While it’s true that AI is moving rapidly toward universal adoption, it is in the enterprise where AI is having its greatest impact. Enterprise AI certainly is an area of massive resource investment: Research pegs the size of the 2025 global enterprise AI market at $97 billion, growing to an astonishing $229 billion by 2030. All forms of AI—agentic AI, generative AI, machine learning, and predictive AI, to name a few—are transforming how, when, where, and why work is done.
A major factor that already influences this impressive adoption and heady growth rate is the rapid scaling of AI workloads, moving quickly from pilots, proofs of concept, and sandboxes to truly enterprise-class AI. This inexorable move toward enterprise AI at scale carries substantial promise of breakthrough capabilities and unprecedented innovation; however, it also requires a widespread transformation of the role of client systems to bring AI closer to the nexus of data creation and data sharing.
This is the logical culmination of what has been described as the four waves of enterprise infrastructure. As the industry transitioned from traditional compute to cloud compute and then AI compute, client solutions certainly improved dramatically. But they still functioned primarily as passive endpoints. In the fourth wave—AI Inferencing—client systems now play a strategic, high-impact role in putting AI to work on premise and on the edge.
In the era of enterprise AI at scale, with its critical focus on AI inferencing on premise and on the edge, client solutions have been re-imagined, re-engineered and redeployed. They now act as intelligent AI nodes, executing AI inference where work is being done. This empowers not only IT professionals, data scientists, and power users to optimize their AI initiatives, but it also turns rank-and-file business users into human AI agents. Whether the solution is a traditional compute device such as a notebook or desktop, a consumer device such as a tablet or a smartphone, a specialized system such as a workstation, or a low-tech wearable device such as glasses, client solutions are critical elements in the move to enterprise AI at scale.
Technology innovators such as Dell have designed and built next-generation client devices not only with the performance, intelligence, and automation necessary to support AI inferencing, but they have taken a “whole solution” approach to these clients. For instance, AI-powered clients incorporate cutting-edge data protection, security, and governance tools that are policy-aware and seamlessly managed as part of an end-to-end AI framework.
These solutions also provide users with varying degrees of technical skills with a personalized user experience that promotes innovation, experimentation, and contextual insights. This requires an architecture that is “AI-ready by design” in such areas as operating systems, application programming interfaces, storage infrastructure, memory management, and chip design.
As a result, enterprise AI at scale is optimized by a new breed of client solutions that are part of an organization’s overarching AI strategy from the start, rather than “bolted onto” legacy systems through patchwork updates and swapping out hardware components. These new client systems are even AI-architected for intense energy management and sustainability requirements that are expected with today’s enterprise AI mindset.
