Will generative A I. be good for U.S. workers?

Generative AI has the potential to increase US labor productivity by 0.5 to 0.9 percentage
points annually through 2030 in a midpoint adoption scenario. The range reflects whether the time freed up by automation is redeployed at 2022 productivity levels or 2030 levels, with both scenarios accounting for the occupational mix expected in 2030. The jobs in the two lowest wage quintiles are disproportionately held today by those with less education, women, and people of color. Women are heavily represented in office support and customer service, which could shrink by about 3.7 million and 2.0 million jobs, respectively, by 2030.

By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources. As marketers strive to scale personalization, generative AI is becoming a powerful ally. The technology aids in customer segmentation, rapid creation of personalized content and further automation of customer journeys. It’s able to learn from the existing material, which aids and informs the creation of new assets that enable more customized content. Generative AI will be pivotal in supporting marketing in its quest to deliver personalization at scale efficiently.

At least in the near term, we see one category of applications offering the greatest potential for value creation. And we expect applications developed for certain industries and functions to provide more value in the early days of generative AI. GPUs and Yakov Livshits TPUs are expensive and scarce, making it difficult and not cost-effective for most businesses to acquire and maintain this vital hardware platform on-premises. As a result, much of the work to build, tune, and run large AI models occurs in the cloud.

Balancing risk and value creation

There will also be a 23% increase in demand for STEM jobs, as companies outside the tech industry continue to integrate A.I. Generative A.I.’s relative competency at performing administrative tasks means demand for jobs like office support and customer service will decline 18% and 13%, respectively, through 2030. Food service can also expect a decline in demand, although at a much lower level of 2% over the same time frame. The reduced job demand in office support roles will disproportionately affect women, while reduced demand in customer service and food service poses outsize risks to Black and Hispanic employees. For near-term access to bleeding-edge capabilities outside the organization, companies can employ “acquihiring,” or strategically acquiring digital-native companies to access their technology offerings and talent pools. L’Oréal created personalized customer experiences through this acquisition, such as lipstick on demand—an AI-powered at-home system that recognizes color from a photo and prepares a lipstick based on it.

Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications. Our goal is to provide you with everything you need to explore and understand generative AI, from comprehensive online courses to weekly newsletters that keep you up to date with the latest developments. Gary Grossman is a senior VP at Edelman and global lead of the Edelman AI Center of Excellence. Improving AI technologies could make this sort of monitoring even easier and more prevalent. If you want to benefit from the AI, you can check our data-driven lists for AI platforms, consultants and companies.

Some of the key issues shaping generative AI’s future

Generative AI and foundation models now open many new opportunities for data-centric applications by automating content generation based on data. These models essentially supersede much more of the value chain than traditional, discriminative AI models that are used to predict labels or classifications. For example, companies can leverage marketing data and generative AI to automatically create and deliver hyperpersonalized messages with virtually no incremental costs.

In general, fine-tuning foundation models costs two to three times as much as building one or more software layers on top of an API. Talent and third-party costs for cloud computing (if fine-tuning a self-hosted model) or for the API (if fine-tuning via a third-party API) account for the increased costs. To implement the solution, the company needed help from DataOps and MLOps experts as well as input from other functions such as product management, design, legal, and customer service specialists. While foundation models serve as the “brain” of generative AI, an

entire value chain is emerging to support the training and use of this technology (Exhibit 2).1For more, see “Exploring opportunities in the generative AI value chain,” McKinsey, April 26, 2023. Specialized hardware provides the extensive compute power needed to train the models. MLOps and model hub providers offer the tools, technologies, and practices an organization needs to adapt a foundation model and deploy it within its end-user applications.

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.

Prompt engineering refers to the process of designing, refining, and optimizing input prompts to guide a generative AI model toward producing desired (that is, accurate) outputs. The value of these metrics is only as great as the degree to which CDOs act on them. CDOs will need to establish data-performance metrics that can be reviewed in near real time and protocols to make rapid decisions. Effective data governance programs should remain in place but be extended to incorporate generative AI–related decisions. Each pair of bars is under a different topic, with data representing developer respondent’s feelings with and without the involvement of generative AI in their work.

Developing new features was around 50 percent faster, while refactoring authentication was roughly 25 percent faster. That’s the percentage of hours worked today that could be automated by 2030 with the addition of gen AI, according to new research led by McKinsey senior partners Kweilin Ellingrud and Oliva White and colleagues. The jury is out on whether gen AI may cause job losses, but historically, technological advances fuel employment and economic growth despite short-term disruptions. “The biggest impact for knowledge workers that we can state with certainty is that generative AI is likely to significantly change their mix of work activities,” note the McKinsey researchers.

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

With generative AI able to take on many time-consuming tactical tasks, marketers can focus on strategic initiatives that directly impact the customer, including strategy development and campaign management. If worker transitions and risks are well managed, generative A.I., combined with other automation technologies, could boost productivity and foster better jobs, contributing to genuinely sustainable and inclusive growth. Such consumer insight is just part of the overarching talent that sets successful product leaders apart—the ability to deliver enhanced Yakov Livshits customer experiences creatively. Product managers have a vast canvas of modern tools and platforms to help serve customers and consumers in new, innovative ways. This capability is particularly important in the consumer retail environment, where offering product experiences that deliver value as well as establish and exceed high user expectations is a true differentiator. The technology’s improved ability to understand natural language has the potential to transform worker productivity by automating 60% to 70% of tasks that absorb employees’ time currently.

CEOs and their teams will also want to stay current with the latest developments in generative AI regulation, including rules related to consumer data protection and intellectual property rights, to protect the company from liability issues. Countries may take varying approaches to regulation, as they often already do with AI and data. Organizations may need to adapt their working approach to calibrate process management, culture, and talent management in a way that ensures they can handle the rapidly evolving regulatory environment and risks of generative AI at scale. For example, the lifeblood of generative AI is fluid access to data honed for a specific business context or problem. Companies that have not yet found ways to effectively harmonize and provide ready access to their data will be unable to fine-tune generative AI to unlock more of its potentially transformative uses.

The economic potential of generative AI: The next productivity frontier

Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies. Organizations continue to see returns in the business areas in which they are using AI, Yakov Livshits and
they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

Application programming interface (API) is a way to programmatically access (usually external) models, data sets, or other pieces of software. The goal of this article is to help CEOs and their teams reflect on the value creation case for generative AI and how to start their journey. First, we offer a generative AI primer to help executives better understand the fast-evolving state of AI and the technical options available. The next section looks at how companies can participate in generative AI through four example cases targeted toward improving organizational effectiveness.

• Companies that use specialized or proprietary data to fine-tune applications can achieve a significant competitive advantage over those that don’t.
• It is becoming even more urgent to solve occupational and geographic mismatches and connect workers with the training they need to land jobs with better prospects.
• And we are optimistic that many of the jobs created will be highly skilled and well paid.

Interestingly, though, when it comes to which job titles use gen AI the most for work and/or outside of it, both C-suite and senior managers clocked in at 24% regular usage for work and outside of work, combined. Midlevel managers were close behind at 23%, although they were more likely to have had no exposure, as well (19%). Ok, so we know companies and individuals are getting their hands on gen AI, but who is using it the most, and for what? In the workforce oscillate between alarm bells of mass unemployment and fantasies of a future utopia where everyone is free to pursue their passions instead of grinding out a career. Will transform the workforce may be debatable, experts and workers alike believe that change is inevitable.