AI investments struggle to deliver expected productivity boosts

Many organisations are finding it challenging to convert their investments in traditional and generative artificial intelligence (AI) systems into significant enhancements in worker productivity, according to a recent survey conducted by Gartner.

The survey, which gathered insights from 724 respondents between June and August 2024, highlighted that 37% of teams utilising traditional AI reported notable productivity gains. However, teams using generative AI reported a slightly lower productivity improvement rate of 34%.

Randeep Rathindran, Distinguished Vice President of Research in the Gartner Finance practice, discussed these findings regarding AI's influence on Chief Financial Officers (CFOs) and finance leaders during a conference held in Sydney. "Despite the excitement surrounding AI, its impact on productivity has been inconsistent, leading to what some describe as the AI productivity paradox," Rathindran stated. "While AI has shown potential to boost productivity at the segment level, such as in call centres, broader organisational benefits have been harder to achieve. Therefore, CFOs should recalibrate expectations on how AI will truly impact worker productivity and headcount."

The survey reveals that several factors contribute to this inconsistency. One major issue is the inflated expectations surrounding AI's capabilities, which often result in disappointment when substantial productivity improvements are not realised. AI's potential to automate tasks and provide valuable insights does not inherently result in across-the-board productivity increases, and the benefits of AI often face implementation delays.

There is an uneven distribution of productivity gains across various business functions. For instance, marketing teams reportedly experience significant productivity enhancements from AI deployment, whereas legal and human resources departments have not witnessed similar benefits. This difference underscores the need for contextual applications of AI tailored to specific organisational functions.

"The most successful teams approach AI with an openness to learn and explore new use cases, rather than fearing job displacement," Rathindran added. "By redesigning structures and workflows to eliminate process bottlenecks and shifting time to value-added tasks, these teams maximise AI's potential and achieve meaningful productivity gains."

CFOs and business leaders are advised to reassess their expectations about AI's influence on productivity, rather than considering it a comprehensive solution for driving efficiency. The focus should be on creating conducive internal conditions for AI to achieve its full potential, which involves questioning assumptions about cost or headcount reductions in AI-related business cases. Companies must adopt a structured approach to capitalise on AI's productivity benefits effectively.

Rathindran emphasised, "As AI and GenAI continue to evolve, their transformative promise remains undeniable. However, organisations must ground their expectations in current realities and focus on the factors that truly drive productivity gains. By understanding the nuances of AI's impact and fostering a culture of acceptance and learning, organisations can harness AI's potential to achieve sustainable success."