AI PC adoption is accelerating, with more than eight in 10 organizations having deployed, piloted or planned near-term adoption of AI PCs, according to a recent IDC white paper sponsored by AMD. This momentum reflects enterprises moving from AI experimentation into scaled deployment, reshaping how organizations approach infrastructure and enabling real-time, intelligent workflows across their operations.
The research surveyed over 500 IT and business decision-makers across the United States, Japan, France, the United Kingdom and Germany. Results show a clear shift in enterprise strategy, with organizations moving beyond pilot programs and embedding AI more deeply into day-to-day operations. Artificial intelligence is no longer experimental; it is becoming essential infrastructure.
Key Adoption Metrics
The IDC findings detail specific adoption rates and perceived benefits. According to the white paper, 81% of organizations are engaged in planning, piloting or deploying AI PCs. Additionally, 61% are integrating AI directly into workflows, while 59% cite high-performance NPUs (neural processing units) as critical for enabling next-generation AI experiences.
Performance gains are already evident. Organizations using AI PCs report 70% faster performance with reduced latency, while 66% report increased employee productivity. Moreover, 58% cite improved data security as a key benefit of on-device AI processing, suggesting that distributed computing reduces risks associated with centralized data storage.
AI PC Adoption and Agentic AI
The transition to agentic AI represents a significant shift in how computing systems function within enterprises. IDC’s research indicates that 70% of organizations expect agentic AI systems, which can autonomously plan, execute and adapt tasks in real time, to influence employee workflows within the next two years. These systems differ fundamentally from traditional AI applications by operating with greater autonomy and responsiveness.
The PC is evolving beyond its traditional role as a productivity device. Instead, it now functions as an interface for interacting with AI systems and as a local execution layer for processing tasks securely and in real time. This architectural change means compute resources are moving closer to where work actually happens, reducing latency and improving data control.
Enterprise Requirements and Security
As organizations scale AI PC deployments, key requirements around security, manageability and integration with existing IT environments come into focus. The IDC findings highlight these areas as top considerations when deploying AI PCs across large numbers of endpoints. Cybersecurity and consistent IT governance remain critical concerns for enterprise decision-makers.
On-device AI processing offers potential security advantages over cloud-based alternatives. By executing AI workloads locally on computing hardware, organizations maintain greater control over sensitive data and reduce the attack surface associated with data transmission to centralized servers. This aligns with broader enterprise trends toward zero-trust architecture and distributed security models.
Early Results and Business Value
Early adopters deploying AI PCs are already seeing measurable results across performance, productivity and innovation. These outcomes reflect a broader shift toward AI-driven workflows, where employees interact with AI systems in real time. As artificial intelligence becomes more integrated into everyday tools and processes, organizations are placing greater emphasis on responsiveness, data control and security.
The transition to agentic AI represents an important inflection point for enterprise computing. Organizations that invest in AI-ready systems today are positioning themselves to better support the next phase of AI innovation and maintain competitive advantage in increasingly automated business environments.
