Data readiness must be a top concern for all AI projects
Organisations that are working with artificial intelligence (AI) or machine learning (ML) have, on average, four projects utilising these technologies in place, according to a recent survey by Gartner.
The survey finds 59 per cent of respondents have deployed AI.
These respondents expect to add six more projects in the next 12 months, and another 15 within the next three years.
This means that in 2022, those organisations expect to have an average of 35 AI or ML projects in place, says Gartner, in its AI and ML Development Strategies study .
The analyst firm says the study is based on the results of a survey it conducted in December 2018 with 106 Gartner Research Circle members. The latter is a Gartner-managed panel composed of IT and IT/business professionals.
The participants were required to be knowledgeable about the business and technology aspects of ML or AI either currently deployed or in planning at their organisations.
“We see a substantial acceleration in AI adoption this year,” says Jim Hare, research vice president at Gartner.
“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.”
The survey finds customer experience (CX) and task automation are key drivers for AI implementation.
Forty percent of organisations named CX as their top motivator to use AI technology.
While technologies such as chatbots or virtual personal assistants can be used to serve external clients, most organisations (56 per cent) today use AI internally to support decision-making and give recommendations to employees.
Automating tasks is the second most important project type — cited as a top motivator by 20 per cent of respondents.
Organisations may need to reorganise internally to make sure that AI projects are properly staffed and funded
Examples of automation include invoicing and contract validation in finance or automated screening and robotic interviews in HR.
As for top challenges to adopting AI, respondents cited lack of skills (56 per cent), understanding AI use cases (42 per cent), and concerns with data scope or quality (34 per cent).
“Finding the right staff skills is a major concern whenever advanced technologies are involved,” notes 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, says Hare.
“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.”
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