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IT & Data Center

Research: Cloud will not become the standard for AI

Article ImageDespite the many advantages offered by cloud computing, the architecture is unlikely to become the standard for artificial intelligence applications; analyst firm Omdia predicts that by 2025, cloud-based, pay-as-you-go AI software will account for just 20 percent of the $126 billion market.

Several business models for AI software are emerging, including fully in-house, customized approaches and modular approaches using pre-built tools and solutions.

According to Omdia, hybrid solutions will account for 36% of global annual AI software market revenue in 2025, and pre-built AI solutions, for another 20%. On-premises AI solutions are forecast to account for 16%.

Making money

Omdia estimates that the worldwide AI software revenue will increase from $10.1 billion in 2018 to $126 billion in 2025.

“The widespread availability of programming platforms and tools, as well as cloud-based infrastructure, has led to a major shift in the market,” said Keith Kirkpatrick, principal analyst at Omdia. “Enterprises do not need to lock into a single AI vendor; they can hire data science teams and engineers to develop, train and run AI models from scratch.”

Adoption of on-premises solutions, in which AI models are trained or executed on local hardware behind a firewall, will be driven by enterprises with skilled data science software and engineering teams, according to Omdia.

The main challenge for this model is the fact that a significant portion of the enterprise market does not have the skills or the budget to develop AI systems from scratch, Kirkpatrick said.

As the market for AI products and services grows, both enterprise customers and vendors are being challenged to find appropriate business models.

Vendors and customers also are in competition to attract top data scientists, engineers, researchers and project leads.

Meanwhile, the products are evolving to become more service-based, requiring higher support levels and customization, leading to lower margins.

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