Implementing generative AI with speed and safety

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Implementing generative AI with speed and safety

By Oliver Bevan et al., | McKinsey & Company | March 13, 2024

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Generative AI (gen AI) presents a once-in-a-generation opportunity for companies, with the potential for transformative impact across innovation, growth, and productivity. The technology can now produce credible software code, text, speech, high-fidelity images, and interactive videos. It has identified the potential for millions of new materials through crystal structures and even developed molecular models that may serve as the base for finding cures for previously untreated diseases.

McKinsey research has estimated that gen AI has the potential to add up to $4.4 trillion in economic value to the global economy while enhancing the impact of all AI by 15 to 40 percent.  While many corporate leaders are determined to capture this value, there’s a growing recognition that gen AI opportunities are accompanied by significant risks. In a recent flash survey of more than 100 organizations with more than $50 million in annual revenue, McKinsey finds that 63 percent of respondents characterize the implementation of gen AI as a “high” or “very high” priority.  Yet 91 percent of these respondents don’t feel “very prepared” to do so in a responsible manner.

That unease is understandable. The risks associated with gen AI range from inaccurate outputs and biases embedded in the underlying training data to the potential for large-scale misinformation and malicious influence on politics and personal well-being. There are also broader debates on both the possibility and desirability of developing AI in general. These issues could undermine the judicious deployment of gen AI, potentially leading companies to pause experimentation until the risks are better understood—or even deprioritize the technology because of concerns over an inability to manage the novelty and complexity of these issues.

However, by adapting proven risk management approaches to gen AI, it’s possible to move responsibly and with good pace to capture the value of the technology.  In practical terms, enterprises looking to address gen AI risk should take the following four steps:

1. Launch a sprint to understand the risk of inbound exposures related to gen AI.
2. Develop a comprehensive view of the materiality of gen-AI-related risks across domains and use cases, and build a range of options (including both technical and nontechnical measures) to manage risks.
3. Establish a governance structure that balances expertise and oversight with an ability to support rapid decision making, adapting existing structures whenever possible.
4. Embed the governance structure in an operating model that draws on expertise across the organization and includes appropriate training for end users.

The specifics of how to implement these steps and the degree of change required to make them effective will vary with an organization’s gen AI aspirations and nature. For instance, it could be looking to be a maker of the foundation models, a shaper that customizes and scales foundation models, or a taker that adopts foundation models through off-the-shelf applications with little or no customization.

3 key takeaways from the article

1. Generative AI (gen AI) presents a once-in-a-generation opportunity for companies, with the potential for transformative impact across innovation, growth, and productivity. 
2. McKinsey research has estimated that gen AI has the potential to add up to $4.4 trillion in economic value to the global economy while enhancing the impact of all AI by 15 to 40 percent.  
3. While many corporate leaders are determined to capture this value, there’s a growing recognition that gen AI opportunities are accompanied by significant risks.  In practical terms, enterprises looking to address gen AI risk should take the following four steps:  launch a sprint to understand the risk, develop a comprehensive view of the materiality of gen-AI-related risks across domains, establish a governance structure that balances expertise and oversight with an ability to support rapid decision making, adapting existing structures whenever possible, and embed the governance structure in an operating model that draws on expertise across the organization and includes appropriate training for end users.

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Topics:  Technology, Artificial Intelligence, Risk, Productivity