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Why companies plan to double AI projects in the next year

Why companies plan to double AI projects in the next year

Nearly 60% of companies have deployed artificial intelligence and machine learning, according to Gartner.

Artificial intelligence (AI) and machine learning adoption continue to explode across the enterprise, as organizations plan to double the number of AI projects they have in place within the next year, according to Gartner’s Survey Analysis: AI and ML Development Strategies, Motivators and Adoption Challenges, released Monday. 

Of the 106 IT and business professionals surveyed, 59% said they have deployed AI already, Gartner found. Organizations currently working with AI or machine learning have an average of four AI projects currently in place, Gartner found. 

Top AI challenges and motivators

Customer experience (CX) is the top motivator for organizations to adopt AI technologies, the survey found, with 40% of respondents saying so. 

While AI has a number of external uses such as chatbots, the majority of organizations (56%) are currently using the technology internally to support decision making and give recommendations to employees, Gartner noted. Automating tasks—such as invoicing and HR screening—were the second most important type of project, named by 20% of respondents as their top motivator. 

“It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster,” Hare said in the release. 

When it comes to AI adoption challenges, IT and business professionals named a lack of skills (56%), understanding AI use cases (42%), and concerns with data scope or quality (34%) as the top pain points. 

“Finding the right staff skills is a major concern whenever advanced technologies are involved,” Hare said in the release. “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.”