Global artificial intelligence (AI) software spending will grow 21.3 per cent to US$62 billion next year, according to analyst firm Gartner. Knowledge management, virtual assistants, autonomous vehicles, digital workplace and crowdsourced data will make up the top five use cases for AI software spending in 2022. The forecast report focused on applications with AI embedded in them, such as computer vision software, as well as software that is used to build AI systems. "Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders," Gartner senior research director Alys Woodward said. Demand for AI technologies and associated market growth is closely tied to organisational AI maturity levels, the report said.

Recent years have witnessed a swell in the adoption of artificial intelligence solutions, revolutionising industries, and helping businesses boost growth. The rising volume and complexity of business data are set to continue driving AI adoption in the following years, causing a surge in global AI spending. According to data presented by BuyShares, global artificial intelligence spending is expected to surge by 120% and hit $110bn by 2024. Businesses across the world use AI technology to be innovative and scalable. Using automation, deep learning, and natural language processing can improve their decision-making, efficiency, speed, and help predict trends.

The retail category as a whole will spend $5.9 billion this year on tools like automated customer service agents and product recommendation generators, while the banking sector is set to put $5.6 billion toward more security-oriented AI uses like fraud prevention and threat intelligence, according to the report. Total spending from businesses on AI is projected to reach nearly $35.8 billion in 2019--a 44 percent increase from last year--and $79.2 billion by 2022. The retail industry's investment in AI comes as ecommerce companies struggle to compete with Amazon's personalized recommendation and pricing algorithms and increasingly cut out traditional retailers to sell directly to consumers. Efficiency in such a cutthroat environment requires the constant processing of customer and transaction data often made possible by machine learning. Meanwhile, banks see AI as a means to cut costs and beef up effectiveness around expensive security and compliance operations, which are complex but quantitative enough for AI functions to handle. All told, the top-funded use cases included automated customer service agents ($4.5 billion worldwide), sales process recommendation and automation ($2.7 billion) and automated threat intelligence and prevention systems ($2.7 billion).