Technology decision making, financial and operational systems, and customer engagement, experience, and support are the three key business areas where generative artificial intelligence (GenAI) is most likely to impact an organisation's future competitive position or business model, according to a new IDC report.

The survey however, found notable differences in expectations across geographic regions. North American tech buyers identified financial and operational systems as the most likely to be affected by GenAI. Western Europe indicated IT and line-of-business technology decision making, while Asia/Pacific respondents felt customer engagement, experience, and support would be most affected.

Four other business areas were also considered in the survey: customer acquisition and sales; product design and development; data-driven decision making; and talent and staffing. Of these, customer acquisition and sales and talent and staffing were considered least likely to be impacted by GenAI.

"What the survey results confirm is that the application of GenAI across business functions will be broad and its impact will span a wide range of competitive and business model areas," said Matt Acaro, research director for Computer Vision AI Tools and Technology at IDC. "Another thing the results highlight is that GenAI's ability to process, understand, and derive value from a wide range of data sources is its real superpower. It is exactly this capability that enables GenAI models and applications to be integrated and used within so many different business and customer processes and workflows. As organisations gain more experience with GenAI technologies, we expect them to use what they have learned to deploy GenAI in new areas."

The survey results are based on data from IDC's Future Enterprise Resiliency and Spending Survey, Wave 4, which was fielded in April 2024 and included 622 self-identified industry respondents. To convert GenAI's potential into practical use cases that generate business value, IDC suggests a clear understanding of AI's current limitations and challenges is required.