Deep Learning Apps Seen Driving AI Software Revenues
Machine vision, natural language processing, data analytics and other deep learning applications will propel global AI software revenues over the next five years via a growing list of industry segments spanning automotive and health care to financial services and retail.
Market tracker Omdia forecasts AI software revenues will surge through 2025 to $126 billion, a 12-fold increase over a $10.1 billion industry in 2018. “The narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level,” said Keith Kirkpatrick, principal analyst with Omdia.
“AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment for many years,” Kirkpatrick added.
Omdia estimates that more than half of AI revenues will be generated by machine vision and language applications, with deep learning deployments driving the AI market. Deep learning models are proving more capable for perception applications since they can operate without expensive training and are able to tap very large data sets to evolve. Omedia sees those attributes as attractive for applications like cybersecurity, health care and investment trading.
Hence, the market tracker predicts deep learning will account for an estimated $74.5 billion in AI software sales by 2025, or 59 percent of total AI revenues.
The consumer sector has seeded the AI software market via early applications such as digital assistants, smart speakers and automotive applications. Voice and speech recognition apps have so far generated the most AI software revenue.
Those consumer applications tapped into large data sets that resulted in improved AI algorithms and processing engines. Omdia expects other sectors to apply these early uses cases to more ambitious, data-driven applications centered around Internet of Things deployments. Meanwhile, the shift to edge computing and the efforts of infrastructure vendors to move computing and storage resources closer to where data resides are expected to spur development of specialized deep learning algorithms and improved processing capabilities at the network edge.
The market tracker also foresees hybrid AI deployments in which deep learning models are combined with machine vision, natural language processing and “machine reasoning.”
That combination is seen overtaking the role of machine learning for data analytics applications. Hence, deep learning applications will help drive “AI in the long run due to the wide range of use cases that will be enabled now and well into the future,” Omdia said.
That bullish AI software forecast squares with other prognostications. For example, Fortune Business Insights predicted earlier this year that the global market for all AI technologies would grow at a 33-percent clip through 2026, reaching more than $202 billion.