Mobile Artificial Intelligence Market Size, Share & Trends Report By Technology Node (7 nm, 10 nm, 20-28 nm And Others), By Application (Smartphones, Cameras, Drones), By Region, And Segment - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032

The mobile artificial intelligence market size was estimated at USD 14.48 billion in 2022 and is expected to surpass around USD 156.81 billion by 2032 and poised to grow at a compound annual growth rate (CAGR) of 26.9% during the forecast period 2023 to 2032.

Key Takeaways:

• North America is estimated to hold the leading share of 31.8% of the global revenue in 2022.
• The market in Asia Pacific is projected to grow at the highest CAGR during the forecast period.
• The 10 nm segment led the market in 2022, accounting for over 44.86% revenue share of the global revenue.
• 7 nm technology node is the fastest-growing segment accounting for 32.6% CAGR growth over the forecast period.
• The smartphone segment dominated the market and accounted for more than 37.17% share of the global revenue in 2022.

Mobile Artificial Intelligence Market Report Scope

The market growth has been significant in recent years due to several factors. One of the major drivers augmenting the market is the increasing processing power of mobile devices. Modern smartphones and tablets have powerful processors and graphics processing units (GPUs) that can efficiently run AI algorithms and models.

Another factor fueling the market growth of mobile artificial intelligence (AI) is the prominent availability of AI tools and frameworks. Many popular AI frameworks, such as TensorFlow, PyTorch, and Keras, have mobile versions that allow developers to build and deploy AI models on mobile devices. Mobile AI applications are used in healthcare, finance, education, and entertainment to improve efficiency, accuracy, and user experience. For instance, mobile AI analyzes medical images, detects fraud in financial transactions, and provides personalized learning experiences. Moreover, emerging data collection is also a significant factor in market growth. Mobile devices generate vast amounts of data, such as images, videos, and text, which can be used to train AI models. The growth of the Internet of Things (IoT) contributes to data availability by generating data from various sources, such as sensors and connected devices.

Overall, the growth of mobile AI is expected to continue as AI technology becomes more advanced and accessible and as mobile devices become more powerful and ubiquitous. It has the potential to transform various industries and improve the lives of people around the world, as its applications can be used in various domains. Some examples of mobile AI applications include speech recognition, image and video recognition, natural language processing, and predictive analytics.

Investments in various AI-based technologies have increased recently. This element is driving the global market for mobile AI. The rise in demand for processors with AI capabilities on a worldwide scale is another factor driving the market. Several nations' governments are implementing various advantageous policies to support the start-up culture. This reason is increasing the need for mobile AI in the international market. Smartphones, Cameras, drones, AR/VR, automobiles, and robots are a few of the significant industries in which items from the market is used. Limited AI Experts and Expensive AI Processors are the two factors limiting industry expansion. In contrast, the potential includes prominent demand for Edge Computing in IoT and Low-Cost vision applications in mobile devices and AI chips for cameras.

Mobile Artificial Intelligence (AI) Market Dynamics: 

Driver: Growing demand for AI-capable processors in mobile devices

In the past years, cloud-based complex AI algorithms could not accomplish tasks on computers, mobile phones, and other devices.

This limitation became a roadblock for rapid adoption of AI in consumer devices. It has led to tier-one semiconductor hardware manufacturers, including smartphone vendors, increasingly focusing on exploring application processor designs and frameworks that will fetch AI on the device rather than on the cloud. Connectivity in mobile devices experiences high latency, network congestion in densely populated areas, and increased levels of signal collision due to oversaturated use of available spectrums/increasing traffic in available spectrums.

On-device processors can help mobile equipment compute data in real-time with minimum latency (much lower compared to cloud). This low latency attribute is a critical design aspect for drones, augmented reality solutions, cameras, and autonomous and semiautonomous cars, as they require running deep learning algorithms in real-time for making quick decisions. Any delay stemming from latency in the communication can result in disastrous or fatal outcomes. At the moment in the market, Apple (US) and Google (US) are using AI capable processors in their flagship smartphone products. However, with the increased use of AI in autonomous cars, drones, and other mobile devices, more players are expected to enter this market during the forecast period.

Restraint: Limited number of AI experts

AI is a complex system, and for developing, managing, and implementing AI systems, companies require personnel with certain skill sets. For instance, people dealing with AI systems should also be aware of technologies such as cognitive computing, ML and machine intelligence, deep learning, and image recognition.

In addition, integrating AI solutions with existing systems is a difficult task, which requires extensive data processing to replicate the behavior of a human brain. Even minor errors can translate into system failure or malfunctioning of a solution, which can drastically affect the outcome and desired result.  

Professional services of data scientists and developers are needed to customize an existing ML-enabled AI service. Due to AI being a technology still in the early stages of its life cycle, a workforce possessing in-depth knowledge of this technology is limited. The impact of this restraining factor will likely remain high during the initial years of the forecast period

Opportunity: Growing demand for edge computing in IoT

Edge computing technology is used to move data processing close to the source of data rather than limiting the processing power in cloud/data centers.

The combination of IoT and edge computing can reduce latencies and increase the penetration of AI in industries. Self-driving cars, robotics, and predictive maintenance are expected to leverage AI edge computing significantly during the forecast period.  Service robots in industries and homes can use AI technologies such speech and voice recognition, computer vision, and more sophisticated analytics tools.

Moreover, the use of edge computing in motion detectors, video surveillance, environmental sensors, and other security and monitoring devices can further enhance levels of automation in security and monitoring processes. In industries, predictive maintenance is also an area where edge AI can enhance IoT systems' latency and overall performance. Edge computing and AI can together reduce maintenance time and costs in the road transportation, railways, and aerospace industries.

Challenge: Unreliability of AI algorithms in mobile apps

AI is implemented through machine learning using a computer to run specific software that can be trained. Machine learning can help systems process data with the help of algorithms and identify specific features from that dataset.

However, a concern associated with such systems is that it is unclear as to what is going on inside algorithms; internal workings remain inaccessible and unlike humans, the answers provided by these systems are uncontextualized. In July 2017, researchers at the Facebook AI Research (FAIR) lab found that the chatbot they created had deviated from their predefined script and were communicating in a language they created, which humans could not understand.

While one of the important goals of current research is to improve AI-to-human communication, the possibility that an AI system can create its own unique language that humans cannot understand could be a setback. Several scientists and tech influencers, such as Stephen Hawking, Elon Musk, Bill Gates, and Steve Wozniak, have indeed raised concerns about future AI technology leading to unintended consequences.

Technology Node Insights

The 10 nm segment led the market in 2022, accounting for over 44.86% revenue share of the global revenue. Emerging innovations by key players are fueling the segment growth. For instance, Intel's 10nm node is a manufacturing technology that depends on a 13-layer metallization stack and FinFET transistors. The key technologies intended to enable Hyper Scaling include contact overactive gates, the use of cobalt interconnects for the 2 layers to reduce resistance at that area by 52% and decrease electro migration by 6x-10x to shrink interconnects, SAQP for selected metal layers at the rear end of the line, self-aligned quadruple patterning for Fin formation, and self-affiliated double patterning for gate construction at the front end of the line.

7 nm technology node is the fastest-growing segment accounting for 32.6% CAGR growth over the forecast period. The PPA, which is the main ask of the Mobile, handheld device and processor industry is augmenting the mobile artificial intelligence (AI) market. It is evident from the fact that Although Qualcomm is already shipping parts made with the 7nm process for its Snapdragon chips, Apple announced its A13 Bionic chip used in the iPhone 11 in February 2019.

Application Insights

The smartphone segment dominated the market and accounted for more than 37.17% share of the global revenue in 2022. Rapid growth in smartphones across the globe is augmenting market growth. Numerous ground-breaking applications in the fields of manufacturing, transportation, and video games are powered by artificial intelligence. In smartphones, AI takes center stage and extends far beyond features like digital assistants. With the advent of Edge-AI technology, many backend AI capabilities for profiling may now be transferred to the phone itself, accelerating market expansion.

Popular interest in virtual and augmented reality has lately increased due to increased development in VR technology and devices based on smartphones. Virtual reality (VR) is a relatively new technology. Virtual reality technologies, such as VR technology with sports training, simulation, and smartphone technology, may be dynamically duplicated using computer hardware, software, and technology. VR may also be used in IoT, crypto chips, home systems, Industry 4.0, and other applications. This approach can benefit from the use of AI algorithms. High resource integrity and node privacy are necessary to prevent security threats, and AI can offer a solution, particularly when utilized in gaming applications to boost market development.

Regional Insights

North America is estimated to hold the leading share of 31.8% of the global revenue in 2022. It is attributable to the prevalence of large number of market players in the countries in the region. Also, due to various developments made by companies, the region is expected to lead in technology adoption and be home to major AI solution providers for mobile applications based in North America, driving the market to grow significantly in the forecast period. Artificial intelligence and the Internet of Things are becoming more common in many industries, and intelligent automation technologies are becoming more popular. This is increasing the need for mobile AI in the area.

The market in Asia Pacific is projected to grow at the highest CAGR during the forecast period. The significant presence of automotive, electronics, and semiconductor businesses in China and Japan, together with a vast number of manufacturing firms, is fueling the expansion of the mobile ALM market in the Asia Pacific region. APAC has enormous promise for technologies like cell phones, industrial robots, and automobiles. The use of smartphones using Al processors is anticipated to rise in the Asia Pacific in the upcoming years due to the increasing smartphone penetration in nations like China, Japan, India, and South Korea. To speed up Al processes, visual processing units are integrated into smartphones, security cameras, and wearable technology, and Asia Pacific is one of their biggest markets.

Key Companies & Market Share Insights

Apple Inc., Qualcomm Inc., Microsoft Corporation, and other prominent mobile artificial intelligence (AI) competitors have profiles and competitive analyses. Qualcomm, MediaTek Inc. (Taiwan), NVIDIA Corporation (the United States), Intel Corporation (the United States), IBM Corporation (the United States), Huawei Technologies (China), and others are concentrating their investments on developing technologically sophisticated, more affordable, and secure products and solutions for diverse applications.

The merging and acquisitions and innovation of new products by major key players in the market are augmenting the market growth, for instance. In March 2023,Qualcomm Technologies, Inc. revealed the Snapdragon 7+ Gen 2 Mobile Platform, which offers premium experiences that are unique to the Snapdragon 7-series. 4K HDR videography, low-light photography quick, uninterrupted gaming, AI-enhanced experiences, and quick 5G and Wi-Fi networking are all made possible by the Snapdragon 7+ Gen 2's exceptional CPU and GPU capabilities are augmenting segment growth. Some prominent players in the global mobile artificial intelligence (AI) market include:

• Qualcomm Inc
• Nvidia
• Intel Corporation
• IBM Corporation
• Microsoft Corporation
• Apple Inc
• Huawei (Hisilicon)
• GoogleLLC
• Mediatek
• Samsung
• Cerebras Systems
• Graphcore
• Cambricon Technology
• Shanghai Thinkforce Electronic Technology Co., Ltd (Thinkforce)
• Deephi Tech
• Sambanova Systems
• Rockchip (Fuzhou Rockchip Electronics Co., Ltd.)
• Thinci
• Kneron

Segments Covered in the Report

This report forecasts revenue growth at country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2032. For this study, Nova one advisor, Inc. has segmented the Mobile Artificial Intelligence market.

By Technology Node 

• 7 nm
• 10 nm
• 20-28 nm
• Others (12 nm and 14 nm)

By Application 

• Smartphones
• cameras
• Drones
• Automobile
• Robotics
• AR/VR
• Others (smart boards, Laptops, PCs)

By Region

• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East & Africa (MEA)