Level of disruption. Although impacts from generative AI are set to be universally transformative, its potential for disruptive effects will vary across industries. The nature of disruption will also vary, taking the form of business model, operational, and/or competitive disruption.
In some sectors, the threat to business models—for example, the fundamental reshaping of product offerings, pricing models, and customer engagement—will be high. Consider the entertainment industry, for example, where the rapid rise of GenAI-powered content creation tools is driving a complete rethink of content development, distribution, and pricing.
Other industries, such as consumer packaged goods, might find the most significant disruption taking place behind the scenes, in the streamlining of operations and the upskilling of their workforce.
On the competitive front, the early adoption of generative AI can provide a significant leg up in some industries, like professional and legal services, where capturing market share from loyal clients (who often engage multiple firms in this space) is critical. In others, such as telecommunications, barriers for new market entrants will remain high, creating a relatively lower first-mover advantage for some GenAI endeavors, though not all.
Ease of adoption. Adopting generative AI is not a straightforward process, and the path to integration will, in cases, be industry-specific. For certain sectors, customizing AI to suit a business’s unique requirements can represent a major undertaking, heavily dependent on the availability, volume, and intricacy of relevant data, as well as a significant degree of model customization.
Consider insurance, for example, which harbors vast amounts of unstructured data that require considerable, though certainly not prohibitive, computational resources to crunch. A high degree of effort is required to apply industry- and company-specific policies, documents, and data to enable personalized policy generation and claims processing.
The ease of adoption is also influenced by other internal and external factors. The readiness of an industry’s workforce and culture to embrace new technology is one critical internal factor (though naturally with great variability among organizations within an industry). Some industries will see more disruption in the nature of worker tasks and roles than others; for example, sectors like retail and financial services, in which customer service, a function already seeing significant transformation from generative AI, features prominently. Additionally, the ability to engage in responsible AI practices—proper governance, transparency, fairness, security, and the like—presents a varying degree of difficulty to industries based on factors such as customer expectations, the sensitivity of the requisite data, and the current level of progress specific organizations have made toward embedding responsible AI practices into the fabric of their organization. External factors, such as regulatory constraints or enablers, play a role as well.
A look at 22 industries and four adoption scenarios
We plotted 22 industries against the two variables of level of disruption and ease of adoption. As with projected sector impacts from GenAI, there will be plenty of variability among organizations. And, of course, the emergence of AI-native disruptors cannot be reliably predicted. But, broadly speaking, we foresee the march toward generative AI adoption taking shape in four different ways—with companies being disruptors, trailblazers, multitaskers, or streamliners, depending on which quadrant their industry falls into.
