From smarter chatbots to data-driven insights, discover how AI’s evolving ecosystem is transforming industries and driving change.

The AI market isn’t a single entity—it’s made up of three distinct yet interconnected markets, each playing a unique role in shaping the future of technology: the pre-GenAI market, the training market and the enterprise AI market. Let’s dive into each of these three areas to better understand how they merge into the broader AI ecosystem today that drives innovation.

The Pre GenAI Market: Foundational Techniques

The pre-GenAI market is not new. In fact, it’s been evolving for decades before generative AI. These foundational AI techniques and disciplines—things like reinforcement learning, machine learning and computer vision—are still thriving today and are leveraged by generative AI or work alongside generative AI solutions.

At Dell, we’ve seen firsthand just how foundational these methods are. For example, if you’re modernizing a supply chain or optimizing factory workflows, traditional AI is often where you’ll start. These technologies aren’t overshadowed by the rise of generative AI. On the contrary, they complement it. Generative systems depend on data often generated through traditional AI approaches like computer vision and predictive analytics.

The Training Market: Laying the Groundwork for What’s Possible

Next is the training market. This focuses on the largest compute systems ever where the generative AI frontier models are born. It’s a market dominated by a small collection of players using immense quantities of computing resources. Think giga clusters, substantial GPU investments and massive data center buildouts.

The work done here is what powers many of today’s generative AI breakthroughs, laying the groundwork for what’s possible across all industries. This provides real value that extends to enterprises. After all, these same frontier models being built by the AI applications organizations are leveraged for enterprise solutions, such as content creation and coding assistance, which we’ve explored in our own AI initiatives.

The Enterprise Market: Driving Outcomes

Finally, we come to the enterprise market. Unlike the training market, enterprises aren’t looking to build all-encompassing AI systems that can do everything. Instead, they want to use AI to solve specific problems, improve processes and drive business outcomes.

This more targeted approach means the enterprise market is naturally different—more complex, slower-moving and built on reliability and trust. The enterprise market isn’t about chasing hype; it’s about tangible results. When an enterprise implements AI, the stakes are high, but so is the opportunity. With millions of potential customers and use cases, this is where AI can truly transform industries—one process at a time.

How These Markets Come Together

What’s important to understand is that these three markets—traditional AI/pre-GenAI market, the training market and the enterprise market—are not silos. They are deeply interconnected.

For enterprises, traditional AI serves as the gateway to digitization. It collects and prepares the data that is later used by more advanced AI work. Meanwhile, the training market provides the foundation models and the technical breakthroughs—scalable compute, optimized networking and more—that enterprises rely on to activate AI at scale. The real prize is activating enterprises around the world, and the enterprise AI market is trying to apply AI to improve the processes that drive ROI for their companies.

Whether organizations are automating customer support through AI chat interfaces, optimizing software development with advanced coding tools or extracting actionable insights using data agents, each layer of the AI ecosystem drives progress in the others.