In 2018 two global giants set out to harness artificial intelligence to reshape the way they designed and brought products to market. General Motors applied generative-design software using Autodesk’s Fusion 360 to reimagine a humble but critical component—the seat bracket. The AI generated a structure that resembled something nature might have grown—an airy, latticelike form that was 40% lighter and 20% stronger than the original. Yet the part never made it into production. Why? GM’s supply chain and manufacturing system—built for stamped steel—couldn’t handle the complex geometry of the AI-generated design. Retooling the system would have taken years. The innovation stalled.

At the same time, Apple began experimenting with metalenses—ultra-thin, AI-optimized optical components capable of replacing traditional camera lenses. The technology required integrating machine learning, materials science, and semiconductor manufacturing. Within two years, Apple had filed dozens of patents and (at the time of this writing) is reportedly preparing to embed the breakthrough in its Face ID sensors—first in the iPad Pro and then in upcoming iPhone 17 models as well. Unlike GM, Apple didn’t just have a bold idea—it had the system to execute it.

The two stories capture an important truth about AI: The problem isn’t usually with what AI can and can’t do. More often, it’s the misalignment between what leaders want to achieve and what their value chains, operating models, and technology stacks can realistically support. The issue is pervasive—62% of companies cite poor cross-functional fit and 63% flag the need to adjust workflows as leading barriers to successful AI adoption. Only 25% of CEOs say they feel fully prepared to deploy AI organization-wide, according to a survey by Kearney and the Futurum Group.

Unfortunately, the vast majority of companies are more like GM in this regard than like Apple. Studies suggest that many AI initiatives fail to deliver tangible business value: According to S&P Global Market Intelligence, 42% of companies abandoned the majority of their AI initiatives in 2025 (up from 17% in 2024), and on average, 46% of proof-of-concepts were scrapped before reaching production. Moreover, a recent survey of 1,600 enterprise leaders and employees by the AI firm Writer found that only one-third of organizations achieve significant ROI from their AI investments, even though 73% of them spend more than $1 million on AI annually.

In this article we offer a practical framework to help companies improve their return on AI investment. Drawing on our research and experience with firms across sectors—including consumer goods and advanced manufacturing—we identify the two key dimensions that shape AI success: value-chain control and technological breadth. These forces define the landscape of possibility—and point to four distinct approaches companies can adopt to realize AI’s potential: focused differentiation, vertical integration, collaborative ecosystem, and platform leadership. Each comes with its own risks, requirements, and potential for breakthrough innovations. But when the approach fits a firm’s reality, the payoff is substantial.

Let’s begin by taking a closer look at the two dimensions that form the foundation of this framework.