Who Made the Top Generative AI Use Case List?
- Generative AI in business functions. 28% of businesses have generative AI on their board’s agenda, primarily focusing on marketing and sales, product and service development, and service operations.
- Top use cases for generative AI: Marketing and sales dominate with tasks like crafting first drafts, personalized marketing and summarizing documents. Product/service development involves identifying customer trends, drafting technical documents and creating new designs.
- Alignment on customer experience. Gartner’s assessment highlights customer experience/retention as the top focus for generative AI investments, surpassing revenue growth, cost optimization and business continuity.
Marketing and customer care continue to play a prominent role in the use of generative artificial intelligence.
The McKinsey Global Survey released this month found 28% say generative AI use is already on their board’s agenda. The most common reported business functions? Marketing and sales, product and service development and service operations, such as customer care and back-office support. And that’s the same top business functions for AI use in general.
This falls in line with at least one major earlier assessment of generative AI investment. Gartner earlier this year reported that 38% of respondents consider customer experience/retention as their primary focus of generative AI investments. That was No. 1, ahead of revenue growth (26%), cost optimization (17%) and business continuity (7%).
“This suggests that organizations are pursuing these new tools where the most value is,” McKinsey researchers noted. “In our previous research, these three areas, along with software engineering, showed the potential to deliver about 75% of the total annual value from generative AI use cases.
Personalized Marketing, Customer Service Chatbots Among Top Use Cases
Marketing and sales took the top spot as far as use cases for generative AI go, ahead of product and/or service development, service operations, risk, strategy and corporate finance, HR, supply chain management and manufacturing.
And here are the most common functions within those use cases:
Marketing and Sales
Product and/or Service Development
Identifying trends in customer needs
Drafting technical documents
Creating new product designs
Use of chatbots (e.g., for customer service)
Forecasting service trends or anomalies
Creating first drafts of documents
And who will see the most disruption from generative AI?
“Industries relying most heavily on knowledge work are likely to see more disruption — and potentially reap more value,” McKinsey researchers found. “While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI — adding value equivalent to as much as 9% of global industry revenue — knowledge-based industries such as banking (up to 5%), pharmaceuticals and medical products (also up to 5%), and education (up to 4%) could experience significant effects as well.”
And if you’re using generative AI for product and service development, you’re in good company. High performers — organizations that say at least 20% of EBIT (Earnings Before Interest and Taxes) in 2022 was attributable to AI use — are achieving “significant value” from AI and are already using generative AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management, according to McKinsey researchers.
“When looking at all AI capabilities — including more traditional machine learning capabilities, robotic process automation, and chatbots — AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products,” researchers found. “These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.”
Related Article: Where Are Marketers on the Generative AI Adoption Curve?
Marketing, Customer Care Teams Need More Risk, Security Programs
One thing even the high performers can get better at as they deploy generative AI into marketing and customer service scenarios? Risk and security.
How many have established policies governing employees’ use of generative AI? Only about 21%.
Further, about 32% mitigate the accuracy of these generative AI tools, and we all know how big a deal that can be with generative AI having a mind of its own and even perhaps getting dumber as some reports say.
“The real trap, however, is that companies look at the risk too narrowly,” Alexander Sukharevsky, senior partner and global leader of QuantumBlack, AI by McKinsey, said in the McKinsey report. “There is a significant range of risks — social, humanitarian, sustainability — that companies need to pay attention to as well. In fact, the unintended consequences of generative AI are more likely to create issues for the world than the doomsday scenarios that some people espouse.”
What do companies who take risk seriously do? According to Sukharevsky, those companies:
- Experiment with and use it while having a structured process in place to identify and address these broader risks.
- Put in place beta users and specific teams that think about how generative AI applications can go off the rails.
- Work with the best and most creative people in the business to define the best outcomes for both the organization and for society more generally.
“Being deliberate, structured, and holistic about understanding the nature of the new risks and opportunities emerging,” he said, “is crucial to the responsible and productive growth of generative AI.”