Usage of artificial intelligence is expected to accelerate this coming year, with organisations expecting to double the number of projects underway.
According to Gartner research, companies that have already adopted AI internally have roughly four projects underway and are likely to more than double that number over the next year.
The research, which surveyed 106 Gartner research circle members, found that 59 per cent of IT professionals have already deployed AI and are likely to have 10 underway by 2020 and 35 by 2022 if the growth rate continues.
“We see a substantial acceleration in AI adoption this year,” said Jim Hare, research vice president at Gartner.
“The rising number of AI projects means that organisations may need to reorganize 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.”
Of the professionals surveyed, 40 per cent claimed customer experience to be their top priority when implementing AI or machine learning technology, largely through internal use to support decision-making and give recommendations to employees.
Other usage included technologies such as chat bots or virtual personal assistants can be used to serve external clients.
“It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster,” Hare said.
Following on from customer experience was usage for automating tasks, with 20 per cent citing that as a main motivator. Tasks included invoicing and contract validation in finance or automated screening and robotic interviews in HR.
However, at the same time, respondents cited challenges in adopting AI, in particular lack of skills among talent, which accounted for more than half.
Understanding AI use cases was cited by 42 per cent as the main issue, while concerns with data scope or quality accounted for a third.
“Finding the right staff skills is a major concern whenever advanced technologies are involved,” added 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.”
