What's Happening?
A recent study by Wavestone reveals that while many businesses anticipate significant benefits from artificial intelligence (AI), a substantial number lack mechanisms to measure its return on investment (ROI). Despite the high failure rate of AI projects,
with 85% failing outright, businesses continue to invest in AI, expecting improvements in efficiency, customer service, and productivity. The study surveyed 500 technology and business leaders across the US, UK, France, Germany, Singapore, and Hong Kong. It found that 39% of respondents expect AI to streamline operations within two years, while 37% foresee enhanced customer satisfaction and product development. However, 46% of organizations admitted they do not have structured ROI measurement frameworks, relying instead on informal assessments or case-by-case evaluations.
Why It's Important?
The findings highlight a critical gap in the adoption of AI technologies, where businesses are investing heavily without clear metrics to assess their impact. This could lead to inefficient allocation of resources and missed opportunities for optimization. The lack of ROI measurement frameworks may deter investors, who are increasingly cautious about the tangible benefits of AI investments. As businesses face economic uncertainty, demonstrating clear value from AI becomes crucial for maintaining competitive advantage and securing future funding.
What's Next?
Organizations may need to develop more robust ROI measurement frameworks to justify continued investment in AI. This could involve standardizing metrics and leveraging expert assessments to provide a clearer picture of AI's impact. As AI technologies evolve, businesses will likely focus on integrating these frameworks to ensure that investments translate into measurable benefits.
Beyond the Headlines
The struggle to measure AI's ROI reflects broader challenges in digital transformation, including workforce adaptation and ethical considerations. As AI becomes more integrated into business operations, companies must address issues such as data privacy, algorithmic bias, and the potential for job displacement.
