AI projects are on the rise and will double within the next year according to a recent Gartner survey — improved customer experience and task automation are key drivers of this growth.
Currently, organisations working with AI or machine learning have, on average, four AI projects in place. And, of all respondents, 59% said that they have already AI deployed today.
“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 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.”
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AI projects on the rise
Today, the average number of AI projects in place is four, but respondents of Gartner’s survey expect to add six more projects in the next 12 months, and another 15 within the next three years (see Figure 1). This means that in 2022, those organisations expect to have an average of 35 AI or ML projects in place.
Customer experience (CX) and task automation
How AI impacts CX and task automation are the key drivers behind the technology’s adoption, according the survey. In fact, 40% 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%) today use AI internally to support decision making and give recommendations to employees. “It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster,” Hare said.
Automating tasks is the second most important project type — named by 20% of respondents as their top motivator. Examples of automation include tasks such as invoicing and contract validation in finance or automated screening and robotic interviews in HR.
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AI projects: the challenges
The top challenges to adopting AI for respondents were a lack of skills (56%), understanding AI use cases (42%) and concerns with data scope or quality (34%). “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.”
Measuring the success of AI projects
The survey showed that many organisations use efficiency as a target success measurement when they seek to measure a project’s merit.
“Using efficiency targets as a way of showing value is more prevalent in organisations who say they are conservative or mainstream in their adoption profiles. Companies who say they’re aggressive in adoption strategies were much more likely instead to say they were seeking improvements in customer engagement,” said Whit Andrews, distinguished research vice president at Gartner.