The number of enterprise projects using artificial intelligence (AI) or machine learning (ML), are set to double within the next year, but the skills needed to back such AI projects remain a significant concern.

According to research published by Gartner today, the average number of ML or AI projects deployed by companies is set to see dramatic growth over the next few years.

At present, companies report an average of four deployed projects using the technologies, but by 2020 this is expected to rise to an average of 10. It will rise even further in subsequent years, with the research and advisory company reporting an average of 20 by 2021 and 35 by 2022.

“We see a substantial acceleration in AI adoption this year,” said Jim Hare, research vice president at Gartner.

“It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster.”

AI projects surge highlight skills shortages

While organisations are increasingly investing in the implementation of ML and AI projects, they require significant skills in order to complete these successfully. And according to Gartner, this is an area that many struggle with.

The organisation found that 56% of respondents saw a lack of skills as a key barrier to adopting AI in enterprises – the single biggest challenge.

A lack of high-quality data, as well as understanding suitable use cases for AI, were also key concerns.

“Finding the right staff skills is a major concern whenever advanced technologies are involved,” said Hare.

“Skill gaps can be addressed using service providers, partnering with universities, and establishing training programs for existing employees.

“However, establishing a solid data management foundation is not something that you can improvise. Reliable data quality is critical for delivering accurate insights, building trust and reducing bias. Data readiness must be a top concern for all AI projects.”

This advice was echoed by others in the industry.

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“As Gartner has identified, many organisations who planned to deliver AI solutions found that they struggled to find the talent, or were unable to access or generate the data required, as well as allocate sufficient resources to properly deploy them,” said Rob Dalgety, industry specialist at Peltarion.

“Instead, these organisations could seek out the right partnerships so that the technology can be deployed in a way that supports their business model. “

Companies that face a skills shortage are advised to look beyond their own organisation to ensure they get the most out of their AI projects.

“Organisations could deploy an operational AI platform that takes away some of the core challenges in this area,” Dalgety.

“By giving AI projects a graphical interface and abstracting above the underlying complexity, using pre-built AI workflows and models with better integration into IT infrastructure, organisations can reduce the cost, skills and infrastructure required to run these projects and move AI projects from concept to production much faster.”


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