Generative AI can give you superpowers, new McKinsey research finds

As a result, economic activity will tend to shift downstream in the GenAI value chain, drawing in an expanding set of players—those with the best data in a specific domain—to either partner with tech firms or directly fine-tune models themselves. Businesses must therefore examine what data they should collect (and aren’t) in order to fine-tune models that they can not only use themselves, but also monetize as services for third parties. Especially with the rise of multimodal GenAI (encompassing text as well as images, video, and even sensor data from machines), the range of valuable data is far greater than many leaders realize. In Asia, there is a major opportunity for the business process outsourcing industry—so pivotal to many economies—to be an early mover in seizing potential efficiency gains. Generating new content based on cumulative data input makes gen AI worthwhile in many industries.

Meanwhile, as the Philippines strives to become Asia’s leading creative economy by 2030, generative AI can play a key role in professionalizing the work of the country’s freelancers. Given generative AI’s ability to provide outputs in a variety of formats—text, images, the economic potential of generative ai video, audio, computer code, and synthetic data—Asia is likely to see an explosion of new content. “While innovation will continue to need a human spark, generative AI can play a role in supporting the creative process,” says Ahmed Mazhari, president of Microsoft Asia.

Generative AI’s potential impact on knowledge work

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions. Fine-tuning is the process of retraining a foundation model (whether an LLM or LMM) using specialized data to adjust weights or add new layers to the model. The retraining of parts of the model with specialized data leads to improved performance on a specific set of tasks.

The study also predicted that AI could increase labor productivity by up to 40% in some industries. Of CEOs surveyed by IBM, 75% believe businesses leveraging the most advanced generative AI will garner a distinct competitive advantage. The technology’s ability to widen the range of tasks AI can automate has already led to a reduction in time-consuming work and a subsequent surge in productivity. I believe the time is now for businesses to think about how to capitalize on generative AI to augment workflows, gain a competitive advantage and create their ideal future.

Industry impacts

Gen AI is a big step forward, but traditional advanced analytics and machine learning continue to account for the lion’s share of task optimization, and they continue to find new applications in a wide variety of sectors. Organizations undergoing digital and AI transformations would do well to keep an eye on gen AI, but not to the exclusion of other AI tools. Just because they’re not making headlines doesn’t mean they can’t be put to work to deliver increased productivity—and, ultimately, value.

By now, the risks of gen AI—such as its tendencies toward unpredictability, inaccuracy, and bias—are widely known. For example, the technology can be misused to spread political propaganda or compromise national security. Confidential government data can be leaked or stolen if government employees inadvertently introduce that information into foundation models through prompts. While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge.

This points to a potential disconnect between the AI’s recommendations and the specific needs or capabilities of these less successful businesses. If you are a software developer, generative AI can do the more routine tasks of converting legacy code, debugging and testing, so you can spend more time developing new functionality. In early use cases in call centers, we see generative AI helping representatives with less tenure, say at level one, advance to a level four much faster. With AI, they have techniques similar to those of their higher-skilled counterparts, such as quality scripts and detailed customer context, preparing them to handle increasingly complex situations more quickly.

The third example is pharma and medical products, with an estimated total value per industry of $60 billion–$110 billion, and a value potential increase of 15–25% of operating profits based on average profitability of selected industries in the 2020–22 period. The adoption of generative AI is expected to significantly impact various industries and job markets, including manufacturing, healthcare, retail, transportation, and finance. While it is likely to lead to increased efficiency and productivity, it is also expected to lead to job displacement for some workers. While the timeline of when this labor productivity boom would occur is relatively uncertain, there is no question that the economic impacts will be significant. If generative AI lives up to its foreseen capabilities in the coming decades, we could see a technological revolution as impactful as the automobile and the personal computer.