It’s amazing how quickly the conversation around generative AI has evolved. Just a few months ago, the conversation in the C-suite was pretty rudimentary, focused on trying to understand what it was and seeing what was hype versus what was reality. Now in just about six months, business leaders are having much more sophisticated conversations. As we can see from the survey results, almost a third of companies are using generative AI in at least one business function. This underscores the degree to which companies understand and accept that generative AI is viable in business.
The next question will be how companies will take the next step, and whether generative AI will follow the same pattern we observed with AI more generally, where adoption has plateaued at around the 50 percent mark. We see from the data that the promise of generative AI is leading almost half of companies already using AI to plan on increasing their investments in AI, driven in part by the understanding that broader capabilities are needed to take full advantage of generative AI.
To take that next step, where generative AI can go from experiment to business engine and ensure a strong return on the investment, requires companies to tackle a broad array of issues. Those include identifying the specific opportunities for generative AI in the organization, what the governance and operating model should be, how to best manage third parties (such as cloud and large language model providers), what is needed to manage the wide range of risks, understanding the implications on people and the tech stack, and being clear about how to find the balance between banking near-term gains and developing the long-term foundations needed to scale. These are complex issues, but they are the key to unlocking the really significant pools of value out there.
Alex Singla
McKinsey commentary
Senior partner and global leader of QuantumBlack, AI by McKinsey
