Published on February 9, 2024, 6:19 am
Generative AI: Navigating the Hype Cycle and Preparing for Productivity
Generative Artificial Intelligence (AI) has been making waves in recent years, with its rapid adoption and democratization leading to mainstream usage. However, despite the current level of hype and excitement surrounding gen AI, it still has a journey ahead before reaching peak productivity.
The analogy often used to describe the trajectory of generative AI is that of the lightbulb. Just as the invention of the lightbulb brought practical use cases for electricity to the masses nearly 150 years ago, generative AI is doing the same for AI today. However, like any technological advancement, mainstream adoption is fueled by powerful and proven initial use cases.
Currently, we find ourselves at the peak of inflated expectations in Gartner’s hype cycle. One example that showcases this is ChatGPT, which gained over 100 million monthly active users in just two months last year. While we are on the verge of mainstream adoption, with almost half the general population utilizing gen AI, there is still more to come.
In the world of generative AI, we are discovering both its strengths and weaknesses in real-time. As we experiment with gen AI applied to various data sets, we are learning what works well and what doesn’t. This understanding will help us navigate through generative AI’s hype cycle.
As CIOs lead their organizations into this transformative era of gen AI, they should ensure they present a realistic perspective. While it is crucial to emphasize its potential benefits, it’s equally important to acknowledge its shortcomings. Some downsides include issues such as the black box problem, vulnerability to misguided human arguments, hallucinations, and more.
To navigate these challenges effectively, CIOs should consider implementing a corporate use policy accompanied by relevant training programs. This will educate employees on both risks and best practices while maximizing business value without compromising organizational security.
Additionally, organizations should review potential use cases for gen AI on a case-by-case basis. While there are excellent applications, it’s crucial to assess the clear benefits of utilizing generative AI and avoid using it where it may not provide significant value or may even create more problems.
Training and education should be prioritized within organizations utilizing gen AI extensively. Internal communities of practice can facilitate knowledge sharing, raise awareness, and promote best practices across the organization. This approach will help ensure that employees understand the limitations and constantly learn from their experiences.
Moreover, organizations must have a plan in place for when gen AI makes mistakes. While corporate use policies set guardrails, IT’s governance processes must monitor and react to situations effectively. Distinguishing between right and wrong decisions, assessing business impacts, and establishing remediation processes are essential components of this plan.
Generative AI is on the cusp of its lightbulb moment, where its productivity will reach new heights. However, before this happens, we need to navigate through the trough of disillusionment and ascend the slope of enlightenment. The gaslighting moments and continuous learning along the way are all part of this transformative process.
In conclusion, as generative AI continues its journey toward mainstream adoption, it’s essential to maintain a balanced perspective that acknowledges both the potentials and limitations of this technology. By implementing practical strategies and fostering a culture of experimentation and learning, organizations can navigate through the hype cycle and prepare for a future where generative AI truly shines.