The Center of Market Growth Is Not Always Where Competitive Advantage Is Built
Investment in AI is accelerating globally. Major technology companies are increasing capital spending on data centers and AI infrastructure, driving demand for semiconductors and related infrastructure. Against this backdrop, some semiconductor- and AI-related stocks have also been major contributors to gains in Japan’s Nikkei Stock Average (Nikkei 225).
As expectations rise, market growth can easily become the dominant lens through which companies assess the AI opportunity. For corporate strategy, however, the fact that a market is expanding does not mean that every part of it offers the same potential for sustained profitability or differentiation.
In the early stages of a new technology cycle, technologies and infrastructure can themselves be powerful sources of differentiation because relatively few companies are able to provide them. As the technology matures, however, the number of providers grows and comparable capabilities become more widely available. As scarcity gives way to broader availability, the basis of competition begins to shift.
For companies developing their AI strategies, it is therefore not enough to ask how large the AI market will become. They also need to consider how the basis of competition may evolve and where AI can create sustainable competitive advantage.
Deciding What to Share and Where to Differentiate
A useful way to think about AI strategy is to distinguish between two layers. The shared foundation consists of systems and capabilities that create more value when they are broadly accessible than when individual companies repeatedly recreate them. Above this sits the application layer, where companies can concentrate resources on solving specific problems and developing their own sources of competitive advantage.
National digital identity and payment systems illustrate how this structure can work. In India, Aadhaar provides a common identity system, while UPI provides a common payment infrastructure. Businesses can use these systems rather than build their own identity and payment mechanisms from the ground up, allowing them to concentrate resources on the products and services through which they create value for customers. The shared foundation does not eliminate competition; it shifts the focus of differentiation to what businesses build on top of it.
The relevance to AI lies in the same strategic choice: whether companies should devote resources to differentiating the underlying foundation or use an increasingly accessible foundation to create value at the layer above it. Investment in data center capacity is expanding, while companies can choose from a growing range of AI foundation models. Differences in performance remain, but model selection increasingly involves other considerations, including cost, usability, and integration with existing systems.
As viable alternatives multiply, access to any one underlying technology becomes less likely to provide a lasting advantage for the companies using it. The AI application layer therefore becomes increasingly important as the arena in which those companies can pursue differentiation.
Competitive Advantage from AI Comes from Solving Specific Business Problems
The question, then, is what makes differentiation at the application layer defensible.
Two sources of advantage emerge at this layer. The first is context-specific execution. Consider AI used to optimize project scheduling on construction sites, reduce inventory losses in retail, or assess conversion potential in B2B sales. These may appear to be highly specific AI use cases, but delivering meaningful results requires more than technical capability. It requires a detailed understanding of workflows, available data, decision-making processes, and the behavior of the people involved. Companies must determine which problems are worth addressing, where AI can change the way work is performed, and how that change translates into measurable business outcomes.
The second is cumulative advantage. As companies build and refine these applications, they accumulate domain knowledge, proprietary data, and practical expertise in how AI interacts with their operations. Those assets can improve subsequent applications, making the resulting capabilities progressively harder for competitors to replicate even when the underlying technology is equally accessible.
The implication is that AI investment should be evaluated not only by the immediate performance of an individual use case but also by what the organization learns and accumulates through implementation. The strongest applications can create value twice: first through the problem they solve, and again through the knowledge, data, and capabilities they leave behind.
More broadly, this raises a question that extends beyond AI: companies need to consider not only how to compete, but whether they are competing in the areas where differentiation can create the greatest value and build sustainable competitive advantage.
How IGPI Can Help
Building competitive advantage requires deliberate choices about where to compete and where to rely on shared foundations. In AI, this means identifying the business problems where differentiation matters, determining where AI can materially improve outcomes, and considering how each implementation can strengthen the capabilities that support future growth.
IGPI helps management teams determine where to focus their resources and translate those choices into executable strategies. By connecting technology decisions with business priorities and sources of differentiation, we help organizations build AI strategies that support sustainable competitive advantage.
