The Rise of Generative AI in Business: A Look at ROI and High-Payoff Applications
Business leaders are increasingly turning to generative AI (GenAI) as a strategic investment, with a recent survey by KPMG indicating that 97 percent of leaders in U.S. companies with at least $1 billion in revenue plan to invest in GenAI over the next year. These investments, often totaling $100 million or more, reflect a growing recognition of the potential for generative AI to drive significant returns on investment (ROI).
Measuring ROI and mitigating risks
Leaders are keenly aware of the need to measure the ROI of their generative AI investments. According to the KPMG survey, metrics such as productivity gains, employee satisfaction, and revenue generated by AI chatbots are key indicators of success. However, they are also mindful of the risks associated with generative AI, particularly regarding data security and workforce preparedness. Companies are investing in robust data security measures, governance frameworks, and workforce training programs to mitigate these risks.
One of the key factors driving investments in generative AI is the potential for high-payoff applications that deliver significant value to businesses. These applications typically exhibit several characteristics, including a quantum leap in revenue and productivity, the ability to attract and retain customers, and difficulty for rivals to replicate. Two examples of such applications are highlighted below.
Bullhorn’s AI-driven candidate matching
Bullhorn, a Boston-based technology provider for temporary worker placement, leverages generative AI to enhance its customers’ recruiting processes. By analyzing successful job placements, Bullhorn’s AI model helps recruiters match candidates to jobs more effectively and efficiently.
This results in increased revenue and profitability for Bullhorn’s clients and improved recruiter productivity. Bullhorn’s AI application is also difficult for competitors to replicate, further solidifying its value proposition.
Dynatrace, a software observability services provider based in Massachusetts, recognizes both the potential and challenges of generative AI in customer service. CEO Rick McConnell highlights the importance of identifying “killer apps” that can significantly enhance customer experience. While some AI-driven customer service interactions can be highly successful, others may fall short, potentially alienating customers. McConnell emphasizes ensuring that AI applications deliver value and positively impact customer relationships.
As businesses increasingly embrace generative AI, the focus shifts towards identifying high-payoff applications that deliver tangible ROI. By investing in robust data security measures, governance frameworks, and workforce training programs, companies can mitigate the risks associated with AI adoption.
Ultimately, the success of generative AI initiatives hinges on their ability to drive revenue growth, enhance productivity, and improve customer satisfaction. As technology evolves, businesses must remain agile and adaptive to capitalize on its full potential.
Generative AI holds tremendous promise for businesses, but success requires careful planning, strategic investment, and a focus on delivering tangible value. By embracing AI-driven innovations and leveraging them to address real-world challenges, companies can position themselves for long-term success in an increasingly competitive marketplace.
Read More
The Rise of Generative AI in Business: A Look at ROI and High-Payoff Applications
Business leaders are increasingly turning to generative AI (GenAI) as a strategic investment, with a recent survey by KPMG indicating that 97 percent of leaders in U.S. companies with at least $1 billion in revenue plan to invest in GenAI over the next year. These investments, often totaling $100 million or more, reflect a growing recognition of the potential for generative AI to drive significant returns on investment (ROI).
Measuring ROI and mitigating risks
Leaders are keenly aware of the need to measure the ROI of their generative AI investments. According to the KPMG survey, metrics such as productivity gains, employee satisfaction, and revenue generated by AI chatbots are key indicators of success. However, they are also mindful of the risks associated with generative AI, particularly regarding data security and workforce preparedness. Companies are investing in robust data security measures, governance frameworks, and workforce training programs to mitigate these risks.
One of the key factors driving investments in generative AI is the potential for high-payoff applications that deliver significant value to businesses. These applications typically exhibit several characteristics, including a quantum leap in revenue and productivity, the ability to attract and retain customers, and difficulty for rivals to replicate. Two examples of such applications are highlighted below.
Bullhorn’s AI-driven candidate matching
Bullhorn, a Boston-based technology provider for temporary worker placement, leverages generative AI to enhance its customers’ recruiting processes. By analyzing successful job placements, Bullhorn’s AI model helps recruiters match candidates to jobs more effectively and efficiently.
This results in increased revenue and profitability for Bullhorn’s clients and improved recruiter productivity. Bullhorn’s AI application is also difficult for competitors to replicate, further solidifying its value proposition.
Dynatrace, a software observability services provider based in Massachusetts, recognizes both the potential and challenges of generative AI in customer service. CEO Rick McConnell highlights the importance of identifying “killer apps” that can significantly enhance customer experience. While some AI-driven customer service interactions can be highly successful, others may fall short, potentially alienating customers. McConnell emphasizes ensuring that AI applications deliver value and positively impact customer relationships.
As businesses increasingly embrace generative AI, the focus shifts towards identifying high-payoff applications that deliver tangible ROI. By investing in robust data security measures, governance frameworks, and workforce training programs, companies can mitigate the risks associated with AI adoption.
Ultimately, the success of generative AI initiatives hinges on their ability to drive revenue growth, enhance productivity, and improve customer satisfaction. As technology evolves, businesses must remain agile and adaptive to capitalize on its full potential.
Generative AI holds tremendous promise for businesses, but success requires careful planning, strategic investment, and a focus on delivering tangible value. By embracing AI-driven innovations and leveraging them to address real-world challenges, companies can position themselves for long-term success in an increasingly competitive marketplace.
Read More