\n<\/p>\n
By Jeff Koyen<\/em><\/p>\n Enterprise IT leaders spent the last two years trying to figure out how to bring AI into the business. As organizations move past experimentation, they must navigate the operational strain AI deployments can create at scale.<\/p>\n The puzzles don\u2019t end with model selection; they start there. How do you support the network and infrastructure demands AI suddenly creates? Where should particular workloads run? How do you manage 50,000 employees with different devices and varying needs?<\/p>\n Reducing this operational complexity is the job for IT teams. The question of how to best achieve that goal defines the work of technology leaders like Hasmukh Ranjan, senior vice president and CIO at AMD.<\/p>\n \u201cYou have to be very thoughtful that if you are going to spend a lot of money on AI tokens, you are not bothered or worried if this PC is configured correctly,\u201d Hasmukh says. \u201cThose operational layers need to be more abstracted so you\u2019re focused on the bigger-value solutions for your enterprise.\u201d<\/p>\n Below, we explore how AI is complicating this goal and share how an enterprise foundation with cutting-edge performance, security and manageability features can help organizations thrive in the AI era.<\/p>\n Cost And Performance Are Converging Yet Conflicting<\/strong><\/p>\n AI consumes resources, such as compute, memory, network bandwidth and electricity, at an astonishing scale that older enterprise infrastructure wasn\u2019t built to handle. The bill is coming due. \u201cIf you are an enterprise and you have adopted AI, the data has exploded,\u201d Hasmukh says.<\/p>\n Network traffic and bandwidth consumption have surged in step. The typical AI query pulls data from multiple sources, runs it through a model and returns results. As enterprise systems process more data at higher speeds, the underlying storage, data and network layers all need an upgrade.<\/p>\n Faced with rising AI demands, an IT manager\u2019s first instinct may be to solve the problem with additional cloud infrastructure. In Hasmukh\u2019s view, that approach is not only limiting but unnecessary. Instead, he counsels enterprises to optimize their AI for the most cost-effective infrastructure that can actually handle it.<\/p>\n Inside AMD data centers, that means carefully considering where and how compute resources are deployed. Not every AI workload requires water-cooled GPUs or radical server room redesigns, for example. Many run well on air-cooled cards with lower power consumption and less need for infrastructure overhauls.<\/p>\n \u201cWe are being thoughtful in making sure that we prepare for the AI enterprise in different segments,\u201d Hasmukh says. \u201cThat\u2019s how we are investing.\u201d<\/p>\n Those same pressures are increasingly extending to the endpoint, where employee devices are beginning to handle more AI workloads locally. Enterprises now need PCs that can balance local AI performance with efficiency and low overhead.<\/p>\n At the same time, tech teams need the manageability, deployment scalability and lifecycle consistency to support large device fleets. AMD PRO, an enterprise platform for commercial PCs, is designed to address these complex operational realities, helping businesses balance AI-ready performance with the manageability and long-term support large fleets require.<\/p>\n Security, AI And Data Governance<\/strong><\/p>\n Even before the cloud era, IT leaders were thinking about data security as a perimeter problem. Keep the wrong people out. Encrypt what moves between systems. Audit what gets logged.<\/p>\n Now, with most AI interactions inside an enterprise leaving the building, maintaining perimeter security becomes even more crucial.<\/p>\n \u201cToday, pretty much 99% of traffic goes to a frontier model,\u201d Hasmukh says. \u201cThat is not a solution that will stay at scale and remain efficient.\u201d<\/p>\n As AI agents become more autonomous, that efficiency challenge grows quickly. Unlike traditional chat interactions, agents continuously reason, retrieve information, call tools and execute workflows, driving significantly higher token consumption across the enterprise. AMD recently explored how this shift<\/u><\/a> is pushing organizations toward more hybrid AI architectures, where some workloads run locally or on dedicated infrastructure to improve cost efficiency, latency and control.<\/p>\n For CIOs, these new transactions can create governance risks as well. Sensitive customer data, proprietary code, financial information and internal documents shouldn\u2019t pass through AI workflows outside the IT team\u2019s direct control.<\/p>\n That\u2019s why AMD is rethinking where AI workloads should run. Some belong in the cloud, some in the data center and some on the device itself. Simple, routine queries may stay local, while frontier models are reserved for workloads that truly require them.<\/p>\n This shift further underscores the importance of security and governance at the endpoints. As AI workloads become more distributed, enterprise endpoints must balance local AI performance with robust security and policy compliance at scale. AMD PRO addresses that need by delivering enterprise-level security with built-in, multilayered protections that are always in place.<\/p>\n The Rising Complexity Of IT As A Service Provider<\/strong><\/p>\n If some AI workloads should run locally for cost, and some should run locally for security, employee devices must be capable of handling them. And IT needs to manage these devices at scale.<\/p>\n \u201cIt\u2019s not enough to put a more powerful processor along with a specific graphics processor on a device,\u201d Hasmukh says. \u201cWhen I think of a device, I think about 50,000 devices I have to manage, and they have to be productive.\u201d<\/p>\n As more AI workloads move onto employee devices, the management burden for IT teams grows with them. This is the environment AMD PRO processors are designed for: helping enterprises balance local AI performance with security, manageability and simplified deployment across large device fleets.<\/p>\n For Hasmukh, the implication is crystal-clear: if AI is going to run at the edge, the edge needs both top-notch performance and centralized management.<\/p>\n Discover AMD PRO systems for today\u2019s enterprise IT challenges<\/u><\/a><\/p>\n<\/div>\n