In spite of hype, many companies are moving cautiously when it comes to generative AI
What is the primary factor slowing companies down in implementing generative AI projects, according to Aamer Baig?
The primary factor slowing companies down in implementing generative AI projects is complexity, according to Aamer Baig. Companies face challenges in areas such as technical complexity, data readiness, and finding employees with appropriate skills. Additionally, organizations must navigate through issues related to governance, security, and technology. Baig emphasizes the importance of a centralized approach to AI and avoiding too many independent projects within the company.
According to Mike Mason, what is a significant part of the readiness deficit for AI in companies?
According to Mike Mason, a significant part of the readiness deficit for AI in companies is the data piece2. In the Gartner survey, 39% of respondents expressed concerns about a lack of data as a top barrier to successful AI implementation12. Mason also emphasizes the importance of data readiness in executing AI successfully, but notes that it is only part of the equation, as organizations also need to address other aspects such as platform building and data cleansing.
How many companies have successfully implemented generative AI projects at scale, as reported by Aamer Baig from McKinsey and Company?
Aamer Baig, senior partner at McKinsey and Company, reported that just 10% of companies are implementing generative AI projects at scale13. He also mentioned that only 15% of companies were seeing any positive impact on earnings from these projects. This suggests that the hype surrounding generative AI might be ahead of the reality that most companies are experiencing.