HCLTech’s AI alignment and Foundry depth set the stage for a pivotal 2026

During an October update from HCLTech executives about the company’s overall AI strategy, TBR noted two areas that should differentiate HCLTech from its peers in the IT services and consulting space. Given the rapidly changing nature of the AI ecosystem, disruptive changes coming to IT services and consulting organizations, and broad uncertainty about AI adoption in the near term, any advantages HCLTech develops and leverages will be critical to the company’s ability to sustain market-leading growth.
 

Aligning alliance, acquisition and industry strategies around AI and then executing on that alignment should be one of HCLTech’s strengths. Taking full advantage of the breadth and depth of HCLTech’s AI Foundry should be another. HCLTech’s efforts in these areas have positioned it well in a wildly fluctuating market. TBR has written extensively about challenges in the AI space, particularly for IT services companies and consultancies. Based on TBR’s research, 2026 should be a pivotal year for HCLTech.

A tightly coordinated approach across acquisitions, alliances and industries sets HCLTech apart in AI

Acquisitions, alliances and industry strategies align in market-leading IT services companies, particularly around emerging technologies such as AI. Looking at HCLTech’s activities over 2025, TBR sees a layered approach to expanding AI-enabled solutions and offerings, guided by strategic decisions within acquisitions, alliances and industries. For example, in the last year the company acquired Nuance as well as Hewlett Packard Enterprise’s (HPE) Communications Technology Group, bolstering HCLTech’s AI capabilities and complementing previous acquisitions, such as Zeena and ASAP. Given the company’s modest acquisition pace relative to peers such as Accenture and Cognizant, by favoring AI-related acquisitions HCLTech has demonstrated its overall AI strategy encompasses an acquisition-forward approach.
 

In concert with those acquisitions, HCLTech has also increased the depth and breadth of its AI technology partnerships, most notably across its ecosystem of hyperscalers as well as Databricks and Snowflake. In presenting the company’s alliances strategy, HCLTech executives noted “best-of-breed partnerships” across applications & products, models, data, infrastructure, and physical AI. Although each partnership may not be singular in the highly competitive IT services and AI market, TBR believes HCLTech’s strategy of choosing specific, named partners and going to market together with joint offerings and solutions provides some differentiation from the traditional technology-agnostic approach. HCLTech takes it a few steps further by including startups and academic institutions in the company’s overall alliances strategy. Extending an understanding of the ecosystem beyond just the largest commercial players echoes HCLTech’s decision to expand its AI alliances strategy across acquisitions and industries.
 

“We’re actually building some very innovative industry solutions, going beyond the horizontal data analytics and AI value chain. Our plan, obviously, is to expand the industry-focused solutions so that you shift from the horizontal value chain of data analytics and AI to more packaged industry solutions that our customers can deploy faster to create the value with AI.” — HCLTech Executive, October 2025

Rounding out the trio of aligned strategies, perhaps with AI at the center of a Venn diagram, HCLTech’s industries strategy reflects the influence of alliances and acquisitions. In a recent presentation to TBR, HCLTech executives highlighted use cases across financial services, manufacturing, utilities, and life sciences & healthcare, tracking closely with the company’s largest and fastest-growing industries. In a coordinated approach across acquisitions and alliances, HCLTech has launched at least eight AI-enabled solutions specifically attuned to HCLTech’s ecosystem partners, such as InsightGen, an Amazon Web Services (AWS)-based tool for financial services clients. Further, HCLTech has established AI-enabled HCLTech Industry Focused Repeatable Solutions, demonstrating the company’s emphasis on playing to its strengths and developing additional revenue growth from its top industries.
 

In TBR’s view, HCLTech’s overall AI strategy benefits from a coherent and aligned story around alliances, acquisitions and industry plays. AI-enabled solutions, AI factories, AI platforms and AI partnerships have become table stakes for every scaled IT services company, making differentiation nearly impossible on those three areas alone. Orchestrating acquisitions and industry strategies along with alliance partners to help them sell customers on HCLTech’s value can truly become differentiators as part of the company’s long-term plan. In the near term, keeping these elements aligned will be essential for 2026 growth.

HCLTech’s AI Foundry emerges as a full-stack engine for talent, trust and future AI growth

AI Foundry was launched more than a year ago, and HCLTech now considers it a cornerstone of the company’s overall AI program, as noted in the recent HCLTech presentation to TBR. One HCLTech executive said the “evolved” AI Foundry has become about “managing the data-to-dollar value chain better. It’s powered by robust, world-class infrastructure that is hybrid and multicloud, giving customers flexibility to use HCLTech software or other ecosystem tools.”
 

“Legacy — and maybe more accurately, proven — skills and capabilities lend immediate credibility to what HCLTech brings to clients and partners with AI Force. Further, a significant part of what separates HCLTech from immediate peers is the company’s IP-driven services model, a strategic difference that becomes increasingly relevant as clients ask for more GenAI-enabled services and less labor-dependent services. HCLTech’s business model is not simply enhanced by AI Force and other IP-driven solutions; it might actually be saved by those capabilities as the entire IT services business model undergoes significant, AI-induced change.” — TBR special report, HCLTech AI Force: Scalable, Modular and Backed by Proven AI Expertise

With components including business templates, AI agent, AI policies & governance, and prebuilt analytics & AI utilities, HCLTech AI Foundry appears to capture nearly every aspect of an enterprise client’s AI needs. As an HCLTech executive said during a recent briefing, AI Foundry is “unique because it leverages specialized AI agents for different personas across the data life cycle, enabling automation, actionable insights and significant productivity improvements.” TBR cannot assess the uniqueness of HCLTech’s AI Foundry but notes that building this capability brings three immediate benefits to HCLTech.

• First, AI Foundry’s completeness sends a clear message to clients and technology partners that HCLTech has the commitment to and the capabilities in AI to be a trusted, scale-delivering partner and provider. In a hype-filled market around an ever-changing emerging technology, the most compelling stories include breadth and depth, which AI Foundry offers.
• Second, a high-functioning AI Foundry should help HCLTech recruit and retain AI talent. Every discussion TBR has with IT services companies, consultancies, technology decision makers, startups and ecosystem players includes a lament about the lack of AI talent. Demonstrating leadership commitment and a sustained willingness to invest should bolster HCLTech’s attractiveness to potential and existing talent.
• Third, AI Foundry helps position HCLTech for growth. Four years ago, enterprises were not investing in generative AI (GenAI) licenses, and two years ago, IT services companies were selling robotic process automation but not robots (agentic AI). In a year, a new evolution of AI could reignite the AI hype cycle, and HCLTech is well positioned to take advantage given the completeness of its AI strategy, including AI Foundry. As one HCLTech executive recently said, “Our entire AI strategy is full stack … from physical AI to eventually the AI product, apps and application integration.” TBR would add that governance, prebuilt utilities and ecosystem tools within AI Foundry underscore how “full stack” HCLTech’s AI strategy has become.

2025 Brooklyn 6G Summit, Brooklyn, New York, Nov. 5-7, 2025 — More than 300 in-person attendees and 1,600 virtual attendees from academia, technology standards bodies, the public sector, industry analyst firms, network infrastructure and device vendors, communication service providers (CSPs), satellite network operators, semiconductor firms, hyperscalers and other stakeholders of the broader wireless technology ecosystem gathered at the Tandon School of Engineering at New York University in Brooklyn for a mix of presentations, fireside chats and panel discussions regarding the future of wireless networks, with a focus on 6G. There were more than 35 exhibits on display from graduate students at various U.S. universities as well as from a range of telecom ecosystem entities showcasing 6G-related innovations. A broad range of other 6G-related topics were discussed, including the role of AI and machine learning in networks, sustainability (especially related to energy), standards development, spectrum policy, public-private collaboration, and nonterrestrial networks’ role in the ecosystem. The 12th annual event was hosted by Nokia and NYU WIRELESS. This year’s event provided the usual cellular ecosystem updates, but one of the major themes was how the telecom industry should approach converting wireless innovation into value, especially economic value. Bell Labs’ 100th anniversary was also commemorated at the event.

TBR perspective

There is no doubt that the global economy is in the midst of an AI super cycle. What is doubtful, though, is whether the telecom industry will rise to the occasion to support (and monetize) opportunities that arise from AI. There are several fundamental challenges endemic to the telecom industry that could keep it from participating in the AI economy in a significant way, and TBR believes the AI economy could disintermediate at least a portion of the telecom industry.

The AI ecosystem currently operates in 18-month innovation cycles, and the telecom industry remains mired in its decade-long generational cycles; this dissonance will create friction and could be the biggest impediment to the convergence of the AI economy with the telecom industry. Will the rapidly evolving AI ecosystem wait for the telecom industry to support it, or will it be forced to move beyond telecom incumbents and institutions to avoid being held back?

One thing is certain: AI will fundamentally change how networks are utilized, necessitating a new network architecture. The telecom industry, which includes the cellular ecosystem, moves incredibly slowly, and this slowness is diametrically opposed to how the AI ecosystem (and other tech theme areas) moves. Something is going to break, and the real questions are what and when.

Impact and opportunities

A new network architecture is required for AI, as current networks will not suffice

One key aspect of AI workloads, especially those emanating from end-user devices, is that they are uplink-intensive, meaning they rely more heavily on uplink resources from the network than on downlink resources. This is a fundamental issue because macro, cellular-based networks are optimized for downlink capacity (typically a 10-1 downlink-uplink ratio from a resource-allocation perspective). CSPs will need to make significant investments in new network technologies and rethink how spectrum resources are utilized, to optimize networks for uplink.

AI traffic also tends to require lower latency than current networks and can support higher bursts of traffic than video and other media consumption. AI networks require uplink bandwidth, lower latency (compared to current networks) and the ability to handle higher bursts in traffic patterns at scale, and none of these requirements can be achieved just by increasing capacity. These requirements are the opposite of how networks are architected today — optimized for downlink, best-effort or good-enough latency, and optimized for more predictable traffic patterns — necessitating significant investment by CSPs. This will be a gradual transition, as there is no silver bullet to address this problem quickly. The best approach seems to be decoupling the downlink from the uplink for transmit power differentials, which would enable network resources to dynamically adapt to traffic demands in real time. Additionally, there is concern as to how willing CSPs will be to invest in uplink when ROI is uncertain.

6G will primarily be a software upgrade

Unlike prior Gs, 6G is unlikely to be a massive hardware refresh, but will instead build on top of existing 5G RAN and 5G core (the 5G SA [standalone] architecture) infrastructure as a software upgrade. This is the most likely outcome as CSPs are highly unlikely to have the appetite or the financial wherewithal to invest in another massive network refresh with unclear ROI.

There will be some hardware elements to invest in, such as processing capability that enhances programmability and maximizes AI support, or radios that support new frequencies not covered by existing RAN via software-defined radio technology, but hardware investments will be a fraction of what was spent in prior cellular generations. As a result, TBR expects a shallower capex curve during the 6G cycle, more closely resembling the 5G Advanced spend curve thus far.

Trump likes 6G

The Trump administration has designated several technologies and resources as strategic national priorities. Much as he did during his first administration, designating 5G as a national strategic priority, Trump views 6G as equally, if not more important. 6G will be leveraged for a broad range of defense and public safety, as well as societal use cases, which makes investment and success with the technology a public issue. Integrated sensing and communication (ISAC) is a 6G-related use case of particular interest to the U.S. government, for national security considerations. The government is heavily involved in 6G R&D-related endeavors, both directly and indirectly, through proxy programs such as the Defense Advanced Research Projects Agency and the U.S. National Science Foundation, as well as government agencies such as the U.S. Department of War.

TBR expects the Trump administration to apply similar approaches to seeding and bolstering U.S. innovation in 6G, much like it has in the production of rare earth metals, AI, nuclear power, and other key technologies. As part of this, it is reasonable to expect U.S. government equity investments and policy support. The recent Nokia deal with NVIDIA and the U.S. and Nokia agreement both align with this policy.

Cellular standards fracturing goes beyond geopolitics

Cellular standards are not only being regionalized due to geopolitical and nationalistic considerations but are also showing cracks within regional ecosystems. For example, non-standardized technologies are increasingly coexisting with 3rd Generation Partnership Project (3GPP)-based cellular technology. Standards in cloud data centers, optics, chip design and end-user devices are fragmenting, creating technological walled gardens.

For example, hyperscalers now have enough global scale to justify pursuing their own technology standards, and it is reasonable that these companies could, at some point, rival or eclipse traditional standards bodies, such as the 3GPP, in ecosystem influence and market power. Potential catalysts for a change such as this could include the need for AI-native networks (thereby reducing dependencies on CSPs for infrastructure investment and innovation road map alignment) and the need to support the rapidly evolving AR/VR market, which holds multitrillion-dollar revenue potential for hyperscalers.

Unlocking stranded spectrum assets: A prerequisite for 6G leadership

The mobile industry continues to beat the drum for more spectrum, but it should instead focus on fully utilizing the spectrum already allocated. TBR notes there are vast tranches of spectrum in the U.S. market that are broadly underutilized, either for technical or economic reasons. And challenges will only worsen as the industry aims to bring upper midband frequencies into the fray, which have greater propagation challenges and are less suited for macro coverage.

The U.S. needs to do a better job, guided by government institutions like the Federal Communications Commission, of utilizing CBRS, C-Band, 6GHz and mmWave bands, which are woefully underutilized today. For example, only a relatively small portion of midband spectrum has been deployed in the U.S. market to date, implying that well over half of it has not yet been put to use. (CSPs are either sitting on it or hoarding it.) Most 4G and 5G network traffic in the U.S. today runs over low bands such as 600MHz to 800MHz and the lower midband (1GHz to 2.6GHz, especially 2.5GHz), with C-Band increasing but nowhere near its full utilization potential. MmWave bands hold promise, but for economic and technical reasons, they were used only in very specific situations, mostly for LAN capacity.

Additionally, spectrum warehousing entities like EchoStar (which recently reluctantly agreed to sell a portion of its vast spectrum holdings to Starlink and AT&T) and private equities are still sitting on large tranches of unused spectrum. The government should redirect its efforts toward addressing these market dislocations rather than straining to bring new spectrum to a market that might not even be used (case in point, CBRS). This should be a mandatory government push because dislocations created by negative externalities of capitalism threaten to keep the U.S. behind China in key technological domains; specifically, corporations with a scarcity mindset are hoarding, but not necessarily using, the spectrum resources they have. The U.S. is already behind China across several key technologies (e.g., 5G, hypersonic missiles, electric batteries and vehicles, rare earth element processing); it is unacceptable for the U.S. to also fall behind on 6G.

TBR believes that the spectrum already allocated to CSPs will be deployed once the business case for its use is secured. Spectrum resources will come out of the shadows once ROI is clear. The government can help in this regard.

Data protectionism is stifling innovation and holding back the U.S.

A major limitation for academia and the broader mobile ecosystem is the lack of raw data to leverage for innovation. Data is viewed as a strategic and valuable asset, and the reality is that companies and governments do not want to share their data for various reasons. This limits academia, research institutions and other companies in what can be used for educational purposes as well as their ability to innovate by training their models and fine-tuning their technologies. Here again, China has an advantage over the U.S. because its authoritarian model can accelerate the pace of innovation in targeted areas.

Conclusion

The AI and cellular ecosystems are moving at different speeds rather than converging onto parallel tracks, which is a major issue and could lead to a breaking point for the technology industry. If hyperscalers feel like CSPs are holding them back, they can and will, as history validates, work around CSPs and take greater control over their own destinies.

This could include building their own standards for network infrastructure, much like they have for IT infrastructure and at the endpoint-device level and build out AI-optimized networks far greater in scope than they have been building (see TBR’s Hyperscaler Digital Ecosystem Market Landscape and Hyperscaler Capex Market Forecast reports for more information). Since it is unlikely the telecom ecosystem will fundamentally change, as it is not geared to do so, greater disintermediation and changing competitive dynamics are likely to occur. It is very possible that 6G could be the last “G.”

Lenovo reaffirmed its commitment to Hybrid AI for All during the company’s annual Global Industry Analyst Conference, held Oct. 20 to 23 at the company’s U.S. headquarters in Morrisville, N.C. The conference featured a series of closed-door sessions during which Lenovo executives briefed roughly 70 industry analysts on all things Lenovo, from liquid cooling to agentic AI solutions. Throughout the conference, Lenovo executives provided updates on the company’s overall strategy and ambitions to shift its perception from PC vendor to full-stack, end-to-end solution provider.

From PC vendor to full-stack solution provider

At its core, Lenovo is an engineering company with a particular strength in computing, having acquired IBM’s PC business and later its x86 server business. However, Lenovo’s investments in scope expansion underpin its transformation into a solutions- and services-led technology provider.

Since the formation of its Solutions and Services Group in 2020, Lenovo has not looked back. In 2024 the company announced its Hybrid AI Advantage framework, which serves as a powerful example of how the company’s portfolio is widening and how the company’s culture and go-to-market approach are evolving to emphasize its increasingly solutions- and services-led model and its growing focus on full-stack AI.

However, despite the company’s investments in transformation, Lenovo has retained its core competencies in engineering and manufacturing, leveraging its expertise and global footprint to drive innovation, cost efficiencies and scale. For example, in Lenovo Infrastructure Solutions Group, the company has leaned into its unique engineering and manufacturing capabilities to establish its rapidly growing ODM+ business, which targets cloud services providers.

Additionally, within the Lenovo Intelligent Device Group, the company has leveraged these capabilities to mitigate the impacts of tariffs and drive design innovation in an otherwise increasingly commoditized PC market. The company’s Solutions and Services business acts like a margin-enriching interconnective tissue over the company’s robust client and data center hardware portfolios and a catalyst to move the company further up the value chain.

As a dual-headquartered company (North Carolina and Beijing), Lenovo’s global footprint is unmatched by its hardware OEM peers, and the company’s business in China largely operates in its own silo. China is one of several regional launch markets for early pilots. However, Lenovo follows a local-first, global-by-design approach: Solutions are incubated to meet local data, content and regulatory requirements, and when there’s Rest of World (ROW) demand, Lenovo reimplements features for global compliance rather than porting code or models 1:1. No China-based user data, models or services are reused in ROW products.

Building on this global theme, during the conference Lenovo noted that it is hiring Lenovo AI Technology Center (LATC) engineers across the world, in places like Silicon Valley; Chicago; Raleigh, N.C.; Europe; Tel Aviv, Israel; and China. Additionally, the company highlighted its investment in establishing new AI centers of excellence to centralize and expand regional AI talent and support independent software vendors in their development of industry- and use-case-specific solutions. Rather than making a net-new investment, Lenovo has leveraged this strategy successfully to expand its AI library, a catalog of preconfigured AI solutions ready to be customized and deployed by Lenovo. In addition to industry- and use-case-specific AI solutions, the company also works with regional independent software vendors to develop solutions tailored to the preferences of customers in specific geographies, such as China.

While Lenovo’s portfolio and go-to-market strategy may differ slightly by geography, the company’s pocket-to-cloud and One Lenovo initiatives remain the same around the world and are the basis for the company’s differentiation in the market — a theme during every session of the conference. From smartphones to servers, Lenovo is vying for share in every segment, and by investing in the unification and openness of its portfolio, whether it be through the development of homegrown software or new ecosystem partnerships, the company is positioning to grow in the AI era. Changing its perception from a PC powerhouse to a solution provider remains one of Lenovo’s largest challenges, but the company’s work in sponsoring and supporting FIFA and F1 with its full-stack technology capabilities demonstrates its willingness to invest in overcoming this hurdle.

Lenovo is investing to win in enterprise AI and bring smarter AI to all

Lenovo’s AI strategy spans all three of the company’s business units and echoes the Smarter AI for All mantra and pocket-to-cloud value proposition.

During the conference Lenovo reemphasized its belief that the meaningful ramp-up of enterprise AI is on the horizon as AI inferencing workloads continue to proliferate. Lenovo has high-performance computing roots and its Infrastructure Solutions Group (ISG) derives a significant portion of its revenue from cloud service provider (CSP) customers, in contrast to some of its closest infrastructure OEM competitors, but Lenovo’s investments and the composition of the company’s portfolio emphasize the company’s intent on driving growth through its Enterprise and Small/Medium Business (ESMB) segment, supporting all levels of on-premises AI computing, from the core data center to the far edge.

Additionally, Lenovo continues to go against the grain on the notion that AI workloads belong exclusively on the GPU and makes a case for lighter workloads being deployed more efficiently on the CPU and in smaller edge server form factors. Lenovo’s view is heterogeneous AI: Training and high-throughput inference lean on GPUs; latency-sensitive and personal workloads increasingly run on NPUs and optimized CPUs, and the company’s portfolio spans all three — multi-GPU-ready workstations, edge servers with GPU/CPU mixes, and Copilot+ PCs with NPUs for local inference — so customers can place the right workload on the right engine.

However, perhaps what differentiates Lenovo’s infrastructure portfolio most is the company’s Neptune liquid cooling technology, which comes in three flavors: Neptune, Neptune Core and Neptune Air. Intensive AI and machine-learnings workloads require dense compute infrastructure that, in some cases, generates so much heat it requires liquid cooling as opposed to traditional air cooling. In addition, even less-intensive workloads often benefit from liquid cooling, which generally operates at lower costs once implemented. This is where Neptune liquid cooling comes in.

The company’s flagship Neptune solution offers full system liquid cooling, making it ideal for the most demanding AI and high-performance computing workloads. The company’s other two offerings — Neptune Core and Neptune Air — deliver lower levels of heat removal but are more easily implemented. For instance, while Neptune Air offers the lowest levels of heat removal, the simplicity of the solution makes it easier to implement, especially in existing data center environments, supporting lower cost transitions to liquid cooling.

TBR sees Lenovo’s family of Neptune solutions as a major advantage, as the variety of offerings targets customers and environments in different stages of liquid cooling adoption. Lenovo’s experience in retrofitting data centers with liquid cooling also presents a strong services opportunity for the company and supports enterprise adoption of higher-power AI servers in their on-premises environments. Further, because liquid cooling is more efficient than air cooling, Neptune supports Lenovo’s sustainability initiatives and delivers strong total cost of ownership savings in many scenarios, which is something IT decision makers tend to scrutinize heavily when making investments.

Unlike its close competitors that have invested heavily in data management and orchestration layers leveraging their networking and storage solutions, Lenovo does not play in the data center networking space, instead choosing to be networking-agnostic and partner-first in this area, which the company sees as an advantage due to geographical differences in customer preference. However, the company’s results have yet to prove that this networking strategy is materially advantageous. Additionally, while complex, networking is typically more margin rich than compute and storage while also presenting myriad attach and services opportunities for OEMs with first-party full-stack infrastructure portfolios.

Adjacent to the company infrastructure offerings, during the conference Lenovo executives stated that there should more adoption of workstations as part of enterprises’ on-premises AI adoption and solution development. Compared to sandboxing AI solutions in the cloud, Lenovo sees its workstations, which can support up to four NVIDIA RTX discrete GPUs, as a more practical and economical solution compared to cloud resources. However, in addition to the company’s Windows-based workstations, Lenovo also showed off its NVIDIA DGX Spark inspired desktop geared more heavily toward use in conjunction with NVIDIA DGX cloud.

Rather than running Windows OS, Lenovo’s DGX Spark inspired desktop runs DGX OS, a Linux-based operating system and is ideal for buyers that already have DGX cloud resources. With desktop offerings for AI spanning multiple operating systems, Lenovo’s Intelligent Device Group showcases the company’s ambition to create AI systems for all types of users. Looking ahead, both TBR and Lenovo expect the adoption of GPU-enabled workstations to grow as an increasing number of enterprises experiment with the development and/or customization of preconfigured AI solutions.

Through a services lens, Lenovo’s enterprise AI strategy centers on the company’s Hybrid AI Advantage framework. Similar to frameworks used by competitors such as Dell Technologies and HPE, Hybrid AI Advantage includes NVIDIA AI Enterprise software components intended to allow for the development of industry- and use-case-specific AI solutions. However, while NVIDIA AI Enterprise can be thought of as a collection of foundational tools to build AI agents, Lenovo’s AI library goes a set up further, offering more out-of-the-box types of industry- and use-case-specific AI solutions.

The composition of Lenovo AI library is largely predicated on solutions developed in conjunction with ISVs through Lenovo’s AI Innovators program. As Lenovo expands its footprint of AI centers of excellence, TBR expects the number of customizable, near-plug-and-play AI solutions to grow, further cementing the company’s differentiation in the marketplace. Additionally, Lenovo argues that its Agentic AI Platform further differentiates Hybrid AI Advantage from what is offered by competitors.

Lenovo’s Solutions and Services Group is equipped with strengthening auxiliary services to support customers wherever they are on their AI journey. This is where the company’s Hybrid AI framework comes into play. The framework has five components: AI discover, AI advisory, AI fast start, AI deploy and scale, and AI managed. The first two components underscore Lenovo’s growing emphasis on delivering professional services, while the third component — where many customers enter the framework — is where Lenovo aligns customers with a solution from the company’s AI library. The last two components highlight Lenovo’s ongoing interest in delivering deployment and ultimately managed services through the company’s maturing TruScale “as a Service” business that caters to both infrastructure solutions and devices deployments.

At the end of the day, Lenovo understands that hardware — specifically compute hardware like PCs and servers — is its strength, but by developing prebuilt solutions and overlaying its expanding services capabilities, the company is investing in moving up the value chain to drive margin expansion and deepen customer engagement.

Intelligent Device Group is doubling down on its unified ecosystem play

At 67.3% of total reported segment revenue in 2Q25, Lenovo’s Intelligent Device Group accounts for the lion’s share of the company’s top line, and TBR estimates 88.5% of the segment’s revenue is derived from the sale of PCs. Over the trailing 12-month (TTM) period ending in 2Q25, TBR estimates the company’s PC business generated nearly $40 billion, growing 14.6% year-to-year and resulting in an approximate 130-basis-point expansion in PC market share, according to TBR’s 2Q25 Devices Benchmark.

In line with the company’s ambitions to change its perception from a PC vendor to a solutions provider, and due to the company’s already established footprint in the PC market, much of the general sessions during the conference focused on the company’s AI position, with specific emphasis on Solutions and Services Group and Infrastructure Solutions Group. However, during the Intelligent Device Group briefings, Lenovo executives confirmed the company has no intention of giving up share in devices — the business on which the company’s success has been predicated. Lenovo touted its leadership position in several segments of the PC market, with the largest being the commercial space. Business leaders acknowledged that it is a good time to take share in the commercial Windows PC market, and by investing in the development of proprietary components and feature sets, the company is actively dispelling the notion that the PC market is fully commoditized. Lenovo has continued its engineering collaboration with Microsoft on CoPilot+ PCs.

Beyond the PC, Lenovo’s investments in growing the share of its Motorola smartphone business have been paying off, and the company is not taking its foot off the gas. To drive cross-selling opportunities, the company is deploying a marketing strategy targeting a younger customer base and is leaning into creating a unified device ecosystem integrated with AI features and capabilities, something the company refers to as its One AI, Multiple Devices strategy. However, unlike other unified device ecosystem plays, such as that of Apple, Lenovo’s play is more open, with the company supporting cross-device features between its PCs and smartphones running both Android OS and iOS. Thus far, Lenovo has seen limited traction in cross-selling smartphones and PCs in the commercial space; however, TBR believes the company’s One AI, Multiple Devices strategy could help shift the tide.

2Q25 TTM Windows PC Market Share and Estimated 2025 Lenovo PC Revenue Mix (Source: TBR)

During its Global Industry Analyst Conference, Lenovo’s focus on maintaining and even expanding its leadership position in the commercial PC segment was obvious, but what was perhaps more interesting was how the company is marketing several of its PCs that are in direct competition with Apple in an effort to appeal to a younger segment of the PC market. Lenovo is promoting its device brand as premium, trusted and innovative — aspects supported by the company’s leadership in PC design and engineering as well as its ongoing investments in device security through proprietary software developments. Lenovo also showcased innovative designs, such as motorized expanding screens, and developments down to the motherboard level, which harkened back to the company’s core legacy competencies in engineering and manufacturing.

Through partnerships and portfolio innovation, Lenovo is gradually changing its perception in the industry

Lenovo’s FIFA and F1 partnerships underscore the company’s investments in growing its brand recognition globally and changing its perception from PC vendor to solutions provider. For example, Lenovo infrastructure will power semi-automated offsides calls during the World Cup via computer vision technology. Additionally, Lenovo continues to leverage its Neptune liquid cooling technology as a key differentiator. During the conference Kate Swanborg, SVP, Technology Communications and Strategic Alliances, DreamWorks Animation, discussed how Neptune has allowed DreamWorks to consolidate its data center footprint from 210 air-cooled servers down to 72 liquid-cooled servers.


By leveraging its global engineering and manufacturing footprint in combination with its expanding ecosystem of ISV partners, Lenovo’s emphasis on hardware innovation and supply chain agility aligns with the company’s ever-growing AI library and its establishment of AI centers of excellence, to support Lenovo’s ambitions of driving enterprise AI adoption across all kinds of on-premises environments. Constant investments in IT operations management platforms and unified device ecosystem software demonstrate Lenovo’s focus on driving cross-selling within and across its hardware portfolios while increasing the value proposition behind the company’s TruScale managed service offerings.