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Enterprise organizations plan to double AI deployments by 2020

Over half of organizations already have at least one AI or ML project in place.
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Written by Charlie Osborne, Contributor on

Enterprise organizations with artificial intelligence (AI) or machine learning (ML) deployments plan to double their project numbers within the next year. 

On Monday, Gartner said that 59 percent of organizations have some form of AI deployment in operation today. Based on a survey of IT and business executives conducted online in December 2018, the research agency estimates that the average company has four AI or ML projects in place.

AI and ML provide a loose definition which can be applied to everything from smart chatbots to natural language processing, task automaton, or object recognition -- and survey respondents expect to add up to six more projects to the roster within the next 12 months. 

The majority of organizations, 56 percent, said that AI is mainly used for internal purposes including decision-making support and employee task recommendations. 

AI is generally seen as a valuable tool for streamlining business processes and reducing workloads through the automation of tasks. In total, close to 20 percent of respondents said automating tasks such as invoicing and screening are a top motivator for the implementation of AI and ML. 

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Furthermore, Gartner's respondents said that up to 15 additional AI projects are due for implementation within three years. At high estimates, this could see the average organization making use of up to 35 AI-based projects by 2022. 

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According to the research agency, the main challenges associated with implementing artificial intelligence and machine learning projects are a lack of skilled help -- cited by 56 percent of respondents -- and concerns with data quality and scope, mentioned by 34 percent of those surveyed. 

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In addition, 42 percent of organizations said it was often difficult to understand AI use cases. Without evidence, tests, and clear goals for an AI project, it might not be possible for an organization to see or measure a return on investment (ROI), which could, in turn, hamper or prevent AI deployment from being successful. 

"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 organizations may need to reorganize internally to make sure that AI projects are properly staffed and funded."

See also: AI is changing the entire nature of compute

Innovative artificial intelligence, machine learning projects to watch

Previous and related coverage


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