Despite the spike in adoption of generative AI (gen AI), we are still in the experimentation phase, with many organizations seeking relatively simple, one-step solutions. Although it varies by industry, roughly half of our survey respondents say they are using readily available, off-the-shelf gen AI models rather than custom-designed solutions. This is a very natural tendency in the early days of a new technology—but it’s not a sound approach as gen AI becomes more widely adopted. If you have it, your competitor probably has it as well. Organizations need to ask themselves: What is our moat? The answer, in many cases, likely will be customization.
But even there, the answer is not so simple. 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. In fact, with gen AI we are moving from a binary world of “build versus buy” to one that might be better characterized as “buy, build, and partner,” in which the most successful organizations are those that construct ecosystems that blend proprietary, off-the-shelf, and open-source models. Finally, leaders must understand that gen AI models generally comprise just
15 percent of any given solution. In other words: it’s not just tech. To create value, organizations must have all the elements in place—domain reimagining abilities; relevant skill sets (including the upskilling of nontechnical colleagues); a robust operating model; proprietary data. It’s only when those factors are in place that organizations will be able to unlock impact and move from experimentation to scale.
Alexander Sukharevsky
McKinsey commentary
Senior partner and global coleader of QuantumBlack, AI by McKinsey
