Over Half of Organizations Cite Lack of Skill as Barrier to AI Development

Over Half of Organizations Cite Lack of Skill as Barrier to AI Development

According to recent data from analyst house Gartner, Inc., leading organizations expect to double the number of AI projects in place within the next year. Gartner’s study, “AI and ML Development Strategies”, reveals that on average, most organizations engaged with AI and ML technology have four projects in place, but expect to add over 20 projects within the next three years. By 2022, these organizations expect to have an average of 35 projects in place.

Our 2019 Application Development Buyer’s Guide helps you evaluate the best solution for your use case and features profiles of the leading providers, as well as a category overview of the marketplace.

“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 organizations 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 Center of Excellence to distribute skills, obtain funding, set priorities and share best practices in the best possible way.”

Among the reasons to experiment with new technology, forty percent of organizations cite customer experience as a top motivator. Over fifty five percent of organizations use AI internally to support decision making, or provide assistance and recommendations to employees. Organization leaders want to enable their employees to make decisions faster, as opposed to replacing their human workers. But implementing emerging technology can be difficult, and comes with it’s own set of challenges.

 “Finding the right staff skills is a major concern whenever advanced technologies are involved,” says Mr. 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.”

Anna Birna Turner

Anna is an enterprise technology writer covering Marketing Automation, Customer Relationship Management (CRM), and Application Development. Feel free to reach out to her at any time at abturner@solutionsreview.com
Anna Birna Turner