Generative AI’s remarkable achievements are creating a growing misconception among executives that all previous artificial intelligence technologies will become obsolete. The resulting over-emphasis on generative AI is counterproductive, leading to the compartmentalizing of AI talent and resources. This ultimately limits AI’s potential because generative AI alone cannot solve every type of problem. That’s why the next phase in the evolution of AI use won’t hinge on a new technological breakthrough. Instead, it will emerge as executives adopt a cohesive, strategic approach to AI—what we call a One-AI approach—that pairs the latest generative AI with other forms of AI to achieve far more than each can do on its own. At the heart of all modern AI technologies lies the same fundamental ability to recognize and learn from sophisticated patterns in data. The practical differences among types of AI arise from the end-use applications they are best suited for. Large language models, for example, deploy pattern-recognition capabilities to anticipate the likely next word. These and other forms of generative AI are mostly applied to content creation and creative problem solving. On the other hand, predictive AI, which has been around for more than a decade, connects historical data to forecast future events, anticipate behaviors, and give recommendations for more informed decision-making. The different uses of each form of AI complement one another, which is why Mastercard’s chief innovation officer, Ken Moore, has rightly argued that “combining the two facets of AI [i.e., generative and predictive] can produce superior results.” Yet companies’ tendency to segregate AI resources makes it hard to achieve those results, as it slows the process of AI adoption and threatens investment in predictive AI in particular, despite its demonstrated ROI.
Full commentary : What C-Suite thinks of AI and how it will impact the future.
