Although virtual assistants have been around for a while, and most consumers are used to dealing with them, the application is limited and the experience can be poor. However, there is a whole new level of virtual assistance on the way called advanced virtual assistants, which are capable of functioning as virtual billing agents, virtual AI/VR agents or even virtual driver/car agents.
The impact will be substantial in industries, organizations, and consumer interactions. But advanced virtual assistants are just one of 23 of the most impactful technologies featured on the Gartner Emerging Technologies and Trends Impact Radar for 2021.
Let’s look at some of the technologies that I find especially interesting. I’ll examine a few that are right around the corner (e.g., advanced virtual assistants) and one that is further out (AR cloud). These technologies align to three themes:
You can see these technologies here on our 2021 Emerging Technologies Impact Radar. The rings represent the range, which estimates the number of years it will take until the technology or trend crosses from early adopter to early majority adopter. The size and color of the emerging technology — or trend radar blip — represent the technology’s mass; in other words, how substantial will the impact of the technology or trend be on existing products and markets.
Advanced virtual assistants (AVAs)
Advanced virtual assistants, sometimes referred to as AI conversational agents, process human inputs to deliver predictions and decisions. They are powered by a combination of the conversational user interface, natural language processing (NLP), and semantic and deep learning techniques such as deep neural networks (DNNs), prediction models, decision support, and personalization.
Time to market: 1-3 years
The estimated time to market is driven by the expansion of current, limited function virtual assistants (which have been around for years now) to advanced virtual assistants that target a multitude of jobs and functions — propelling the expansion of AI conversational agents into every sphere of consumer lives, business interactions, and operations.
The impact potential of advanced virtual assistants is high because the technology can be utilized in virtually every vertical segment and almost all disciplines. It has the potential to transform the nature of how an application is used for the workforce and how consumers interact with devices and the IoT (Internet of Things) while enhancing customer experience and engagement.
Transformer-based language models
Transformer-based language models are DNNs that process words as sequences in a sentence. This approach preserves the context or meaning of surrounding terms. It also substantially improves translation, transcription, and natural language generation. These models are trained on enormous datasets of billions of phrases.
Time to market: 1-3 years
The time to market is driven by the effectiveness of the training tools, runtime efficiency, and ease of deployment. Transformer-based language models, such as GPT-3, have the capability to generate paragraphs of text that are indistinguishable from those written by a well-educated human.
The impact potential of transformer-based language models is very high because they are displacing recurrent neural networks (RNNs) systems at a surprising rate. And new tools deliver substantial improvements in advanced text analytics and all related applications, such as conversational user interfaces, intelligent virtual assistants, and automated text generation.
Packaged business capabilities
The composable business enables organizations to create custom application experiences composed of application components that they buy or build. To support the composable business, technology providers should deliver packaged business capabilities, which represent a well-defined set of business features that are recognizable as such by a business user.
Time to market: 3-6 years
The time to market is driven by the high number of vendors that have modularized their offerings. That being said, despite this progress, smaller providers and providers transitioning from older technologies still find themselves in the earliest stages of API adoption.
The impact potential of packaged business capabilities is medium because the technology typically represents a repackaging of existing capabilities, but the widespread implementation of the composable business will transform the way that traditional providers market, sell, and deliver their solutions.
AR cloud enables the unification of physical and digital worlds by delivering persistent, collaborative, and contextual digital content overlaid on people, objects and locations to provide people with information and services directly tied to every aspect of their physical surroundings.
For example, any individual can receive fare, route, schedule, and routing information about public transit based on their context (personal status, geolocation, calendar appointment, travel preferences, etc.), by simply “looking at” a bus or bus station with your phone, tablet or head-mounted display (HMD). Further information can be crowdsourced, such as users noting how often the bus has been late in recent weeks.
Time to market: 6-8 years
The time to market comes from a need for numerous, underlying elements such as edge networking, high bandwidth, and low-latency communications, standardized tools and content types for publishing into the AR cloud, management and delivery of content, and interoperability to ensure seamless and ubiquitous experiences.
The impact potential of the AR cloud is very high because it will transform how people will interact with the world around them. AR cloud will provide a digital abstraction layer for people, places, and things and will space across the business and consumer applications and impact every industry regardless of geography. This will enable new experiences and, in turn, new business models and ways to interact and monetize the physical world.
The original article by Tuong Huy Nguyen, senior principal analyst at Gartner, is here.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Alexey Surgay