How will AI adoption play out in your industry?

Recent PwC research shows that the adoption of generative AI has the potential to transform—and disrupt—different industries in different ways. By assessing the cumulative use case impact on benchmarked profits and losses for 22 industries, and then factoring in current and soon-to-come GenAI capabilities, the study calculated the potential boost in operating profit margin attributable to GenAI in those industries. Not surprisingly, the tech sector stands to see considerable gains, thanks to the potential for use cases like AI-assisted coding and data feature extraction to yield a major productivity dividend. The study then looked at two crucial factors governing what AI implementation could look like in each industry: the level of expected disruption (ranging from full-scale business model reinvention to operational streamlining and new competitive pressures) and the expected ease of adoption (which depends on variables such as data complexity, workforce readiness and regulatory hurdles). 

The chart above visualises both of those dimensions, as well as the potential margin gains, for each of the 22 industries studied and, instructively, groups those industries into four categories. Disruptors are in a prime position to leverage GenAI for transformative applications in the near term, and have the potential to upend the status quo with compelling AI implementations, though they might not come easy. With the prospect of major disruption on the near horizon, trailblazers recognise the critical importance of staying relevant and are thus motivated to integrate GenAI into their business models. In the near term, streamliners are more likely to use GenAI primarily to enhance efficiency rather than to completely reinvent business models. As for multitaskers, they may see generative AI as one of many technologies that can support existing operations. 

Knowing where your business sits on this matrix will help inform the potential pace and extent of GenAI adoption in your organisation. But regardless of industry, charting the right path forward demands a dual vision that prioritises efficiency gains while simultaneously laying the groundwork for more transformative applications. That vision, in turn, requires recognising the patterns—that is, the common model architecture, tooling and design elements—that enable each of GenAI’s six primary capabilities: net-new creation, augmentation, transformation, dialogue, information retrieval, and summarisation. Build these elements out for one use case and they can be repurposed for use cases that deliver the same capability. And simply thinking about applying generative AI in this way, as opposed to focusing on what specific tasks GenAI can automate, sets a flywheel in motion that can provide the foundation for implementations that ignite reinvention. Getting that flywheel spinning is critical to breaking out of isolated solutions and driving the kind of long-term value-creation that can keep pace with the imminent wave of AI-native disruptors.