How to Capitalize on Generative AI

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How to Capitalize on Generative AI

By Andrew McAfee et al., | Harvard Business Review Magazine | November–December 2023 Issue

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How Will Generative AI Affect Your Company’s Jobs?  Predictions of the kinds and numbers of jobs that will be replaced by generative AI abound. But it’s actually more helpful to think about the cognitive tasks that the technology could perform or help perform.  A study shows that 80% of U.S. workers have at least 10% of their tasks exposed to generative AI, and 19% of workers have more than half of their tasks exposed. But “exposed” doesn’t mean that those tasks will or should be automated. In many cases the best use of generative AI will be to make human workers more productive or creative, not to replace them. 

Every board should expect its CEO to develop an actionable game plan. Doing so is a three-part process.  First, do a rough inventory of knowledge-work jobs: How many of your people primarily write for a living? How many data analysts, managers, programmers, customer service agents, and so on do you have?  Next, ask two questions about each role. The first is, “How much would an employee in this role benefit from having a competent but naive assistant—someone who excels at programming, writing, preparing data, or summarizing information but knows nothing about our company?” Today’s publicly available LLMs are like such an assistant. They can write code, for example, but they don’t know what your software development or systems integration needs are. They can create a project plan or critique an existing one, but they don’t know what projects you’re working on.  The second question is, “How much would an employee in this role benefit from having an experienced assistant—someone who’s been at the company long enough to absorb its specialized knowledge?”  Finally, once your company’s knowledge-work roles have been inventoried and those two questions have been answered, prioritize the most-promising generative-AI efforts. This task is straightforward: Choose the ones with the largest benefit-to-cost ratio. 

Remedying the “Confabulation” Problem.  Given the major impact that generative AI promises to have on a wide variety of businesses in the near future, the response to one of its biggest shortcomings—that it can fabricate information—shouldn’t be to avoid the technology. Rather, it should be to safeguard against that danger. Here are ways to do so.  Build multilevel LLMs or combine one with another system.  Supplement the LLM with a human.  Don’t use an LLM for some tasks that are too risky for generative AI to be involved at all. 

3 key takeaways from the article

1. Predictions of the kinds and numbers of jobs that will be replaced by generative AI abound. But it’s actually more helpful to think about the cognitive tasks that the technology could perform or help perform.
2. Every board should expect its CEO to develop an actionable game plan. Doing so is a three-part process.  First, do a rough inventory of knowledge-work jobs: How many of your people primarily write for a living? Next, ask two questions about each role. The first is, “How much would an employee in this role benefit from having a competent but naive assistant. The second question is, “How much would an employee in this role benefit from having an experienced assistant?”  Finally, prioritize the most-promising generative-AI efforts. Choose the ones with the largest benefit-to-cost ratio. 
3. To protect your organization from AI major short comings i.e., it can fabricate information: build multilevel LLMs or combine one with another system, supplement the LLM with a human, don’t use an LLM for some tasks that are too risky for generative AI to be involved at all. 

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Topics:  Technology, Artificial Intelligence, Productivity