Generative AI and the future of work in America

In a recent Business Insider article, Suleyman said that generative AI would soon become pervasive. While he warns about potential risks posed by AI — especially in combination with synthetic biology — he also predicted that within five years everyone would have access to an AI personal assistant. In this vision, everybody will have access to an AI that knows you, is super smart, and understands your personal history. No one, including corporate executives, knows exactly what the AI future will mean for businesses and workers across the country.

Companies seeking to go one step further can acquire and build their own internal ecosystem—an in-house B2B solution hub that provides access to other players in the market—to accelerate digital transformations and optimize the customer journey. But what exactly does it take to keep up and make that level of technology innovation part of a consumer or retail organization’s DNA? According to our research and experience, six principles are critical for consumer and retail organizations to leverage tech effectively and perform more like software companies. Those principles align broadly with cross-sector trends examined in McKinsey’s recent software transformation research. Many retail and consumer players recognize this reality and have already made decisive software and technology investments. For example, Starbucks developed Deep Brew, a tool to leverage AI for various applications.

“Forward-thinking C-suite leaders are considering how to adjust to this new landscape.”

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. The deployment of generative AI and other technologies could help accelerate productivity Yakov Livshits growth, partially compensating for declining employment growth and enabling overall economic growth. In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations.

However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity. Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures.

The Great Attrition obscured deeper shifts

This means companies should be careful of integrating generative AI without human oversight in applications where errors can cause harm or where explainability is needed. Generative AI is also currently unsuited for directly analyzing large amounts of tabular data or solving advanced numerical-optimization problems. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction.

The collaboration is designed to merge Salesforce’s popular customer relationship management (CRM) software, including its intelligent Einstein applications and Data Cloud, with McKinsey’s specialized AI and data models. • A trove of unstructured and buried data is now legible, unlocking business value. Previous AI initiatives had to focus on use cases where structured data was ready and abundant; the complexity of collecting, annotating, and synthesizing heterogeneous datasets made wider AI initiatives unviable. By contrast, generative AI’s new ability to surface and utilize once-hidden data will power extraordinary new advances across the organization.• The generative AI era requires a data infrastructure that is flexible, scalable, and efficient.

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.

Furthermore, the technology has already made its way onto the board’s agenda for 28% of these organizations. Previous generations of automation technology often had the biggest mid-term impact on occupations with lower-middle wages, the McKinsey analysts wrote. Lower-wage occupations were more immune from automation because their employers paid them lower salaries in the first place. In those cases, the cost savings of automation wouldn't be as big, whereas the skills required for higher-wage roles were harder to automate. On the lower end of the job market—those making less than $38,200 a year—automation and other structural changes have already had big effects. Could accelerate these trends, resulting in lower wage workers being 14 times more likely to need to shift occupations than high-wage workers.

These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many Yakov Livshits deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning.

It encompasses a set of practices that span the full ML life cycle (data management, development, deployment, and live operations). Many of these practices are now enabled or optimized by supporting software (tools that help to standardize, streamline, or automate tasks). Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.

Companies use this software to monitor web browsing, track keystrokes, and capture random screenshots of workers' screens. About three-quarters of respondents in the Resume Builder survey said they had fired employees based on findings from their tracking software. Just two months after its November launch, ChatGPT reached 100 million users, and a report by the Swiss banking giant UBS said it might be the fastest-growing consumer app ever. August marked the third consecutive monthly decline in ChatGPT's global web traffic, and the average time spent on the platform has fallen.

Their most striking advance is in natural language capabilities, which are required for a large number of work activities. While ChatGPT is focused on text, other AI systems from major platforms can Yakov Livshits generate images, video, and audio. What is clear from the job switching and occupational shifts of the past three years is that the US labor market accommodated a higher level of dynamic movement.

• But high tech and banking will see even more impact via gen AI’s potential to accelerate software development.
• Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time.
• The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities.
• Such a group can not only help identify and prioritize the highest-value use cases but also enable coordinated and safe implementation across the organization.
• McKinsey’s report estimates 12 million people will switch careers by 2030, 25% more than it projected just two years ago.