Leading firms ‘to double AI projects’

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Leading businesses are set to increase their number of AI projects from four to ten over the next 12 months, Gartner says.

Forward-thinking organisations around the world are set to more than double the number of artificial intelligence (AI) projects they are running over the next 12 months as excitement about the potential of the technology grows.

This is according to research from Gartner, which found almost six out of ten leading firms (59 per cent) in this sector have already deployed AI or machine learning (ML) initiatives.

On average, organisations have four such projects in place today, but respondents expect to add six more in the next 12 months. The pace of adoption is set to continue increasing in the coming years, with enterprises forecasting they will have a total of 20 AI/ML projects in the works by 2021 and a further 15 on top of that in 2022.

Jim Hare, research vice-president at Gartner, said this significant acceleration of AI projects will require many firms to rethink how they approach these technologies to ensure each initiative is getting the right support and resources.

He commented: “The rising number of AI projects means that organisations may need to reorganise internally to make sure that AI projects are properly staffed and funded. It is a best practice to establish an AI Centre of Excellence to distribute skills, obtain funding, set priorities and share best practices in the best possible way.”

Gartner’s research also found improving customer experience is the main driver of AI/ML projects. Some 40 per cent of respondents cited this as their top motivator for investing in the technology.

Meanwhile, one in five (20 per cent) said automating tasks was their top priority, with tasks such as invoicing and contract validation in finance or automated screening and robotic interviews in HR among those set to be delegated to AI.

The survey also found the top challenge to adopting AI is a lack of skills, which more than half of people (56 per cent) identified as an issue. This was followed by understanding AI use cases (42 per cent) and concerns with data scope or quality (34 per cent).

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