* [Topics](/en/topics "Topics") * What are intelligent applications? What are intelligent applications? ================================== Updated May 12, 2026•*4*-minute read Copy URL Jump to section --------------- OverviewBenefitsUse casesChallengesHow Red Hat can help Overview -------- Intelligent applications, or intelligent apps, are software applications that incorporate [artificial intelligence (AI)](/en/topics/ai) to augment a human workflow. Intelligent applications apply AI to a specific business problem and use data to solve the problem efficiently. These data-driven, AI-enhanced applications can automate routine tasks, thereby reducing error-prone manual work. They also can learn and improve over time, adjusting based on user interactions and adapting to changing situations. A classic example of intelligent applications is credit card fraud-detection systems that apply AI models to detect suspicious activity. Another is email applications that use AI to prioritize the messages you’re most likely to care about. [Explore Red Hat AI](/en/products/ai) Benefits of intelligent applications ------------------------------------ AI tools can answer questions they weren’t specifically programmed for and haven’t encountered before. By making use of AI capabilities, intelligent applications can deliver benefits that exceed what’s possible with applications that rely entirely on rule-based logic. This is what's called [AI inference](/en/topics/ai/what-is-ai-inference). It's the operational phase of AI, where the model is able to apply learning from training and apply it to real-world situations. Let’s take a look at a few of the main benefits of intelligent applications. #### Adaptability Intelligent applications can learn from new information and improve their accuracy over time. This is useful when conditions change. Consider the example of credit card fraud detection. An intelligent application could refine its recommendations in real time as a response to new data about new types of fraud. Intelligent applications can also learn from user interactions and improve to be more responsive, like getting better at recognizing content a user is most likely to engage with. [How do you apply AI at the enterprise?](/en/topics/ai/what-is-enterprise-ai) #### Information processing AI-based intelligent applications can help people process incoming information in business situations, like incoming messages, presentations, or financial data. Some intelligent applications use [generative AI](/en/topics/ai/what-is-generative-ai) and [large language models (LLMs)](/en/topics/ai/what-are-large-language-models) to create content in response to problems to be solved, like a chat application that offers suggestions when you’re composing a reply to a message. #### Automation Through [event-driven automation](/en/topics/automation/what-is-event-driven-automation), intelligent applications can take action based on changes in the software ecosystem around them. For example, an intelligent application applied to IT automation can rapidly respond to outages or bring more systems online when demand increases. [What is agentic AI?](/en/topics/ai/what-is-agentic-ai) #### Adaptive experiences An intelligent application can react to the user’s needs in order to accurately answer questions and perform tasks. Think of a chatbot that understands when a user is requesting an image and can respond by generating a picture instead of text alone. [What is generative AI?](/en/topics/ai/what-is-generative-ai) Red Hat resources ----------------- [Keep reading](/en/resources "Keep reading") Use cases of intelligent applications ------------------------------------- Businesses and software developers continue to find new use cases for intelligent applications. Here are a few examples. #### IT automation Managing IT systems involves responding to events by making adjustments, such as shutting down or starting up a particular process. An intelligent application can analyze data and trigger some action as part of a pipeline or workflow. #### Customer experience From streaming video services to online shopping, AI-powered personalized recommendations are a part of many products we enjoy. The same idea can apply to interactions across industries. The ability to recognize what a customer expects and deliver it to them at the right moment—thanks to an intelligent application—can increase customer loyalty and retention and be a significant competitive advantage. #### [Explore generative AI use cases](/en/products/ai/generative-ai) #### Decision making Business decisions—about supply chains, logistics, finances, and many other areas—require analyzing large amounts of information in real time. Intelligent applications can help process that data and provide reliable and accurate recommendations. #### [Explore predictive AI use cases](/en/products/ai/predictive-ai) #### Data analytics AI can find patterns in data that humans might miss, making intelligent applications useful to scientific researchers, business analysts, and anyone else who works with data. [Explore agentic AI use cases](/en/products/ai/agentic) #### Industrial edge Applying intelligent applications to edge computing—computing done at or near the physical location of the data—can help provide insights faster where they’re needed most. Picture using an image-recognition algorithm to inspect products as they roll down an assembly line. Being able to spot defects immediately on the factory floor can improve quality. [Explore operationalized AI use cases](/en/products/ai/operationalize-ai) Challenges of implementing intelligent applications --------------------------------------------------- Building an intelligent application requires resources beyond what a standard logic-based application needs. To deliver an intelligent application, a software development team usually has to: * Gather and prepare data. * Develop or tune an AI model. * Orchestrate, integrate, test, and embed the model. * Integrate the model into the application development process. * Monitor, manage, and retrain the model as needed. [Learn more about AI/ML environments](/en/resources/building-production-ready-ai-environment-ebook) The first step is gathering and preparing data, which plays an outsized role in the success of an intelligent application. There’s often a [machine learning (ML)](/en/topics/ai/what-is-machine-learning) step, as data scientists train or tune a model to make predictions based on data. Next is testing, another crucial step to make sure the model behaves responsibly and delivers useful results. [MLOps](/en/topics/ai/what-is-mlops) practices help keep data scientists, engineers, and IT teams synchronized as they follow these steps. Then the model has to be made accessible to the intelligent application that needs it. Whether it’s a newly trained model or an existing model, developers can choose from a range of models and architectures when it’s time to optimize and deliver the AI model. AI environments are complex. The methodologies of [cloud-native application development](/en/topics/cloud-native-apps) are a natural fit for intelligent applications. [Microservices](/en/topics/microservices), [serverless architecture](/en/topics/cloud-native-apps/what-is-serverless), and [DevOps](/en/topics/devops) processes can help bring intelligent applications to users more efficiently. [Why you should care about AI inference](/en/topics/ai/what-is-ai-inference) How Red Hat can help with intelligent applications -------------------------------------------------- [Red Hat® AI](/en/products/ai) is built for fast, flexible, and efficient inference through its [vLLM-powered](/en/topics/ai/what-is-vllm) server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end-to-end at any scale. The Red Hat AI portfolio includes [Red Hat AI Inference](/en/products/ai/inference), an inference stack that provides the operational control to run any model on any accelerator across the hybrid cloud. Get fast, efficient, and cost-effective inference at scale. [Explore Red Hat AI](/en/products/ai) The official Red Hat blog ------------------------- Get the latest information about our ecosystem of customers, partners, and communities. [Keep reading](/en/blog "The official Red Hat blog") All Red Hat product trials -------------------------- Our no-cost product trials help you gain hands-on experience, prepare for a certification, or assess if a product is right for your organization. [Keep reading](/en/products/trials "All Red Hat product trials") Keep reading ------------ ### What is agentic AI? Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. [Read the article](/en/topics/ai/what-is-agentic-ai "article | What is agentic AI?") ### What is generative AI? Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. [Read the article](/en/topics/ai/what-is-generative-ai "article | what is generative ai?") ### What are large language models? A large language model (LLM) is a type of artificial intelligence that uses machine learning techniques to understand and generate human language. [Read the article](/en/topics/ai/what-are-large-language-models "article | What are large language models?") 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