In the context of the survey, Gartner defines “AI-mature” organizations as those that have deployed over five AI use cases across various business units and processes, which have been in production for more than three years.
Among the notable insights, 52% of organizations report that risk factors are a critical consideration when assessing new AI use cases. While AI-mature organizations are more likely to consider AI for every possible use case, they are also more likely to weigh risk factors before moving forward.
Erick Brethenoux, Gartner Distinguished VP Analyst, emphasized that an AI-first strategy is pivotal for AI maturity and increased ROI. He stated that “an AI-first strategy is a hallmark of AI maturity and a driver of increased return on investment. However, AI-first does not mean AI-only”. This highlights that AI-mature organizations not only explore AI in various use cases but also place a strong emphasis on evaluating the risks involved.
The study also highlighted key differentiators among AI-mature organizations. The latter are 3.8 times more likely to involve legal counsel at the ideation stage of AI use cases. This involvement indicates a greater focus on addressing ethics, legality, and privacy concerns from the inception of an AI project.
Another key finding is that 52% of AI-mature organizations evaluate the return on AI investment relying on a combination of technical and business metrics – showcasing a well-rounded approach to assessing ROI. In contrast, less mature organizations primarily use technical metrics to measure the value of AI use cases.
Further, AI-mature organizations tend to focus on customer success-related business metrics, with 41% leveraging these metrics to estimate ROI, compared to 24% of less mature organizations. The survey also found that 47% of AI-mature organizations identify customer service as one of the top three business functions benefiting from AI, compared to 34% in other organizations. This suggests that AI-mature organizations are more adept at using AI technology to attract and retain customers, leading to a clearer demonstration of AI’s impact on their business.
Besides, AI-mature organizations are proactive in defining metrics at the ideation phase of every use case, with 67% doing so compared to 44% in less mature organizations. This proactive approach ensures that the AI projects are well-aligned with business goals and objectives from the outset.
In conclusion, by considering AI for various use cases and closely evaluating risk factors, organizations can ensure a successful AI deployment. Furthermore, involving legal counsel at the ideation stage and focusing on a combination of technical and business metrics for ROI assessment further contributes to the success of AI-mature organizations in today’s competitive landscape.
