In the race to dominate the most strategic technology market of our time, the competitive strategies of the major players are a subject of intense focus. The Applied AI Market Share is being contested through a variety of approaches, from providing foundational platforms to building highly specialized applications. With the market projected to explode to over USD 2.1 trillion by 2035 at a blistering 25.32% CAGR, the stakes are incredibly high. The winning strategies are those that can successfully navigate the complex landscape of infrastructure, platforms, and applications to deliver scalable and valuable AI solutions to a massive global customer base, solidifying their position in this transformative market.
The major cloud hyperscalers—AWS, Microsoft Azure, and Google Cloud—are pursuing a platform strategy. Their goal is to be the foundational "operating system" for AI. They are building a comprehensive suite of services, from the basic infrastructure (like GPU-powered virtual machines) to high-level, pre-built AI services and APIs (for things like image recognition or text-to-speech). Their strategy is to make their cloud the easiest and most powerful place for other companies to build, train, and deploy their own AI applications. By winning the platform war, they aim to capture a significant share of the value from every AI workload that runs on their infrastructure, a highly scalable and defensible position.
In contrast, the major enterprise software companies like SAP, Salesforce, and Oracle are pursuing an "embedded AI" strategy. Their approach is to infuse AI capabilities directly into their existing, market-leading business applications. For example, Salesforce has integrated its "Einstein" AI across its CRM platform to provide features like predictive lead scoring and automated customer service responses. This strategy leverages their massive incumbent customer base and the fact that their software already holds the critical business data needed to power the AI. For their customers, this offers a seamless way to adopt AI within the familiar workflows of the tools they already use every day.
A third strategic approach is that of the "AI-native" startups and specialized vendors. These companies are not trying to be a general-purpose platform; they are focused on building a best-in-class AI solution for a single, specific problem. This could be a cybersecurity company using AI to detect threats, a biotech company using AI to design drugs, or a generative AI company building a tool for video creation. Their strategy is to use their deep domain expertise and their focus to build a product that is superior to the more generic offerings of the large platform players. This specialization allows them to capture a significant share of high-value vertical markets, creating a vibrant and innovative ecosystem.
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