The era of experimenting with AI on the sidelines is over. Across boardrooms and IT departments worldwide, enterprises are making a decisive move: integrating AI-capable PCs directly into their core operations and daily workflows. Driven by demand for faster processing, stronger data security, and the rise of autonomous AI agents, this pivot is not a future consideration — it is happening right now, and the numbers confirm it.
From Pilot to Production: The AI PC Tipping Point
According to research from IDC, more than eight in ten organisations have already deployed, piloted, or planned near-term adoption of AI PCs. This is not a slow rollout — it reflects a broad, decisive transition in how enterprises think about their computing infrastructure.
A separate IDC study reinforces the momentum: 60% of enterprises are already testing or have fully rolled out AI PCs, with another 21% planning to introduce them within the next year. Among organisations that have already deployed AI PCs, a strong majority report real, measurable gains — with 70% citing faster processing speeds and improved responsiveness as immediate benefits.
The trajectory is equally striking at the market level. Gartner projects that AI laptops already account for 51% of total laptop shipments in the current year, with AI PCs expected to surpass 50% of all PC sales and grow to represent 94% of all PCs in use by 2028. Large enterprises, Gartner notes, will likely have no option but to buy AI PCs — as vendors phase out standard non-AI models entirely.
Why Enterprises Are Moving AI to the Device
The case for on-device AI processing — rather than relying solely on the cloud — is growing stronger by the month, driven by three core priorities that enterprises consistently rank at the top of their AI agendas.
Security and privacy lead the charge. According to Forrester Research, 55% of US adults specifically value the fact that AI PCs keep private interactions on-device rather than transmitting data to the cloud. For enterprise IT leaders, this directly addresses their top concern: keeping sensitive business data out of shared cloud environments.
Productivity is the second major driver. Among enterprises adopting AI PCs, 59% cite productivity gains as their primary motivation, followed by innovation and competitive differentiation (39%) and stronger security (35%). AI capabilities are now embedded directly into operating systems — such as Windows 11's Copilot — and into major productivity suites like Microsoft Office and Adobe Creative Suite, enabling employees to leverage AI without switching between applications.
Latency and cost round out the picture. Running AI workloads entirely in the cloud introduces latency, concurrency challenges, and escalating cost-per-query concerns. On-device processing via dedicated Neural Processing Units (NPUs) addresses all three, enabling faster, more responsive AI experiences without the overhead of constant cloud round-trips.
"The question for businesses is which AI PC to buy rather than should they buy one. Businesses will purchase AI PCs for future-proofing and because this is their only choice that offers a more secure and private computing environment."— Ranjit Atwal, Senior Director Analyst, Gartner
Agentic AI: The Next Frontier for Enterprise PCs
Beyond conventional AI tools, enterprises are now preparing for the next wave: agentic AI — systems that can autonomously plan, execute, and adapt tasks in real time without constant human instruction. IDC research shows that 70% of organisations expect agentic AI to influence employee workflows within the next two years.
For agentic AI to work effectively at the enterprise level, it needs powerful local compute. The PC is evolving from a simple productivity device into a local execution layer — capable of processing context-aware, real-time AI tasks directly on the device. This architectural shift is driving demand for systems built specifically to handle these emerging agent-driven workloads, with NPUs, high-bandwidth memory, and AI-optimised software stacks becoming standard requirements.
Gartner further predicts that by the end of next year, 40% of software vendors will focus on AI built specifically for PCs — up from just 2% in 2024. Small Language Models (SLMs) running locally on devices are expected to play a central role, offering fast, efficient, and highly customisable AI capabilities without cloud dependency.
From Experimentation to Enterprise-Wide Deployment
Enterprise AI strategy is maturing rapidly. After years of isolated pilots and proof-of-concept projects, organisations are now embedding AI more deeply across their operations — a shift analysts describe as moving from the "year of proof" to the "year of scale." AI is no longer confined to IT or innovation teams; decisions about data, governance, and security now directly shape business outcomes at every level.
Enterprise AI spend is also consolidating. Rather than testing multiple tools simultaneously, organisations are rationalising their AI investments — cutting experimentation budgets, eliminating overlapping tools, and concentrating resources on the platforms that have delivered proven returns. This means a smaller number of AI vendors capturing a larger share of enterprise budgets.
Successful AI PC integration, however, requires more than new hardware. Experts consistently emphasise the importance of employee training, workflow redesign, and governance frameworks to ensure AI tools are used effectively, responsibly, and in compliance with data regulations. Companies that invest upfront in these foundations are best positioned to unlock lasting competitive advantage.
What Enterprises Should Do Now
Industry analysts are clear on the strategic imperative: enterprises should take a proactive approach to AI PC adoption by aligning hardware upgrades with broader AI strategies — not treating them as isolated IT refresh decisions. This means planning for enterprise-wide deployment that supports productivity, security, and long-term scalability rather than simply replacing ageing devices.
Investing early in AI-capable devices allows organisations to prepare their workforce for more advanced use cases, including autonomous and agent-driven AI — positioning them ahead of competitors still waiting for the technology to mature. Those that delay risk being locked out of the most capable hardware at the best price points, as supply tightens and the AI PC market accelerates.