Not a day goes by without a new achievement, investment or national plan powered by artificial intelligence being reported. AI is embedded in many apps and software we use, and it makes features like voice interaction a reality.
Yet the adoption of AI itself is largely absent from most organizations we interact or work with directly. While apps that were only a dream a few years ago have gone mainstream, their development is still limited to a handful of savvy companies.
For example, Meta (formerly Facebook) is building the world’s largest supercomputer. The company said its power was needed not to run the metaverse, but to train AI models “that can learn from billions of examples; work in hundreds of different languages; Seamlessly analyze text, images and video together. . . and much more”.
The number of days or months required to train an AI model can determine the extent of innovation and competitiveness. For a company like Meta, shortening development time to enable faster experimentation is key to being competitive.
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But adoption of AI is painfully slow in most businesses, unlike other technological disruptions, from cars to digital photography to smartphones. The reason for this is the specific requirements for the adoption of AI. For many technologies, it is simply a matter of buying an innovation, such as a smartphone, where local applications speed up processes.
In other cases, such as the Internet or social media, significant local infrastructure and support is needed to create relevant content and drive network effects and adoption.
This leads to relatively slower adoption curves. AI requires even more complex preconditions and active business involvement. In “Artificial Intelligence as Increased Automation: Implications for Jobs” – my article with Feichin Ted Tschang in the Academy of Management Perspectives journal – we highlight how AI enables companies to modularize and control work routine and, in doing so, requires the transformation of their structures.
The disturbance can be significant. In the early eras of automation, the loss of jobs was compensated by the growth of new sectors and jobs, and the loss of routine and medium-skilled jobs with a polarization of jobs between high and low-skilled jobs. In the age of AI automation, this can be made even worse.
Such hurdles mean that we are seeing a two-speed adoption in which AI can seem to be everywhere except in our own organizations. Yet AI is increasingly essential to compete effectively, offering zero marginal cost and rapid scalability.
The consequence is a large productivity gap between the “frontier” companies and the others. The amount of information is greater in service industries than in manufacturing, so service companies are where the differentiation is most important – and those without AI risk falling even further behind.
The introduction of AI leads to the translation of routines into code and the creation of new tasks that cannot be done by other means. For many interconnected OECD countries, wage increases are being driven by inflation and worker mobility, meaning it will be essential for businesses to tackle productivity with AI to stay competitive.
We are seeing the integration of AI into products and solutions. The best examples are warehouses using bots and the widespread deployment of recommendation engines, image recognition software, fraud detection and prediction systems, and chatbots.
However, the adoption of AI requires changes in the business and operating models of organizations. This, combined with an ever faster pace, explains our two-speed world. This also explains why non-border organizations face increasingly tough competition.
Acceleration requires new capabilities, including both sufficient AI talent and ways to foster innovative practices through a more supportive, can-do culture. Effective talent generation requires a network to produce, attract and retain qualified people. This can mean leading universities and research centers for training and developing expertise, as well as the prospect of high salaries and challenging enough projects to ensure specialist staff can be recruited and motivated to stay.
Computing power is also needed. While cloud platforms are now widely available, harnessing their potential also requires the presence of cloud-savvy universities and organizations.
Finally, organizations need specific data to give them a competitive edge. This can be internally derived, which means it must be collected and processed; or externally, in which case it must go beyond basic transactional data to be useful.
Capacity alone is insufficient. Progress in AI goes through competitiveness clusters. While knowledge has gone global, innovation remains local. Without a better understanding of all these factors, a growing number of businesses will be left behind by the AI revolution.
But as AI-driven automation increasingly replaces labor and most of the remaining jobs are concentrated in a smaller, highly technical workforce, we also need to think about how to use the new technologies to promote sustainable forms of work and livelihoods.
Esteve Almirall is Associate Professor in the Department of Operations, Innovation and Data Science at Esade Company & Faculty of Law in Barcelona