Key Insights
Key takeaways from "Harnessing the value of generative AI: Top use cases across industries" report published by Capgemini Research Institute
CFTE summarised “Harnessing the value of generative AI: Top use cases across industries” report by Capgemini Research Institute. In the newest Capgemini Research Institute report, Harnessing the value of generative AI: Top use cases across industries, they delve into the transformative potential of the technology specifically from the perspective of organizations. This report is the second part of our series on generative AI and follows their previous report that looked at generative AI through the consumer lens.
Key Aspects
- Generative AI is rapidly integrating into various industries, with 96% of organizations considering it at a board level.
- It’s primarily seen as a growth accelerator rather than a disruptor.
- Executives expect generative AI to enhance growth, capabilities, and offer new opportunities.
Table of Contents
- Introduction
- Five Broad Themes
- Generative AI in Different Sectors
- Workforce Impact
- Risk and Compliance
- Implementation Strategies
- Conclusion
Key Findings and Insights
This report will give you an insight into:
- Generative AI is a top boardroom agenda in 96% of organizations, indicating its rising strategic importance.
- It's viewed more as a growth accelerator than a disruptor, with 21% of executives expecting significant industry disruption.
- The technology is seen as a powerful tool to enhance growth, capabilities, and unlock new opportunities.
- Executives in high-tech and industrial manufacturing are most likely to anticipate significant industry disruption.
- Generative AI's potential extends to all functions and industries, with IT, sales, customer service, and marketing identified as key areas.
- About 60% of executives globally are strong advocates of generative AI, while 39% are taking a "wait-and-see" approach.
- Risks are acknowledged, but 74% of executives believe the benefits of generative AI outweigh the risks. Environmental considerations are also noted due to the carbon-intensive nature of training new models.
- The report concludes with recommendations on creating a robust generative AI strategic and operational architecture, adopting a human-centric approach, building user and consumer trust, and prioritizing sustainable development.
Key infographics
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