Lenovo Group released its full-year and fourth-quarter results for its latest fiscal year before the Hong Kong market opened on May 22. The results exceeded market expectations, according to IPO Zaozhidao. Full-year revenue reached RMB 589.9 billion (USD 86.8 billion), up 20.3% from a year earlier, while adjusted net profit rose 42.1%.

Fourth-quarter revenue was nearly RMB 150 billion (USD 22.1 billion), up 27.1% year-on-year and the highest fourth-quarter revenue Lenovo has reported. Adjusted net profit doubled, while net profit under HKFRS (Hong Kong financial reporting standards) was nearly six times the level recorded in the same period last year.

Lenovo’s Hong Kong-listed shares rose more than 11% within about 15 minutes of the market open on the day the results were released. By the close on May 22, the stock was up nearly 20%, bringing its market capitalization to almost HKD 200 billion (USD 25.5 billion). Compared with its low in mid-April last year, Lenovo’s market value has nearly doubled. Its share price has risen more than 70% so far this year. In April, it was the second best performer among Hang Seng Tech Index constituents, outperforming the index by 21.5 percentage points.

Part of the rally reflects Lenovo’s revenue and profit growth, stronger expectations for enterprise artificial intelligence adoption, and continued buying through southbound trading channels, which allow mainland Chinese investors to invest in Hong Kong-listed stocks. Lenovo said full-year AI-related revenue grew 105% from a year earlier. In the fiscal fourth quarter, AI-related revenue rose 84% and accounted for 38% of total revenue.

The more important shift is happening inside Lenovo’s Infrastructure Solutions Group, or ISG.

For fiscal 2025/26, ISG’s full-year revenue exceeded RMB 136 billion (USD 20 billion), up 32% from a year earlier and setting a record for the business. Its operating profit improved by about RMB 1 billion (USD 147.1 million).

The improvement suggests Lenovo is finding better economics in AI infrastructure as enterprises move from testing AI tools to deploying production systems. Its “AI factory” model groups servers, storage, networking, liquid cooling, software platforms, and services into integrated deployments, rather than selling servers and storage as isolated components.

That matters because enterprises are no longer just buying computing power. They are also trying to use tokens more efficiently, protect data, shorten deployment cycles, and link AI systems to measurable business outcomes. For Lenovo, that shift could help move ISG toward higher-value systems and recurring service revenue.

AI moves from pilots to production

DeepSeek’s rise in early 2025 broadened corporate interest in AI, especially in China. In the months that followed, enterprise deployment often remained at the proof-of-concept stage. Individuals and teams bought AI applications for fragmented needs in customer service, office work, coding, marketing, and data analysis.

By early 2026, as large models became more useful for coding and workflow automation, AI became more credible as a productivity tool for business operations.

Token consumption also grew sharply. OpenRouter said it analyzed more than 100 trillion tokens from real-world large language model interactions. In China, token calls among domestic large models stood at about 100 billion per day in early 2024. By March this year, they had exceeded 140 trillion per day, representing growth of more than 1,000 times in two years.

That growth points to a change in how AI is used. It is moving from occasional queries to continuous production. The infrastructure bottleneck is also shifting, from raw computing power to inference efficiency, energy control, and system operations.

According to Deloitte’s 2026 enterprise AI report, the share of employees with access to AI rose 50% in 2025. The report also found that the share of companies with at least 40% of AI projects in production is expected to double within six months.

Once AI moves from individual tools to company-level deployment, enterprises must manage more than standalone applications. They may need multiple agents and models to run together reliably. Fragmented procurement can quickly create problems, including insufficient computing power, slow response times, weak data protection, high maintenance costs, and unstable performance. These risks are greater in agentic AI, where systems must plan, call tools, generate content, and complete tasks in closed loops.

Lenovo’s AI factory approach is designed for that environment. Under Nvidia’s definition, an AI factory is dedicated computing infrastructure that manages the AI lifecycle, from data acquisition, training, and fine-tuning to high-throughput inference. Its output is intelligence, with tokens, the units of text processed or generated by AI models, generated per second serving as one performance metric.

For enterprise buyers, that changes how infrastructure is evaluated. Rack count, server scale, and utilization still matter, but companies are increasingly focused on token output per unit of power, inference latency, model launch efficiency, and whether AI agents can complete business tasks reliably.

Lenovo’s hardware and software stack

Lenovo’s current product and service lines map onto this shift. Its AI factory model combines hardware, software, and services for enterprise AI deployment.

On the hardware side, Nvidia’s GB300 NVL72 rack-scale solution is part of Lenovo’s AI infrastructure portfolio. These systems are designed for high-density AI training and inference, with significant requirements for integration, cooling, power management, and supply chain execution.

Lenovo said products built on the Nvidia GB300 NVL72 platform were fully shipped during the fourth quarter. It completed the first batch of GB300 NVL72 rack solution deliveries in the previous quarter. A platform based on Nvidia’s Rubin architecture is advancing as planned and is targeted for market release in the second half of this year. The platform is intended to shorten time to first token and support scaled deployment by customers.

Inference is likely to be closer to day-to-day enterprise AI use than training. As AI agents spread across customer service, office work, coding, industrial, financial, and medical use cases, demand for low-latency and high-throughput inference infrastructure should continue to rise. Lenovo has launched AI inference servers including the ThinkSystem SE455i, SR650i, and SR675i for enterprise inference workloads.

Liquid cooling is another part of Lenovo’s AI infrastructure offering. Revenue from Lenovo Neptune liquid cooling rose 300% from a year earlier in the previous quarter. Lenovo’s latest results show that its annual server manufacturing capacity exceeds 70,000 racks across AI, compute, and storage systems, including more than 11,000 direct liquid-cooled racks designed for AI workloads.

For AI data centers, liquid cooling affects energy consumption, deployment density, and operating costs. Lenovo said Neptune liquid cooling can help data centers bring power usage effectiveness below 1.1 in high-intensity AI computing environments.

In storage, Lenovo’s acquisition of Infinidat, completed in early April, strengthened ISG’s high-end enterprise storage capabilities.

The software layer is also becoming more important. Lenovo Group executive vice president and China president Liu Jun said ISG’s growth has been driven not only by hardware, but also by the Wanquan platform, which can centrally manage and schedule diverse computing resources. Under the same hardware conditions, it can help customers improve performance by more than 20%, according to Liu.

In compute-as-a-service, Lenovo TruScale has become a growth driver for the Solutions and Services Group, or SSG. It shifts traditional hardware procurement into a subscription model based on usage, addressing cost pressure, idle resources, and technical complexity.

In endpoint AI applications, the Qira personal AI agent supports cross-device collaboration and task transfer. It is part of Lenovo’s effort to move beyond hardware into personal AI interfaces, while supporting its broader hybrid AI strategy across devices, edge environments, cloud systems, and private enterprise infrastructure.

Ken Wong, Lenovo Group executive vice president and president of SSG, said during an earnings call that the primary purpose of the AI factory is to improve AI production efficiency. It can significantly reduce the cost of each token while increasing the business value generated by each token.

“This means Lenovo is upgrading into an end-to-end AI service provider,” Wong said. “It can help customers truly build AI compute systems, bring AI into real business scenarios, continuously optimize operations, and ultimately turn AI into business value.”

ISG’s next test is profitability

Lenovo’s AI factory model is contributing to ISG’s revenue and profit recovery. It also shows how Lenovo is trying to shift more of its profit base from one-off hardware sales toward higher-value infrastructure and services.

A Huatai Securities research report cited by IPO Zaozhidao said one reason for ISG’s profit recovery is its expansion from cloud service providers to higher-margin enterprise and small and midsize business customers. Compared with cloud service providers, these customers tend to have stronger demand for bundled solutions and may offer more opportunities to integrate Lenovo’s SSG services.

At Lenovo Tech World 2026, the company highlighted its global manufacturing footprint and supplier relationships as competitive advantages.

That matters in an environment where memory, CPUs, and other key components remain in tight supply. A resilient supply chain can help Lenovo improve order predictability and manage logistics costs by shifting production across its global factory network. That capability may be harder for pure ODMs (original design manufacturers) to replicate. For cloud vendors and enterprise customers, predictable delivery can be as important as price.

Lenovo could also gain market share and improve margins during the current storage and CPU supply upcycle if it secured inventory earlier than competitors.

The link between ISG and SSG is central to Lenovo’s strategy. In fiscal 2025/26, SSG revenue exceeded USD 10 billion for the first time. Its operating profit margin reached 22.4%, its revenue growth was about four times the overall IT services industry growth rate, and it recorded year-on-year growth for 20 consecutive quarters.

For Lenovo, AI infrastructure can become more than a hardware sale. ISG can bring in customers through AI factory deployments, while SSG can add managed services, software, and ongoing support. That structure gives Lenovo a clearer path to recurring revenue if customers continue to expand their AI systems.

This article was adapted based on a feature originally written by MD and published on IPO Zaozhidao. KrASIA is authorized to translate, adapt, and publish its contents.

Note: HKD, RMB figures are converted to USD at rates of HKD 7.83 = USD 1 and RMB 6.80 = USD 1 based on estimates as of May 26, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates.