Intelligent Applications Explained

Research firm Gartner has recently released its Top 10 Strategic Technology Trends for 2024 that can propel your business forward in this AI-driven era. With a variety of technologies integrating AI capabilities into their core functionality, Gartner shines a spotlight on the top enterprise technologies that can generate the maximum value for your digital transformation initiatives.

The idea of following a strategic technology trend is to ensure that all business and technology decisions around your AI and digital investments will guide your digital transformation trajectory over the next three years. By stretching the impact horizon of your AI investments, you can scale your business operations, expect predictable outcomes, and plan for changing circumstances.

In this blog, we’ll review the strategic technology trend of Intelligent Applications.

What are Intelligent Applications?

Intelligence is the ability of a system to learn, reason, adapt, and understand information. This is especially relevant in complex environments where several internal and external parameters may impact system performance. Intelligent systems can learn from experience — or data.

For example, an ML algorithm can be used to train a system model. This model can emulate the true behavior of the system and can then be used to:

• Reason.

• Draw logical conclusions.

• Make decisions based on new information generated in complex and uncertain environments around it.

What’s more, intelligent systems can adapt. Based on new behaviors and experience — or data — the model can be trained to improve predictions based on true, changing, dynamic, and real-world behavior of the applicable internal and external uncertainties.

An Intelligent Application is a tool that embodies these capabilities. An AI-powered application can use the intelligence attributes for several use cases that are relevant for usage in an enterprise IT environment. Common use cases include:

• Automation

• Personalization

• Predictions

• Analysis

What can Intelligent Applications do for your business?

A key value proposition of Intelligent Applications, or applications with AI capabilities as their core embedded functionality, is that they help optimize business decision-making.

This is a proven technology trend adopted in many industry verticals: AI forecasting is a primary focus area for financial institutions, energy and utility companies, government institutions such as meteorology services, health care institutions, and others.

Gartner believes that enterprise IT companies will develop function-specific apps with their own Large Language Models (LLMs). Vendors will want their customers to use their proprietary generative AI tools integrated across their offerings. Across multiple vendor products, this means that organizations will now have multi-modal, multi-model generative applications.

• Multi-modal refers to the various modes of information interactions: text, speech, multimedia such as audio, video, and images, as well as network logs and system performance metrics.

• Multi-model refers to the different AI models across multiple vendor products.

Gartner also predicts that personalization and custom functionality will be a significant advantage of AI apps in the near future. According to Gartner, 30% of new apps will use AI capabilities for personalized adapted user interfaces. It’s likely that these LLMs do not integrate with competing products — an added layer of training will be required for multiple vendor products and services.

The future impact of Intelligent Applications

McKinsey predicts that the explosive growth of generative AI tools will significantly impact their business. Indeed, 40% of the organizations responding to their survey are already investing in new capabilities and resources to prepare for the future powered by generative AI tools.

Despite the hype around AI tools, McKinsey finds that organizations are still at an early stage of evaluating generative AI capabilities while leading enterprises are already ahead on the generative AI adoption trends.

As for organizations lagging behind, the adoption of strategic technology trends such as Intelligent Applications will still require:

• Expertise

• Business value alignment

• An impact assessment (like a BIA)

To deliver value from these applications, organizations need technologies and expertise that fit their own organizational needs, develop a roadmap for large-scale adoption among non-specialist users, and work closely with business stakeholders on areas such as portfolio lifecycle management and software delivery cycles.

While some may argue that we’re already at the peak of inflated expectations, especially as it relates to the hype around AI tools such as ChatGPT, organizations are rapidly moving toward pilot projects for their own generative AI-based intelligent applications. The challenge for organizations here is to invest in technologies that will present the best long-term impact in solving problems that are unique to their own business — and this is where the role of Intelligent Applications is most relevant as a top strategic technology trend of 2024.

To get started, organizations can establish a center of excellence to define the scope of Intelligence necessary for their Intelligent Applications. On the technical side, you’ll need to establish a data pipeline and platform for efficient data access, model training, and insights processing.

Finally, you must evaluate a wider portfolio of services and solutions impacted by the adoption of vendor-specific Intelligent Applications.

More strategic technology trends

Learn about more of Gartner’s Top 10 Strategic Technology Trends:

• Continuous Threat Exposure Management (CTEM)

• AI TRiSM: AI Trust Risk & Security Management

• Machine Customers

• Industry Cloud Platforms

FAQs about Intelligent Applications