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6 Major Factors Affecting AI Adoption and Diffusion in Firms

People are quick to sound the alarm on AI. Yells of widespread automation and labour obsolescence pervade the media. Yet, beneath the punditry and prognostications is a more complex story. Central to this complexity is predicting how AI is adopted by firms and diffused throughout economies.

Firms are the primary driver of economic activity in economies. If firms are slow or fail to adopt AI, then its effects are naturally restricted. The ‘promise of AI’ can only be fulfilled if these technologies are adopted by firms, absorbed in workflows, and broadly diffused.1 Otherwise, they’re just isolated use cases.

This consideration, however, has largely been ignored. Much of the recent research on the economic impacts of AI has focused on AI’s estimated capabilities if the technologies are broadly diffused. For example, the prominent study by Frey and Osborne2 estimated that 47% of occupations face a near-term risk of automation from AI. These results were based on the assessments of a small panel of Machine Learning experts who were asked to identify which of 70 jobs were ‘completely automatable’ in 2013. But these projections rely on some shaky assumptions. Chief among them is that firms will quickly and efficiently adopt AI for commercial use. This should not be taken as a given. As James Bessen et al.3 points out, just because new technologies have commercial applications does not mean that they will be adopted and diffused in a timely manner.

Therefore, understanding the factors that influence the adoption and diffusion of AI in firms is important. It enables more accurate forecasting and better planning for policymakers, businesses, and civil society. Identifying the main levers that drive the growth of AI applications can help to expedite the many positive use cases in the pipeline; such as Machine Learning disease diagnosis systems in healthcare and optimised renewable energy distribution at scale. It also empowers governments and firms to make pragmatic and timely decisions on the negative implications of AI diffusion. For instance, workers at risk of displacement by automation can be better identified and more targeted transition support can be provided.

Developing robust forecast models, however, first requires identifying and measuring the variables that influence adoption and diffusion. A discussion of the major factors that affect the rate of AI adoption follows.

Explanatory variables for AI adoption and diffusion

Research on the factors that affect firms’ decisions to adopt digital technologies is well established. Researchers have closely examined the adoption dynamics of innovations such as personal computers,4 the Internet,5 and social media.6 AI builds upon these digital technologies. And the factors that influence the adoption of AI by firms differ by degree but not by kind.

Building upon past research, and accounting for the unique characteristics of AI, there are six major variables that explain the rate at which firms adopt AI:

  1. Competition: Recent modeling conducted by McKinsey Global Institute7 found that the extent of rivalry within markets has the largest effect on AI adoption. This is consistent with game theory, where the marginal propensity to adopt AI depends on the proportion of rivals that have already decided to adopt. Assuming the new technology becomes broadly diffused, then early adopters typically enjoy disproportionate rewards. But as more firms adopt, the marginal incentive to adopt diminishes as the technology provides less competitive advantages. This is why laggard firms are punished with shrinking market shares. These competitive forces, therefore, drive adoption rates as firms jostle to assert a competitive edge and advance market share. However, adoption decisions are made with imperfect information. It’s difficult for a firm to know what its competitors are doing behind closed doors. So, these competitive forces (or FOMO!) can drive rapid periods of adoption growth.
  2. Firm characteristics: The size, income level, and industry of firms have all been shown to affect the rate that a new technology is adopted.8 For example, larger firms, by headcount and income, typically adopt digital technologies earlier and at faster rates than smaller firms. Also, firms in Financial Services and ICT industries tend to adopt digital technologies earlier and at faster rates than firms in Agricultural and Construction industries.9
  3. Workforce skill capabilities: Emerging technologies, such as AI, often require specific skills. The availability of workers with these skills can influence the extent of adoption and diffusion. The ability to access such labour competencies, however, varies between firms, industries, and economies. The implementation of AI requires strong technical competencies. These competencies are unevenly distributed between firms, industries, and economies. Therefore, the more firms are able to access relevant skilled labour, the greater the likelihood that firms will adopt AI.
  4. Digital maturity: Previous research has shown that the adoption of new digital technologies often depends on the adoption of previous digital technologies. For instance, broadband infrastructure supports the adoption of more sophisticated digital applications.10 This relationship also appears to hold for AI. Firms that have adopted and absorbed cloud infrastructure and ‘web 2.0 technologies’ (such as mobile technologies and CRM systems) are more likely to adopt AI technologies.11
  5. Expected return on AI investment: Firms’ perceptions of the value that AI can create also influences adoption rates. Unsurprisingly, firms that are positive about the business use cases of AI are more likely to adopt earlier and faster. Conversely, firms that are uncertain about AI use cases are slower or less likely to adopt, which delays aggregate adoption rates.
  6. AI complements: The more a firm invests in one type of AI, the more likely it will invest in another. For example, a retailer that implements robotic process automation to retrieve stock is more likely to adopt computer vision to identify inventory items than a retailer that hasn’t adopted any AI technologies. Capital investment deepens as AI is increasingly absorbed in workflows.

Of course, there are other variables that can influence the firm-level adoption of AI. For instance, regulatory effects can be important to consider when comparing the adoption rates between country economies; it’s plausible that the more stringent data protection regulations in Europe could delay AI adoption in European firms compared to US firms. Relative rates of performance improvement for a technology can also affect diffusion rates, particularly in the earlier phases of adoption. But in order to simplify a forecast diffusion model of AI, the explanatory variables listed above account for a significant proportion of firm-level adoption decisions.

Future posts will explain how these explanatory variables can be used in a model to forecast the adoption of AI in an economy.

  1. Working Definitions: Adoption is defined as investment in a particular technology; Diffusion is defined as how adoption spreads—the process by which an innovation is diffused in a market or social system; and Absorption is how technology is used within a firm.
  2. Frey, Carl Benedikt, and Michael A. Osborne. 2017. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change 114 (January): 254–80.
  3. Bessen, James E., Stephen Michael Impink, Robert Seamans, and Lydia Reichensperger. 2018. “The Business of AI Startups.” https://doi.org/10.2139/ssrn.3293275.
  4. Thong, J.Y.L.1999. An integrated model of information systems adoption in small businesses, “Journal of Management Information Systems”, Vol. 15, No. 4, pp. 187-214.
  5. Andrés, Luis, David Cuberes, Mame Diouf, and Tomás Serebrisky. 2010. “The Diffusion of the Internet: A Cross-Country Analysis.” Telecommunications Policy 34 (5): 323–40.
  6. Bughin, Jacques. 2016. “The Diffusion Pattern of Enterprise 2.0 Technologies: Worldwide Estimates of a Bass Co-Diffusion Model for the Last 10 Years.” Journal of Contemporary Management, December. http://www.bapress.ca/jcm/jcm-article/1929-0136-2017-02-31-12.pdf.
  7. Bughin, Jacques, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi. 2018. “Modeling the Impact of AI on the World Economy.” McKinsey Global Institute. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Notes%20from%20the%20frontier%20Modeling%20the%20impact%20of%20AI%20on%20the%20world%20economy/MGI-Notes-from-the-frontier-Modeling-the-impact-of-AI-on-the-world-economy-September-2018.ashx; Bughin, Jacques, Jeongmin Seong, James Manyika, Lari Hämäläinen, Eckart Windhagen, and Eric Hazan. 2019. “Notes from the AI Frontier: Tackling Europe’s Gap in Digital and AI.” McKinsey Global Institute. https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/tackling%20europes%20gap%20in%20digital%20and%20ai/mgi-tackling-europes-gap-in-digital-and-ai-feb-2019-vf.ashx.
  8. Hall, Bronwyn H., and Beethika Khan. 2003. “Adoption of New Technology.” Working Paper Series. National Bureau of Economic Research. https://doi.org/10.3386/w9730. pg. 20.
  9. For example, see Australian Bureau of Statistics. 2019. ‘Catalogue 8129.0 – Business Use of Information Technology’. https://www.abs.gov.au/ausstats/abs@.nsf/mf/8129.0
  10. Andrews, Dan, Giuseppe Nicoletti, and Christina Timiliotis. 2018. “Digital Technology Diffusion: A Matter of Capabilities, Incentives or Both?” OECD. https://doi.org/10.1787/7c542c16-en.
  11. Bughin, Jacques, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi. 2018. “Modeling the Impact of AI on the World Economy.” McKinsey Global Institute. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Notes%20from%20the%20frontier%20Modeling%20the%20impact%20of%20AI%20on%20the%20world%20economy/MGI-Notes-from-the-frontier-Modeling-the-impact-of-AI-on-the-world-economy-September-2018.ashx.