Generative AI is evolving at a rapid pace, and CEOs are exploring its business value and potential risks. To that end, we've provided a core summary of generative AI for CEOs.
Since the advent of ChatGPT, Bard, Claude, Midjourney, and other content generation tools, there have been high expectations for generative AI. CEOs are naturally wondering: Is this a technology hype, or an opportunity to disrupt the industry landscape, and if so, what value can generative AI bring to their business?
The mass version of ChatGPT attracted 100 million users in just two months. It has driven the adoption of AI in an unprecedented way and has become the fastest-growing application to date. Unparalleled ease of use sets generative AI apart from all previous AI technologies. Users don't need to specialize in machine learning to interact and gain value. Just like other breakthrough technologies such as the personal computer or the iPhone, which can ask questions, almost human, etc., a generative AI platform can give rise to many applications for users of all ages and education levels, and people can use the Internet from anywhere.
All of this is achieved by the underlying models that drive generative AI chatbots, which are massive neural networks trained on large amounts of unstructured, unlabeled data in various forms such as text and audio. The base model can handle a wide variety of tasks. In contrast, previous AI models are often more "narrow" in scope, often only performing one task, such as predicting customer churn rates. A basic model can generate a summary of a 20,000-word quantum computing technology report, draft a market entry strategy for a gardening company, and give five different recipes based on 10 ingredients in the refrigerator. However, behind its rich functions, there are still shortcomings in the inaccurate results, which also makes people pay attention to the risk management of AI again.
When properly regulated, generative AI can not only open up new use cases for businesses, but also accelerate, expand, or improve existing ones. In the case of telemarketing, for example, AI models that have been specially trained can help salespeople identify upsell opportunities, but until now, these models have only been able to determine the likelihood of upselling based on static customer data such as demographics and purchase patterns collected before the call. Generative AI tools leverage internal customer data, external market trends, and social media influencer data to provide salespeople with real-time upsell recommendations based on actual conversations. At the same time, generative AI can also write sales collateral for salespeople to adapt to their specific situations.
The above examples show only one side of the potential impact of AI technology on human work, when in reality, almost all knowledge workers have the potential to benefit from the use of generative AI. While generative AI may eventually automate some of the work, its value will come more from the use of knowledge workers when embedded in everyday tools, such as email or word processing software. These upgraded tools can dramatically increase productivity.
CEOs want to know if action should be taken now, and if so, where to start. Some may see an opportunity to overtake the competition by reinventing the way humans and generative AI applications work together. Others may want to err on the side of caution and try out a few use cases to improve their understanding of generative AI before making a large-scale investment. Companies also need to assess whether they have the necessary technical expertise, technical and data architecture, operating model, and risk management processes to further deploy generative AI.
This article aims to help CEOs and their teams think about the value creation scenarios of generative AI and how to get started. First, we've put together a primer guide to generative AI to help CEOs better understand the ever-changing state of AI and the available technology options. The second part will explore how companies can apply generative AI through 4 case studies aimed at improving organizational effectiveness. These stories come from our observations of early adopters and describe the various options in terms of technology, cost, and operating model requirements. Finally, we'll explore how CEOs can play a key role in using generative AI to lead businesses to success.
The expectations for generative AI are clear, and it's only natural for executives to take advantage of this to plan and move forward quickly. We hope this article will give business leaders a more comprehensive understanding of the future potential of generative AI.
