Why Your Business Should Consider Using Intelligent Applications

Intelligent applications tap into AI to give users the ability to make highly informed strategic business decisions. It’s a smart move many organizations are currently evaluating and deploying.

John Edwards, Technology Journalist & Author

January 18, 2024

4 Min Read
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At a Glance

  • AI-enriched intelligent applications can empower workers with increased data accessibility.
  • Organizations need to understand how data is used before implementing intelligent applications.
  • While intelligent applications can boost business success, care must be taken in implementation.

As their name suggests, intelligent applications are applications enriched with artificial intelligence (AI) and packed with data supplied by transactions and other external sources. Like generative AI (GenAI), intelligent applications learn from their interactions, gradually improving autonomous responses over time, transforming the experience of customers, architects, developers, and other users.

The technology’s biggest benefit is it makes data science accessible, taking it out of the hands of data scientists and giving actionable insights to everyone in the organization, says Michelle McGuire Christian, a principal with Deloitte Consulting in an email interview. “This means that companies can provide every level of their workforce with the data they need to make more strategic business decisions -- all the way up to the C-suite.”

Intelligent applications rapidly analyze large amounts of data, uncovering patterns and insights that humans may not notice, explains Mayank Jindal, an Amazon software development engineer, via email. “They allow real-time responses to industry variations, increasing business flexibility.” Intelligent applications also have the potential to lower operating expenses by automating routine tasks and resource allocations.

Related:How Intelligent Applications Can Boost Sales

Most adopters find that intelligent applications minimize the time needed to conduct complex operational tasks, access new insights, and create a truly data-driven and scalable business model in which all team members have visibility to valuable information, Christian says. “With this [technology], companies can generate new ideas, outcomes, and experiences for their customers, including better support and more personalized engagements.”

Many organizations are under increased pressure, caused by accelerated digital transformation, talent shortages, and sustainability requirements, says Julien Moutte, CTO at infrastructure software engineering firm Bentley Systems in an email interview. “Intelligent applications can increase productivity, improve engineering quality, create a more resilient infrastructure, and ensure that sustainability requirements are met.”

Gaining Intelligence

Intelligent applications offer enhanced data analysis capabilities, utilizing machine learning to drive deeper insights into big data, Jindal says. “These kinds of applications can enable personalized experiences based on consumer behavior and preferences, and boost customer engagement,” he explains. “They also assist risk management and decision-making via the prediction of future outcomes and trends.”

Related:3 Factors Driving Software Transformation Across Product Life Cycles

AI and ML capabilities, when used in in intelligent applications, can deliver significant value to an organization, Moutte says. “Applications with intelligent capabilities can automate tedious tasks and streamline workflows across the infrastructure.” He points out that engineers and asset owners, for instance, can use computer vision AI techniques to quickly analyze, detect, and identify objects from thousands of CAD drawings.

Intelligent applications are especially useful in customer relationship management, where they can personalize interactions and anticipate customer needs, Jindal says. “In supply chain management, they can optimize logistics and inventory management with predictive analytics.” He adds that intelligent applications can also make a measurable impact in financial services when applied to fraud detection and personalized investment advice. “In healthcare, AI applications can help with diagnosis and treatment planning.”

When implementing intelligent applications, begin with a solid foundation using cloud-native tools and ensure that strong data hygiene is practiced. “Organizations should federate a company-wide, single source of data and create a set of data governance rules for ingesting and storing information in a way that eliminates duplicative sets,” Christian advises. From there, adopters should consider their business challenges, objectives, and opportunities, then focus on a small set of initial use cases.

Related:AWS Angles Cloud Resources For Generative AI-Dominated World

First Steps

Regardless of industry or sector, the simplest and fastest way to get started with intelligent applications is by observing how data is organized, managed, used, and discarded, Moutte says. “Because intelligent applications are powered by data, your outcomes will only be as good inputs,” he warns. “Companies that master data management will be best positioned to unlock the potential of intelligent applications, digital twins, AI, and machine learning.”

Before moving toward intelligent applications, businesses need to identify areas where such applications can be most beneficial. “Keeping the right blend of technical and domain experts is essential for creating a team,” Jindal says. It’s crucial to invest in a scalable AI infrastructure and tools that support business objectives. “Lastly, pilot projects can provide a look at the technology’s capabilities and limitations before full-scale deployment.”


Christian says she sees a lot of new adopters trying to implement intelligent applications for everything at once. “Our advice to our clients is practical -- don’t try to tackle everything at once and prioritize the high-yield use cases. Overlooking the foundation and infrastructure needed to support intelligent applications is a common pitfall, she says. “Cloud-native tools and one source of data, as well as data governance, are critical in getting started.”

Solid data privacy and security practices and technologies are a must, Moutte says. “This is especially true in the infrastructure sector,” she notes. “Engineering firms and owner-operators need to clearly understand how their proprietary data and intellectual property are being used to train generative AI models.”

Leading Players

Current market players include tech giants, such as IBM with its Watson platform, Amazon Web Services (AWS), Google Cloud AI, and Microsoft Azure AI. “Salesforce offers AI solutions for customer relationship management through its Einstein platform,” Jindal says. “Amazon Web Services (AWS) provides a range of AI options, including machine learning and data analytics capabilities.” He adds that smaller, specialized firms such as Palantir and OpenAI are also significant contributors to the market.

About the Author(s)

John Edwards

Technology Journalist & Author

John Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.

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