While AI is a general-purpose technology that’s useful for many things, it isn’t good for every task under the sun. How can we decide which concrete use cases to build? If you’re helping a business figure out where to apply AI, I’ve found the following recipe useful as a brainstorming aid:
- Consider the jobs of the company’s employees and contractors, and break down the jobs into tasks.
- Examine each commonly done task to see if it’s amenable to either assistance (augmentation) or automation using AI tools such as supervised learning or generative AI.
- Assess the value of doing so.
Rather than thinking of AI as automating jobs — a common narrative in the popular press and in conversations about AI leading to job losses — it’s more useful to think about jobs as collections of tasks, and to analyze AI’s ability to augment or automate individual tasks. This approach is based on a method developed by Erik Brynjolfsson, Tom Mitchell, and Daniel Rock for understanding the impact of AI on the economy. Other researchers have used it to understand the impact of generative AI. Workhelix, an AI Fund portfolio company co-founded by Brynjolfsson, Andrew McAfee, James Milin, and Rock, uses it to help enterprises asses their generative AI opportunities.
In addition to economic analyses, I’ve found this approach useful for brainstorming project ideas. For example, how can AI be used to automate software businesses? Can it do the job of a computer programmer?
Typically, we think of computer programmers as writing code, but actually they perform a variety of tasks. According to O*NET, an online database of jobs and their associated tasks sponsored by the U.S. Department of Commerce, programmers perform 17 tasks. These include:
- Writing programs
- Consulting with others to clarify program intent
- Conducting trial runs of programs
- Writing documentation
and so on. Clearly systems like GitHub Copilot can automate some writing of code. Automating the writing of documentation may be much easier, so an AI team building tools for programmers might consider that too. However, if consulting to clarify the intent behind a program turns out to be hard for AI, we might assign that a lower priority.
Another example: Can AI do the job of a radiologist? When thinking through AI’s impact on a profession, many people gravitate to the tasks that are most unique about that profession, such as interpreting radiological images. But according to O*NET, radiologists carry out 30 tasks. By taking a broader look at these tasks, we might identify ones that are easier or more valuable to automate. For example, while AI has made exciting progress in interpreting radiological images, part of this task remains challenging to fully automate. Are there other tasks on the list that might be more amenable to automation, such as obtaining patient histories?
O*NET listings are a helpful starting point, but they’re also a bit generic. If you’re carrying out this type of analysis, you’re likely to get better results if you capture an accurate understanding of tasks carried out by employees of the specific company you’re working with.
An unfortunate side effect of this approach is that it tends to find human tasks to automate rather than creative applications that no one is working on. Brynjolfsson laments that this leads to the Turing Trap whereby we tend to use AI to do human work rather than come up with tasks no human is doing. But sometimes, if we can do something that humans do but do it 10,000x faster and cheaper, it changes the nature of the business. For example, email automated the task of transmitting messages. But it didn’t make the postal system cheaper; instead it changed what and how frequently we communicate. Web search automated the task of finding articles. Not only did this make librarians more effective, it also changed how we access information. So even if AI tackles a task that humans perform, it could still lead to revolutionary change for a business.
Many jobs in which some tasks can be automated aren’t likely to go away. Instead, AI will augment human labor while humans continue to focus on the things they do better. However, jobs that are mostly or fully automatable may disappear, putting people out of work. In such cases, as a society, we have a duty to take care of the people whose livelihoods are affected, to make sure they have a safety net and an opportunity to reskill and keep contributing. Meanwhile, lowering the cost of delivering certain services is bound to increase the demand for some jobs, just as the invention of the car led to a huge explosion in the number of driving jobs. In this way, AI will create many jobs as well as destroy some.
Some programmers worry that generative AI will automate their jobs. However, programming involves enough different tasks, some of which are hard to automate, that I find it very unlikely that AI will automate these jobs anytime soon. Pursuing a long-term career in software is still a great choice, but we should be sure to adopt AI tools in our work. Many professions will be here for a long time, but workers who know how to use AI effectively will replace workers who don’t.
I hope you find this framework useful when you’re coming up with ideas for AI projects. If our projects affect someone else’s work, let’s work hard to protect people’s livelihoods. I hope that by building AI systems, we can create — and fairly share — value for everyone.