Keith Weiss: How Generative AI Could Affect Jobs

As companies integrate generative AI into enterprise software, a wide variety of jobs that depend on requesting or distributing data could be automated.

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Welcome to Thoughts on the Market. I'm Keith Weiss, Head of Morgan Stanley's U.S. Software Team. Along with my colleagues bringing you a variety of perspectives, today I'll discuss the significant potential impact from generative A.I on enterprises. It's Tuesday, October 10th, at 10 a.m. in New York.

You may remember the generative A.I powered chat app that reached 1 million users in only five days after its launch late last year. While much of the early discussion on the use of generative A.I focused on the consumer opportunity, we see perhaps an even bigger opportunity in enterprise software.

The advantages from traditional A.I to generative A.I are rapidly broadening the scope of the types of work and business processes that enterprise software can automate, and this could ultimately have an impact on industries across the entire economy.

Of course, one of the biggest questions everyone seems to have is how will generative A.I impact jobs? We forecast 25% of labor could be impacted by generative A.I capabilities available today, likely rising to 44% of labor in three years. Further, by looking at the wages associated with those jobs, our analysis suggests generative and A.I technologies can impact the $2.1 trillion of labor costs attached to those jobs today, expanding to $4.1 trillion in three years in the U.S. alone. This drives an approximately $150 billion revenue opportunity for software companies in our view.

An important caveat here, we believe it's too early to make any definitive claims on the number of jobs that will be replaced by generative A.I. So we used the term impact to denote the potential for either an augmentation or further automation of these jobs on a go forward basis.

So what are the jobs we think are most likely to be impacted? Based on the current capabilities of generative A.I technologies like large language models, we believe the common characteristics are skills amongst the jobs most impacted are the need to retrieve or distribute information. For example, billing clerks, proofreaders, switchboard operators, general office workers and brokerage clerks. On the other side of the equation, jobs that are least impacted today are those that require some aspect of physical labor, including ophthalmologists, extraction workers, choreographers, firefighters and manufactured building and mobile home installers.

Over the next three years, as this more generalized A.I. technology focuses in on more specific use cases, we believe the impact of generative A.I will shift into more specialized jobs, such as general and operations managers, as well as registered nurses, software developers, accountants and auditors, and customer service reps. Of these, the General and Operations Manager jobs could experience the highest potential cumulative wage impact. In fact, our analysis suggests a $83 billion impact amongst general and operations managers today.

The magnitude of the enterprise impact marks only one side of the equation, as the timing of the realizable opportunity becomes increasingly important for investors to navigate this evolving technology cycle. To be clear, the rapid adoption of these consumer technologies are not going to be indicative of the pace of adoption we're likely to see amongst the enterprise. There are several notable frictions to enterprise adoption related to items such as finding a good return on investment, enabling good data protection, the skill sets necessary to run and operate these new technologies and legal and regulatory considerations, all which necessitate significantly longer adoption cycles for the enterprise. For this reason, we think generative A.I remains in the early stages of the opportunity.

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