The global AI market is currently worth around $300 billion, according to Fortune Business Insights, with businesses worldwide adopting AI to gain operational and strategic advantages.
However, adoption is only part of the story. Success also comes down to value realisation. Research from BCG shows that only around 5% of companies are meaningfully capturing value from AI. This “AI performance gap” indicates AI must move from experimentation to implementation. Targeted investments in the right tools are now vital to success.
The crucial role of AI at the edge
As business AI deployments mature, focus is increasingly shifting from AI models (AI systems trained to recognise patterns, make predictions, or generate outputs) to inferencing models (optimised versions of AI models deployed to make real-time operational decisions). Analysis by Brookfield suggests that as much as 75% of future AI compute demand will come from inference by 2030.
As AI moves from training to execution, the case for investing in AI endpoints mounts up. AI PCs bring intelligence to the edge, enabling fast, secure, and energy-efficient AI experiences without relying solely on the cloud.
Powered by components such as AMD Ryzen™ processors, which integrate high-performance CPU, GPU, and dedicated NPU capabilities, AI PCs can run local inference for tasks including real-time language processing, image enhancement, and predictive analytics.
Endpoints vs. the cloud
These devices are crucial to the future of AI for several reasons. First, running AI in the cloud consumes huge amounts of energy. Moving inferencing workloads to more energy-efficient NPUs can help reduce the total carbon footprint of AI systems.
Corbett Hoxland, Chief Technologist at HP, commented: “AI PCs use NPUs that are custom-made for AI workloads and are much more power efficient than datacenter GPUs or CPUs. Given that inferencing accounts for around 90% of an AI model’s total life cycle energy use, moving these workloads to the edge would go a long way toward making AI sustainable.”
Security is another critical driver. When AI relies exclusively on remote, cloud-based datacenters, businesses must accept that growing volumes of sensitive data are processed beyond their direct control. From a data-sovereignty standpoint, this raises fundamental questions about where AI data resides, who governs access to it, and how it is protected throughout its lifecycle.
AI PCs, which are equipped with on-device processing and secure local inference capabilities, can directly address these concerns. With a dedicated AMD Ryzen™ AI hardware accelerator, for example, AI PCs including the latest HP EliteBook devices, can process data rapidly without relying on external servers. On-device inferencing helps keep sensitive business data local, protected, and compliant.
Making the move to AI-enabled devices
If businesses are to avoid being locked into cloud-centric AI systems, they should start work on their PC migration strategies sooner rather than later.
Here, insights from digital experience platforms such as HP’s Workforce Experience Platform (WXP) can help. These tools provide data on how employees use devices and applications; insights that can help IT leaders create prioritised roadmaps for their AI PC deployments. By focusing first on user groups that will benefit most from AI PCs, IT leaders can ensure that their investments start paying a return from day one.
To date, AI has largely been focused on the cloud. This trend will change rapidly over the months and years ahead. To future-proof their AI investments, it’s therefore crucial that businesses start at the endpoints with AI PCs. Doing so will unlock greater value and help organisations meet their security and sustainability requirements.
Learn more on how HP and AMD can help optimise your AI investments.