How AI Can Empower the Generalist
Many experts on the future of work highlight the importance of adaptability and flexibility. Workers will need to constantly re-skill as the needs of the economy shift along with technology’s rapid pace. The term “serial mastery”, coined by Lynda Gratton at London Business School, illustrates this important point. Workers will need to master multiple areas sequentially throughout their careers. As one area becomes defunct, employees will need to quickly become experts in another area. The idea of serial mastery also elevates the importance of the subject matter expert as opposed to the generalist. I used to strongly agree with this argument, but as I learn more about AI’s capabilities, I find the idea less intriguing. With numerous specialized AI chatbots at their disposal, tomorrow’s employees may be better off taking the generalist route.
Before my MBA, I worked for 2.5 years in management consulting. My teams were full of highly talented and smart individuals who were mostly generalists. My team members exceled in core consulting skills, but our lack of subject expertise consistently held us back and even sometimes frustrated clients. We would need to enlist the help of subject matter experts, who were often expensive and difficult to reach, to push engagements forward. The most successful and efficient engagements involved significant involvement from senior managers and partners with deep knowledge of the client and industry. Engagements led mostly by managers and seniors often floundered. These experiences led me to take stock in specialization and expertise as opposed to soft skills and generalist roles. Generalists could help in project management roles, but experts were needed to actually get work done.
My attitude toward the value and effectiveness of generalists is changing as AI advances. Specialized chatbots can now play the role of subject matter experts and can be accessed on demand. Anything from industry overviews, advanced computer code, or even Excel modeling can be done through versions of Chat GPT, Gemini, or Claude. Generalist consultants no longer have to wait for input from industry or functional experts for crucial slide deck content. Now, they can simply access their firm’s licensed chatbot to gain key insights and solve core problems. In the future, employers may place a higher value on skills like communication, relationship building, critical thinking, and information synthesis. These are all domains of the generalist. The value of the expert seems to be quickly eroding as AI becomes more productive.
One of the benefits of the generalist model is its flexibility. Generalists have skills that are useful across many different areas, allowing them to float from area to area. If one subject area becomes defunct due to changing technology trends, the generalist can simply move to another one with their same portfolio of skills. In comparison, subject matter experts open themselves to risk by choosing depth of knowledge over breadth. If technology advances or economic trends shift demand away from their area, the subject matter expert faces a much harsher transition than the generalist. The generalist model then creates more safety for individual workers, while also presenting lower barriers to entry. Generalist positions then offer a much more attractive value proposition to prospective members of the labor force, while also creating more economic value for organizations.
Yet, the rise of AI does not spell the end of the expert. I still think my original intuition about generalists and experts is correct. Generalists offer many useful aspects to problem solving, but experts are the ones who push initiatives forward. Projects can be done well in their entirety by experts, but not by just generalists. For AI to become an effective expert in different domains, human experts will still need to serve many crucial roles. Firstly, humans will need to innovate to provide AI with more training data. The most effective training data appears to be human generated, not synthetic. If AI is to advance as expected, it will need more and more quality, human-generated data. Subject matter experts will be needed to create this data through innovative projects. Secondly, humans will need to be able to guide AI and provide safeguards. If AI becomes the sole expert in many fields, society will be open to significant risk. AI’s could hallucinate or offer harmful suggestions without our knowledge. Human experts will then play the important function of ensuring that AI operates within safe bounds. Thirdly, human experts are still sometimes the most effective communicators. AI may not always be able to clearly pass on knowledge to generalists. Experts then will be needed to clarify points and more aptly describe complex topics.
I no longer believe that serial experts will be the most effective and successful workers in tomorrow’s economy. Experts will always be needed to innovate, articulate complex points, and provide a safeguard for society. Yet, AI can play the role of subject matter expert sufficiently well for many domains. The new technology unlocks knowledge and potential for generalists, while also making the generalist model more attractive to workers. There are still important needs for serial mastery, but we just need fewer experts and more generalists.
Sources:
https://www.nytimes.com/2012/09/22/business/to-stay-relevant-in-a-career-workers-train-nonstop.html
