Situation
Our client wanted to explore the future impact of Generative AI across various industries. They aimed to create a comprehensive dossier that would provide insights into the most impactful use cases of Generative AI and how it would shape industries in the coming years.
Objective
The project’s objective was to conduct research and gather perspectives on the potential impact of Generative AI. Our client wanted to identify the most significant use cases of Generative AI in different industries, both present and the future.
Our Work
To support our client in achieving their objective, 10EQS undertook the following activities:
Identification and Qualification of Subject Matter Experts (SMEs): 10EQS identified and qualified senior strategic decision-makers from large, global companies across various industries. The targeted industries included:
● Consumer
● Energy, Resources & Industrials
● Financial Services
● Government & Public Sector
● Life Sciences & Health Care
● Technology, Media & Telecom
Interview Guide Development:
In collaboration with our client, 10EQS developed an interview guide that would be used to conduct interviews with the identified SMEs. The focus could be adjusted throughout the project as new insights emerged from the interviews.
Primary and Secondary Research:
10EQS conducted extensive primary research by interviewing 30 experts from Financial Services, Technology, Media and Telecommunications, Life Sciences & Healthcare, Consumer, Government, Energy, Resources, & Industrials sectors.
In addition, secondary research was conducted to explore Generative AI market trends, developments, applications across industries, and investment flows.
Deliverables:
The deliverables provided by 10EQS included cleansed interview transcripts, secondary research results, and a comprehensive summary report that synthesized vital themes and takeaways across all the interviews.
Results
The research conducted by 10EQS identified critical themes regarding the usage of Generative AI across industries. While the following insights provide a general overview, it is essential to highlight that our client received in-depth insights from 30 expert interviews and extensive secondary research.
This allowed us to delve into the specific, nuanced differences in use cases, challenges, and feasibility based on the unique industry contexts.
These findings provide our client with a comprehensive understanding of the broader themes related to Generative AI while capturing the industry-specific nuances that shape its implementation.
Common themes emerged from the research.
Common Themes
Generating Net New Ideas and Content:
Generative AI has been recognized for its ability to create new ideas and content based on existing information, fostering innovation across industries.
Democratizing Access to Data and Analytics:
Organizations have explored the potential of Generative AI to democratize access to data and analytics, empowering a broader range of stakeholders to leverage these capabilities.
Identifying New Potential Patterns:
Generative AI has shown promise in identifying new potential patterns and generating synthetic data without relying on training models with preset scenarios.
Partnering with Other Technologies:
Generative AI has exhibited synergy with other technologies like simulations and digital twins, leading to the development of more powerful and comprehensive solutions.
However, it is essential to acknowledge that implementing Generative AI presents challenges and considerations.
Challenges and Considerations
Data Access:
Limited access to relevant and high-quality data has been a significant challenge for organizations across industries. The availability and quality of data play a critical role in successful implementation.
Regulatory and Security Concerns:
Organizations face regulatory and security concerns that pose obstacles to the widespread adoption of Generative AI. Addressing these concerns is vital for enabling broader implementation.
Feasibility:
While many use cases demonstrate technical feasibility, the need for appropriate data, data annotation, and data quality checks can impede implementation. Nuanced factors must be considered in each industry context.
Adoption Priorities:
Companies have prioritized commercial or customer-facing use cases of Generative AI. However, the potential impact on manufacturing and back-end applications should not be overlooked. Customer comfort and potential backlash might affect the timing of certain customer-facing use cases.
Talent and Bandwidth Constraints:
The limited availability of talent and organizational bandwidth present challenges for implementing Generative AI. These constraints may result in the deprioritization of specific applications in certain industries.
While these insights capture critical themes discussed in public domains, organizations must recognize the industry-specific nuances that shape the use cases, challenges, and feasibility of Generative AI implementation.
Conclusion
The research conducted by 10EQS provided valuable insights into the future of Generative AI and its potential impact across industries. The identified use cases, roadblocks, and considerations highlighted the opportunities and challenges organizations might face while adopting Generative AI.
These findings can guide organizations in formulating strategies and making informed decisions to leverage the benefits of Generative AI effectively.
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