Greater China region leads throughout 2017-2025; Asia-Pacific emerges as the the fastest-growing region, going from 0.2% in 2017 to fourth overall with a total of 12.3% by the year 2030
AI promises to be the most disruptive technology of the next 10 years due to advances in computational power alongside the volume, velocity and variety of data, as well as advances in deep neural networks (DNNs). AI comes in two forms — quantitative techniques that can predict behavior from data and neural network technology that can classify complex objects, such as images, video, speech and sound. Organizations using AI technologies can harness data to both extract new insights and automate processes that are uneconomical endeavors for human labor to perform. Any industry with very large amounts of data — so much that humans cannot possibly analyze or understand it on their own — can use AI. Some industries, such as health care, are ripe for such disruption. AI applications will bring new levels of customer service, decision quality, scale and operational efficiency to processes formerly operated by human labor.
The growth in AI businesses’ value shows the typical S-shaped curve pattern associated with an emerging technology (Figure 1). In 2017, the growth rate was estimated to be 70%, but this growth will quickly drop to 39% in 2020. After 2020, the curve will flatten, resulting in a low growth rate of only 7% in 2025. The value that employing AI will bring to businesses comes from the following areas:
Creation of insights that personalize the customer experience
New automated processes that reduce friction and improve business efficiency
AI systems will enhance customer engagement and commerce, expand revenue-generating opportunities, and enable new business models driven by data insights.
Key points in the evolution of AI are as follows:
By 2020, AI technologies will be entirely pervasive in almost every new software product and service.
By 2020, AI will become a positive net job motivator, creating 2.3 million jobs, while only eliminating 1.8 million jobs.
By 2021, AI augmentation will generate US$2.9 trillion in business value and recover 6.2 billion hours of worker productivity.
By 2022, more than 90% of AI technologies in use in enterprises will be embedded in broader products, rather than being assembled or created from scratch by the enterprise itself or its agents.
Business Value According to Geography
The Greater China region leads in business value throughout the forecast period, although only by a whisker until 2019. North America accounts for a third of business value in
2018, dropping to a fifth by 2025 and finishing the forecast period at 18.2% of the total
(Figure 2). Emerging Asia-Pacific is the fastest-growing region, going from 0.2% in 2017
to fourth position at 12.3% by 2030. Both
Latin America, the Middle East and North Africa grow from less than 0.5% to 5% in the
same time frame. Decision support/augmentation and decision automation accelerate more quickly in market mature regions where there is a significant level of automation presence — this growth takes longer to affect emerging regions.
Business Value According to Industry
Adoption and value from AI will be
based on bringing differentiated business
values to operations within a vertical
parameter. Therefore, there are only modest fluctuations in the proportion of business
value from AI generated by each individual industry.
The heavy industry sector leads from 2017 throughout the forecast period (Figure 3). The transportation and construction organizations are focused on becoming more data driven in order to support their goals of growth, digital transformation and profitability improvements. The advance of the IoT will increase the amount of data available, demanding new AI systems that identify otherwise undetected patterns that presage unexpected costs. These systems will also enable predictive maintenance techniques, which Gartner believes will save asset operators about US$1 trillion a year. Agents will be the main variety of AI in the early years, but by 2019 decision support/augmentation will be the leading AI type for heavy industry. Minimal uplift came from decision automation in 2017, but by 2022 decision automation (particularly in the form of predictive maintenance) will have overtaken products in terms of generating business value.
The communications, media and services’ business value from AI will dip by 2023 but will peak by the end of the forecast period. Agents will be the dominant type of AI in the early years, but by 2025 the proportion of AI types will stabilize, with decision support/augmentation reaching 45% and a significant proportion coming from decision automation (18%). AI systems will enhance content by performing indexing and classification that are otherwise unavailable, encouraging consumers to find and consume more content.
Natural resources and materials have long used machine learning for exploring data regarding extraction of resources and finding new areas and new potential resources to extract. DNNs can detect new opportunities indicated by datasets that are not detectable employing conventional processing techniques. Mining and oil extraction are examples
of resources that will benefit from the superior technology of DNNs to detect undiscovered opportunities hidden in the large, diverse
areas in this industry. Decision support/augmentation will be the top AI type from 2021 onward.
For the consumer products industry, the proportion of the entire AI business value will peak around 2023 and 2024. Adoption of products such as smart home devices (e.g., Amazon Echo and Google Home), smart personal care devices (e.g., toothbrushes) and smart
toys will become mainstream, with new categories emerging (e.g., smart toiletries). In terms of spatial experience, digital business models are emerging that will blur the lines between the digital and physical worlds. Companies are leveraging IoT to connect people, businesses and objects to drive revenue and efficiency. AI applications create the leverage necessary to enhance interactions with potential customers.
Analyst(s): John-David Lovelock, Susan Tan, Jim Hare, Alys Woodward, Alan Priestley
Text provided by Gartner Inc., All rights reserved