As companies integrate generative AI into enterprise software, a wide variety of jobs that depend on requesting or distributing data could be automated.
----- Transcript -----
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.
Thank you for listening. If you enjoy the show, please leave us a review on Apple Podcasts and share Thoughts on the Market with a friend or colleague today.
U.S. Consumer: Mixed Holiday Spending Expectations
Ed Stanley: The Cutting Edge of AI
Ellen Zentner: 2024 U.S. Economic Outlook
Serena Tang: The Return of the 60/40 Portfolio
Special: What Should I Do With My Money?
Macro Economy: The 2024 Outlook Part 2
Macro Economy: The 2024 Outlook
Andrew Sheets: Will the Bond Market Suffer from Tax-Loss Selling?
Ed Stanley: Weight Loss Drugs and the Global Economy
Michael Zezas: Are the Worst Bond Returns Behind Us?
Matt Cost: How AI Could Disrupt Gaming
Mike Wilson: Will the Equity Market Rally Last?
Andrew Sheets: Upgrades and Downgrades in Corporate Credit
US Economy: What Generative AI Means for the Labor Market
Michael Zezas: What the New U.S. Speaker Means for Markets
U.S. Housing: The Impact of High Mortgage Rates
Mike Wilson: 2023 Stock Market Comes Full Circle
Andrew Sheets: Optimism in Corporate Credit
Asia Equities: China’s Risk of a Debt Deflation Loop
Vishy Tirupattur: Implications of the Treasury Market Selloff
Create your
podcast in
minutes
It is Free
The emPOWERed Half Hour
Now, What’s Next?
Access and Opportunity
At Scale: A Sustainability Podcast