Spotlight Report Archives

IT Infrastructure Market Forecast

06.11.25/by TBR

TBR Spotlight Reports represent an excerpt of TBR’s full subscription research. Full reports and the complete data sets that underpin benchmarks, market forecasts and ecosystem reports are available as part of TBR’s subscription service. Click here to receive all new Spotlight Reports in your inbox.

Organizations will continue to prioritize spending on AI infrastructure

Growth drivers

Investment in, renewed focus on and adoption of enterprise AI are increasing demand for high-performing infrastructure.
Private and hybrid cloud deployments increase demand for hyperconverged infrastructure form factors.
Organizations are prioritizing investments in denser and more energy-efficient infrastructure solutions to make way for AI.
Edge deployments are creating new-new workload opportunities for OEMs.

Growth inhibitors

The enterprise and SMB spend environment remains cautious and fragile as trade wars erupt.
ODMs are largely capturing cloud growth as they produce low-cost, custom, commoditized hardware for hyperscalers.
Commodity hardware and the popularity of software-defined infrastructure reduce OEMs’ pricing power.
Heightened demand for InfiniBand threatens traditional Ethernet-based networking solutions providers.

IT Infrastructure Market Forecast for 2024-2029 (Source: TBR)

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

TBR predicts that the top 5 covered IT infrastructure OEMs will achieve double-digit revenue growth from 2024 to 2029 but their respective market shares will decline

IT Infrastructure Market Share for 2024 and 2029 (Source: TBR)

Despite shipping over $11B in Blackwell products in 4Q24, NVIDIA is racing to increase production to meet the market’s seemingly insatiable demand for AI servers

Within the OEM market, AI server demand continues to be driven primarily by services providers and model builders, but sovereigns are showing increased interest in OEMs’ AI infrastructure solutions, presenting the OEMs with a major opportunity. Additionally, although enterprise demand for on-premises deployments of AI infrastructure remains soft, especially for the most powerful and thereby highest-revenue-generating systems, the industry expects enterprise AI demand will accelerate throughout 2025 and 2026 as customers pursuing tailored AI solutions increasingly transition from the prototyping phase to the deployment phase.

TBR predicts Dell will lead covered vendors in terms of storage revenue growth due in part to increased attached sales opportunities associated with the company’s growing server business

Key takeaways

TBR forecasts the storage market will grow at a 13.4% CAGR from 2024 to 2029 as organizations across a variety of industries invest in modernizing and hybridizing their storage estates to support current and future workloads, including those related to AI. Organizations’ data volumes will continue to grow over the next five years as the rise of AI further underscores the value behind organizations’ proprietary data.

The storage market typically lags trends in the traditional server market, as is presently the case. However, as organizations increasingly transition from prototyping to deploying AI solutions, data management and orchestration has risen toward the top of key customer pain points. Recognizing this, storage OEMs are selling customers on the capabilities of their storage platforms, comprising software, adjacent services and sometimes hardware. Additionally, storage OEMs are forming partnerships with hyperscalers and other ecosystem players, like NVIDIA, to have their storage solutions validated and certified for operability and AI system reference architectures. TBR believes Dell and Hewlett Packard Enterprise (HPE) are well positioned for growth in storage over the next five years due to their strong data management capabilities and increased opportunities around attaching storage sales to server deals.

In 2024 TBR estimates Lenovo overtook NetApp for the third-largest storage market share among covered vendors. The storage market has become more of a priority for Lenovo in recent years due to the segment’s higher margins, evidenced by the company’s recently announced acquisition of Infinidat. TBR forecasts NetApp will outperform Lenovo in five-year storage revenue CAGR, but Lenovo will retain its positioning in the market among covered vendors.

Storage Revenues and Market Share of Top 5 Vendors for 2024 and 2029 (Source: TBR)

IT infrastructure OEMs are expanding manufacturing capabilities in Saudi Arabia

EMEA market changes and vendor activities

Relative to the U.S., European economies have had more difficulty recovering from the pandemic; however, looking ahead to 2029, TBR forecasts covered vendors’ IT infrastructure revenue derived from the EMEA region will grow at a 12.9% CAGR due in large part to AI. While the EMEA market pales in comparison to that of the Americas, TBR believes the region’s strong growth will be driven both by rising AI adoption — especially among sovereigns — as well as rapidly increasing technology and infrastructure investments in countries like Saudi Arabia.

TBR believes HPE is among the best-positioned covered vendors in the EMEA geography. Sovereigns in the region already have a strong working relationship with HPE due to their legacy investments in high-performance computing based on Cray systems, and the company has made some of the strongest commitments among covered vendors to develop infrastructure manufacturing capacity in Saudi Arabia and the rest of the Middle East.

AI & GenAI Model Provider Market Landscape

06.11.25/by TBR

Interest in AI capabilities has not waned as enterprises view the technology as critical to long-term competitive positioning

The buzz around GenAI persists as enterprise interest is leading to adoption. Yet it is still early days, and many enterprises remain in exploration mode. Some use cases, such as data management, customer service, administrative tasks and software development, have already moved from the proof-of-concept stage to production. Still, the exploration phase of AI adoption will be a slow burn as enterprises seek opportunities beyond these low-hanging fruit. As seen in the graph to the right, most enterprises are evaluating AI qualitatively, forgoing quantitative measures to keep up with peers based upon the assumption that the technology will bring transformational improvement to business operations.

Source: TBR 2H24

Reasoning models excel at performing complex, deterministic tasks, and have become the most popular models at the back end of agentic AI

The capability improvement brought by the iterative inferencing process has made reasoning models the focal point of frontier model research. In fact, most of the models sitting atop established third-party benchmarks are reasoning models, except for OpenAI’s GPT-4.5, which the company stated would be its last nonreasoning LLM. Put simply, the difference in output quality is too pronounced to ignore, especially regarding complex, deterministic tasks. As seen in the graph, reasoning models outperform their nonreasoning predecessors across the board, with the greatest distinction appearing in coding and math benchmarks. The strength in complex, deterministic tasks makes reasoning models particularly adept at powering agentic AI capabilities, offering a wider range of addressable use cases and greater accuracy. In addition, reasoning frameworks can be leveraged at any parameter count, with available reasoning models ranging from fewer than 10 billion parameters to more than 100 billion.

As SaaS vendors continue to build proprietary, domain-specific SLMs [small language models] to power their agentic capabilities, incorporating reasoning frameworks will be an important part of their development strategies. Although the capabilities of reasoning models are impressive, the models bring new challenges and are not necessarily the best choice for every application.

Simple content generation and summarization, for instance, do not necessarily require iterative inferencing. Moreover, the greater compute intensity caused by repeated processing at the transformer layer will compound existing challenges to scaling AI adoption. Not only will these models be more expensive to run for the customer, but they will also exacerbate the persistent supply shortages facing cloud infrastructure providers. Microsoft has noted infrastructure constraints as a headwind to AI revenue growth in the past several quarters, and the emerging need for test-time compute adds to these infrastructure demands. As discussed in TBR’s special report, Sheer Scale of GTC 2025 Reaffirms NVIDIA’s Position at the Epicenter of the AI Revolution, NVIDIA’s CEO Jensen Huang stated that reasoning AI consumes 100 times more compute than nonreasoning AI. Of course, this was a highly self-serving statement, as NVIDIA is the leading provider of GPUs powering this compute, but we are dealing with magnitudes of difference. For the use of reasoning models to continue scaling, this high compute intensity will need to be addressed.

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

SaaS vendors will need to get on board with the new Model Context Protocol to ensure customers can use their model of choice

SaaS vendor strategy assessment

From a strategic positioning perspective, TBR does not expect the rising popularity of the Model Context Protocol to have an outsized impact, primarily because we anticipate all application vendors will adopt the framework to ensure customers can leverage the model of their choice. Furthermore, cloud application vendors are positioned to benefit from the standardization of API calls between models and their workloads. Through a standardized API calling framework, these vendors will be better positioned to drive cost optimization and improve workload management for embedded AI tools.

Recent developments

The Model Context Protocol is becoming the standard: The idea of the Model Context Protocol (MCP) has been steadily gaining popularity following its release by Anthropic in November 2024. At its core, MCP aims to address the emerging challenge of building dedicated API connectors between LLMs and applications by introducing an abstraction layer that standardizes API integrations. This abstraction layer — commonly referred to as the MCP server — would establish a default method for LLM function calling, which software providers would need to incorporate into their applications to access LLMs.

This standardization offers several benefits for model vendors, such as eliminating the need to build individual connectors for each service and promoting a modular approach to AI service integration, potentially unlocking long-term advantages in areas such as workload management and cost optimization.

For SaaS vendors, there is little reason to resist the shift toward MCP, and its growing popularity may make adoption inevitable. Application vendors like Microsoft and ServiceNow have already begun implementing the protocol by establishing MCP servers for the Copilot suite and Now Assist, respectively, and TBR expects other vendors to follow.

It is important to recognize, however, that this approach better suits vendors taking a model-agnostic stance — meaning they aim to empower enterprises to use any LLM to automate agentic capabilities. A possible exception lies with vendors that are less model-agnostic. For instance, Salesforce’s emphasis on proprietary models reduces the need for MCP and favors the company’s focus on native connectors between Customer 360 workflows and xGen models.

Ultimately, TBR expects Salesforce to adopt MCP, but there is an important distinction in how different SaaS vendors may approach standardization. Today, the BYOM [bring your own model] philosophy remains a priority for Salesforce, but if the company were to eventually push customers to use its proprietary models exclusively with Customer 360, its commitment to MCP could be deprioritized in favor of tighter customer lock-in.

Google enhances AI capabilities with the launch of Gemini 2.5 Pro, revolutionizing search functionality, healthcare solutions and multimodal content generation

Google remains differentiated in the AI landscape through the deep integration of its proprietary models across a broad product ecosystem, including Search, YouTube, Android and Workspace. Although many competitors focus on niche capabilities or open-source development, Google positions Gemini as a comprehensive, multimodal foundation model designed for widescale consumer and enterprise adoption. Google’s infrastructure, proprietary TPUs (Tensor Processing Units), and access to vast and diverse data sources provide a significant advantage in training and deploying next-generation models. Gemini 2.5 Pro is a testament to this strength, offering the best performance and largest context window available on the market. Although TBR expects the top spot to continue exchanging hands, we believe Google’s models will remain among the frontier leaders for years to come.

OpenAI advances AI development with GPT-4.5, cutting-edge agent tools and a premium ChatGPT Pro subscription to expand capabilities and improve user experiences

OpenAI is the most valuable model developer in the market today, largely due to the company’s success in productizing its models via ChatGPT. The mindshare generated by ChatGPT is benefiting the company’s ability to reach custom enterprise workloads, though OpenAI must be mindful of the widening gap in price to performance relative to peers. From a sheer performance perspective, TBR believes the company’s emphasis on securing compute infrastructure via the Stargate Project, as well as its ongoing partner initiatives to gain access to high-quality training data, will ensure its models remain near the top of established third-party benchmarks over the long term.

ServiceNow Ecosystem Report

06.11.25/by TBR

ServiceNow’s evolving value proposition, centered on seamless tech integration and sales alignment, provides a strong backbone in its alliances’ strategy, appealing to client-mindshare-hungry partners

Key trends

The need to unlock data and break down integration barriers between the back, middle and front office is as relevant as ever as customers look to deploy generative AI (GenAI) within their workflows. Acting as an abstraction layer on top of the enterprise system of record (SOR), ServiceNow is in a strong position to message around business transformation and to have more outcome-based conversations with clients, which is aligned with the IT services companies and consultancies’ business models. IT services companies and consultancies that have experience reducing organizations’ technical debt and implementing systems like SAP, Workday and Salesforce are well positioned to use ServiceNow to deliver added value. As evidenced by ServiceNow’s introduction of consumption-based pricing for AI Agents, ServiceNow is focused on selling value as part of its GenAI portfolio, which is certainly in step with the market, though outcome-based pricing may be something for ServiceNow to consider to further align with the global systems integrator (GSI) ecosystem and stay ahead of its growing list of SaaS competitors.

Go-to-market strategy

As ServiceNow continues to grow and pursue new market opportunities, the company is doing a better job of enabling the ecosystem in both sales and delivery. Unlike some of its SaaS peers, ServiceNow is not as established in the market, underscoring a clear need to leverage partners that have the C-Suite relationships, particularly in the line of business (LOB) that can articulate ServiceNow’s value as it exists alongside core enterprise applications. Despite its rapid expansion into more SaaS markets, ServiceNow remains a platform company at its core, but being a true platform company requires an ecosystem that can build on that platform. We suspect the Build motion, where partners sell custom, often industry-specific offerings they develop on the Now Platform, will be an increasingly critical motion, helping ServiceNow capitalize on opportunities.

Vendors

Given the smaller size, ServiceNow is unsurprisingly among the fastest-growing practice area within the GSIs, with average practice-related revenue up 12.9% year-to-year in 4Q24. Several partners have more than $1 billion commitments with ServiceNow, and in early 2025 Infosys and Cognizant joined their competitors in the Global Elite tier of the ServiceNow Partner Program. Cognizant is also the inaugural partner for ServiceNow’s Workflow Data Fabric platform, a key offering that rounds out ServiceNow’s portfolio, offering zero-copy integrations with key platforms, including Google Cloud and Oracle, to feed ServiceNow’s AI Agents. On the technology side, ServiceNow is also strengthening its partnerships with hyperscalers beyond Microsoft, which could unlock new points of engagement for services partners as they start to embrace the multiparty alliance structure. For example, Deloitte is looking at how it can build agents for ServiceNow-specific use cases, with an immediate focus on the front office, on Google Cloud Platform (GCP), while Accenture included ServiceNow on its partner list for the recently announced Trusted Agent Huddle for agent-to-agent interoperability.

Emergence of multipartner networks will test vendors’ trustworthiness and framework transparency

Prioritizing the needs of partners and enterprise buyers over internal growth aspirations will position vendors across the ICT value chain as leading ecosystem participants. It sounds like an idea born in marketing, but positive digital transformation (DT) outcomes will require multiparty business networks that bring together the value propositions of players across the technology value chain. By leading with their core competencies, players can establish needed trust among partners and customers alike, increasing their competitiveness against other players that have spread themselves too thin with aspirations of being end-to-end DT providers.

To better understand these approaches, we have identified three back-office ecosystem relationship requirements that guide how the parties work together.

TBR Ecosystem Value Chain (Source: TBR)

TBR has identified 4 cloud ecosystem relationship requirements that guide how the parties work together

ServiceNow ecosystem relationship best practices

1.Consider PaaS layer and its role in the SaaS ecosystem: As discussed throughout our research, the value is shifting from “out of the box” to “build your own,” and customers clearly believe building their own custom solutions around a microservices architecture will give their business a competitive advantage. Naturally, we expect ServiceNow wants partners to take the lead in Now Assist delivery, but for the GSIs to see value, GenAI has to actually change the business process.

2.Drive awareness through talent development efforts: ServiceNow’s growing portfolio outside the core IT service management (ITSM) space is creating new channel opportunities for services partners to capitalize on, compelling them to invest in training and development programs. Gaining the stamp of approval from a ServiceNow certification program enhances services partners’ value proposition, especially in new areas such as the Creator Workflow and Build portion of the ServiceNow portfolio, which positions them to drive custom application and managed services opportunities. Standing out in a crowded marketplace where services and technology providers vie for each other’s attention will elevate the need to invest in consistent messaging and knowledge management frameworks that elevate buyer trust.

3.Prioritize IT modernization ahead of GenAI opportunities and scaling NOW deployment: Some vendors have made GenAI capabilities available only to cloud-deployed back-office suites, meaning customers still on legacy systems must first migrate to the cloud before they can adopt the emerging technology. Partners must account for this modernization prerequisite by prioritizing traditional migration services through broader programs like RISE with SAP if they hope to pursue new opportunities over the long term. Reducing legacy technical debt will also free up resources, both human and financial, which will allow for broader ServiceNow portfolio adoption.

4.Set up outcome-based commercial models to scale adoption across emerging areas and protect against new contenders: Aligning commercial, pricing and incentive models that resonate with buyer priorities and achieving business outcomes can allow partners to expand addressable market opportunities, especially as scaling GenAI adoption necessitates greater trust in the portfolio offerings. ServiceNow’s consumption-based model provides a short-term hedge against potential tech partner disruptors, which may take on the risk to offer similar solutions but are able to better align with services partners’ messaging through the use of outcome-based pricing.

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Acting as an abstraction layer, ServiceNow has a unique opportunity to further expand into the back office to address integration pain points but risks further overlapping with its SOR peers

ServiceNow positions as system of action to expose gaps in core system of record

Existing as a platform layer that orchestrates and integrates workflows, ServiceNow has long been able to successfully enter new markets without encountering a lot of head-to-head competition. But this is changing as ServiceNow, a $10-plus billion company, continues to drive traction with the LOB buyer by challenging a lot of the fragmentation that exists within front- and back-office systems. Over the past several quarters, ServiceNow has continued to launch new products in areas like talent management, finance and supply chain. One of the company’s biggest moves was in the front office with the launch of Sales & Order Management (SOM), giving customers the ability to use CPQ (configure, price, quote) and guided selling in a single product. Though ServiceNow famously integrates with all of the systems of record, these new innovations could pose a risk to the likes of SAP, Workday and Salesforce, which perhaps do not have the platform capabilities to build custom processes that can be tied back to the workflow, at least in a truly modern way. To be clear, ServiceNow is not interested in being a core CRM, ERP or human capital management (HCM) provider, and today acts as a service delivery system. But having customers store their data in the service delivery layer, as opposed to the core system of record so they can use that data against a specific workflow, is how ServiceNow aims to position as a “system of action.”

DXC Technology’s ServiceNow Ecosystem Strategy in Review

TBR assessment

DXC Technology has an established history and deep expertise within the ServiceNow ecosystem, with a partnership spanning more than 15 years, a talent pool of over 1,800 ServiceNow experts, and a track record of more than 7,200 global implementations with over 350 instances managed worldwide, all of which position the company as a mature and experienced service provider for ServiceNow. Notable client wins, such as with the city of Milan (medical supply delivery during a crisis), Nordex Group (workplace safety management) and Swiss Federal Railways (unified customer inquiry management) underscore DXC’s ability to leverage the partnership to address diverse and critical business challenges across different industries and sectors. These successes highlight DXC’s capacity to translate its deep ServiceNow knowledge and implementation capabilities into tangible business value for its clients, suggesting a well-established and impactful ServiceNow practice.

Strategic portfolio offering

Through a strategic alliance with ServiceNow and bolstered by a dedicated global business group and acquisitions such as Syscom AS, TESM, BusinessNow and Logicalis SMC, DXC delivers a comprehensive range of ServiceNow-focused solutions. This approach enables DXC to digitize processes, enhance user experiences, and transform service management across the full ServiceNow platform, driving business innovation at scale, including specialized solutions such as those for the insurance industry, where DXC has had core competencies and long-lasting customer relationships. DXC’s offerings span enterprise applications transformation, security solutions, and compute and data center modernization, all designed to maximize client efficiency and agility utilizing the ServiceNow platform. The establishment of a new Center of Excellence in Virginia in November 2024, combining DXC’s industry strengths with ServiceNow’s solutions, further solidifies the two companies’ commitment to streamlining AI adoption and delivering cutting-edge solutions.

DXC Technology’s ServiceNow Ecosystem Strategy in Review (Source: TBR ServiceNow Ecosystem Report)

U.S. Mobile Operator Benchmark

05.14.25/by TBR

Most operators will sustain wireless service revenue and connection growth in 2025 but face headwinds from macroeconomic challenges and Trump administration immigration policies

Most benchmarked operators sustained service revenue growth in 4Q24, driven by connection growth and higher ARPA

Total wireless revenue from benchmarked U.S. operators increased 4.5% year-to-year to $78.9 billion in 4Q24, mainly due to continued postpaid phone subscriber growth and higher average revenue per account (ARPA). Although the market is maturing, operators are maintaining postpaid phone net additions due to factors including population growth and more businesses purchasing mobile devices for employees. Higher ARPA is being driven by operators increasing connections per account, including from growing fixed wireless access (FWA) adoption, uptake of premium unlimited data plans and recent rate increases implemented over the past year.

Though most U.S. operators expect to continue to grow wireless service revenue and connections in 2025, they will face headwinds from factors including macroeconomic pressures (including layoffs within the private and public sectors and uncertainty around tariff impacts) and immigration policies under the Trump administration (including mass deportations).

U.S. operators increase focus on cross-selling mobile and broadband services

U.S. operators are focused on advancing their convergence strategies by offering plans bundling mobile and broadband services. The bundles create a stickier ecosystem to reduce churn long-term via the convenience of enrolling in broadband and mobility services from the same provider as well as by providing discounted pricing compared to purchasing those services separately.

Operators including AT&T, Charter, Comcast, T-Mobile and Verizon are growing their ability to offer these bundles via the expanding service availability of their broadband services (including wireline and FWA offerings). Operators are also targeting acquisitions to strengthen their convergence strategies, such as Verizon’s pending purchase of Frontier Communications and T-Mobile’s proposed joint ventures to acquire Metronet and Lumos. Cable operators also have significant opportunity to increase sales of converged services as a relatively low portion of cable broadband customers are enrolled in their service provider’s mobile offering.

AI is providing cost savings and revenue generation opportunities for U.S. operators

U.S. operators are focused on more deeply implementing AI technologies in areas including optimizing customer service and sales & marketing functions as well as enhancing network operations. For instance, deeper AI implementation will help AT&T reach its goal of generating $3 billion in run-rate cost savings between 2025 and 2027, while leveraging AI technologies will help T-Mobile meet its target of reducing the number of inbound customer care calls by 75%.

AI will also help operators optimize energy usage, especially as it pertains to network operations. Examples include using AI for optimal, dynamic traffic routing and to determine when to turn on and turn off radios to optimize energy usage. AI, especially providing network and real estate resources to support AI inferencing workloads, will create significant revenue opportunities for operators.

For instance, Verizon views telco AI delivery as having a $40 billion total addressable market, and the company has already secured a sales funnel of over $1 billion in business by leveraging its existing infrastructure and resources.

Operators are focused on cost-cutting initiatives, including streamlining headcount and more deeply implementing AI technologies, to improve margins

The impacts of inflation and challenging macroeconomic conditions, such as lower consumer discretionary spending, higher network operations and transportation expenses, and increased labor-related costs, are limiting profitability for U.S. operators. These challenges are leading operators to implement cost-cutting and restructuring initiatives to improve profitability, such as AT&T’s goal of generating $3 billion in savings from 2025 to the end of 2027 through its latest cost-cutting program.

Operators are streamlining headcount as part of their cost-cutting initiatives. For instance, about 4,800 employees are expected to leave Verizon by the end of March 2025 as part of the company’s latest voluntary separation program.

To increase cost savings and operational efficiencies, operators are more deeply implementing AI technologies in areas including customer service, field technician support and fleet vehicle fuel consumption.

T-Mobile is improving profitability, evidenced by its EBITDA margin growing by 220 basis points year-to-year to 35.4% in 4Q24, which was impacted by the company’s higher revenue and lower network costs aided by greater merger-related synergies. T-Mobile’s 2025 guidance for core adjusted EBITDA* is between $33.1 billion and $33.6 billion, compared to $31.8 billion in 2024. Service revenue growth as well as cost-cutting initiatives and merger-related synergies will all contribute to higher core adjusted EBITDA.

*Core adjusted EBITDA reflects T-Mobile’s adjusted EBITDA less device lease revenues.

Wireless Revenue, EBITDA Margin and Year-to-year Growth for 4Q24 (Source: TBR)

T-Mobile continued to lead the U.S. in postpaid phone and broadband net additions in 4Q24 and recently launched new FWA pricing plans

Operators are attracting FWA customers, mainly because FWA offerings have lower price points compared to other broadband services and are available to customers in markets with limited other high-speed broadband options, such as within rural markets. Though consumers account for the bulk of FWA connections, FWA is also gaining momentum among businesses seeking to reduce connectivity expenses and/or companies needing to quickly launch new branch locations, as the technology can be installed faster than fixed broadband.

In 4Q24 T-Mobile continued to lead the U.S. in broadband subscriber growth, driven by its FWA services, aided by the company continuing to gain market share against cable companies including Comcast and Charter, which reported steeper broadband customer losses in 4Q24 both year-to-year and sequentially. T-Mobile also reported its highest-ever year-to-year broadband ARPU growth in 4Q24, which was aided by the company revamping its 5G Home Internet and Small Business Internet plans in December.

Total FWA Net Additions for 4Q23-4Q24 (Source: TBR)

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Wireless capex moderated for most U.S. CSPs in 2024 as they are in the later stages of 5G rollouts

Verizon’s consolidated capex will increase to a guidance range of $17.5 billion to $18.5 billion in 2025, compared to $17.1 billion in 2024 (higher consolidated capex is mainly due to increased wireline capex to support Verizon’s accelerated Fios build). TBR estimates Verizon’s wireless capex in 2025 will be relatively consistent compared to 2024 as the company will focus on the continued expansion of C-Band 5G services into suburban and rural markets.

AT&T’s 2025 guidance for capital investment, which includes capex and cash paid for vendor financing, is in the $22 billion range, consistent with $22.1 billion in capital investment in 2024. Capital investment in 2025 will entail materially lower vendor financing payments compared to 2024, while capex is expected to increase year-to-year in 2025. TBR estimates AT&T’s wireless capex will be about $10.6 billion in 2025, which will help to meet AT&T’s goals, including providing midband 5G coverage to over 300 million POPs by the end of 2026 and completing the majority of its transition to open-RAN-compliant technologies by 2027.

T-Mobile’s capex guidance for 2025 is around $9.5 billion, compared to $8.8 billion in capex spent in 2024, with spending focused on continued 5G network deployments as well as investments in IT platforms to enhance efficiency and customer experience.

Enterprise Edge Compute Market Landscape

05.14.25/by TBR

Healthcare, industrial and consumer goods respondents have the highest rates of edge computing technology adoption

Modern edge computing use cases remain a relatively new concept for the majority of organizations, although they are gaining popularity as 36% of respondents indicate their organization is currently using edge technology compared to 29% in 2023. However, the majority of respondents plan to deploy edge solutions in the future or observe edge use cases in their industry.

Seventy-eight percent (78%) of respondents in the healthcare and industrial verticals either plan to use or currently use edge computing.

Consumer goods respondents also have a keen interest in edge computing, which may be driven by use cases at brick-and-mortar locations and the emergence of packaged solutions for retail.

2Q24 Plans to Use Edge Computing (Source: TBR)

GenAI will not have a significant impact on enterprise edge market growth, at least in the near term, as customers prioritize their investments in the IT core and cloud

Forecast assumptions

TBR continues to revise its enterprise edge forecast to account for changes in the traditional IT and cloud markets, including the advent of generative AI (GenAI). Although the enterprise edge market benefited from the hype surrounding AI in 2024, many pilot projects may not enter production and more concrete use cases around edge AI need to be developed.

The enterprise edge market is estimated to grow at a 19.9% CAGR from 2024 to 2029, surpassing $144 billion by 2029. Professional and managed services will remain the fastest-growing segment, followed by software, at estimated CAGRs of 22.4% and 19.3%, respectively.

Enterprise Edge Spending Forecast by Segment for 2024-2029 (Source: TBR)

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Given customers’ preference for first migrating to the public cloud and then extending data out to the edge for certain use cases, AWS has a compelling reason to consolidate its edge portfolio

TBR assessment

The evolution of networking bandwidth, maturity of Amazon Web Services’ (AWS) public cloud services, and overall strategic shift around GenAI have AWS reevaluating some aspects of its edge portfolio. In November 2024 the cloud giant discontinued three generations of Snowball edge devices for both storage and compute, forcing customers to look for alternative data migration solutions like DataSync and Direct Connect. AWS also announced that the Fleet Hub feature of AWS IoT Device Management will reach end of life in October 2025. In our view, these discontinuations, particularly for the Snow family of hardware, are a big reaffirmation of customers’ preference for extending the public cloud to the edge as opposed to taking an “edge-native” approach. We have long discussed how the hyperscalers are encroaching on the edge opportunity, and customers appear to be overwhelmingly initiating online migrations to the cloud and using native services, such as AWS DataSync, to move data from the cloud to edge environments. That said, expanding the company’s IaaS footprint, through offerings like Outposts and Local Zones to support customers’ cloud-to-edge strategies, is still very much a focus for AWS.

Recent developments

In late 2024 AWS announced Satellite Resiliency for AWS Outposts, a managed solution backed by partners like Accenture, Capgemini, Deloitte, Kyndryl and Tata Consultancy Services (TCS), that connects Outposts to commercial satellites. This solution greatly expands the reach of where AWS services can be consumed, and helps enable local data processing while supporting connectivity in the event of outages or network disruptions.
NetApp announced it is bringing its ONTAP storage arrays to AWS Outposts. This means customers that leverage either AWS Outpost servers or racks can manage and provision NetApp storage solutions directly via the AWS Management Console.
Customers can now use different storage classes including S3 Express One Zone and S3 One Zone-Infrequent Access in AWS Dedicated Local Zone environments. These storage classes are designed to accommodate specific data perimeters and support customers’ data residency use cases.

Key strategies

Offer the broadest set of infrastructure options with Local Zones and Wavelength Zones to appeal to customers looking to run their applications with millisecond latencies to mobile devices and/or end users.
Integrate security offerings, such as Identity and Access Management, with networking offerings to drive adoption.
Work with ISVs to expand availability of edge solutions on the AWS Marketplace.

Cloud Components Benchmark

05.14.25/by TBR

Behind healthy server backlog and new software IP, hardware vendors drive the cloud components market, particularly as software pure plays prioritize entirely public cloud migrations

Hardware-centric vendors continue to make their move into software

Over the past several years, the cloud software components market has shifted. Microsoft and Oracle are no longer dominating the market as they prioritize their native tool sets and encourage customers to migrate to public cloud infrastructure. Driven largely by weaker-than-expected purchasing around Microsoft Windows Server 2025, aggregate revenue growth for these two software-centric vendors was down 3% year-to-year in 3Q24. Over the same compare, total software components revenue for the benchmarked vendors was up 14% and total cloud components revenue was up 8%. In some ways, this dynamic has made room for hardware-centric vendors such as Cisco and Hewlett Packard Enterprise (HPE) to move deeper into the software space, particularly as they buy IP associated with better managing orchestration infrastructure in a private and/or hybrid environment.

Backlog-to-revenue conversion for AI servers fuels market growth

Though revenue mixes are increasingly shifting in favor of software, driven in part by acquisitions (e.g., Cisco’s purchase of Splunk), hardware continues to dominate the market, accounting for 80% of benchmarked vendor revenue in 3Q24. Industry standard servers being sold to cloud and GPU “as a Service” providers are overwhelmingly fueling market growth, more than offsetting unfavorable cyclical demand weakness in the storage and networking markets. This growth is largely driven by the translation of backlog into revenue, but vendors are still bringing new orders into the pipeline, which speaks to ample demand from both AI model builders and cloud providers. However, large enterprises are increasingly adopting AI infrastructure as part of a private cloud environment to control costs and make use of their existing data.

Cloud Revenues by Segment for 3Q23-3Q24 (Source: TBR)

Ample scale and strong demand from both CSPs and enterprises extend Dell’s lead in the cloud components market

Cloud components vendor spotlights

Dell Technologies [Dell]

From a revenue perspective, HPE and Cisco once threatened Dell’s cloud components leadership, but the company has been able to distance itself from its nearest competitors. This is largely due to Dell’s performance over the past year, with strong server demand, particularly from Tier 2 cloud service providers (CSPs), propelling the company’s corporate and cloud components revenue growth rate to the double digits. Meanwhile, in 3Q24 Dell shipped $2.9 billion worth of AI servers while backlog reached $4.5 billion, reflecting 181% year-to-year growth during the quarter and indicating strong future revenue performance.

Hewlett Packard Enterprise

Like its peers, HPE is benefiting from AI-related server demand, and in 3Q24 the company reported $1.5 billion in total AI systems revenue. HPE continues to benefit from its ongoing efforts to shift the sales mix in favor of software and services via GreenLake. In 3Q24 HPE completed its acquisition of Morpheus Data, officially equipping HPE with a suite of infrastructure software that allows customers to take core hypervisors, such as KVM and VMware, and use them to build complete private cloud stacks.

Cisco

With its acquisition of Splunk, Cisco has emerged as the leader of the software components market, even surpassing Microsoft in related revenue. But networking still accounts for the bulk of Cisco’s components business, and, as evidenced by a 32% year-to-year decline in total hardware revenue for 3Q24, Cisco is facing headwinds in the core networking business. That said, the company is actively taking steps to build out its portfolio, particularly by integrating more security components into the networking layer, which is where most cyberattacks originate, to boost its long-term competitiveness in the market.

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Infrastructure agnosticism and flexible cloud-enabled delivery are core attributes of the service delivery market, cementing IBM’s leadership

Dedicated orchestration tools continue to have their place in the market, both in on-premises and cloud environments, but growth is largely driven by application lifecycle management and orchestration tools that span multiple environments. IBM has a rich history in this space and remains a revenue leader. Cisco used to have a foothold in the market but no longer sells its CloudCenter suite.

Vendor spotlight: IBM

After taking steps to bring watsonx into Maximo in 2Q24 for greater process automation, IBM strengthened its commitment to the asset performance management space with the acquisition of Prescinto. Prescinto offers AI tools and accelerators designed for asset owners and operators with a focus on renewable energy and operators. This deal is designed to support IBM’s play in certain verticals, particularly energy and utilities.

Service Delivery and Orchestration Revenue Growth vs Cloud Software Components Revenue Growth for 3Q24 (Source: TBR)

AI PC and AI Server Market Landscape

05.14.25/by TBR

Despite hyperscalers’ increasing investments in custom AI ASICs, TBR expects demand for GPGPUs to remain robust over the next 5 years, driven largely by the ongoing success of NVIDIA DGX Cloud

The world’s largest CSPs, including Amazon, Google and Microsoft, remain some of NVIDIA’s biggest customers, using the company’s general-purpose graphics processing units (GPGPUs) to support internal workloads while also hosting NVIDIA’s DGX Cloud service on DGX systems residing in the companies’ own data centers.

However, while Amazon, Google and Microsoft have historically employed some of the most active groups of CUDA developers globally, all three companies have been actively investing in the development and deployment of their own custom AI accelerators to reduce their reliance on NVIDIA. Additionally, Meta has invested in the development of custom AI accelerators to help train its Llama family of models, and Apple has developed servers based on its M-Series chips to power Apple Intelligence’s cloud capabilities.

However, even as fabless semiconductor companies such as Broadcom and Marvell increasingly invest in offering custom AI silicon design services, only the largest companies in the world have the capital to make these kinds of investments. Further, only a subset of these large technology companies engage in the type of operations at scale that would yield measurable returns on investments and total cost of ownership savings. As such, even as investments rapidly rise in the development of customer AI ASICs, the vast majority of customers continue to choose NVIDIA’s GPGPUs due to not only their programming flexibility but also the rich developer resources and robust prebuilt applications comprising the hardware-adjacent side of NVIDIA’s comprehensive AI stack.

Data Center GPGPU Market Forecast for 2024-2029 (Source: TBR)

Companies across a variety of industry verticals want to take a piece of NVIDIA’s AI cake

Scenario Discussion: NVIDIA faces increasing threats from both industry peers and partners

NVIDIA GPGPUs are the accelerator of choice in today’s AI servers. However, the AI server and GPGPU market incumbent’s dominance is increasingly under threat by both internal and external factors that are largely related. Internally, as Wall Street’s darling and a driving force behind the Nasdaq’s near 29% annual return in 2024, NVIDIA’s business decisions and quarterly results are increasingly scrutinized by investors, forcing the company to carefully navigate its moves to maximize profitability and shareholder returns. Externally, while NVIDIA positions itself largely as a partner-centric AI ecosystem enabler, the number of the company’s competitors and frenemies is on the rise.

Despite NVIDIA’s sequentially eroding operating profitability, investor scrutiny has not had a clear impact on the company’s opex investments — evidenced by a 48.9% year-to-year increase in R&D spend during 2024. However, it may well be a contributing factor to the company’s aggressive pricing tactics and rising coopetition with certain partners. While pricing power is one of the luxuries of having a first-mover advantage and a near monopoly of the GPGPU market, high margins attract competitors and high pricing drives customers’ exploration of alternatives.

Additionally, the fear of vendor lock-in among customers is something that comes with being the only name in town, and while there is not much most organizations can do to counteract this, NVIDIA’s customers include some of the largest, most capital-rich and technologically capable companies in the world.

To reduce their reliance on NVIDIA GPUs, hyperscalers and model builders alike have increasingly invested in the development of their own custom silicon, including AI accelerators, leveraging acquisitions of chip designers and partnerships with custom ASIC developers such as Broadcom and Marvell to support their ambitions. For example, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP) and Meta have their own custom AI accelerators, and OpenAI is reportedly working with Broadcom to develop an AI ASIC of its own. However, what these custom AI accelerators have in common is their purpose-built design to support company-specific workloads, and in the case of AWS, Azure and GCP, while customers can access custom AI accelerators through the companies’ respective cloud platforms, the chips are not physically sold to external organizations.

In the GPGPU space, AMD and, to a lesser extent, Intel are NVIDIA’s direct competitors. While AMD’s Instinct line of GPGPUs has become increasingly powerful, rivaling the performance of NVIDIA GPGPUs in certain benchmarks, the company has failed to gain share from the market leader due largely to NVIDIA CUDA’s first-mover advantage. However, the rise of AI has driven growing investments in alternative programming models, such as AMD ROCm and Intel oneAPI — both of which are open source in contrast to CUDA — and programming languages like OpenAI Triton. Despite these developments, TBR believes NVIDIA will retain its majority share of the GPGPU market for at least the next decade due to the momentum behind NVIDIA’s closed-source software and hardware optimized integrated stack.

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Microsoft Copilot+ PCs represent a brand-new category and opportunity for Windows PC OEMs industrywide

PC OEMs expected the post-pandemic PC refresh cycle to begin in 2023, but over the past 18 months, their expectations have continually been delayed, with current estimates indicating the next major refresh cycle will ramp sometime in 2025. While the expected timing of the refresh cycle has changed, the drivers have remained the same, with PC OEMs expecting that the aging PC installed base, the upcoming end of Windows 10 support — slated for October 2025 — and the introduction of new AI PCs will coalesce, driving meaningful rebounds in the year-to-year revenue growth of both the commercial and consumer segments of the PC market.

As organizations graduate from Windows 10 devices to Windows 11 devices, TBR expects many customers will opt for AI PCs to future-proof their investments, understanding that the overall commercial PC market will be dominated by devices powered by Windows AI PC SoCs in a few years’ time. However, while TBR expects the Windows AI PC market to grow at a 44.3% CAGR over the next five years, the driver of this robust growth centers on the small revenue base of Windows AI PCs today.

While Apple dominated the AI PC market in 2024 due to the company’s earlier transition to its own silicon platform — the M Series, which features onboard NPUs — TBR estimates indicate that among the big three Windows OEMs, HP Inc.’s AI PC share was greatest in 2024, followed closely by Lenovo and then Dell Technologies. Without an infrastructure business, HP Inc. relies heavily on its PC segment to generate revenue, and as such, TBR believes that relative to its peers — and Dell Technologies in particular — HP Inc. is more willing to trade promotions and lower margins for greater number of sales, which is a key factor in the current increasingly price-competitive PC market. TBR estimates Lenovo’s second-place positioning is tied to the company’s growing traction in the China AI PC market, where the company first launched AI PCs leveraging a proprietary AI agent in a region where Microsoft Copilot has no presence.

Windows AI PC Market Forecast for 2024-2029 (Source: TBR)

The PC ecosystem increases its investments in developer resources to unleash the power of the NPU

Currently available AI PC-specific applications, such as Microsoft Copilot and PC OEMs’ proprietary agents, are focused primarily on improving productivity, which drives more value on the commercial side of the market compared to the consumer side. However, it is likely more AI PC-specific applications will be developed that harness the power of the neural processing unit (NPU), especially as AI PC SoCs continue to permeate the market.

Companies across the PC ecosystem, including silicon vendors, OS providers and OEMs, are investing in expanding the number of resources available to developers to support AI application development and ultimately drive the adoption of AI PCs. For example, AMD Ryzen AI Software and Intel OpenVINO are similar bundles of resources that allow developers to create and optimize applications to leverage the companies’ respective PC SoC platforms and heterogenous computing capabilities, with both tool kits supporting the NPU, in addition to the central processing unit (CPU) and GPU.

However, as it relates to AI PCs, TBR believes the NPU will be leveraged primarily for its ability to improve the energy efficiency of certain application processes, rather than enabling the creation of net-new AI applications. While the performance of PC SoC-integrated GPUs pales in comparison to that of discrete PC GPUs purpose-built for gaming, professional visualization and data science, the TOPS performance of SoC-integrated GPUs typically far exceeds that of SoC-integrated NPUs, due in part to the fact that the processing units are intended to serve different purposes.

The GPU is best suited for the most demanding parallel processing functions, requiring the highest levels of precision, while the NPU is best suited for functions that prioritize power efficiency and require lower levels of precision, including things like noise suppression and video blurring. As such, TBR sees the primary value of the NPU being extended battery life — an extremely important factor for all mobile devices. This is the key reason why TBR believes that AI PC SoCs will gradually replace all non-AI PC SoCs, eventually being integrated into nearly all consumer and commercial client devices.

One of the reasons PC OEMs are so excited about the opportunity presented by AI PCs is that AI PCs command higher prices, supporting OEMs’ longtime focus on premiumization. Commercial customers, especially large enterprises in technology-driven sectors like finance, typically buy more premium machines, while consumers generally opt for less expensive devices, and TBR believes this will be another significant driver of AI PC adoption rising in the commercial segment of the market before the consumer segment.

2024-2029 Devices Market Forecast

04.03.25/by TBR

TBR expects cyclical PC market dynamics and AI adoption to drive a single-digit devices CAGR from 2024 to 2029

After declining for several quarters due to market saturation and tightened corporate IT budgets, PC demand is gradually recovering, particularly on the commercial side of the market as organizations begin to refresh their fleets of devices. TBR expects the devices market to grow at roughly a 2.7% CAGR from 2024 to 2029 as this recovery in PC demand is supplemented by growing smartphone and tablet revenue. TBR also expects demand for AI advisory and consultancy services will increase as organizations invest in implementing AI across IT infrastructure and client devices.

The proliferation of AI across the IT space presents devices vendors with a range of growth opportunities. PC OEMs will remain focused on driving AI PC adoption and gradually increasing these devices as a mix of total PC shipments to drive long-term revenue growth and average revenue per unit (ARPU) expansion. To help speed this adoption and increase services revenue, vendors will also continue to build out suites of services designed to help organizations take advantage of the productivity gains offered by AI PCs. TBR expects vendors to continue to increase their non-PC revenue mix, capitalizing on growth opportunities presented by AI and sheltering their top lines and margins from potential fluctuations in the PC market.

Devices Market Share, 2024 and 2029 (Source: TBR)

Dell’s strong direct sales motion will help it maintain its top position in PC services revenue through 2029

PC services revenue is closely correlated with PC hardware revenue, but revenue recognition is often deferred. As a result, the trends in the overall devices market are slower to materialize in the PC services segment. PC sales declined for several quarters throughout 2022 and 2023, offering vendors less opportunity to attach services and constraining revenue growth in this segment. As 2025 progresses and PC unit shipments continue to expand year-to-year, PC services revenue growth momentum will gradually accelerate. Recovery in the commercial PC space will boost PC services performance, as commercial PCs carry higher services attach rates than their consumer counterparts.

Vendors will remain focused on mitigating the impacts of negative margin pressures on PC hardware by continuing to focus on increasing their PC services revenue mix relative to PC hardware revenue, with an emphasis on sustainability, cybersecurity and predictive analytics.

As AI becomes increasingly prevalent in the devices space, vendors will also continue to promote offerings designed to help organizations effectively adopt AI infrastructure and AI-enabled end-user devices. Offerings such as Lenovo’s AI Fast Start program and Dell Technologies’ Implementation Services for Microsoft Copilot are both designed to help organizations take advantage of the productivity gains that AI PCs offer.

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Although China is a weak market for some vendors, TBR expects Lenovo to take advantage of AI PC opportunities in the country to expand APAC revenue

TBR expects the APAC devices market to grow at a 2.7% CAGR from 2024 to 2029 — a rate on par with the global devices market.

Over the last several quarters, vendors such as Apple and HP Inc. have reported China as being a particularly weak market for their devices businesses due to persistent softness in demand.

TBR expects that among the vendors included in this forecast, Lenovo will reap the greatest benefit from recovering PC demand in China due to its already large market share and its AI PC strategy in the country. In May 2024 Lenovo rolled out a lineup of devices in the country it dubbed its “five-feature” AI PCs, including a personal agent and local large language model (LLM). The company reported strong initial uptake of these devices during its 2Q24 and 3Q24 earnings calls, and TBR expects ongoing momentum in China will help drive Lenovo’s PC segment and top-line growth throughout the forecast period.

3Q24 IT Services Vendor Benchmark

04.03.25/by TBR

Overall revenue growth year-to-year for the vendors in TBR’s IT Services Vendor Benchmark will accelerate during 2025 aided by managed services activities, innovative portfolios and upskilling

As we review financial results from 3Q24 and 4Q24, we will likely see that macroeconomic uncertainty caused an overall IT services revenue deceleration in 2024. However, IT services spending will continue as clients seek run-the-business managed services opportunities to operate in challenging market conditions.

A new wave of outsourcing demand is picking up speed as buyers are inclined to switch from innovation to business resiliency in the event of economic turbulence. Multiple IT services providers are racing to capture as much business in the managed services space as they can before GenAI [generative AI] picks up and threatens the core value proposition centered on human-backed service delivery.

While vendors are experiencing a slowdown in consulting activities due to limited discretionary spending, the U.S. Federal Reserve’s three federal funds rate reductions during 4Q24 and general market expectations of two cuts in 2025, will likely improve buyers’ confidence and boost discretionary spending. TBR expects IT services peers with established consulting capabilities will race to capture the potential rise in spending, compelling vendors to constantly evaluate their value propositions to ensure trust and service quality. This is especially important as the prolonged slowdown in discretionary spending has given buyers an opportunity to assess digital stacks and vendors’ performance.

TBR expects IT services revenue growth for benchmarked vendors to accelerate during 2025 compared to rates in 2024. Broad-based investments in innovative portfolio offerings and acquisitions along with upskilling existing employees and recruiting higher-caliber talent in onshore and nearshore locations will contribute to revenue growth acceleration.

Overall Revenue and Growth for Benchmarked IT Services Vendors, 2024 Est. and 2025 Est. (Source: TBR)

To position for growth in 2025, vendors continue to pursue opportunities around AI and GenAI, expand outsourcing capabilities through engineering services, and build local relationships in India

3Q24 Trends

Vendors invest in AI and GenAI solutions: While the overall short-term revenue growth slowdown in TBR’s IT Services Vendor Benchmark suggests some vendors might be in financial distress, the pipelines of AI- and GenAI-related opportunities suggest vendors are capturing emerging opportunities. Procurement, sales and marketing, customer service and software development are the go-to use cases around GenAI adoption. Use cases with demands on data, dependencies on external data and/or long horizons to ROI remain the subjects of innovation sessions, proofs of concept and road maps.

Vendors develop engineering services portfolios: Multiple IT services providers are enhancing their outsourcing capabilities by investing in engineering services and silicon design services. Such initiatives position vendors for a new wave of outsourcing services funded by operational technology buyers, which are a new type of buyers for some of the vendors. GenAI provides a new conduit for managed services opportunities, as the integration of new applications and infrastructure gives vendors a natural starting point to build their managed services pipelines. Enhancing outsourcing capabilities by investing in engineering services and silicon design services will help vendors control the GenAI-related disruption of human-driven service delivery models and mitigate potential revenue cannibalization.

Vendors develop innovation capabilities in India: India remains a region of investment — from both a revenue generation and a global service delivery standpoint — as vendors strive to build relationships with local clients to diversify geographic reach. According to TBR’s Fall 2024 Global Delivery Benchmark, “investing in India remains a priority for most vendors, as the low-cost labor in the country continues to offer a way for vendors to improve profitability. The difference this time is that vendors also aspire to drive sales from within the country. Lack of native industry-skilled consultants, combined with a smaller number of industries that are ripe for digital transformation compared to Western economies, might create a profitability headwind if vendors lack the C-Suite permission and brand recognition.”

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Vendors use acquisitions to deepen functional and industry expertise and grow revenues

IT services providers are gradually accelerating acquisition activities compared to 2023 as they strive to diversify portfolios with niche expertise and expand client reach. In 3Q24 IBM used acquisitions to expand client reach across sectors and enhance capabilities.

During the first nine months of 2024 IBM completed three acquisitions in IBM Consulting and five acquisitions in IBM Software, supporting IBM’s strategy to expand in hybrid cloud and AI. IBM paid a collective cost of $2.8 billion. In comparison, IBM completed seven acquisitions during the first nine months of 2023 for an aggregate cost of $5 billion, approximately $4.6 billion of which was for the Apptio acquisition.

Accenture’s acquisition strategy remains unrivaled, with the company spending $6.6 billion on 46 transactions in FY24 (ended in August 2024), up from $2.5 billion in FY23 and $3.4 billion in FY22. The company plans to spend another $3 billion in FY25. The broad-based targets Accenture pursues highlight the company’s efforts to maintain a diversified portfolio and skill sets while ensuring it captures inorganic revenue boosts and positions itself for long-term organic revenue growth.

Industry view

While financial services is a leading revenue contributor for some of the 17 benchmarked vendors with available data, accounting for 25% of total revenue in 3Q24, revenue growth in the vertical has declined for the past three consecutive quarters. Macroeconomic headwinds in financial services subsectors, such as mortgages, asset management, investment banking, cards and payments, have been negatively affecting vendors’ performance in the sector.

These headwinds have been driving revenue growth deceleration for some of the vendors in the U.K. & Ireland and in North America, typically the two largest revenue-contributing geographies for IT services activities in financial services for the covered vendors. Other vendors are alleviating revenue growth pressures by ramping up activities with large banking clients in areas such as cost takeout and transformation programs.

Financial services will remain a leading revenue-generating segment for some of the subset of 17 benchmarked vendors with available data. TBR expects revenue growth pressures in financial services to ease in 2025, as some of the leading IT services providers in the segment, including India-centric Cognizant, TCS, Infosys, Tech Mahindra and Wipro, experienced revenue growth in 3Q24, albeit in the low single digits.

Such vendors are benefiting from increased interactions among financial services clients, as these clients look to drive IT modernization and optimization for efficiency and enhanced experience. Financial services is a leading revenue generator for India-centric vendors. These vendors — along with Capgemini, which has stabilized revenue growth in the sector — are optimistic about future revenue performance in financial services, as discretionary spend continues to improve among clients in the capital markets, mortgage and card processing sectors in the U.S., a trend that began in 2Q24.

Other vendors, such as Accenture and Kyndryl, continue to report declining revenues but are ramping up activities with alliance partners and winning new deals, which will contribute to revenue generation and help alleviate revenue growth pressures during 2025.

1H24 Cloud Data Services Market Landscape

04.03.25/by TBR

Vendors lead with the data lake architecture and emerging frameworks to sell a message of data intelligence amid rampant GenAI adoption

The race for Apache Iceberg mindshare is on

Data lakes remain a valuable way for enterprises to simultaneously store structured and unstructured data, particularly as the latter increases due to generative AI (GenAI) and large language models (LLMs). Data lakes are also directly attributable to the rising popularity of Apache Iceberg, an open-source format regarded by developers for its ability to store data in tables and freely move that data across any data lake architecture.

Whether a customer is creating their own data lake (e.g., on Amazon Web Services [AWS]) or deploying a data lake platform as a product (e.g., Databricks), Iceberg is playing an increasingly larger role in helping customers navigate their big data estates with the most limited vendor lock-in.

How the two data lake giants — Snowflake and Databricks — are investing best speaks to the budding role of Apache Iceberg and its growing community. Earlier this year Snowflake adopted Apache Iceberg as the native format for its platform and subsequently launched Polaris, a tool that allows customers to catalog that data stored in Iceberg tables.

In only a matter of days, Databricks, which was born out of Delta Lake, an Apache Iceberg alternative, moved into the space with its acquisition of Tabular. Tabular was created by the founders of Apache Iceberg, marginalizing Snowflake’s recent investments and intent to attract more Iceberg-heavy users, which generally include digital and cloud-native companies. The hyperscalers, primarily AWS and Microsoft, work closely with Snowflake and Databricks and benefit from their respective integrations to boost interoperability for joint customers through Iceberg.

For example, Microsoft announced its data platform Fabric, which is based on a data lake architecture (OneLake), will support Iceberg via Snowflake. This is a major win for Snowflake that elevates the company’s role as an ISV partner in the Microsoft Fabric ecosystem and further challenges Databricks, which due to its native first-party integration with Azure, has always had a rich and unique relationship with Microsoft.

A select number of vendors are leading the shift to data intelligence

Though somewhat influenced by a degree of marketing hype vendors use to differentiate themselves, data intelligence has become an emerging topic in the market, led by GenAI. At its core data intelligence refers to the use of AI on data to deliver insights tailored to the business, but the other core component of data intelligence is the underlying data architecture foundation.

Databricks is largely associated with formalizing the concept of data intelligence and even markets its platform as the Data Intelligence Platform to convey the value of having both the data lakehouse architecture and the AI components (in Databrick’s case, Mosaic AI) that allow customers to build, train and fine-tune models. Other vendors have similarly adapted their messaging around data intelligence.

For example, as part of what it now calls its Data Intelligence vision, Oracle Analytics announced Intelligent Data Lake, a reworking of existing OCI (Oracle Cloud Infrastructure) services like cataloging and integration, to create a single abstraction layer that will support both Apache Iceberg and Delta Lake formats.

If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click the Access Research button.

Access Research

Hyperscalers are taking different approaches to address the symbiotic relationship between data architecture and AI

Microsoft and Google Cloud are integrating and productizing their data services as complete solutions, exposing a lack of maturity in AWS’ fragmented approach

Microsoft made a big move when it launched Fabric, which essentially integrates seven disparate Azure data services — from data warehousing up to analytics — as part of a single platform underpinned by a unified data lake. Today, Fabric has amassed over 14,000 paid customers and a growing ecosystem of global systems integrators (GSIs) and ISVs building and selling applications on top of the platform.

Google Cloud, which has always had a strong play in data analytics, is trying to better unify key data and analytics capabilities in BigQuery to deliver a more complete, single-product experience. This includes BigLake, Google Cloud’s storage abstraction layer and services like Dataplex, so customers can apply governance tasks like lineage and profiling in Dataplex without having to leave the BigQuery interface.

Though Google Cloud’s approach may lack the level of integration compared to Microsoft Fabric, it is clear to see the direction the company is heading to help customers simplify their data estates, and ultimately capture more analytics and AI workloads.

AWS’ approach is different. Though offering the broadest set of data tools and services, from storage and ingestion up to governance, AWS is still lacking the platform mindset and strategy of its peers.

To be fair, the company has been working to better integrate services within its own ecosystem by improving data sharing between the operational database and the data warehouse (e.g., “zero-ETL” integration between Aurora and Redshift), but customers continue to stress that they have to take on more burden in the back end when crafting a data architecture on AWS.

This dynamic only reinforces the importance of AWS’ partnerships with complete data cloud platforms like Snowflake and Databricks, but of course Microsoft is also making sure it keeps these companies elevated within the Fabric ecosystem.

The GSIs are playing a prominent role in multiple facets of data, which could speak to maturing ecosystems and hyperscalers’ efforts to productize the entire data life cycle

Customers indicated that the GSIs play a prominent role in all aspects of the data strategy from change management to data architecture to governance. Just 12% of respondents say the GSIs were involved in their analytics stack, but this seemingly low percentage could be for many different reasons.

First, establishing the data architecture, or re-architecting disparate IT assets, such as data warehouses, is top of mind for many customers right now as they recognize it is a necessary step in GenAI deployment.

Secondly, the hyperscalers and pure play data platform companies are becoming more adept at delivering integrated solutions that deliver upper-stack capabilities, such as analytics based on a holistic data lake architecture. Microsoft Fabric, which has a growing ecosystem of both GSI and ISV partners, is a top example.

TBR’s newly launched Voice of the Partner Ecosystem Report found that cloud providers expect data strategy and management to be the biggest growth area coming from partners over the next two years. In fact, data strategy and management ranked higher than GenAI on its own, which is telling of what the cloud providers expect from their partners.

Graph: Role of GSI Partners in Data Strategy, 1H24 (Source: TBR)

Role of GSI Partners in Data Strategy (Source: TBR 1H24)

Though Informatica’s cloud-first vision will erode lucrative license and support revenue streams, the company is showing early signs in its ability to expand margins

Despite no longer selling perpetual licenses and actively migrating its support base to Information Data Management Cloud (IDMC) in the cloud, Informatica’s gross margins continue to expand.

Meanwhile, GAAP operating margin increased over 300 basis points year-to-year in 2Q24 as Informatica continues to benefit from economies of scale, and sign larger, more strategic contracts with customers.

Recognizing that it is navigating a highly competitive landscape, Snowflake’s investments in R&D are increasing. For context, Snowflake’s R&D accounts for a notable 50% of total revenue.

Graph: Data Cloud Platform Revenue, Growth and Profitability, 2Q24 (Source: TBR)

2Q24 Data Cloud Platform Revenue, Growth and Profitability (Source: TBR)