AI Productivity at Scale: From Everyday Assistance to Autonomous Execution

The conversation about enterprise AI has changed. A few years ago, the focus was experimentation: give employees access to AI, explore use cases, and learn where technology could add value. We are well beyond that stage.

AI is now embedded in how work gets done across AMD. Employees use it to find information, create and translate content, generate code, navigate enterprise processes, make decisions faster, and eliminate work that previously required significant manual effort. The impact is visible in productivity, execution speed, and quality.

From an IT leadership perspective, the challenge is no longer enabling AI adoption. It is industrializing AI productivity across the enterprise, making AI pervasive, connected, measurable, and increasingly autonomous.

AI Has Become Part of the Way We Work

Employees are no longer trying AI to understand what it can do. They are building it into real workflows and saving meaningful time on everyday tasks.

Enterprise productivity does not come from occasional interactions with an AI assistant. It comes when thousands of employees repeatedly remove minutes and hours from the work they do every day.

A faster search for engineering knowledge, an approval completed without navigating multiple systems, content created in minutes rather than hours, or code developed and reviewed more quickly—each looks incremental in isolation. Across an organization, they represent significant additional capacity, and our focus is on capturing that capacity systematically.

ACE: Bringing AI and Action Into the Flow of Work

One of the ways we are doing that is through ACE-AMD Concierge for Employees. ACE is an internal agentic system that gives employees a common front door to productivity directly within Microsoft Teams—the environment where they already communicate and collaborate. Its value goes well beyond quick answers.

ACE connects employees to enterprise processes and lets them take action without navigating the complexity underneath. Approvals from different platforms come into a single experience, and employees can reach capabilities spanning HR, IT, engineering knowledge, compute resources, and more.

Historically, enterprise technology required employees to understand our systems: which application to open, where information lived, which workflow to follow, and how different platforms connected. AI reverses that relationship. The result is not simply a better interface. It is less friction in the way the company operates.

Aria: Enterprise AI Designed for Advanced Work

Our custom-built AI platform, the AMD Aria AI Suite, provides employees with advanced AI capabilities. In AI Chat, they can choose from more than a dozen large language models—from cloud-hosted frontier models to open-source models running in our own data centers on AMD Instinct accelerators.

We do not view enterprise AI as a one-model environment. Different models have different strengths, and different jobs require different capabilities. Access to a range of models lets employees select the right capability for the work at hand while staying within an enterprise platform.

Aria also extends beyond chat with advanced translation, text-to-voice, flowchart generation, and other content-development tools that move employees from idea to executable output. When AI can help someone analyze, create, translate, visualize, and act in the same environment, the opportunity is far larger than making an individual task faster.

An AI Ecosystem, Not an AI Product

ACE and Aria are important elements of our strategy, but enterprise AI productivity is broader than either platform. AMD employees also use commercial technologies supporting collaboration, software development, code generation, and other specialized workflows. At our level of maturity, the IT challenge is orchestrating an AI ecosystem.

The right capabilities available for the right use cases, reducing unnecessary fragmentation, connecting AI securely to enterprise knowledge and processes, and letting employees move easily between general-purpose and specialized tools. It also means treating AI as a fundamental layer of enterprise technology—not an isolated application category.

Productivity Has to Show Up in Business Outcomes

At scale, usage alone is not a sufficient measure of success. Adoption matters because employees must use these capabilities for them to create value, but the more important question is whether AI is changing the operating performance of the company. We are seeing evidence that it is.

The impact is appearing in the top-level KPIs we use to evaluate performance and productivity. The goal is not workforce reduction. Instead, AI helps employees focus more time on higher-value work and enables teams to scale their impact. More importantly, we are seeing accelerated timelines and improved quality.

The question is not, “How many hours did AI save?” It is:

• Can the organization absorb more work without resources increasing at the same rate?
• Can teams complete initiatives faster?
• Can employees spend more of their time on engineering, innovation, decision-making, and other high-value activities?
• Can we improve speed without sacrificing quality and improve both at once?

Those outcomes are where AI productivity becomes visible at the enterprise level.

What’s Next: Scaling Autonomous AI

Autonomous AI is an important part of our productivity strategy. Within IT, self-healing systems detect, diagnose, and resolve issues in real time.

The value is not automating an individual task. It is eliminating entire cycles of detection, triage, diagnosis, resolution, and recovery that traditionally required people. When that happens autonomously—sometimes before an employee is aware there is a problem—we create capacity while improving reliability and the employee experience on both sides: IT teams spend less time responding to issues, and employees face fewer disruptions.

We are working with business functions across AMD to implement fully autonomous capabilities that can further increase productivity and accelerate business processes. That progression—from assistance to action to autonomy—will define the next phase of enterprise productivity.

AI Productivity Is Becoming an Enterprise Operating Capability

It requires an integrated ecosystem of models and tools, trusted access to enterprise knowledge, integration with workflows and transactional systems, an employee experience that removes complexity rather than adding another layer of it, and measurement tied to business outcomes.

Most importantly, it requires us to keep expanding what is possible. We have moved from isolated productivity tools to enterprise platforms, from measuring activity to seeing impacts in business-level metrics, and from AI that assists employees to AI that executes work autonomously. The question is no longer whether AI can improve productivity—we are seeing that today. The opportunity now is to scale it, and to keep moving work from manual execution to autonomous operations.