McKinsey Technology Trends Outlook 2023

After a turbulent 2022 for technology investment and talent, the first half of 2023 saw a resurgence of enthusiasm about technology’s potential to catalyze progress in business and society. Generative AI deserves much of the credit for ushering in this renaissance, but it is just one of many advances on the horizon that can drive sustainable, inclusive growth and solve complex global challenges.

To help executives track the latest developments, the McKinsey Technology Council has once again identified and interpreted the most important technology trends unfolding today. While many trends are in the early stages of adoption and scaling, managers can use this research to plan ahead by developing an understanding of potential use cases and identifying the critical skills needed as they hire or retrain talent to meet these opportunities. to bring to fulfillment.

Our analysis examines quantitative measures of interest, innovation and investment to measure the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into underlying technologies, uncertainties and questions surrounding each trend. This year, we added an important new dimension to analysis—talent. We provide data on talent supply and demand dynamics for the roles most relevant to each trend. (For more, please see the sidebar, “Research Methodology.”)

New and noteworthy

All of last year’s 14 trends remain on our list, although some have experienced accelerated momentum and investment, while others have experienced a downturn. One new trend, generative AI, has made a strong entrance and has already shown potential for transformative business impact.

This new entrant represents the next frontier of AI. Building on existing technologies such as applied AI and industrializing machine learning, generative AI has high potential and applicability in most industries. Interest in the topic (as measured by news and internet searches) increased threefold from 2021 to 2022. As we wrote recently, generative AI and other foundational models are changing the AI ​​game by taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to non-technical users. Generative AI is poised to add as much as $4.4 trillion in economic value from a combination of specific use cases and more diffuse uses—such as helping with email drafts—that increase productivity. While generative AI can unlock significant value, enterprises should not underestimate the economic significance and growth potential that underlying AI technologies and industrializing machine learning can bring to various industries.

Investment in most technology trends has intensified year over year, but the potential for future growth remains high, as further indicated by the recent recovery in technology valuations. Indeed, absolute investments remained strong in 2022, at more than $1 trillion combined, indicating great faith in the value potential of these trends. Trust architectures and digital identity grew the most out of last year’s 14 trends, increasing by nearly 50 percent as security, privacy and resilience become increasingly critical across industries. Investment in other trends—such as applied AI, advanced connectivity, and cloud and edge computing—has declined, but this is likely due, at least in part, to their maturity. More mature technologies may be more sensitive to short-term budget dynamics than more emerging technologies with longer investment time horizons, such as climate and mobility technologies. As some technologies also become more profitable, they can often scale further with lower marginal investment. Given that these technologies have applications in most industries, we have little doubt that mainstream adoption will continue to grow.

Organizations should not focus too much on the trends that attract the most attention. By focusing on only the most hyped trends, they may miss the significant value potential of other technologies and hinder the chance for targeted capability building. Instead, companies seeking long-term growth should focus on a portfolio-oriented investment across the technology trends most important to their business. Technologies such as cloud and edge computing and the future of bioengineering have shown steady increases in innovation and ever-increasing use cases across industries. In fact, more than 400 edge use cases across various industries have been identified, and it is projected to achieve double-digit growth globally over the next five years. In addition, emerging technologies, such as quantum, continue to develop and show significant potential for value creation. Our updated analysis for 2023 shows that the four industries likely to see the earliest economic impact from quantum computing—automotive, chemicals, financial services, and life sciences—will potentially gain up to $1.3 trillion in value by 2035. By carefully evaluating the evolving landscape and with taking a balanced approach, businesses can capitalize on both established and emerging technologies to drive innovation and achieve sustainable growth.

Technical talent dynamics

We cannot overestimate the importance of talent as a key resource in developing a competitive advantage. A lack of talent is a top issue limiting growth. There is a huge gap between the demand for people with the skills needed to capture value from the technology trends and available talent: our survey of 3.5 million job postings in these technology trends found that many of the skills in the largest demand less than half as many qualified practitioners per placement as the global average. Companies need to be on top of the talent market, ready to respond to significant shifts and to deliver a strong value proposition to the technologists they hope to hire and retain. For example, recent layoffs in the tech sector may be a silver lining for other industries that have struggled to attract the attention of attractive candidates and retain senior tech talent. In addition, some of these technologies will accelerate the pace of workforce transformation. In the coming decade, 20 to 30 percent of the time workers spend on the job could be transformed by automation technology, leading to significant shifts in the skills needed to be successful. And companies must continue to look at how they can improve roles or individuals to meet their customized job requirements. Job postings in fields related to technology trends grew by a very healthy 15 percent between 2021 and 2022, even as global job postings overall declined by 13 percent. Applied AI and next-generation software development together posted nearly one million jobs between 2018 and 2022. Next-generation software development had the largest growth in number of jobs (exhibit).

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Small multiples of 15 slope maps show the number of job postings in different fields related to technology trends from 2021 to 2022. Overall growth of all fields combined was about 400,000 jobs, with applied AI having the most job postings in 2022 and a 6% experienced increase from 2021. Next-generation software development had the second-highest number of job postings in 2022 and has 29% growth from 2021. Other categories shown, from most job postings to least in 2022, are as follows: cloud and edge computing, trusted architecture and digital identity, future of mobility, electrification and renewable energy, climate technology beyond electrification and renewable energy, advanced connectivity, immersive reality technologies, industrializing machine learning, Web3, future of bioengineering, future of space technologies, generative AI and quantum technologies.

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This bright outlook for practitioners in most fields highlights the challenge facing employers struggling to find enough talent to keep up with their demands. The shortage of qualified talent has been a persistent limiting factor in the growth of many high-tech fields, including AI, quantum technologies, space technologies, and electrification and renewable energy. The talent crisis is particularly pronounced for trends such as cloud computing and industrialization of machine learning, which are required in most industries. It is also a major challenge in areas that employ highly specialized professionals, such as the future of mobility and quantum computing (see interactive).