What are intelligent applications?

Intelligent applications, or intelligent apps, are software applications that incorporate artificial intelligence (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.

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. 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.

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 and large language models (LLMs) 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, 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.

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.

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.

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.

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.

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.

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.

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) 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 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 are a natural fit for intelligent applications. Microservices, serverless architecture, and DevOps processes can help bring intelligent applications to users more efficiently.

Red Hat delivers the common foundations for your teams to build and deploy intelligent applications with transparency and control.

Red Hat® Enterprise Linux® AI provides a platform for working with LLMs in enterprise applications.

Red Hat® OpenShift® AI is a platform that can train, prompt-tune, fine-tune, and serve AI models for your unique use case and with your own data.

For large AI deployments, Red Hat OpenShift offers a scalable application platform suitable for AI workloads, complete with access to popular hardware accelerators.

Additionally, Red Hat’s partner integrations open the doors to an ecosystem of trusted AI tools built to work with open source platforms.