Intelligent Applications and Machine Customers: Top Tech Trends

It’s almost Thanksgiving, and Gartner has published its Top Strategic Technology Trends for 2024. No surprise — AI figures in half of them. In this post we’ll take a look at three of these trends, Democratized Generative AI, Intelligent Applications and Machine Customers, and discuss the technological and organizational changes coming our way. 

Democratized Generative AI

First, a look at why by 2026, Gartner predicts that 80% of enterprises will deploy Generative AI models, APIs and/or AI-enables applications, and create massive amounts of new content in many formats. Whereas currently, Gen AI is primarily the territory of Big Tech, often related to the high costs of implementing these solutions, as Gen AI moves into the cloud and more developments are open sourced, more types of users will be able to build applications and take advantage of all that it has to offer, enabling true Democratized Generative AI. 

This newfound access to information and skills, often using natural language interfaces, will boost productivity and lower costs of operations. In addition, Gen AI’s ability to summarize large bodies of data is another way that enables the dissemination of data, enabling insights based on more holistics data sets. To optimize the use of Gen AI, organizations can prioritize employee training, and encourage onboarding tools that will assist employees automate routine tasks. Employees may need reassurance that their positions will not become redundant once they integrate AI tools, and that they will in fact benefit from this integration. While it is difficult to quantify the benefits of this democratization, enterprises can gain much from staff that are able to concentrate on more creative and analytical projects, instead of routine tasks. 

But it’s not all upside regarding increased access to data — enterprises must adopt responsible AI practices and governance policies to ensure that accessible data is free of offensive material and that information that should be private remains such. New regulations will be required on local and national levels, and companies may have to make certain decisions about where to draw the lines. 

Another challenge facing organizations with onboarding Gen AI and democratizing data is how  to unify numerous, disparate sources of data and process it in near real-time. An operational data hub such as Smart DIH provides a high performance, highly available and resilient data store for the use of near real-time digital services. The platform decouples systems of record from digital applications, enabling enterprises to drastically shorten the development and deployment cycle for new digital services. 

Intelligent Applications

Users worldwide spend about 5 hours a day on their apps – so it’s no surprise that Gartner sees much value in apps that are augmented by AI to offer more powerful capabilities.  Intelligent applications (I-Apps) are consumer or business applications that are augmented with AI and various connected data from transactions and external sources. These apps can push insights within apps that businesses are currently using, reducing the need for separate business intelligence tools. AI can add actionable insights and predictions, allowing customization and personalization which improves outcomes and enables data-driven decisions.

“Intelligent applications recommend or automate actions instead of just providing analysis, so they can drive improvements — including better personalization, more efficient use of resources, improved accuracy, increased automation, more finely grained responses and decision support. Customers are increasingly demanding these types of intelligent outcomes.”

• Jim Hare, Gartner Distinguished VP Analyst

I-Apps incorporate the power of predictive and prescriptive analytics, consumer data, cutting-edge technologies, and operational data, with application development tools and the latest user-centric design to provide a high-end user experience. These apps study user behavior and provide personalized and actionable outcomes using predictive analytics. Practical examples include security tooling, customer assistance, virtual personal assistants among others. Intelligent apps can magnify the capabilities of cognitive computing and AI/ML.

Also critical to the success of these apps is the unification of data from different sources, including websites, mobile apps, IoT sensors and more, all in real-time. For example, ecommerce applications must offer accurate data on product availability information, estimated delivery dates, and order tracking details. This transparency fosters trust and enhances the overall customer experience. Delayed synchronization can result in missed sales opportunities, as businesses may not be able to fulfill orders promptly or offer real-time inventory visibility. 

Machine Customers

Should your next purchasing decision be made by a machine? 

That seemingly far-out question is close to reality as ‘machine customers’ make their way into a number of sectors. A machine customer is a non-human economic actor who obtains goods and/or services in exchange for payment. Gartner analysts Don Scheibenreif and Mark Raskino  state that we are now in the first phase of the machine customers’ evolution. These machine customers automatically perform limited functions on behalf of the owner, think of Amazon Dash Replenishment. For now, people set the rules, and the machine executes them within a specific and prescribed ecosystem.

Moving along to the next phase, people will still set the rules for machines as ‘adaptable customers’ but AI will be able to choose a course of action, with minimal intervention for specific tasks — think of robotrading and autonomous vehicles. An advantage of machines over humans is that they can also process large amounts of data very very quickly. Another advantage, at least in some situations, is that machines are logical and not emotional; they can make decisions based on defined rules, and not upon the vendor who wined and dined them last week. 

In the third phase, machine customers will be able to own most of the process steps associated with a transaction. For these types of transactions to be successful, the machine customers require comprehensive, real-time data — especially for systems that require immediate response — back to robotrading and autonomous vehicles. Without accurate, timely data, machine customers will not be able to make correct decisions, making them in effect, useless. 

Since machine customers can process data much faster than their human counterparts, they require greater volumes of accurate data in near real-time. Smart DIH provides a high performance, highly available and resilient data store for the use of near real-time digital services — be they machine or human customers. This solution enables the integration of data from Gen AI technology with cloud technology and legacy systems into a unified data layer that serves the entire organization. Smart DIH offers a high-performance, highly available platform that scales to handle hundreds of thousands of transactions with millisecond response times. 

Last words

Wherever we look, AI in its various forms is becoming more embedded in our apps and technology. When AI is used effectively, it promises to accelerate value for customers and internal users. Organizations must make educated decisions when determining which technologies to adopt and incorporate. Gartner recommends investigating revenue potential, by quantifying the potential value and unique outcomes from within the organization’s customer base.