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June 3, 2024

Global Survey By McKinsey: GenAI Adoption Starting To Deliver Value

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Investments in GenAI are beginning to create value for organizations according to a new global survey by McKinsey, a leading management consultancy firm. 

While 2023 was a year of investing in GenAI initiatives, 2024 is about deriving business value from this new technology.

In just one and half years since OpenAI’s ChatGPT was launched, 65% of organizations are now regularly using AI, according to the McKinsey report. This is nearly double the percentage from last year’s survey. 

The report suggests that organizations are now using AI in more parts of the business. More than half of the respondents shared that their organization has adopted AI in two or more business functions. The most commonly reported GenAI use cases include marketing and sales, content support for marketing strategy, and professional services. 

Organizations have seen the most meaningful cost reductions from GenAI use in HR and revenue increases in supply chain and inventory management.

“In 2024, gen AI is no longer a novelty,” said Alex Singla, senior partner and global co-leader of QuantumBlack, AI by McKinsey. “The technology’s potential is no longer in question. And while most organizations are still in the early stages of their journeys with gen AI, we are beginning to get a picture of what works and what doesn’t in implementing — and generating actual value with — the technology.”


The report analyzed GenAI adoption across industries and one of the key findings is that organizations are about equally as likely to invest more than 5% of their digital budgets in GenAI as they are in analytical AI across industries. Most respondents (67%) expect their organizations to invest more in AI initiatives over the next three years. 

McKinsey classifies companies using generative AI into three categories based on how they implement the technology. The “takers” are those who prefer to use off-the-shelf AI tools. The “shapers” use publicly available AI tools, but customize them to better fit their needs. Lastly, there are “makers” who build their own AI models from scratch. 

The report’s findings show that roughly half of the organizations use pre-built AI tools, while the other half uses significantly customized or built-from-scratch AI tools. McKinsey predicts that in the future we will see more hybrid ecosystems that combine off-the-shelf, proprietary, and open-source AI models. 

“The spine and brain of the enterprise of the future will rely on a well-orchestrated mix of multiple foundational models — both off-the-shelf solutions and tools that have been finely tuned to the enterprise’s specific needs.” said Alexander Sukharevsky, senior partner and global co-leader of QuantumBlack, AI by McKinsey. 

As businesses start to reap the benefits of their GenAI investments, they also recognize the risks associated with the technology, with 44% of respondents saying their organizations have already experienced negative consequences from GenAI use. The report highlights inaccuracy, cybersecurity, and IP infringement as top concerns of GenAI use.


Last year, a report by Lucidworks also highlighted security and accuracy as the top two concerns for GenAI adoption in the financial services sector. 

The inaccuracy of AI models can have a major negative impact across the GenAI value chain, ranging from strategic planning to customer experiences. However, respondents shared that their organizations are actively working on mitigating the risk of inaccuracy. 

McKinsey also identifies “high performers” who are more likely to experience challenges with data. These are organizations further along their GenAI adoption journey and typically allocate a higher share of their budgets to GenAI deployment.  

While these high performers may face more challenges in GenAI adoption, they address these concerns by following a range of best practices including an increased GenAI risk awareness and clear processes to mitigate the risks. They also curate learning journeys for their workforce to build GenAI skills and establish clear KPIs to measure and track the value of GenAI. Following the examples of these high performers, other organizations can also learn how to derive more value from their GenAI investments. 

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