Businesses should focus on practical, targeted AI use cases rather than hype-driven applications, according to new findings from Gartner.

A survey of C-suite executives found that the most popular AI applications are not necessarily those delivering the greatest business value.

Cybersecurity threat detection and response and IT service desk automation were each reported by 54% of respondents as the most popular uses, while automated code generation and refactoring was reported by 44%.

However, automated code generation and refactoring was the only one of the three most popular use cases to rank among those delivering positive operational or financial returns. Intelligent IT asset and cost optimisation was identified as successful by 40% of respondents, synthetic data generation by 28%, and automated code generation and refactoring by 23%.

“AI tools are increasingly being embedded into small and midsize businesses as they work to stay competitive, however, this rapid shift means digital skills are no longer a ‘nice to have’, but critical for business growth,” Richard Bovey, Chief for Data at AND Digital, commented.

“When employees and business owners have accessible ways to build confidence with essential AI and digital tools, it puts them in a much stronger position to thrive in today’s workplace.”

Sheila Flavell, CBE, COO of FDM Group said: “As AI continues to be embedded across different areas of business, a baseline understanding of AI tools and applications is becoming a core employability requirement.

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“The graduates who thrive in the workplace will be the ones who are equipped with both AI fluency and given the opportunity for continuous upskilling and reskilling to keep pace with employers’ evolving needs.”

The findings highlight a disconnect between the AI applications businesses are prioritising and those delivering measurable results, reinforcing the importance of identifying clear business objectives and focusing on specific processes where AI can deliver value.

Gartner also found that only 22% of respondents had successfully scaled AI across multiple business units. The research points to the importance of building strong foundations, including the right data, skills and infrastructure, before attempting to scale AI across the enterprise.