Shawn Chang is General Manager of ASUS North America, where he drives consumer and commercial business strategy in United States and Canada.
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As we move further into 2026, the disconnect between AI ambitions and hardware reality is becoming impossible to ignore. Small businesses are downloading the latest AI productivity tools, subscribing to cloud-based AI services and experimenting with automation only to find their existing devices simply can't handle the workload.
The problem isn't just about processing power. Modern AI applications are resource-intensive in ways that extend beyond raw computing capabilities. They require sustained performance during extended processing sessions, stable memory management when handling multiple AI agents simultaneously and reliable thermal performance that doesn't degrade over time. A laptop that was perfectly adequate for email and spreadsheets two years ago is now struggling under the demands of local AI models and continuous cloud connectivity.
Forward-thinking SMBs are now evaluating their hardware through an AI-first lens. Questions have shifted from "Can this device run our current applications?" to "Will this device support AI workloads reliably for the next three to five years?" The total cost of ownership calculation has changed dramatically when you factor in the productivity gains from consistent AI availability versus the losses from frequent failures and workarounds.
The good news is that the market is responding. Manufacturers are now producing devices specifically engineered for AI workloads, with enhanced cooling systems, optimized power management and components selected for sustained rather than burst performance.
In step, according to IDC, "organizations increased spending on compute and storage hardware infrastructure for AI deployments by 97% year-over-year in the first half of 2024, reaching $47.4 billion." This explosive growth reflects a market-wide recognition that existing hardware simply can't support modern AI demands, forcing businesses into earlier-than-planned replacement cycles.
As AI tools demand consistent processing power and uninterrupted operation, modern SMBs need devices that can provide reliable performance. SMBs need to move past basic hardware and investigate adopting hardware that can support dynamic working environments and AI workflows.
If an SMB finds themself with software that overpowers their hardware, it isn't uncommon for hardware to fail, which then directly impacts revenue. In 2024, the average cost of downtime for businesses was $14,056 per minute across all organization sizes, with a 65% increase in per-minute costs from 2022 specifically for organizations with fewer than 10,000 employees. Unreliable hardware doesn't just inconvenience teams; it directly erodes the bottom line, making every infrastructure decision a critical business calculation.
As you evaluate your infrastructure needs, consider where AI fits into your transformation roadmap. If AI is central to your strategy, and for most SMBs it should be, then hardware durability and reliability are prerequisites for success.
Look for advanced vapor chambers and thermal compounds, as well as smarter fan control systems, which allow devices to safely pull more power from modern silicon and do it more quietly. This capability is essential when running multi-agent tools or models locally without throttling.
Most modern hardware has optimized power management, which means the device can sustain AI workloads without rapid battery drain. New NPUs enable low-power local inference for productivity assistants and background agents. More powerful integrated GPUs and discrete graphics support heavier on-device AI workloads and next-generation connectivity like Wi-Fi 6E and Wi-Fi 7, which enables stable, fast access to cloud models.
The SMBs that will thrive in the AI era are those recognizing that transformation requires a solid foundation. You can have the most sophisticated AI strategy, the best-trained team and the most innovative use cases, but if your hardware can't deliver consistent performance, none of it matters. The infrastructure you choose today will either enable or constrain your AI journey for years to come.
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