The global artificial intelligence (AI) market was valued at $93.5 billion last year and is forecast to expand at a compound annual growth rate (CAGR) of 38.1% between 2022 and 2030. AI is proving to be a transformative factor in today’s digital era and is being applied in every area possible, from self-driving cars to life-saving medical equipment. The big tech giants continue to invest in research and innovation that is driving the adoption of advanced AI technologies in industry verticals, including healthcare, finance, automotive, retail, and manufacturing. AI offers better efficiency, and many companies are also adopting AI technology to offer better customer experiences.
The software solutions segment leads the AI market, accounting for a revenue share of more than 38% in 2021. This large share is due to advances in data storage capacity, data extraction, computing power, parallel processing to deliver high-end services, and the ability to provide real-time insights that aid in decision-making. These AI software solutions utilize video analytics, linear algebra, inference, sparse matrices, and hardware communications to help businesses analyze and understand visual content to gain insights and make smarter decisions.
Machine & Deep Learning
The deep learning segment of the AI market accounted for a revenue share of about 37% in 2021. This large share can be attributed to complicated data-driven applications, including text and speech recognition. As a result, deep learning offers profitable investment opportunities and includes both AI platforms and cognitive applications, such as tagging, alerting, filtering, clustering, categorizing, hypothesizing navigation, and visualization. These elements enable the development of intelligent solutions.
As cloud-based computing and on-premises hardware equipment for mass data storage grow, it paves the way for the expansion of analytics platforms. There is increasing investment in R&D by leading firms that will play a major role in adopting AI tech. The natural language processing (NLP) segment is largely anticipated to gain momentum as it is becoming more widely used by various businesses to improve decision-making processes by understanding customer preferences, trends, and purchasing behaviors.
The advertising and media segment accounted for a revenue share of more than 20% in 2021. However, the healthcare sector is expected to gain a leading share by 2030. It is further segmented based on areas such as robot-assisted surgery, virtual nursing assistants, dosage error reduction, hospital workflow management, clinical trial identifiers, preliminary diagnosis, and automated image diagnosis.
As the banking, financial services, and insurance (BFSI) segment performs financial analysis, risk assessment, and investment and portfolio management, AI has a growing share in the sector due to high demand for risk and compliance applications.
Other verticals highly focused on AI usage include retail, law, automotive, transportation, and agriculture. One of the most utilized applications by every vertical is the conversational AI platform, which is a type of AI technology that allows people to interact with devices in a way that mimics human conversation, such as chatbots.
Retail is expected to see a significant increase in this area to provide enhanced shopping experiences. In the entertainment and advertising industries, companies are using AI technologies to promote products and connect with customers based on data mining solutions.
North America dominates the global AI market, accounting for a revenue share of more than 42% in 2021. In this region, favorable government initiatives encourage the adoption of AI across industries.
In the Asia-Pacific region, the market is expected to see a significant CAGR through 2030 as investment in AI grows. Additionally, an increasing number of AI start-ups in the region are driving the adoption of AI to bolster operational efficiency and enable process automation.
AI TRENDS FOR 2023
Hyperautomation, also known as digital or intellectual automation, is the process of applying innovative technology to simplify and speed up tasks with minimal need for human interaction and manpower. Hyperautomation is key to performing research in order to increase profits, such as robot-assisted process automation (RPA), AI and machine learning (ML), and cognitive process automation. For example, conversational AI and RPA can improve customer support by automating responses to customer emails, and a company’s productivity can be improved by incorporating technology into labor-intensive processes. Through such implementation, manual labor is reduced and productivity is boosted.
Many companies work with large flows of information, and require automation to retrieve it effectively. Data knowledge and data analysis are being used every day by sectors such as financial, manufacturing, insurance, and marketing. It is creating demand for the roles of application architects, machine learning specialists, data engineers, and enterprise architects.
One of the most critical components of technology is information security. AI and ML technology make it possible to create new ways of protection, and cybersecurity can become an automated and lower risk. This is because AI can be used to classify, process, cluster easily, and filter large amounts of incoming information. In addition, ML allows applications to analyze past data and provide the best solutions as well as instructions to identify threats or malware. Essentially, AI and ML help to impede anyone trying to infiltrate the system. It also sets parameters, allowing information to be matched and any threats quickly tracked. Because of this, AI is helping enterprises to strengthen cloud migration methods, bettering performance when dealing with large amounts of data.
Business Forecasting & Analysis
Using AI for forecasting and analysis in business is quite simple and highly accurate. For example, financial companies are using AI to understand the demand for currency pairs based on market conditions and gauge consumers’ behavior and fear levels in real time. This allows businesses to provide the financial and technical sector with the proper amount of supply to meet demand.
Augmented intelligence combines machine learning with human intuition. It is a different approach from AI as it maintains a human aspect. According to Gartner, by 2023, 40% of infrastructure and operations teams will use automation with AI to achieve increased IT productivity. Augmented intelligence is also referred to as intelligence amplification, cognitive augmentation, machine-augmented intelligence, and enhanced intelligence.
By using augmented intelligence, different platforms quickly gather information by structuring it by templates. Then, the data is extracted from different sources to give a company a complete picture of the product or customer. This trend is expected to grow quite a bit in 2023 in the financial services, healthcare, travel, and commerce sectors.
Intersection of AI and ML with loT
AI and ML are making our devices smarter and more secure. Data from Gartner suggests that by the end of 2022, more than 80% of Internet of Things (IoT) projects in corporations will use AI and ML. All devices must be connected to the Internet to respond promptly to situations based on the information gathered, determine patterns, and detect anomalies. Think wearable devices, smart homes, and smart cities. The concept uses the help of intelligent sensors and additional devices that gather data such as temperature, pressure, humidity, air quality, sound changes, and computer vision. It allows control of lighting, climate, and appliances to enhance comfort, safety, and convenience.
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