Intelligent Applications Drive Enterprise Opportunities

Generative AI has revealed applications’ potential to operate intelligently, which has created the expectation for intelligent applications. IT leaders must understand the foundational changes affecting applications and decide their strategy to ensure continued alignment to target business outcomes.

What are Intelligent Applications?

Intelligent applications include intelligence — which we define as learned adaptation to respond appropriately and autonomously — as a capability. This intelligence can be utilized in many use cases to better augment or automate work. 

As a foundational capability, intelligence comprises a number of AI-based services — especially machine learning, semantic enginesvector stores and connected data. Consequently, intelligent applications deliver experiences that dynamically adapt to user context and intent. Sometimes, user experiences are no longer necessary because applications interoperate with other applications autonomously.

Intelligent applications can synthesize their interfaces between other applications (self-integrating applications) — as well as users — in ways that are appropriate to the prevailing circumstances, and they can do so proactively (see Figure 2). For example, an intelligent application can pull functionality (i.e., ordering software from a catalog) into a conversational interface based on user intent and context, or adapt it to external APIs for data exchange.

Why Is This Trend Important?

The way applications work is changing dramatically. Intelligence — in the form of a suite of AI features and functionalities — is becoming a foundational capability. This is expanding the roles that applications can play across a broad range of employee- and customer-facing business activities, and between applications themselves: increasing their level of agency.

Intelligent applications transform the experiences of customers and employees, further impacting product owners, architects, developers and governing roles. As applications play a fundamental and pervasive role throughout our working and social lives, these transformations will have far-reaching consequences (e.g., in terms of the types of jobs available to future generations).

AI is surpassing the limits reached and imposed by traditional programming that uses explicit rules, relationships and instructions. AI learns rules implicitly. Combined with access to connected data, AI can model context and intent to operate autonomously. This can improve work through augmentation, or eliminate it through automation.

As AI continues to advance, it’s causing us to reappraise its capabilities and applications. The progress and speed of such advances — especially in the wake of generative AI applications such as ChatGPT — are providing insight into the nature of intelligence itself. AI can now mimic human behavior so successfully that it can not only help or even replace people at work, but it can also, in some circumstances, fool people into believing it’s human. As such, the scope of AI’s application to work and automation is shifting from routine and mundane tasks, such as invoice processing, to nonroutine and creative tasks, such as copywriting.

Why is this Trending?

Business disruption due to talent/skill shortages is one of the biggest external threats to business after economic threats, according to the 2023 Gartner’s Board of Directors Survey. Workforce (e.g., retention and hiring) is the second biggest priority for 2023 and 2024. The top priority is digital technology initiatives, with AI/machine learning considered the top breakthrough technology. 

Intelligent applications have entered the mainstream. Over 50% of respondents to the Gartner AI Use-Case ROI Survey reported that they have a form of intelligent application in their enterprise application portfolios. Yet, a lack of effective automation/tools is the biggest barrier to worker productivity, according to one-third of respondents to the 2023 Gartner Workforce Optimization Survey. 

Key to AI’s advance is content — facts modeled for human comprehension. Content includes text, image, video and audio formats. AI can now identify and extract facts from content and remodel these as data for processing. It can use this data as the source from which to synthesize new content — the generative in generative AI. Most enterprise data is in the form of content, such as documents, and central to all activities that involve people. 

Content also makes up the interfaces through which users interact with applications, and code is itself content. As such, intelligence extends to adapting applications’ form and function through re-composition, re-engineering their parts to optimize performance, extend reach and expand purpose.

What are the Business Implications?

Intelligence as a capability can apply to all applications. The impact and implications are therefore pervasive across all use cases touched by applications (operational-, employee- and customer-centric use cases). Examples include:

• Optimization and automation of business processes, such as inventory management. For example, generative AI working with AI-based automated stockout measures can deliver natural language insights to managers — ensuring the right level of inventory to match demand. This improves customer satisfaction and related financial metrics. 
  • Example: GA Telesis leveraged an AI-based application using Google’s Vertex Generative AI Platform, with its sales processes to synthesize purchase orders for aircraft replacement parts automatically.1 This significantly cut GA Telesis’ response times to sales inquiries, thus optimizing and preserving the customer experience.
• Assistance throughout the digital workplace to help with many tasks, including drafting documents, automating process workflows, answering questions and generating business insights. For example, digital workplace application suite vendors and their intelligent assistants, such as Microsoft Copilot and Google Duet AI. 
  • Example: Bank of England created a cognitive search application solution using Squirro to enhance its document search and internal knowledge management capabilities.2 This application used machine learning term extraction workflows, coupled with dashboards, to provide a more unified knowledge search system and streamline data management.
    
     
• Customer relationship management with chat-based interfaces facilitating agent-based and self-service support. For example, generative AI can produce an automated summary of a customer service agent’s audio interaction with a customer. The code writes one summary for the customer, indicating what advice was given. The code writes a second summary that summarizes the client’s issue and adds to the customer service knowledge base. 
  • Example: CallRail partnered with AssemblyAI to provide capabilities, such as automatic transcript highlights, and redaction of personally identifiable information.3 This not only provided customer service agents with essential insights much more quickly, but also improved CallRail’s call transcription accuracy by 23%.
    
     

The opportunities created by intelligent applications should be focused on expected outcomes, such as:

• Simplification and personalization of experiences for both employees and customers.
• Optimization of processes combined with a reduction in human error.
• Simplification of applications, and reduction of their number, to deliver business processes.