Despite artificial intelligence’s (AI) promise for enhancing efficiency, many organisations struggle to turn investments in traditional and generative AI (GenAI) into material improvements in worker productivity, according to a survey by Gartner.
Gartner’s survey of 724 respondents from a range of business functions, taken June through August 2024, revealed that among teams who primarily used traditional AI, 37% reported high productivity gains, while GenAI-using teams fared marginally worse at 34%.
Randeep Rathindran, distinguished vice-president: research, in the Gartner Finance practice, says: “Despite the excitement surrounding AI, its impact on productivity has been inconsistent, leading to what some describe as the AI productivity paradox.
“While AI has shown potential to boost productivity at the segment level, such as in call centers, broader organisational benefits have been harder to achieve. Therefore, CFOs should recalibrate expectations on how AI will truly impact worker productivity and headcount.”
Several factors contribute to the limited productivity gains from AI. For example, the inflated expectations of AI’s capabilities lead to disillusionment. While AI can automate certain tasks and provide valuable insights, it does not automatically translate into substantial productivity improvements across the board.
Additionally, measuring productivity gains can be challenging, and implementation lags often delay the realisation of benefits.
Further, the distribution of productivity gains across functions is uneven. Marketing teams, for instance, report the highest productivity gains from AI implementation, while legal and HR functions fall behind. This disparity highlights the importance of context and the specific applications of AI within different organisational functions.
“The most successful teams approach AI with an openness to learn and explore new use cases, rather than fearing job displacement,” says Rathindran. “By redesigning structures and workflows to eliminate process bottlenecks and shifting time to value-added tasks, these teams maximize AI’s potential and achieve meaningful productivity gains.”
Rather than viewing AI as a silver bullet for driving efficiency, CFOs and business leaders should reset their initial expectations about AI’s impact on productivity and focus on creating the internal conditions that enable AI to deliver its full potential.
This involves challenging assumptions about cost or headcount savings in AI-related business cases and sensitising C-suite and finance leaders to organisational behaviors that heighten AI’s impact. By adopting a structured, explorative, and collaborative approach, organizations can position themselves to capture the productivity benefits that AI can deliver.
“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,” says Rathindran. “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.”