Artificial Intelligence, and all its sub-components, is one of the most intriguing and potentially transformational of all of the currently emerging technology areas. In February 2022, Transforma Insights unveiled its forecastsOpens a new window for the trajectory of deployments, including which device types it will be deployed on, which will be the dominant use cases, and which countries and vertical sectors will see the greatest use of AI. Our expert contributor, Matt Hatton, founding partner of Transforma Insights, summarizes how the market is evolving based on their latest study.
How Big Will AI Be?
In the Transforma Insights projection of the AI market growth, the focus is on the number of instances of AI that will be deployed. Instances are the best way to understand the market growth because they paint a true picture of the importance of each use type and how they are deployed.Â
Measuring revenue is fraught with confusion because there are so many ways of thinking about AI ‘revenue’. Does revenue measure the spending by companies on AI? Or the cost savings associated with it (as most AI is aimed at doing the same things cheaper? Or the total revenue associated with use cases that make use of some form of AI, even if it is only a small part of that implementation? Measuring revenue does not fairly reflect how and where AI is really used. For that reason, our efforts to quantify the growth and adoption of AI focuses instead on the number of instances. Â
Based on a highly granular analysis of each of dozens of different use cases across all vertical sectors in every country, we assess that there are currently 1.8 billion instances of AI deployed globally. By 2030 we expect this to grow to 21 billion, a more than ten-fold growth.Â
IoT Dominates AI Instances
Of the 21 billion AI instances in 2030, around 99% will be deployed on Internet of Things (IoT) devices, a roughly constant figure throughout the forecast period. The other device types upon which AI will be deployed, be it cloud, handsets/tablets/PCs, or edge computing nodes, will account collectively for 1%. It should be noted that qualitatively those AI instances deployed in non-IoT devices may be individually more impactful. After all, a chat-bot deployment in the cloud may serve millions of unique users. Contrast that with AI deployed in an autonomous vehicle which will be predominantly relevant only to the vehicle upon which it is deployed (although it will be very relevant to other nearby vehicles).Â
AI Instances with Share for Non-IoT
The vast majority of IoT AI instances will be in consumer-facing products, with ‘AV Equipment’ such as smart TVs being the single most prominent use case, along with vehicle head units and personal electronics. Other significant use cases are associated with security and public safety, including the adoption in CCTV for behavior analysis and identification of public safety threats. Many IoT devices, including autonomous vehicles, AR/VR devices and delivery robots, will all require some form of AI, meaning that as those IoT use cases grow, so will the requirement for AI.
The analysis also includes an equally granular assessment of each of the other device types. For instance, cloud deployments will tend to focus on enterprise-wide requirements. These are dominated by two prominent use cases. Firstly, those related to IT security, such as identifying hacking attempts and phishing. Secondly, those associated with business efficiency such as robotic process automation, workflow optimization and other business processes.
See More: 2022: The Year IoT Pivots to ‘Hyperscale’
Where Will AI See the Biggest Adoption?
Because the forecasts are built on a country-by-country basis, we can identify which countries will see the greatest adoption. China and the US will lead the way, collectively accounting for 54% of AI instances. This is a greater proportion even than the US and China’s share of the global economy. This is even more pronounced for cloud-related AI instances, with China alone accounting for 53% and the US 18%.Â
Total AI Instances by Country 2030
Delving into the specific use cases, we find a few interesting trends. Natural Language Processing (NLP) is the single most widely adopted use case in every region. After that, there is a bifurcation, with some regions (including Europe, North America, Japan and Australasia) focusing more on commercial use cases such as chatbots and customer behavior analysis. The other regions (including China, India, and Southeast Asia) tend to favor applications related to security, particularly CCTV (e.g., image processing or activity recognition) or IT, such as traffic monitoring.
How Will AI Evolution Affect the Forecasts?
In the next decade, many possible qualitative changes might occur in the AI field. There are some predictions of breakthroughs occurring in moves to Artificial General Intelligence, for instance, through quantum computing. There are other predictions of a third ‘AI winter’ where further progress is pending new technological developments.Â
Neither of these scenarios is expected to have a big impact on our forecasts. The forecast period is only for the next ten years, and it is unlikely that either will result in big changes in that time. More importantly, most AI deployments are only using the simplest of AI in the form of relatively well-understood machine learning techniques. Today’s challenge isn’t one of sophistication, it’s one of practical implementation. The capabilities available today can deliver the 21 billion instances that we forecast.
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