According to a new survey conducted by GBK Collective, a leading marketing strategy, insights, and analytics firm, enterprise adoption of generative AI has hit a tipping point. Not only are the majority of enterprise executives using generative AI, but they are also planning a significant increase in generative AI investments over the coming year.
The poll, which was conducted with 672 senior leaders from U.S. organizations with annual revenues over $50 million, also provides some eye-opening findings on the adoption of generative AI by functional area, emerging applications and use cases by industry, and adoption drivers and hurdles.
The comprehensive report was prepared by artificial intelligence expert Dr. Stefano Puntoni, Sebastian S. Kresge, professor of marketing at The Wharton School and co-director of Wharton’s AI department, and Jeremy Korst, former chief technology officer at Microsoft and T-Mobile, and currently the president of GBK. Dan Ives, managing director of equity research at Wedbush Securities, also contributed to the report.
“The results of our study show that we have crossed a critical turning point in the field of generative artificial intelligence,” said Prof. Puntoni. “This is not another metaworld. “Enterprise decision-makers across industries are adopting generative AI en masse, and this wave will only continue to grow, with spending increasing by over 25% over the next 12 months.”
Summary of Insights:
Intensity of use of generative artificial intelligence by industry and company size:
According to a new survey conducted by GBK Collective, a leading marketing strategy, insights, and analytics firm, enterprise adoption of generative AI has hit a tipping point. Not only are the majority of enterprise executives using generative AI, but they are also planning a significant increase in generative AI investments over the coming year.
The poll, which was conducted with 672 senior leaders from U.S. organizations with annual revenues over $50 million, also provides some eye-opening findings on the adoption of generative AI by functional area, emerging applications and use cases by industry, and adoption drivers and hurdles.
By industry, technology dominates with 60% of leaders frequently using generative AI, followed by industrial/construction and finance with 43% and 39% respectively. On the other hand, only 26% of leaders in retail and 36% in professional services and manufacturing routinely use generative AI.
Factors and barriers hindering the implementation of generative artificial intelligence
Three-quarters of company leaders have a generally positive attitude on generative AI, although caution lingers among all respondents, especially those who utilize the technology less regularly.
The key drivers for using generative AI include increased staff productivity, improved corporate operations, more employee creativity, the development of new products and services, and reaching new audiences or markets.
In contrast, the top impediments to adoption include concerns about erroneous results, consumer privacy, internal pushback, ethical challenges, and expense. those with $50M-$200M in revenue are most concerned about data confidentiality, while those with $2B+ in revenue are most concerned about accuracy.
“While optimism about generative AI is widespread, concerns remain about accuracy, bias and the role of AI in decision-making,” said Prof. Puntoni. “In addition, leaders are driven by psychological concerns about the potential for workplace change, especially among those who have not yet used the technology. As generative AI becomes more embedded in teams, striking the right balance in AI management and employee education will be key.”
Will AI replace or supplement human talent?
For the time being, the survey indicates that generative AI is perceived as more advantageous than detrimental to employees. Senior leaders who are already employing the technology are more inclined to believe that generative AI would improve rather than replace staff talents (48% vs. 36% strongly agree).
Furthermore, most business leaders do not believe that technology can totally replace human expertise. However, it can potentially improve work quality (55% strongly agree that AI will enable higher quality with the same personnel versus 43% who strongly agree but with fewer employees).
“Generative AI, while revolutionary, is not immune to errors,” said Korst. “It’s crucial for leaders to have strong quality control mechanisms in place to monitor and validate AI-generated output from data analysis to content. This not only ensures accuracy but helps to mitigate risks and maintain the integrity of the brand.”
Investments in Gen AI Poised to Surge
Despite the dangers and hurdles, investment in generative AI is on course for significant growth, with companies across industries aiming to boost investments by 25% over the next year, driven by organizations with revenues surpassing $2 billion (which plan a 28% increase in spend). Retail and professional services, which are currently underperforming in generative AI adoption, are expected to have the most significant investment increases, with forecasted growth rates of 27% and 28%, respectively.
Emerging Applications and Use Cases
“Use cases for generative AI continue to explode, with enterprises across industries now viewing AI as a major strategic initiative in the coming years,” Dan Ives, Managing Director at Wedbush Securities and a report contributor, said. “We continue to view AI as the most transformational tech trend since the birth of the Internet in 1995.”
When asked what use cases and applications for generative AI would be most prominent, enterprise leaders unanimously point to a future in which these AI models become crucial co-pilots in the workplace. Decision-makers across the board agree that generative AI will be widely employed in the next 3-5 years for creating data analysis (89%), marketing content and creation (text, graphics, video) (87%), and investigating customer and competition insights.
Document editing and summarising (84%), customer assistance or internal help desk activities (82%), and automated email production (82%) are also among the top applications. Legal contracts (57%), recruitment (67%), and supply chain management (71%), are predicted to be the least popular applications for generative AI.
“The results of our survey show a dynamic future for generative AI, with investment and applications expanding rapidly,” says Prof. Puntoni. “However, not all approaches are equal. While AI can analyse mountains of data in seconds, human oversight and asking the correct questions are critical to ensuring the accuracy and responsible usage of AI-generated outputs.”
Depending on the scale of the company, generative AI investments may be prioritized differently. Smaller businesses ($50-$200M) are substantially investing in generative AI for sales content (91%), whereas mid-sized businesses ($250M-$2B) are more focused on email production and internal support (86%). This reflects not only differing priorities but also the distinct strategic problems that organizations of various sizes face.