AI is no longer just a buzzword in enterprise software. It is becoming a practical layer that helps teams create content faster, improve search, support customers better, and reduce repetitive development work. Liferay DXP has been moving in this direction by adding AI-powered capabilities across content management, commerce, search-related metadata, translation, support experiences, and developer tooling. Official Liferay documentation describes AI in DXP as a way to accelerate content creation, streamline operations, and provide more context-aware assistance.
For organizations already using Liferay DXP, the bigger question is not “Does Liferay have AI?” The real question is: How can we use AI in Liferay in a way that actually improves day-to-day productivity for business users and developers?
This article answers that with real examples.
Why AI in Liferay DXP matters
Liferay DXP already sits in the center of many enterprise workflows: websites, customer portals, partner portals, knowledge bases, employee intranets, commerce experiences, and headless integrations. Adding AI to this layer can help teams do three things better:
- Create and manage content faster
- Improve discoverability and customer experience
- Reduce manual work for technical teams
Liferay’s official AI documentation lists practical capabilities such as generating text, generating images, translating content, auto-tagging assets, generating product descriptions, enabling automated support chat, using AI tools in Workspace, and even exposing Liferay DXP through MCP for AI assistants.
How AI helps Liferay business users
Business users are usually the first group to benefit from AI in Liferay because they spend a lot of time on content, assets, localization, support, and product information.
1) Faster content creation for marketers and content authors
Liferay includes AI-assisted content generation so authors can create draft content by providing direction such as topic, tone, and word count. Liferay’s own feature list states that the AI Creator tool helps content authors generate content in just a few clicks.
Practical use case
A marketing team managing a customer portal can use AI to generate:
- landing page copy
- campaign descriptions
- product highlights
- knowledge base drafts
- internal announcement content
Instead of starting from a blank editor, the author starts with a first draft and then refines it for accuracy, compliance, and brand voice.
Business value
This reduces time spent on first-draft writing and helps teams publish faster without waiting for every line to be written manually.
2) AI image generation for faster campaign execution
Liferay also supports AI image generation in Documents and Media through the AI Creator tool when an OpenAI API key is configured. This feature is available in newer DXP releases and allows teams to generate images directly inside the platform.
Practical use case
A content editor creating a campaign page for a seasonal promotion can generate a supporting banner image directly from Liferay instead of switching between multiple tools.
Business value
This is useful when teams need quick visual placeholders, campaign graphics, or concept imagery for internal portals and microsites.
3) Better product onboarding in Liferay Commerce
Liferay Commerce supports AI-generated product descriptions. Editors can generate product descriptions from the product screen and guide the output with description, tone, and approximate word count. The documentation also notes that users can regenerate the text if they do not like the first result.
Practical use case
A commerce team importing hundreds of SKUs can use AI to generate:
- short descriptions
- long descriptions
- benefit-led product summaries
- tone-adjusted content for different audiences
Business value
This is especially helpful when a business is launching a new catalog, onboarding third-party products, or cleaning inconsistent product data.
4) Faster localization and multilingual publishing
Liferay can integrate with third-party translation services such as Amazon Translate, DeepL, Google Cloud Translation, and Microsoft Translator to generate automatic translations for pages and web content.
Practical use case
A global enterprise managing customer help content in English can translate articles into Spanish, French, or German much faster, then route those translations for review before publication.
Business value
This shortens time-to-market for multilingual content and helps regional teams avoid copying and pasting content into separate translation tools.
5) Smarter content classification with auto-tagging
Liferay supports auto-tagging for text assets and images. Official documentation notes that Liferay can use local or cloud-based services to automatically assign tags to content and that this improves discoverability through search and faceting.
Practical use case
A knowledge management team uploading thousands of PDFs, blog posts, and support articles can let Liferay auto-tag content so users can find information faster.
Business value
Manual tagging is often inconsistent and time-consuming. AI-assisted tagging improves content organization and helps search results become more relevant.
6) Always-on support experiences with automated chat
Liferay also supports integration with automated live chat systems, which can add a chat window to sites and improve support and user experience. Liferay documentation highlights supported integrations including platforms such as Zendesk and HubSpot.
Practical use case
A customer self-service portal can offer AI-enabled chat for:
- order status questions
- policy lookup
- account guidance
- routing users to support content or live agents
Business value
This can reduce support load, improve response times, and give users help outside business hours.
How AI helps Liferay developers
Developers benefit from AI in a different way. For them, the value is not only content generation. It is also about speed, integration, code generation, modernization, and reducing repetitive setup work.
1) AI tools in Liferay Workspace
Liferay has official support for AI-oriented workspace context. Its documentation says developers can use AI-based tools in Liferay Workspace with assistants such as Claude, Copilot, Cursor, Gemini, and Windsurf, and that Workspace provides context files to help those assistants generate better Liferay-related output.
Practical use case
A developer starting a new project can use an AI assistant to:
- scaffold modules
- generate starter code
- explain project structure
- create service boilerplate
- help document customizations
Developer value
This cuts down setup friction and helps teams onboard faster, especially when working in large Liferay workspaces with many modules.
2) Liferay as an MCP server for AI assistants
One of the more interesting newer capabilities is Liferay DXP functioning as an MCP server when the beta feature is enabled in supported releases. Liferay states that AI applications such as GitHub Copilot or Cursor can be configured to interact with Liferay DXP through MCP, and those AI services inherit the permissions of the authorized user.
Practical use case
A developer using an AI coding assistant can connect it to Liferay DXP and potentially ask it to work with live platform context such as content structures, APIs, or available resources, based on permissions and configuration.
Developer value
This can move AI from being a generic coding helper to being a more context-aware assistant for Liferay-specific work.
Important note
Because the AI service uses the authorized user’s permissions, governance and access control matter here. This is powerful, but it should be rolled out carefully.
3) Faster custom app and integration development
Liferay already provides strong development tooling through Workspace, Blade CLI, and headless APIs. AI makes that tooling more productive by helping developers produce code, configs, and documentation faster. Liferay’s development docs position Workspace as the center for creating, building, deploying, and testing projects.
Practical use case
A team building a custom employee portal can use AI to:
- create REST integration stubs
- generate object definitions and service layers
- draft front-end fragments
- document deployment steps
- explain unfamiliar APIs
Developer value
Less time is wasted on repetitive scaffolding and more time goes into business logic and architecture.
4) Supporting upgrades and modernization efforts
While not a pure generative AI feature, Liferay Workspace also includes tooling to help with source code upgrades when moving to newer releases. Combined with AI assistants, this can make modernization less painful by helping teams understand breaking changes, refactor code, and review migration work.
Practical use case
During a Liferay upgrade project, developers can use:
- Liferay upgrade tooling for structured migration
- AI assistants for code explanations
- AI-generated summaries of deprecated patterns
- faster creation of upgrade checklists and test cases
Developer value
This is useful for large enterprise teams carrying older customizations across multiple Liferay releases.
Real-world AI use case examples in Liferay DXP
Here are a few realistic scenarios where AI can transform productivity.
Use case 1: Global customer support portal
A company uses Liferay DXP for a multilingual support portal.
AI can help by:
- generating first drafts of support articles
- translating those articles for regional sites
- auto-tagging content for better findability
- powering chat-based support for common questions
Result: fewer manual steps, faster publishing, better self-service.
Use case 2: B2B commerce catalog cleanup
A distributor is launching thousands of products in Liferay Commerce.
AI can help by:
- generating missing product descriptions
- rewriting inconsistent product copy
- creating marketing-friendly summaries
- helping internal teams enrich product metadata
Result: faster catalog onboarding and more consistent product content.
Use case 3: Internal employee intranet
An enterprise uses Liferay for HR, IT, and policy information.
AI can help by:
- drafting HR announcements
- tagging documents automatically
- translating updates for regional offices
- using chat to answer repetitive employee questions
Result: less time spent answering the same queries and better access to information.
Use case 4: Developer productivity in a large Liferay program
A development team manages multiple Liferay modules and integrations.
AI can help by:
- generating starter modules in Workspace
- explaining old custom code
- helping produce integration templates
- using MCP-enabled AI assistants for Liferay-aware workflows
Result: faster onboarding and less repetitive engineering work.
Best practices before adopting AI in Liferay
AI can improve productivity, but it should not be treated as magic. A smart rollout usually includes:
Human review
AI-generated text, translations, and descriptions should be reviewed before publishing.
Governance
For MCP and AI integrations, user permissions and data access need to be controlled carefully. Liferay explicitly notes that AI services use the permissions of the authorized user.
Cost awareness
Liferay documentation also notes that AI generation requests consume OpenAI API tokens for relevant AI Creator features.
Start with high-value use cases
The best starting points are repetitive tasks:
- content drafts
- image generation
- product descriptions
- tagging
- translation
- support chat
- developer scaffolding
Final thoughts
Liferay DXP AI capabilities are most valuable when they are used to remove friction from real work. For business users, that means faster content creation, translation, tagging, and support. For developers, it means better tooling, faster project setup, smarter integrations, and more context-aware AI assistance.
The real opportunity is not replacing people. It is helping teams spend less time on repetitive work and more time on quality, strategy, architecture, and customer experience.
That is where AI in Liferay becomes practical.
