliance's internal pipeline is not a good candidate for an integration pipeline. The Salesforce calling Address Validation service example given earlier is like a coffee machine having its own internal water pipeline not requiring an elaborate integration platform or a solution.
Transient Vs. Non-Transient Data
Another way to look at the question of whether to use an integration platform or not is to look at the type of data you are transferring, mediating, transforming, translating, and routing.
If the data you wish to transport is transient i.e. if the data is incomplete and going back-and-forth between a system of record and SaaS services to reach completion, you don't need an elaborate integration here, let the system of record use SaaS services as an augmented functionality as if SaaS service is its external function. The integration platform and solution add undue latency, complexity, and cost here.
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However, if the data you wish to transport is non-transient and is ready to leave the system of record boundary, you need an integration platform and an integration solution to build an integration pipeline. Here, you are abstracting where the data originated and matured and where the data is used downstream within the organization and external stakeholders like partners and customers.
B2B
B2B is a no-brainer! You don't want systems of record to directly connect to external business partners and internal federated organizations to exchange non-transient data.
An integration platform and a B2B EDI integration solution play a key role here, providing abstraction in terms of where the final business data is coming from and going to, and which system of record ends up using it.
B2B
Here is how you can really shine - Opportunities to Lead the Future of Integration
Data Virtualization and AI Readiness
Data Virtualization and AI Readiness
Every organization today has a mandate for Citizen Integration and AI readiness, and this is where you can really shine.
The integration folks are always in the middle of business processes and are always very close to the integration traffic of non-transient data. With that vantage point, they can provide data virtualization for Citizen Integration and AI grounding by providing a directory of integration-fronted API endpoints and their business context.
Citizen Integration
A chatbot connected to the Data Virtualization directory returns business context-driven aggregated business datasets to the chatbot users.
The virtualization directory contains aggregation logic and defines how it maps to the business context for supported chat questions.
With this approach, you can meet the Citizen Integration requirement by giving control via a chatbot to business stakeholders.
The virtualization directory also contains instrumentation and telemetry APIs that can return tracking information for requested integration. The instrumentation APIs can also kick-start ad-hoc integrations or change configurations and this can be done by business stakeholders via a chatbot.
AI Readiness
The data virtualization layer APIs can be hooked into the API training models and the data virtualization APIs can be used for grounding context-driven business datasets with other information sources within your organization to provide context-driven aggregate response. Consider a use case where a business user within your organization wants to combine business partner profile information with the latest top 10 transactions, Data Virtualization APIs that return order information can be grounded with the business partner's profile information stored in SharePoint to provide combined aggregated response as you get it from ChatGPT or Copilot.
Pattern Harvesting
Pattern Harvesting will also provide innovation opportunities with integration platforms and integration solutions.
Integration platforms have started building AI plug-ins that you can place within your integration trajectory that collect pattern harvesting counters and feed into pattern harvesting datastore.
Pattern Harvesting
Your integration solution does its job of transferring messages between endpoints and while it is doing it AI plug-ins keep building patterns and show trends. For example, order integration between ERP and other systems of record over a period starts showing trends that nobody thought of. This provides early opportunities for course corrections and minimizes business losses or helps make profits.
Final Thoughts
Interesting times we integration folks are in; with the advent of cloud computing and AI, the role of integration platforms and integration solutioning approach should change. We should start using integration platforms and build integration solutions as an integration-as-a-service layer with AI-powered data virtualization that provides standard application integration infrastructure as a service wherever it is needed for now or in the future.
 
 
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