Adrian JohnsonTLDR:
Many businesses that incorporated generative artificial intelligence (AI) tools into their operations in the past year have been disappointed with the returns, leading to skepticism about the technology. However, experts believe that 2024 will be the year when businesses will demand concrete results from AI. A study by IBM showed that the average return on investment (ROI) for generative AI projects last year was just 5.9%, well below the typical 10% cost of capital. The gap between the theoretical promise of AI and its practical implementation in the business world needs to be addressed.
Key Points:
- Many businesses that integrated generative AI tools into their operations last year have not seen the returns they expected, leading some to believe the new technology is over-hyped.
- A study conducted by IBM found that the average ROI on generative AI projects last year was just 5.9%, well below the typical 10% cost of capital.
- Lead times for AI implementation are often too long, causing the underlying technology to become outdated before projects are completed.
- Organizations should prioritize getting the technology in the hands of end users quickly and improve it based on their feedback.
Despite the hype surrounding generative AI, businesses have been disappointed with the returns on their investments. A study conducted by IBM found that the average return on investment for generative AI projects last year was just 5.9%, significantly below the typical 10% cost of capital. This has led many businesses to question the effectiveness and value of AI tools in their operations.
However, experts believe that 2024 will be a turning point for businesses and AI. Arijit Sengupta, CEO and founder of AI app developer Aible and creator of Harvard Business School’s course “AI in Market Facing Functions,” predicts that this will be the year when businesses demand concrete results from AI. “2024 is the year businesses are saying ‘show me the money’ from AI,” Sengupta says.
Sengupta points to the long lead times for AI implementation as a major reason for the underwhelming returns. He argues that if implementation takes longer than a month, the underlying technology becomes outdated before the project is completed. He suggests that projects should take hours, days, or weeks at most.
Another key issue is the pursuit of perfect data before implementing AI. Sengupta believes that perfect data is a pipe dream and that businesses should prioritize getting the technology in the hands of end users quickly. He argues that the AI will improve as users provide feedback and the technology is refined based on their needs.
The gap between the theoretical promise of AI and its practical implementation in the business world needs to be bridged. Businesses need to have realistic expectations and understand that AI is not a magic solution that will automatically deliver great results. Proper implementation and continuous improvement based on user feedback are crucial for the success of AI projects.