What Are Intelligent Applications? Benefits and Use Cases

Intelligent applications combine the capabilities of conventional finance, supply chain, HR, and other applications with embedded AI to provide business users with recommendations for a next best action. Generative AI capabilities built into intelligent applications can also help companies with follow-ups to business activities, such as writing a job description, producing summaries of recommended patient treatments, or requesting bids from logistics providers after a new route has been picked for a supply chain.

What Are Intelligent Applications?

Intelligent applications use AI to help business users make and execute decisions and improve various processes. In contrast to older generations of rule-based analytics applications, intelligent applications help companies make decisions using mathematics-based training, which enables these applications to adapt to new circumstances and learn from the past.

For example, companies used earlier versions of inventory management software to identify an inventory shortfall (say, of wool sweaters) compared with demand forecasts. An intelligent inventory management application can tell users the likely causes of the shortfall based on an analysis of sales data from previous seasons and predict future needs based on an analysis of third-party weather forecast data and design trends.

Key Takeaways

• Intelligent applications combine the strengths of traditional enterprise software with AI and third-party data to help business users make better decisions in a more timely manner and with more confidence.
• Businesses can also use intelligent applications to become more efficient by reducing steps from a workflow and eliminating errors caused by manual labor.
• Intelligent applications learn over time, helping businesses adapt to changing market conditions.
• Intelligent applications span a vast number of use cases, including predictive maintenance of industrial equipment and recommendations for patient care.

Intelligent Applications Explained

Intelligent applications use AI embedded in supply chain management (SCM), human capital management (HCM), enterprise resource planning (ERP), and other conventional enterprise applications to help business leaders and process owners make decisions. This AI uses mathematical algorithms rather than a rule-based approach to analytics, which allows it to learn over time.

Intelligent applications often tap data from third-party sources to help make more-informed decisions in real time. For example, using third-party weather data, an intelligent SCM application with embedded AI can recommend transportation providers not affected by a significant weather event, helping logistics managers make better decisions in a fraction of the time.

Intelligent applications can also help business users follow up on decisions they’ve made. In the above supply chain example, the application’s embedded generative AI could, with a click of a button, help logistics managers produce an RFP and send it electronically to selected transportation providers, saving them from having to identify the right set of providers, produce the RFP, and compose an email themselves.

Businesses are using AI in increasing numbers. In a 2024 McKinsey survey, 72% of respondents said their organizations had adopted AI in some form, compared with about 50% in previous years, indicating that use of this technology is becoming table stakes.

Benefits of Intelligent Applications

Intelligent applications provide a range of benefits that help business leaders make decisions and execute processes more quickly and efficiently. Those benefits include the following:

• Automation: Intelligent applications can automate a number of manual tasks business users have to perform, suggest business decisions, and produce documents more quickly and with fewer errors than people can on their own. For example, intelligent HCM applications can use a previous job description to help business leaders create an updated description for the same role. Intelligent applications can also take entire tasks off people’s plates, for example, by automatically approving expense reports in appropriate workflows.
• Real-time decision-making: Insights provided by AI systems let businesses make decisions closer to real time, such as by interpreting sensor data for anomalies that could indicate equipment failure. By taking future production needs into account, such analyses could suggest alternate production lines or other resources that could be used to replace the failing equipment in order to still meet production deadlines. This approach encompasses analyses of disparate data and factors, giving business users greater confidence in the reliability of the recommended actions.
• Continuous learning: Over time, AI models help improve the insights derived from intelligent applications by learning from previous situations and applying those lessons to a new situation. For example, an intelligent demand risk management application can identify that extreme weather conditions have affected ocean-bound shipments, analyze past carrier transit data, and suggest alternate sources for the goods.
• Personalization: Intelligent applications will provide decision support and other services to end users based on their specific needs. For example, GenAI built into a marketing application can produce emails using a marketing executive’s preferred tone and style for a given type of recipient.
• Predictive analytics: Intelligent applications can leverage AI to help companies forecast demand, customer churn, supply chain disruptions, hiring needs, and other trends that can affect business decisions and operations.
• Improved efficiency: Intelligent applications can help automate workflows, for example, by allowing businesses to use chatbots to respond to most customer queries, saving human agents for more complex or higher-value interactions. They can also improve collaboration among team members by automatically routing tasks or by creating meeting summaries.
• Cost savings: Intelligent applications can help businesses lower their expenses by eliminating unnecessary steps in a workflow and streamlining them to be more efficient. And by automating certain manual tasks and workflows, they can also reduce costly errors. AI-informed personalization can help increase the impact of marketing dollars.
• Better customer experience: Intelligent customer experience applications can help customer service agents provide more relevant responses to inquiries—and in some cases, replace live agents with automated bots equipped to handle customers’ low-level, repetitive questions (such as how to reset a password or check on the status of an order) without delay or fuss.

Use Cases and Examples of Intelligent Applications

Intelligent applications can help improve a variety of business processes and tasks, including the ones outlined below.

• Supply chain management: Intelligent SCM applications can help businesses forecast demand, plan more efficient supply routes, and forecast weather and other disruptions.
• HR and talent management: HR teams are starting to use AI-imbued HCM applications for a variety of purposes, including to identify skills gaps, help inform and summarize performance reviews, assist with producing job descriptions, help customize learning programs, and analyze and summarize employee feedback.
• Chatbots and virtual assistants: Companies can deploy chatbots to automate certain aspects of customer service, such as answering common queries (like how to generate return labels), process orders, and handle basic troubleshooting. In an HR context, employers can use chatbots to answer employees’ basic benefits and policy questions.
• Predictive maintenance: Intelligent predictive maintenance applications can use sensor readings in combination with the ability to learn from earlier histories to help identify equipment failures before they occur. Companies can then take that equipment offline under controlled conditions rather than after a major disruption or emergency has occurred.
• Fraud detection: Intelligent financial applications can use pattern recognition and anomaly detection to analyze data flows that might indicate fraudulent activity, such as payment, credit card, identity, and product return fraud.
• Personalized recommendations: Intelligent marketing and customer service applications can help marketers deliver personalized, targeted content and offers that are likely to resonate with individual customers rather than to broader demographic segments.
• Healthcare diagnostics: Intelligent healthcare applications can analyze vast amounts of data from electronic health records, medical research repositories, government regulation libraries, and other sources to assist in diagnoses and help personalize treatment plans. They use predictive analytics to process and interpret large data sets to help providers make more-informed choices and improve patient outcomes.
• Autonomous vehicles: Intelligent driving applications use image recognition and other AI functions to help autonomous and semiautonomous cars and trucks interpret pavement markings, obstacles, road signs, and other data.
• Financial trading: A variety of trading applications use AI to help structure investment portfolios, pick stocks and other investment vehicles, and help portfolio managers manage risk.

The Future of Intelligent Applications

Most intelligent applications currently analyze just historical data, limiting their ability to alert organizations to malicious hacking attempts, predict imminent equipment breakdowns, identify commodity price fluctuations in real time, and provide other actionable insights. Future iterations of intelligent applications will analyze pricing, sensor, expense report, workforce skills, and other data and interact with transactional or execution systems to accomplish recommended tasks once they’re approved by business users.

How Does Oracle Help with Intelligent Applications?

Businesses can use Oracle Fusion Data Intelligence to jump-start their use of intelligent applications. Built for Oracle Fusion Cloud ERP, SCM, HCM, and other Fusion applications, as well as for Oracle industry applications, Fusion Data Intelligence provides ready-to-use analytics and prebuilt AI and machine learning modules to help accelerate business decision-making and provide deeper insights, even across domains.

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Intelligent Applications FAQs

How are intelligent applications different from traditional AI?

Intelligent applications make it so business users can access AI capabilities embedded into the applications they already use rather than having to create an AI tool, extract the application data, and load it into the tool.

What are the functions of intelligent applications?

Intelligent applications help people make better decisions more quickly thanks to the use of AI designed for specific use cases, such as in supply chain, talent, and financial management.

How can intelligent applications improve customer service?

Intelligent applications can provide customer service agents with information in real time or interact with customers directly to respond to common or simple queries.