Generative AI can give you superpowers, new McKinsey research finds

Our estimates intuitively suggest that fewer jobs in EMs are exposed to automation than in DMs, but that 18% of work globally could be automated by AI on an employment-weighted basis. … Our scenario analysis suggests that the ultimate share of work exposed to automation could range from 15-35%, the economic potential of generative ai a range which is consistent with—but on the conservative side of—existing estimates in the literature. Generative AI may have just recently hit the mainstream, but the economics of GenAI already point to a shift in the industry’s balance of power, away from the dominant tech giants.

Companies, policymakers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods. Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read” vast libraries of corporate information stored in natural language and quickly scan source material in dialogue with a human who helps fine-tune and tailor its research, a more scalable solution than hiring a team of human experts for the task. In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity.

Factors for retail and CPG organizations to consider

On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design. In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain.

• Our analysis captures only the direct impact generative AI might have on the productivity of customer operations.
• Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications.
• This big potential reflects the resource-intensive process of discovering new drug compounds.
• Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated sooner than previously expected.

This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks.

What are the implications for organizations as a whole?

Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. It does not account for potential knock-on effects the technology may have on customer satisfaction and retention arising from an improved experience, including better understanding of the customer’s context that can assist human agents in providing more personalized help and recommendations. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data.

Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9). In the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets.