Rachel Thornton has joined AMD to lead marketing for Enterprise and Data Center AI, moving to the semiconductor company after serving as Chief Marketing Officer for Adobe’s enterprise business. Thornton announced the appointment on LinkedIn, describing the move as an opportunity to focus on the changing conversation around enterprise AI from what is possible to what can be deployed in real-world environments.
Thornton brings extensive experience in enterprise technology marketing, with her recent work at Adobe focused on the intersection of marketing, customer experience and artificial intelligence. At Adobe, she worked on how companies could use data, AI and connected systems to reshape customer experiences and marketing operations.
Her move to AMD comes as businesses increasingly shift their attention toward the infrastructure required to run AI at scale. Enterprise AI discussions are expanding beyond model capabilities to questions around deployment, performance, cost, power consumption, software compatibility and vendor choice.
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In her announcement, Thornton highlighted AMD’s portfolio across Instinct GPUs, EPYC CPUs, Pensando networking and the ROCm software stack. She also pointed to the company’s approach to open software, standards and ecosystem partnerships as factors that influenced her decision to join.
Thornton’s initial focus will be on communicating how enterprise customers can evaluate AI infrastructure and make decisions about where and how AI workloads should run. She also said she plans to work closely with customers and partners as businesses navigate their AI infrastructure strategies.
The appointment comes as AMD continues to expand its AI organization. Its current leadership structure includes dedicated executives overseeing artificial intelligence, data center solutions, global AI markets and AI solutions engineering.
For Thornton, the move marks a transition from enterprise marketing and customer experience technology into a broader AI infrastructure conversation. It also reflects a wider change in enterprise AI: companies are increasingly evaluating not only what AI can accomplish, but also how efficiently and sustainably those workloads can be deployed.
