Integrating business processes with AI in Liferay

Artificial Intelligence has moved beyond being a resource reserved for experimental projects or innovation labs.
Its concrete application in enterprise platforms like Liferay is unlocking new possibilities to automate, scale, and optimize business processes that once relied entirely on human intervention. Rather than being a simple technological integration, this represents a profound transformation in how organizations design and execute their internal operations and customer-facing processes.

Liferay, known for its robustness in managing portals, intranets, and digital experiences, has become a core business platform across multiple industries. Its modular architecture and ability to integrate with other systems make it an ideal environment to harness AI capabilities—whether through recommendation engines, predictive analytics, natural language processing, or cognitive automation. This enables decisions to be not only faster but also more informed and tailored to the context of each interaction.

Integrating AI in Liferay doesn’t simply mean connecting an API. It requires rethinking how data flows across systems, how processes are structured, and how value is delivered to both end users and internal teams. True integration occurs when artificial intelligence becomes part of the operational fabric—detecting anomalies in real time, personalizing content based on behavioral patterns, or anticipating user needs before they are expressed.

In this article, we’ll explore how to achieve that integration effectively. We’ll examine use cases, recommended architectural patterns, and key tools that can turn Liferay into a truly intelligent platform. The goal is not automation for the sake of it, but rather to enable a new way of operating the business—more efficient, proactive, and focused on measurable outcomes.

>> What is Liferay Portal and what is it for? <<

Table of Contents:

1. Artificial Intelligence in Business Processes: The New Standard for Digital Efficiency

2. Why Liferay Is the Ideal Platform to Integrate Processes and AI

3. Which Processes to Automate First (and Why)

4. Practical Strategies for Applying AI in Liferay

5. Architecture and Implementation Approach Recommended by ICX

6. Results That Matter: Key Metrics and KPIs

7. From Pilot to Strategy: Scaling Intelligence Across Your Processes
   
   
   

Artificial intelligence in business processes: the new standard for digital efficiency

Many companies already have digital portals, intranets, document management systems, or self-service channels. However, what sets leading organizations apart is not their infrastructure—it’s how they connect their business processes with artificial intelligence to make better decisions, automate tasks, and deliver personalized experiences.

Market leaders are no longer asking whether they should automate, but rather which processes to prioritize and how to use AI to scale operations without friction. The difference doesn’t lie in the tool itself, but in how that tool becomes an active part of the business strategy.

At ICX, we work with organizations that already use robust platforms like Liferay, and we see a common pattern: the infrastructure is in place, the portals are live, but many processes still depend on human judgment, manual tasks, or isolated decisions. That’s where artificial intelligence becomes essential.

This isn’t science fiction or isolated innovation labs. We’re talking about applying AI directly to core processes: customer service, internal workflows, document management, case tracking, personalized recommendations, and operational data analysis—all from the platform they already know: Liferay.

The key is to redesign processes with intelligence from the start—not just layering AI on top. It’s about moving from platforms that merely support operations to platforms that optimize, predict, and execute.

And it's not just about doing more with less. It's about delivering smoother experiences, faster responses, and smarter decisions. That’s what real digital transformation looks like—starting with the practical, measurable integration of AI into business processes, aligned with the core of the organization.

AI integration is not a trend or an experimental side project. It is a strategic decision to reduce costs, eliminate operational friction, and anticipate customer needs.

Why is Liferay the ideal platform to integrate processes and AI?

Liferay is not just a CMS or a tool for building visual portals. It’s a platform designed to orchestrate complex processes, manage large volumes of information, and maintain precise control over interactions between multiple user types—both internal and external. Its true value lies in how it enables organizations to structure and govern workflow across the enterprise.

What makes Liferay especially valuable for intelligent automation projects isn’t a single feature—it’s the way multiple components work together:

Modular and extensible architecture

Liferay allows you to build solutions in parts, without relying on a monolithic approach. This makes it possible to develop functionalities tailored to different business areas, integrate them progressively, and scale without having to rebuild the foundation. When introducing artificial intelligence, this flexibility is critical: it allows you to incorporate models, services, or smart logic without breaking the existing structure.

Robust workflow management

One of Liferay’s most powerful features is its workflow engine. It goes far beyond simple notifications or approval steps. It allows the configuration of processes with multiple stages, conditions, actors, validations, and dependencies. This is essential when embedding AI into the process—whether it’s to make automatic decisions, classify requests, or route cases based on estimated priority.

Real connectivity with external systems

Liferay is designed to integrate, not to operate in isolation. It offers native support for REST and SOAP services and has an ecosystem built to connect with ERPs, CRMs, databases, and external processing engines. When AI models live outside the platform (e.g., in Azure, AWS, or on-premise engines), this interoperability makes the difference between a forced integration and a truly functional one.

Granular access control and permissions management

When automated processes involve sensitive data (such as legal documents, personal information, or administrative workflows), security is not optional. Liferay enables permission definitions by user, role, group, resource type, or action. This ensures that even if AI is making decisions, access to information remains under business control.

Liferay offers exactly that point of connection: an environment where processes live, data flows, and decisions can be automated without losing governance. When it comes to integrating AI into real operations, Liferay is not just a starting point—it’s the space where transformation happens with structure and measurable outcomes.

>> Creating and Configuring Liferay Forms <<

Which processes to automate first (and why)

Not every process needs AI—but there are clear signs that indicate a process is a good candidate:

• It’s repetitive and consumes a large amount of human time.

• It involves data analysis that can’t be done by a person in real time.

• It receives a high volume of inputs and requires similar decisions.

• It has a direct impact on user experience or operational cost.
  
  
  

Typical Use Cases in Liferay:

• Automatic classification of requests (by type, priority, or responsible area) using Natural Language Processing.

• Content or document recommendation based on user profile or behavior.

• Form abandonment prediction in long processes, with automatic alerts or interface enhancements.

• Automatic summarization of comments or tickets in internal digital channels.
  
  
  

Practical strategies for applying AI in Liferay

Integrating artificial intelligence into Liferay is not a one-size-fits-all approach.

The path depends on three factors: the type of process to be automated, the quality of available data, and the organization’s level of technical maturity. Below are three proven and effective ways to implement AI in Liferay:

Connecting to External AI Services

This is the fastest way to get started. It involves leveraging APIs from platforms such as AWS, Azure, or Google Cloud to incorporate pre-trained intelligent capabilities, such as:

• Text classification by intent or topic

• Automatic entity extraction in forms

• Sentiment analysis in messages or tickets

• Anomaly detection in data or user behaviors
  
  

These services integrate with Liferay via REST APIs and can be used as part of workflows, forms, or dynamic validations. It's a practical option for immediate results without having to build custom models.

Implementing custom models

When an organization has sufficient proprietary data and requires a tailored solution, the best approach is to train custom machine learning models. For example:

• Predicting case urgency levels based on historical variables

• Classifying requests or documents using industry-specific criteria

• Estimating the probability of abandonment in digital form processes
  
  

These models are trained outside of Liferay (e.g., in Python, TensorFlow, or MLaaS platforms) and integrated via microservices. This allows for scalability, version control, and maintainability without impacting the portal’s core logic.

Intelligent automation within workflows

Unlike other platforms that require external architectures or complex models to incorporate advanced logic, Liferay enables rules, conditions, and adaptive logic to be integrated directly into its workflows. This allows for intelligent automation without leaving the native environment. From decision-making based on form inputs to flows that dynamically react to changes in user roles, statuses, or business objects, Liferay makes it possible to configure conditional behaviors, run custom scripts, or connect real-time integrations.

This flexibility allows development and operations teams to build powerful, contextual, and sustainable workflows aligned with real business processes, without relying solely on external technical resources or predictive models.

Examples include:

• Redirecting a request to a specific department based on detected keywords

• Speeding up approvals when low-risk criteria are automatically met

• Validating form inputs with contextual feedback before submission

This approach is particularly useful for automating operational decisions without relying on external infrastructure.

Architecture and approach recommended by ICX

At ICX Consulting, we understand that intelligent automation is not just about activating flows—it’s about designing architectures that respond precisely to real business conditions. That’s why our approach in Liferay combines the platform’s native capabilities for executing complex workflows with a modular, flexible logic that avoids rigid coupling and promotes scalability.

We leverage built-in scripting tools, conditional rules, and real-time connectors—but always under principles of governance, reusability, and traceability. This ensures that each workflow not only fulfills its operational role but can also adapt easily to future changes, without requiring a full redesign.

Beyond the technical design, ICX advocates for a data-driven automation model: workflows should interpret real-time information, react to specific user behaviors, and coordinate actions across multiple systems. To achieve this, we recommend an interoperable architecture where Liferay serves as the orchestration core, seamlessly integrated with CRMs, ERPs, and external decision engines. This strategy enables workflows not only to execute tasks but to make context-aware decisions, enhance user experience, and become a strategic asset in the digital ecosystem.

An effective AI architecture in Liferay can be built on four layers:

• Orchestration Layer: Where Liferay operates, managing interfaces, users, and process logic.
  
  

• Data Layer: Responsible for structuring and exposing the information that fuels AI models—such as forms, historical data, or interaction events.
  
  

• Intelligence Layer: Where AI models are hosted and executed—whether in local environments or cloud platforms—processing data to generate predictions or decisions.
  
  

• Integration Layer: The connection point between Liferay and AI services, using APIs or microservices.
  
  

This architecture ensures that artificial intelligence does not function as an isolated system, but as an active and aligned component of the overall digital process.

Results that matter: key metrics and KPIs

Automating a process without measuring its impact is like operating blindly. At ICX, we believe that every intelligent automation initiative must translate into concrete, measurable results aligned with strategic business goals. That’s why every workflow we design is accompanied by a clear set of Key Performance Indicators (KPIs), defined from the outset and tailored to the specific organizational context. It’s not just about counting how many tasks are executed automatically, but understanding how those automations impact operational efficiency, service quality, response times, and ultimately, the customer or employee experience. Metrics such as average time per stage, effective automation rate, reduction of manual errors, or SLA compliance levels are examples of how we move beyond volume metrics to focus on the real value generated.

The goal is not to “implement artificial intelligence” as an end in itself, but to generate tangible improvements that create value for the business and its users. Well-applied AI shows up in the indicators—not just in presentations.

Some of the most relevant KPIs when integrating AI into processes managed through Liferay include:

• Reduction in average response time for automated processes (up to 60% less)
  
  

• Increase in digital self-service adoption (users completing processes without human intervention)
  
  

• Lower error or complaint rates due to more accurate decision-making
  
  

• Time saved by internal teams in tasks like classification, follow-up, or content generation
  
  

You measure before and after. Intelligence is justified by numbers—not promises.

From pilot to strategy: scaling intelligence in your processes

Implementing AI in a single process is a great start, but it’s not enough to transform how an organization operates.

A pilot can demonstrate value, generate useful metrics, and spark internal conversations—but its impact will remain limited unless it becomes part of a structured, long-term strategy.

The next step is to scale with intention. It’s not about blindly replicating the first use case in other areas, but about defining a clear path: which processes to prioritize, how to embed AI into workflow design, and what internal capabilities need to be developed to sustain the change.

To shift AI from an experiment to a true competitive advantage, organizations must work across three key fronts:

• Process: Identify where automation truly improves time, quality, or experience, and redesign those workflows with AI integrated from the start—not added as a patch.
  
  

• Technology: Build an architecture that allows models, services, and business rules to connect without constantly rewriting business logic. Liferay provides that foundation when structured properly from the beginning.
  
  

• Culture: Prepare teams to interpret and coexist with automated decisions, delegate routine tasks to intelligent systems, and focus on supervision, analysis, and continuous improvement.
  
  

Scaling doesn’t mean automating everything—it means automating effectively. It’s about deciding what to do with intelligence, how to measure it, and how to sustain it without creating more complexity.

The good news is that with platforms like Liferay and a clear strategy, that path is entirely achievable. Starting with one process and turning it into a replicable model is what transforms AI from a one-off tool into a true business enabler.

Want to learn more about implementing AI-powered business processes?

Schedule a meeting with us.