McKinsey teams up with Salesforce to deliver on the promise of AI-powered growth

The majority of these shifts came from people leaving jobs in food services, customer service and sales, office support, and production work (such as manufacturing). At the same time, managerial and professional roles plus transportation services collectively added close to four million jobs from 2019 to 2022. Our previous research had anticipated these types of changes over a longer time frame, but the pandemic suddenly accelerated matters. The past few years have been a preview of trends we expect to continue through the end of the decade.

For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks. Yakov Livshits • Some organizations seek to leverage open-source technology to build their own LLMs, capitalizing on and protecting their own data and IP. CIOs are already cognizant of the limitations and risks of third-party services, including the release of sensitive intelligence and reliance on platforms they do not control or have visibility into.

A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work. One surprising result is that baby boomers report using gen AI tools for work more than millennials. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). In this section, we highlight the value potential of generative AI across business functions.

The Future Is AI

In our forward-looking scenario, we refer to people needing to make transitions if demand is projected to decline in their current occupation. While it is impossible to trace individual moves, many people left their previous roles and landed better-paying jobs in other occupations. Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. We’re seeing it deployed in four main categories that enterprises, including automotive, are starting to adopt. One is around software, the ability to co-develop different software applications with the machine, which we call the copilot.

It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces.

So understanding the use cases that will deliver the most value to your industry is key

Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. Generative AI tools can draw on existing documents Yakov Livshits and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts.

The quits rate soared to new heights during the pandemic, with roughly 48 million Americans leaving their jobs in 2021 and 51 million in 2022. Others left the labor force, whether out of discouragement or for personal or health reasons, and it is unclear if or when they will return. Workers have shown a willingness to change career paths, while a tighter labor market has encouraged companies to hire from broader applicant pools. By contrast, occupations in business and legal professions, management, healthcare, transportation, and STEM were resilient during the pandemic and are poised for continued growth. These categories are expected to see fewer than one million occupational shifts by 2030.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Anheuser-Busch InBev used cloud infrastructure to create digital twins of its breweries—digital models of physical assets that identify operational inefficiencies in real time.9Sandy Thin, “What’s making beer got to do with Silicon Valley? All of this is made possible by training neural networks (a type of deep learning algorithm) on enormous volumes of data and applying “attention mechanisms,” a technique that helps AI models understand what to focus on. Traditional AI also might use neural networks and attention mechanisms, but these models aren’t designed to create new content.

Machine learning is founded on a number of building blocks, starting with classical statistical techniques developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them. The overall labor market will have higher demand for social-emotional and digital skills.

As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption.

We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1). The speed at which generative AI technology is developing isn’t making this task any easier. The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use.

More tech-forward companies also want to be part of a retail ecosystem to access consumers, other retailers, and best-in-class technologies for that complex environment. In addition, consumers appreciate the innovation an ecosystem can provide; seven in ten consumers in a McKinsey survey said they value ecosystem offerings that simplify their purchase journey. This approach helps to deliver high-quality, ready-to-use data sets that people across an organization can easily access and apply to various tasks, such as keeping up with changing customer buying patterns and trends. Traditional approaches, such as grassroots (managing data across the organization on a team-by-team basis) and big bang (managing data en masse in a centralized team), are highly complex and inefficient.

Pharmaceuticals and medical products could see benefits across the entire value chain

In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. When machines take over dull or unpleasant tasks, people can be left with more interesting work that requires creativity, problem-solving, and collaborating with others. Workers will need to gain proficiency with these tools and, importantly, use the time that is freed up to focus on higher-value activities.

Installing ChatGPT in the car is one step, very iteratively done, and will continue to evolve from there over many, many increments. It’s not just with every new model cycle or every new model year, but even on a quarterly and why not a weekly or daily basis? I’m a firm believer in those fast feedback loops we see when we’re putting something new into a customer’s hands. I think generative AI can help us a great deal with that initial tedious part of the work. It’s not the car by itself but in connection with the wider world, and with that ability, as Ben said before, to look forward and generate usefulness in your life.

• For the sector to fulfill its ambition of becoming true software innovators, that reality has to change.
• This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms.
• Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope.
• In addition, consumers appreciate the innovation an ecosystem can provide; seven in ten consumers in a McKinsey survey said they value ecosystem offerings that simplify their purchase journey.
• For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity.

Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug.