Bronfman also highlights the potential challenges for GenAI, including the ongoing GPU shortage, the cost of compute for GenAI models, and the environmental impact of GenAI usage. He proposes a strategy of combining GenAI with predictive AI to achieve the greatest impact, using the example of an e-commerce subscription provider using machine learning to predict customer churn and GenAI to craft personalized messages to at-risk customers. He emphasizes that AI should be used to address identified business challenges and opportunities, rather than just experimenting with new tools.
Key takeaways:
- Despite the hype and investment in Generative AI (GenAI), many initiatives are predicted to fail by 2024 due to a lack of focus on the technology's true business potential and the challenges of implementation.
- While GenAI has the potential to contribute significantly to the global economy, traditional machine learning and deep learning still represent a massive unrealized potential that many companies are neglecting.
- Companies should focus on identifying critical business challenges and then select the AI tools that best address those issues, rather than being distracted by the latest algorithms.
- Combining Generative AI and Predictive AI can lead to more efficient and effective solutions, such as customer retention efforts, and this approach is suggested for future AI initiatives.