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Article source:：PChome

Canalys today released its latest analysis of Intel's ambitious AI PC adoption plans in the coming years. At its recent Intel Vision 2024 conference, Intel laid out an ambitious plan to reinvigorate the personal computer (PC) market ahead of the biggest replacement in decades. Intel has undergone a transformation from a chip company to a one-stop systems company, and Canalys believes that Intel is trying to convince customers that making AI ubiquitous is the only way to stay ahead of the competition.

AI is fueling the biggest PC refresh in decades, driven by unprecedented installs and age, the looming software obsolescence due to the imminent demise of Windows 10, and the growing demand for AI capabilities across client devices, edge infrastructure, and data centers. These factors will trigger a wave of PC updates over the next 24 months - both Oems and chip giants are betting heavily on AI to inspire a sense of urgency among consumers to update their devices.

Ecosystem expansion and scale are Intel's biggest strengths

To drive adoption of its AI hardware, Intel has launched an AI PC Acceleration program designed to engage hundreds of independent software vendors (ISVs), hardware vendors, and developers. The goal is to ensure that the new generation of AI applications running on Intel processors have strong software compatibility and optimized performance.

The plan is to bring more than 300 AI-centric features to market by 2024, which will primarily focus on collaboration and design/creative tools, but also include specialized tools such as improved mobile device management and threat detection. Intel's key advantage in this regard is scale, and 80% of the PCS in use today have Intel chips. As a market maker, Intel is in a better position to collaborate in the ecosystem because these ISVs, which must prioritize optimized systems, are more likely to choose partners that hold the largest share of PC installations and have an entrenched history in the industry. Intel plans to ship more than 100 million Core Ultra chips within two years and sees the program as a key driver.

While this leads to a proliferation of AI workloads running locally on the PC, there are some limitations. Many features cater to mass market needs, such as the benefits AI brings to collaborative applications. However, initial adoption is more focused on niche markets of interest to budget holders concerned about total cost of ownership, security, and manageability, rather than end users. In addition, productivity-focused software providers, including Microsoft, are still developing their own end-to-end AI applications. Given that the average brain worker spends most of their time using Office 365 applications, once Microsoft integrates some of the new features on the end side, the adoption rate of AI PCS will increase significantly.

Intel's AI PC strategy comes after several years of declining market share in key markets. Since Apple began transitioning from Intel chips to homemade chips in 2020, Intel has lost 6% of market share in notebook shipments to enterprise customers. AMD and Qualcomm are also stepping up to compete with Intel in the business PC space. Despite the fierce competition, Intel still has a strong influence, with 80 percent of enterprise laptops using Intel chips, and in other market segments, Intel's share has been relatively stable.

One-stop services make AI ubiquitous

Intel's transition from a pure chip vendor to an end-to-end systems company has significant implications not only for the PC market, but for the broader ecosystem of businesses, developers, and consumers. Intel's AI PC strategy is driven by the belief that an open ecosystem and "small" custom language models will be key to enabling transformative AI experiences.

In addition to providing AI PCS, Intel also offers on-premises solutions for edge computing for enterprises, namely AI nodes and clusters, as well as "super clusters" for data centers. At the heart of the product is Intel's new Gaudi3 AI accelerator, which will be a major product for building AI at scale.

In addition to building cutting-edge hardware for AI applications, Intel has also developed retrieval Enhanced Generation (RAG) capabilities that allow businesses to apply AI models on their own data. Intel's new edge platform, combined with the Gaudi3 AI accelerator, will provide a solid foundation for businesses looking to leverage their own data and AI models in end-to-end devices or servers.

Highlight unmet and defined customer needs

There is no doubt that Intel's vision for the future of the PC is ambitious and forward-looking. By injecting AI capabilities into their hardware and software portfolio, the company is committed to positioning the PC as an important strategic asset for businesses, helping them navigate the changing computing landscape.

At the heart of Intel's strategy is the assumption that every business leader wants to integrate AI into their entire organization. Most companies, especially large enterprises, see AI as one of the most transformative technologies, so Intel's plan is fairly sound. However, there are also some obstacles and potential after-sale hazards in the adoption process that must be addressed.

A good product should meet the unmet and undefined needs of the customer. For now, both customers and sellers are largely unclear about the value that AI PCS could create. Customers don't know exactly what AI can do and what quantifiable benefits can be derived from deploying AI. So far, the PC industry as a whole has not been able to adequately answer these questions.

To truly take advantage of the upcoming PC refresh supercycle, vendors and their partners will have to do much more than further showcase the performance and hardware capabilities of their AI PCS. Participants must be able to articulate a clear and compelling direction that resonates with businesses across industries, and should be able to accurately map how AI PCS can actually drive business outcomes, streamline workflows, and increase productivity and innovation. At the same time, vendors also need to focus on reducing the ownership cost of AI PCS, improving security, and improving manageability.