Here Comes KPMG: Client Trust, Alliance Focus and Tech-enabled Strategy Emphasized at 2025 Global Analyst Summit

Executing on its Collective Strategy through integrated scale and backed by robust strategic partnerships and platform-enabled services positions KPMG to remain a formidable competitor in the transforming professional services market

KPMG Global Chairman and CEO Bill Thomas kicked off the firm’s 2025 Global Analyst Summit by reinforcing the firm’s mission to be “the most trusted and trustworthy professional services firm.” As we have discussed at length across TBR’s professional and IT services research, firms like KPMG trade on trust with clients, alliance partners and employees. Putting a stake in the ground from the get-go provided Thomas and KPMG’s executives a strong foundation to rely on during the next two days as trust — at the human and technology level — was an underlying theme during presentations and demos.
 
Continuing the firm’s presentation, Thomas outlined KPMG’s evolving Collective Strategy, noting that the firm is 18 months into its latest iteration focused on “accelerating trust and growth.” Among the key enablers of achieving this goal is KPMG’s collapsing of its organizational structure from 150 country-specific member firms to a cluster of 30 to 40 regionally organized “economic units.” TBR views this pivot as the most natural evolution of KPMG’s operating model. For the Big Four, the biggest challenge is how to demonstrate value through integrated scale. Once completed, the reorganization will allow KPMG to minimize such disruption and better compete for globally sourced opportunities from what the firm calls “transactions to transformation.”
 
Following Thomas’ presentation, Carl Carande, KPMG U.S. & global lead, Advisory, and Regina Mayor, global head of Clients & Markets, amplified KPMG’s strategy, reinforcing the importance of the firm’s people, technology partners and technology — with AI the catalyst and change agent of success. For example, Carande recognized the technology relationships are changing in two ways. Relationships are becoming more exclusive, and the multipartner alliance framework offers a multiplier power — themes TBR has discussed at length throughout our Ecosystem Intelligence research stream.
 
Although KPMG continues to manage a robust network of alliance partners, highlighting its seven strategic partners — Google Cloud, Microsoft, Oracle, Salesforce, SAP, ServiceNow and Workday — solidifies its recognition of these vendors’ position throughout the ecosystem. Mayor expanded on Carande’s discussion around alliances through an industry lens, describing “alliance partners leaning in with KPMG” as they realize efforts to only sell the product will be insufficient. Meanwhile, on the KPMG side, alliance sales partners help figure out how to penetrate sector-specific alliance relationships.
 
Taking such a systematic approach across KPMG’s 15 sectors will allow the firm to demonstrate value and support its evolving Collective Strategy to act as a globally integrated firm. Additionally, tools, such as Velocity (discussed in depth on Slide 6) will arm KPMG’s consultants with the necessary collective knowledge management to serve global clients locally, further supporting the firm’s strategy.
 
One could argue that many of KPMG’s steps, including launching partner-enabled industry IP, reinforcing trust, developing regionally organized operations, outlining a select few strategic partners, and investing in platform-enabled service delivery capabilities, resonate with the moves taken by many of its Big Four and large IT services peers. We see two differences.
 
First, KPMG is laser-focused on exactly which of the strategies above to amplify, rather than taking a trial-and-error approach. Second, it is about timing. Some of KPMG’s peers have tried these strategies for some time, with limited success because of poor execution or timing. We believe that as the professional services market goes through its once-in-a-century transformation, KPMG has an opportunity to ride the wave, provided it maintains internal consensus and executes on its operational and commercial model evolution with minimal disruption.
 

KPMG’s evolution will largely stem from orchestrating alliances with seven strategic technology partners

At the event, KPMG asserted the role of tech alliance partners in building the “firm of the future.” Although the firm works with a range of ISVs, a targeted focus on the firm’s seven strategic technology partners has become key to the company’s growth profile — with 50% of its consulting business alliance-enabled in the U.S. — and, as the case of previous audit client SAP shows, KPMG has been able to overcome barriers to ultimately help clients get the most out of technology. The firm’s approach of leading with client outcomes first and technology second is unchanged, but prioritizing a tech-enabled go-to-market approach will support KPMG’s position in the market behind two major trends.
 
The first trend is the overall maturation in partner alliance structures we see from the cloud vendors. Changes in programmatic structure, including bringing sales and partner delivery closer together, and an all-around shift in how partners are viewed among historically siloed vendors, could act as enablers for KPMG’s newer capabilities, including Velocity. Second, there is a big paradigm shift underway on where the value of tech exists. Increasingly, we see the firm moving down the stack, a trend enabled by agentic AI and customers’ need to harness their own data and build new applications. Across the Big Seven, there is no shortage of innovation. As the value of AI shifts down the technology stack, KPMG can leverage the technology to deliver business outcomes to clients.
 
To fully describe KPMG’s evolving technology alliance strategy and the firm’s growing capabilities, KPMG leaders hosted a panel discussion that included leaders from Microsoft, SAP, Salesforce and KPMG clients. Todd Lohr, KPMG’s head of Ecosystems for Advisory, set the stage by saying the firm views ecosystems as more than simply a collection of one-to-one alliances, but ecosystems are, instead, many-to-many relationships, an idea TBR has increasingly heard expressed by consultancies, IT services companies, hyperscalers and software vendors.
 
Having leaders from technology partners on stage to display a very common example of a tech stack — with SAP as the system of record (SOR), Salesforce in the front office, and Microsoft as the platform with Copilot — was a strong way to depict the “many-to-many relationships” structure and KPMG’s role in orchestrating the ecosystem, especially in scenarios where some of these ISVs may not have a native integration and/or formal collaboration with one another. Lohr noted that KPMG “needs to show up understanding how complicated multiparty relationships work before showing up and working them out ad hoc at the client.” That direct acknowledgment of the challenges inherent in multiparty alliances is decidedly not something TBR consistently hears from KPMG’s peers and partners.

KPMG moves away from vendor agnosticism

One of the most important takeaways for TBR from the summit was KPMG’s willingness, in the right circumstances, to aggressively abandon the typical agnostic approach to recommending technologies and instead make a specific technology recommendation. One client example highlighted this new(ish) approach. When the client reached out for advice on a sales-enablement platform, KPMG did not take an agnostic approach and, instead, told the client Salesforce was the only choice, based on KPMG’s evaluation.
 
Part of KPMG’s proposal rested on reworking the client’s processes so Salesforce could work as much out of the box as possible, limiting costs and customizations. As KPMG leaders described it, this reflected the opposite of most consultancies’ (and enterprises’) usual approach of forcing the business processes to work with a new technology. In a competitive bidding process, the lead KPMG partner, according to the client, answered questions on the Salesforce software and implementation issues without turning to others on the KPMG team, demonstrating mastery of Salesforce and the client’s IT environment that reassured the client about KPMG’s recommendations. Further, the client expressly did not want customization layers on top of Salesforce, knowing that would be more expensive over time.
 
Notably, the “fairly comprehensive implementation,” according to the client, took less than a year, including what the client said was “a lot of investment with KPMG in change management.” Recalling best practices TBR has heard in other engagements, the client team and KPMG called the Salesforce implementation Project Leap Frogs to avoid the word “transformation,” enabled champions across the enterprise, and held firm to the approach of making minimal customizations. In discussions with TBR, KPMG leaders confirmed that not being technology agnostic was contrary to the firm’s usual practice but was becoming more common.
 
Reinforcing that notion, a KPMG leader told TBR that the firm had lost a deal after it recommended Oracle and said SAP was not the right fit. The client selected SAP (for nontechnical reasons) but later awarded, without a competitive bidding process, Oracle-specific work to KPMG after noting respect for the firm’s honesty and integrity.

KPMG showcases client-centric innovation in action

ServiceNow implementation

A client story featuring a ServiceNow implementation that brought cost savings and efficiencies to the client notably emphasized change management, a core KPMG consulting capability that is sometimes overshadowed by technologies. The client described the “really good change management program that KPMG brought” as well as the emphasis on a clear data and technology core, out-of-the-box ServiceNow implementation, and limited customizations. In TBR’s view, KPMG’s approach with this engagement likely benefited considerably from the firm’s decades-long relationship with the client, playing to one of KPMG’s strengths, which the firm’s leaders returned to repeatedly in discussions with TBR: Trusted partnerships with clients create long-standing relationships and client loyalty.

Reimagining leaders

One client story centered on a five-day “reimagining leaders” engagement at the Lakehouse facility, conducted by the KPMG Ignition team. Surprisingly, KPMG included an immersive session with an unrelated KPMG team working on an unrelated client’s project that had little overlap with the business or technology needs of the leadership engagement client.
 
According to the KPMG Ignition team, the firm showcased how KPMG works, how innovation occurs at the working level, and how KPMG creates with clients, giving them confidence in KPMG’s breadth and depth of capabilities. Echoing sentiments TBR has heard during more than a decade of visiting transformation and innovation centers, KPMG Ignition leaders said that being enclosed on the Lakehouse campus made it easier for clients to be fully present throughout the engagement and removed from the distractions of day-to-day work.
 
KPMG kept the client in the dark about what to expect from the engagement, which prevented any biased expectations from creeping in before the engagement had even started. KPMG Ignition leaders shared additional insights, noting that it was a pilot program for rising leaders at the client, providing an immersive experience that showcased the power of the KPMG partnership.
 
Throughout the five-day immersion at KPMG Lakehouse, participants learned how to apply the methodologies that fuel innovation at KPMG while staying focused on one theme: reimagining leadership of the overall company and of the participants as next-generation leaders, as well as reimagining leadership capabilities at every level of the organization.
 
KPMG equipped the client’s leaders with methodologies emphasizing storytelling, design thinking and strategic insights, and strengthened the client’s culture by fostering high-performing, collaborative teams.
 
One final comment from the Ignition Center leaders: This pilot program “highlighted the fact that AI can be viewed as a wellness play across the agency if you free up capacity and understand what can be achieved.” Based on the use case and sidebar discussions TBR had with KPMG Ignition leaders, we believe Ignition Centers continue to evolve, although the basics remain the same: Get clients into a dedicated space outside their own office, use design thinking, and focus on business and innovation and leadership and change, not on technology.

The art of the possible

A final client story, presented on the main stage, wove together the themes of AI, transformation and trust. The client, a chemicals manufacturer and retailer, said KPMG consistently shared “what’s possible,” essentially making innovation an ongoing effort, not a one-off aspect of the relationship.
 
The client added that his company and KPMG had “shared values … and we understand each others’ cultures,” in part reinforced by KPMG dedicating the same team to the client during a multiyear engagement.
 
In TBR’s view, KPMG’s decision to highlight this client reinforced everything KPMG leaders had been saying during the summit: Relationships, built on consistent delivery and continually coming to the client with ideas and innovations, plus a commitment to the teaming aspect of the engagement, are KPMG’s superpower. Notably, this client was not a flashy tech company, a massive financial institution or a well-known government agency, and the work KPMG did was not cutting-edge or highly specialized but rather core KPMG capabilities — in short, what KPMG does well.

Velocity and GenAI: KPMG’s client-first approach to AI adoption and transformation

KPMG dedicated the second day of the analyst summit to AI, a decision that reflected the firm’s overall approach: Business decisions come first, enabled by technology. Supporting the firm’s AI strategy, this month and next KPMG will launch Velocity, a knowledge platform and framework for the KPMG workforce that pulls together every capability, offering, and resource across the firm. According to KPMG leaders, Velocity reinforces the firm’s multidisciplinary model and will become the primary way KPMG brings itself to clients.
 
In addition to sharing knowledge across the global firm, Velocity will help KPMG’s clients find the right AI journey by allowing them to select a strategic objective they are trying to achieve, which function(s) they want to transform, and which technology platforms they want to transform on. The platform also reaffirms the firm’s acknowledgment of data’s role in AI. In fact, part of the rationale for Velocity was bringing the data modernization and AI business together while maintaining a focus on a sole client outcome. This means KPMG does not care whether customers build their data foundation with a hyperscaler or internally; as one leader in the AI Journey breakout session said, it is just about “helping customers do what they want to do.”
 
Velocity includes preconfigured journeys based on specific client needs, as developed, understood and addressed in all of KPMG previous engagements. Similar to many consultancies, KPMG begins engagements by developing an understanding of clients’ strategic needs and issues, rather than their technology stack. (TBR comment: easy to say, hard to do, especially when a firm has practices built around specific technologies).
 
Velocity will bring a “tremendous amount of information” and is “highly tailorable,” according to a KPMG leader, who also noted that the platform’s adoption, use and usefulness over time will be key. KPMG leaders said the core aspects of AI — even agentic AI — are all the same, separated only by planning and orchestration. Velocity, then, is a KPMG tool, but every step is client-centric, which comes back to understanding clients’ key business issues, not simply their technology stack.
 
The launch of Velocity internally brings to life KPMG’s approach to AI. Amid caution on the client side around the adoption and implementation of AI technologies, KPMG’s David Rowlands, head of AI, discussed how KPMG wants to be customer zero around AI, helping to ease clients’ ethics and security concerns by working through experimentation and into adoption and scale. Rowlands highlighted the firm’s attention to knowledge and need to fully benefit from AI. Training around AI, including the definition of AI and how to use it; creating trust within AI; and learning effective AI prompts also fit within this strategy, enabling both KPMG and clients to effectively embed AI across people and operational strategies.

Velocity, AI and the future of audits

Three other AI-centric comments from KPMG leaders stood out for TBR:

  • With AI, “the road to value is paved with human behavior and change,” according to Rowlands, reflecting the firm’s emphasis on the business over the technology and the importance of change management — a core KPMG consulting strength.
  • Rowlands also noted that AI is a critical national infrastructure, dependent on energy, connectivity and networks, and should be considered a national investment priority and national security issue. In TBR’s view, framing AI this way — not as just a tool or another service to be sold — adds credibility to KPMG’s AI efforts.
  • According to Per Edin, KPMG’s AI leader, “ROI is clear and documented, but still not enough adoption to be as measurable as desired.” In TBR’s view, Edin’s sentiments track closely with TBR’s Voice of the Customer and Voice of the Partner research, which have repeatedly shown that interest and investments in AI have outpaced adoption, particularly at scale.

In a breakout session, KPMG walked through the firm’s well-established KPMG Clara platform, a tool designed to help the firm complete its audits more quickly and accurately. In essence, KPMG creates a digital twin of an organization, reflecting the firm’s understanding of where AI can be applied. KPMG Clara Transaction Scoring enables auditors to deliver what the firm calls “audit evidence” and note “outlier” transactions. According to KPMG leaders, “AI agents perform audit procedures and document results for human review, just like junior staff.”
 
Critically, KPMG Clara audits every transaction, not just a sample of transactions, increasing the likelihood of catching problems, issues and outliers. By flagging high-risk transactions, KPMG can deploy professionals to focus on solving real problems rather than adjudicating false positives or meaningless issues. In TBR’s view, this represents the proverbial “higher-value task” long-promised by robotic process automation, AI and analytics.
 
When pressed by TBR, KPMG leaders said clients were not looking for rate cuts but rather for higher-quality audits and new insights into their operations. Importantly, clients also expect to spend less time on an audit, freeing up professionals’ time: The client can do what they do, and KPMGers can stay focused on reflected issues and generate new insights.
 
TBR remains a bit skeptical, but if clients do not expect a rate cut when KPMG deploys AI to speed up the audit process and instead expect to spend less time internally on what should be a higher-quality audit, TBR considers that a fantastic way to position AI while also reducing KPMG’s professionals’ time. There are two unanswered questions: What happens to the apprenticeship model, in which less-experienced KPMG professionals learn the art, not the science, of audit? And, in a few years, will 95,000 professionals conduct 400,000 audits (twice the current number) or will 50,000 professionals (half the current staff) complete 200,000 audits?
 
Regardless, as the company rolls out internally developed generative AI (GenAI) tools, the learning and experience captured through the firm’s implementation and change management process will undoubtedly be integrated into customer engagements involving third-party solutions. With SAP and Salesforce in attendance, KPMG zeroed in on each vendor’s AI strategy and how the firm plans to support it. To focus on Salesforce, Lohr echoed Salesforce CEO Marc Benioff in calling Agentforce the most successful Salesforce launch ever, which suggests a recognition from KPMG leadership of Salesforce’s agentic AI strategy.
 
For its part, KPMG highlighted the recent launch of an Agentforce Incubator, an experimental experience that can be delivered to clients from any location — a client site, Salesforce event or a KPMG office — to ignite the ideation stage and begin exploring the road map from proof of concept to production. During one-on-one conversations, TBR explored KPMG’s view of its role in the agentic AI, and we found it to be both pragmatic and valuable — similar to how the firm must be opinionated in broader digital transformation engagements.
 
KPMG’s journey to becoming an AI orchestrator will require the firm to take a stance on a vendor-by-vendor basis and arrive on-site with a preconceived understanding of the best path forward for clients given their goals. In addition to having an opinion, KPMG also recognizes it must help facilitate the road maps it lays out to clients, which will involve a heavy change management component, as well as a more technical design and development element. With the Agentforce example, once a targeted business outcome is established, an AI agent needs to be designed and developed to achieve the outcome. In many cases, a customer may lack the internal technical resources necessary to build the agent and tackle the problem. As KPMG avoids vendor agnosticism, the company can focus on building out technical resources with the vendors it chooses, building deeper benches with technical training associated with its strategic partners.

KPMG’s Lakehouse offers unique setting for analyst event

As it did less than 18 months ago when KPMG broke from the traditional analyst event style, the firm did it again. Hosting 62 analysts and dozens of global executives, clients and partners at a flagship Lakehouse facility for two days of both formal and informal interactions, presentations, client use cases and demos, KPMG demonstrated agility in terms of the delivery and engagement format, yet, with a steady hand, continued to execute on its vision wrapped around its four-part framework: Connected, Powered, Trusted and Elevated.
 
KPMG held one-on-one sessions between analysts and executives midway through the first day so that executives were present and engaged. Additionally, KPMG saved the all-about-AI-and-nothing-else sessions for the second day, which came off as, “We get AI is important, but we are also realistic and keeping our heads on straight and not being ‘me too, me too’ about AI.” KPMG senior executives sat in on both the client case study and platform breakout sessions. Subtle message to analysts: This stuff matters enough across the firm to be worth KPMG partners’ time even if it is not in their area.

Conclusion

As a member of the Big Four, KPMG has brand permission and a breadth of services that are relevant to nearly every role in any enterprise. As the firm executes on its Collective Strategy, TBR believes KPMG will accelerate the scale and completeness of its offerings, building on a solid foundation and expanding the gaps between KPMG and other consulting-led, technology-enabled professional services providers.
 
KPMG’s four-part framework — Connected, Powered, Trusted and Elevate — resonates with clients and technology partners, provides KPMG’s professionals with clear insight into the firm’s strengths and strategy, and underpins a transformation already well underway. The evolving strategy will challenge KPMG’s leaders to execute on the promise of that transformation during the next wave of macroeconomic pressures, talent management battles and technology revolutions. At the same time, KPMG’s leaders recognize that their priorities are transforming the firm’s go-to-market approach, unlocking the power of the firm’s people, reimagining ways of working, and innovating capabilities and service enhancements.
 
Success in executing these priorities, in TBR’s view, will come as KPMG shifts from building a foundation to scaling alongside the growing needs of its clients and as the era of GenAI presents yet another opportunity and challenge. Striking the right balance between elevating the potential of GenAI as a value creator and accounting for commercial and pricing model implications will test the durability of KPMG’s engagement and delivery frameworks.
 
Although the firm has placed in motion many of the aforementioned investments over the past 12 to 18 months, the one opportunity that is changing relates to speed. As one enterprise buyer recently explained to TBR: “GenAI will force all services vendors to change. The [ones] who [will] be [the] most successful will be [those] who do it fast.” With speed comes risk. Departing from a historically risk-averse culture rooted in an audit and assurance heritage will present the biggest challenge — and also an opportunity — for KPMG. Choosing the alternative might not be an option.
 
Special report contributors: Catie Merrill, Senior Analyst; Kelly Lesizcka, Senior Analyst; Alex Demeule, Analyst; Boz Hristov, Principal Analyst

TBR Announces On-demand Availability of 1Q25 TBR Insights Live Webinars

Technology Business Research, Inc. (TBR) is pleased to announce on-demand availability of all TBR Insights Live webinars that aired during 1Q25. Each session features TBR’s top experts discussing key trends, recent analysis and market dynamics across covered industries.
 
1Q25 webinars can be viewed on our YouTube channel, and presentation decks can be downloaded via the linked titles below.

Explore the full lineup of 1Q25 TBR Insights Live webinars

MWC25: Disruptive Technologies and Business Models Create New Opportunities for the Mobile Ecosystem

  • How the telecom industry intends to derive business outcomes from AI
  • How enterprises are progressing in their digital transformations and incorporating private networks
  • Where in the mobile ecosystem new value is being created and what telcos need to do to generate ROI from new opportunities

Ecosystem Intelligence for IT Services, Cloud and Consultancies: Strategic Insights for 2025 Success

  • How to place strategic ecosystem bets on alliance partners that are well-positioned for the next growth wave
  • How competitors are gaining ground with common alliance partners through sales programs, go-to-market motions and training
  • How to create unique value with alliance partners that resonates with end customers

Digital Transformation Outlook: Strategy Rebound, GenAI Impact and Ecosystems Importance in 2025

  • Why strategy consulting will rebound in 2025, and which consultancies will benefit
  • Discussion of ERP consolidation (e.g., S4Hanna, mainframe modernization)
  • Cloud migration services, custom apps development and workflow management
  • How generative AI (GenAI)-enabled solutions will upend organizational structures and business models for IT services and consultancies, with follow-on effects for partners
  • How the emergence of ecosystem intelligence as a strategic priority will impact IT services companies, consultancies and technology alliance partners

Cloud Market 2025: How GenAI Will Shape the Future

  • How GenAI will offset some of the slowdown in cloud revenue growth
  • How Microsoft will challenge Amazon Web Services’ leadership in IaaS and PaaS
  • How SaaS vendors will monetize GenAI in 2025

6G: How Government Intervention Globally Will Shape the Next Generation of Telecom

  • What spectrum bands 6G will likely leverage
  • How 6G will shape communication service providers’ capex investments
  • How governments might get involved to ensure 6G becomes a reality

AI PCs in 2025: Unlocking Mass Appeal and Overcoming Market Challenges

  • The potential impact of AI PCs on PC refresh cycles in 2025
  • How PC makers will set themselves apart
  • Expectations for the PC market overall in 2025 based on TBR’s latest research and analysis

Navigating GenAI: Insights, Strategies and Opportunities for 2025

  • How GenAI is impacting buyer-vendor relationships and what is next in the evolution of their business models
  • How tech and services companies are using alliances to extend their reach within enterprises and across the larger GenAI — and emerging tech — ecosystem
  • Which consultancies, IT services vendors, cloud and software companies, and infrastructure players are best positioned for the next wave of GenAI adoption

 
Visit the TBR Insights Live page to discover what our experts will be discussing next.

Comcast Business Nears $10B in Annual Revenue and Accelerates Enterprise Growth but Faces Headwinds from Competitive and Macroeconomic Pressures

2025 Comcast Business Analyst Conference, Philadelphia, April 2-3, 2025 — A select group of industry analysts gathered at the Comcast Center in Philadelphia to hear from Comcast Business leaders about the unit’s progress and success with its sales and go-to-market strategies. The central theme of the event was “Everything, Everywhere, All at Once,” which reflects Comcast Business’s ability to provide solutions to its customers through advancements in areas including AI implementation, network technologies, industry partnerships, and acquisitions, including Masergy and Nitel. The event was hosted by CNBC Senior Markets Correspondent Dominic Chu and included a State of the Business session from Comcast Business President Edward Zimmermann, a Strategy & Vision session from Comcast Business Chief Product Officer Bob Victor, and an update on Comcast’s network from Chief Network Officer Elad Nafshi. Also included were panel discussions with senior leadership as well as speaker sessions featuring industry partners Cisco and Intelisys and a Comcast Business customer within the brewing industry.

 

TBR perspective

Since its inception in 2006, Comcast Business has consistently grown into a more formidable competitor in the B2B telecom space. Most of this growth has stemmed from the SMB segment, where Comcast Business’ superior DOCSIS-based, hybrid-fiber coax (HFC) fixed broadband offerings were priced right compared to non-fiber-to-the-premises (FTTP) telco offerings and addressed demand for more bandwidth. Comcast Business’ strategy has evolved over the past decade to target additional growth segments including midmarket businesses and multinational enterprises via the operator’s managed services portfolio, strategic acquisitions including Masergy, and partnerships with global operators spanning 130 countries.
 
Other key growth drivers over the past decade include Comcast Business’ increasing focus on the public sector, including federal agencies, as well as the launch of portfolio segments including Comcast Business Mobile and value-added services in areas including SD-WAN, security and unified communications.
 
The success of Comcast Business’ growth strategies has enabled the company to essentially reach its long-term goal of generating $10 billion in annual revenue (Comcast Business generated $9.7 billion in revenue for full-year 2024). Comcast Business, which increased total revenue by about 5% in 2024, is also outpacing incumbent operators competing in the B2B market in revenue growth as service providers including AT&T, Verizon and Lumen continue to face significant revenue erosion from customers disconnecting from legacy data and voice services.
 
Despite its recent strong momentum, Comcast Business will encounter obstacles as it tries to increase revenue due to headwinds including competition from the expansion of fixed wireless access (FWA) and FTTP services, and macroeconomic pressures that will cause businesses to optimize spending on connectivity services. Despite its ability to sustain revenue growth in 2024, driven by increased revenue from enterprise solutions and higher average rates from small business customers, Comcast Business lost a net of 16,000 customer relationships in 2024 (compared to customer relationship net additions of 17,000 in 2023 and 52,000 in 2022).
 
TBR attributes rising B2B FWA adoption, especially for Verizon and T-Mobile, as the primary driver of the net loss as more businesses are gravitating to FWA for its cost savings over traditional fixed broadband services as well as its greater ease of installation, which is helping to support companies seeking to quickly launch new branch locations. Comcast Business will also face competitive pressures from operators including AT&T, T-Mobile and Verizon that are expanding their FTTP footprints via organic builds and acquisitions, which will give these operators new opportunities to offer converged service plans combining mobile and broadband services. Comcast Business will also continue to face pressures from businesses continuing to migrate off of pay-TV and VoIP (voice over IP) services.
 
We expect that current macroeconomic challenges, including mass layoffs within the private and public sectors and uncertainty around tariff impacts, will create headwinds for Comcast Business and the overall U.S. B2B telecom market. Though network connectivity solutions such as broadband will remain essential to businesses, companies will need to optimize spending to counter macroeconomic pressures.
 
TBR believes these challenges will require Comcast Business to become more dependent on providing a stronger value proposition to retain and grow its customer base in addition to its reliance on the strengths of its network and solutions portfolio. Comcast Business is also addressing these industry shifts by evolving its portfolio of adjacent solutions in areas including secure networking, cybersecurity, and managed IT services to augment revenue from its core broadband services.

Impact and opportunities

Comcast Business is leveraging AI to optimize its network performance and sales and customer service capabilities

Sessions throughout the event discussed how deeper AI implementation will enable Comcast to enhance capabilities such as network performance and sales and customer service, thereby improving overall efficiency and customer experience. For instance, AI integration is enabling Comcast to automate over 99.7% of all software changes that it is making on its network, which is supporting network self-healing capabilities that can quickly resolve outages. These capabilities will help Comcast Business to more effectively retain customers as recent disruptions experienced by rivals, such as AT&T’s two major prolonged network outages in 2024, have resulted in customer losses and tarnished brand images for impacted operators.
 
AI is also enabling Comcast to enhance the cybersecurity of its network, including through the development of a next-generation firewall embedded into the network, which leverages GenAI and does not require dedicated CPE (customer premises equipment).
 
The vendor is also focused on training its customer care and sales teams to more effectively leverage AI to improve customer support and enhance operations. Comcast Business is increasing the number of AIOps use cases and applying AI and machine learning (ML) across its managed solutions platform to improve service delivery, assurance and management, both for customers and the internal teams that support customers (e.g., help desk, network operations center [NOC] and security operations center [SOC]).
 
Comcast expects AI to not only improve network and operational efficiencies but also provide significant revenue-generation opportunities, though the company is still in the early stages of developing strategies to do so. For instance, Comcast’s edge computing resources enable the company to support ultra-low latency speeds of less than 1 millisecond to many of its customers. These capabilities will position Comcast to optimize connectivity and user experience for future advanced AI applications in areas such as AR/VR that will be more dependent on ultra-low latency, though current AI applications such as ChatGPT are not as dependent on ultra-low latency as they are mainly text-based.

Comcast Business continues to accelerate its data speeds to incentivize customers, though industry pricing pressures will hamper connection growth

Throughout the event, Comcast Business promoted its accelerating data speeds, which are aided by network advancements such as DOCSIS 4.0 and mid-split upgrades to Comcast’s HFC network. Enhancements to Comcast Business’ connectivity portfolio include extending the availability of its Dedicated Internet solution and upgrading the service to provide symmetrical download and upload speeds up to 200Mbps over HFC or up to 400Gbps over fiber.
 
Comcast expects to accelerate its Dedicated Internet solution to reach symmetrical speeds of 300Mbps over HFC and reach a total of over three million passings this year. Other updates to the Dedicated Internet solution include adding a network reliability guarantee, which provides SLAs ensuring 99.99% network uptime, and enhanced proactive network monitoring, which enables IT teams to optimize performance.
 
TBR believes these updates will help to attract clients with bandwidth-intensive workloads, especially customers with more stringent SLA requirements necessitating minimal network downtime. However, TBR also recognizes that competitive pricing and the overall value proposition provided by operators are becoming more influential factors in contract wins within the B2B market. As evidenced by the robust uptake of FWA, small businesses are especially concerned with the value they are getting for the price paid, and they are migrating to lower-cost broadband offerings to obtain internet access that more closely meets their needs and aligns with what they are willing to pay.
 
T-Mobile and Verizon are feeding this market shift to “rightsized bandwidth” through clever marketing and customer education about what businesses actually need. Comcast Business will need to demonstrate why its cutting-edge broadband offerings are necessary for its customers in order to justify the premium pricing. It also has an opportunity to further strengthen the value proposition of its value-added services when combined with its core broadband services. Comcast Business Mobile is a key portfolio segment Comcast Business can further leverage to combat pressures from rivals’ FWA services and converged service bundles.
 
Though Comcast Business Mobile connections are not reported by Comcast, TBR believes just a small portion of Comcast Business customers are currently enrolled in the service as only 12% of Comcast’s residential broadband customer were enrolled in Xfinity Mobile in 4Q24. Heavier promotional activity, such as offering free lines for a limited time, could help Comcast Business Mobile compete more aggressively against rival B2B smartphone plans while creating a stickier ecosystem to retain high-value broadband customers long-term. Comcast Business Mobile’s impact is limited to the SMB market, however, as the brand supports a maximum of 20 lines per business customer under Comcast’s current MVNO agreement with Verizon.

Comcast will strengthen its enterprise business and expand sales channels via the Nitel acquisition

The analyst event coincided with Comcast Business closing its acquisition of Chicago-based managed service provider Nitel on April 1 from private equity firm Cinven. Nitel is a NaaS (Network as a Service) provider offering solutions in areas such as networking (including SD-WAN and SASE [Secure Access Service Edge]), cloud services and cybersecurity.
 
Through the purchase, Comcast Business will expand its footprint in the midmarket and enterprise customer segments and gain Nitel’s 6,600 clients across the U.S. within verticals including financial services, healthcare and education. Acquiring Nitel also enables Comcast Business to expand its channel distribution strategy to more effectively target new sales opportunities within the midmarket and enterprise segments.
 
Comcast Business is also gaining AI and software tools from the Nitel acquisition that will enable it to enhance its sales and customer service capabilities. These benefits include robust orchestration capabilities, an instant quoting tool that makes it easier to price and establish deals across multiple vendors and sites, and a digital dashboard that offers a single-pane-of-glass view of deployments.

Conclusion

Comcast Business remains in a relatively strong position within the B2B market as the company continues to outpace its competitors in revenue growth and will continue to expand its client base within the midmarket and large enterprise segments by leveraging its Masergy and Nitel acquisitions. Comcast Business also has an opportunity to increase revenue by refining its international strategy and more deeply leveraging assets — such as its managed services portfolio, partnerships with global operators across 130 countries, and numerous acquisitions including Sky, Masergy, Nitel, Deep Blue Communications and Blueface — that enable it to support multinational corporations.
 
However, SMB, which accounts for the majority of Comcast Business’ revenue, is becoming a more challenging segment in which to grow market share as FWA competition and macroeconomic challenges lead to spending constraints. These headwinds will require Comcast Business to become more focused on enhancing its value proposition to retain and grow its SMB client base and combat competitive pressures in the market.

Sheer Scale of GTC 2025 Reaffirms NVIDIA’s Position at the Epicenter of the AI Revolution

As the undisputed leader of the AI market, NVIDIA and its GPU Technology Conference (GTC) are unmatched compared to other companies and their respective annual events when it comes to the enormous impact they have on the broader information technology market. GTC 2025 took place March 17-21 in San Jose, Calif., with a record-breaking 25,000 in-person attendees — and 300,000 virtual attendees — and nearly 400 exhibitors on-site to showcase solutions built leveraging NVIDIA’s AI and accelerated computing platforms.

NVIDIA GTC 2025: Pioneering the future of AI and accelerated computing

In 2024 NVIDIA CEO and cofounder Jensen Huang called NVIDIA GTC the “Woodstock of AI,” but to lead off the 2025 event’s keynote address at the SAP Center, he aptly changed his phrasing, calling GTC 2025 “the Super Bowl of AI,” adding that “the only difference is that everybody wins at this Super Bowl.”
 
While the degree to which every tech vendor “wins” in AI will vary, NVIDIA currently serves as the rising tide that is lifting all boats — in this case, hardware makers, ISVs, cloud providers, colocation vendors and service providers — to help accelerate market growth despite the economic and geopolitical struggles that have hampered technology spending in the post-COVID era. NVIDIA’s significant investments not as a GPU company but as a platform company — delivering on innovations in full-stack AI and accelerated computing infrastructure and software — have provided much of the foundation upon which vendors across the tech ecosystem continue to build their AI capabilities.
 
During the event, which also took place at the nearby San Jose McEnery Convention Center, Huang shared his vision for the future, emphasizing the immense scale of the inference opportunity while introducing new AI platforms to support what the company sees as the next frontiers of AI. Additionally, he reaffirmed NVIDIA’s commitment to supporting the entire AI ecosystem by building AI platforms, rather than AI solutions, to drive coinnovation and create value across the entire ecosystem.

The transformation of traditional data centers into AI factories represents a $1 trillion opportunity

The introduction of ChatGPT in November 2022 captured the attention of businesses around the world and marked the beginning of the generative AI (GenAI) revolution. Since then, organizations across all industries have invested in the exploration of GenAI technology and are increasingly transitioning from the prototyping phase to the deployment phase, leveraging the power of inference to create intelligent agents, power autonomous vehicles and drive other operational efficiencies. As AI innovation persists, driven largely by the vision of Huang and the increasingly capital-rich company behind him, new AI paradigms are emerging and NVIDIA is helping the entire AI ecosystem to prepare and adapt.

The rise of reasoning

On Jan. 27, otherwise known as DeepSeek Monday, NVIDIA stock closed the day down 17.0% from the previous day’s trading session, with investors believing DeepSeek’s innovations would materially reduce the total addressable market for AI infrastructure. DeepSeek claimed that by using a combination of model compression and other software optimization techniques, it had vastly reduced the amount of time and resources required to train its competitive AI reasoning model, DeepSeek-R1. However, at GTC 2025, NVIDIA argued that investors misunderstood implications on the inference side of the AI model equation.
 
Traditional knowledge-based models can quickly return answers to users’ queries, but because basic knowledge-based models rely solely on the corpus of data that they are trained on, they are limited in their ability to address more complex AI use cases. To enhance the quality of model outputs, AI model developers are increasingly leveraging post-training techniques such as fine-tuning, reinforcement learning, distillation, search methods and best-of-n sampling. However, more recently test-time scaling, also known as long thinking, has emerged as a technique to vastly expand the reasoning capabilities of AI models, allowing them to address increasingly complex queries and use cases.

From one scaling law to three

In the past, pre-training scaling was the single law dictating how applying compute resources would impact model performance, with model performance improving as pre-training compute resources increased. However, at GTC 2025, NVIDIA explained two additional scaling laws in effect — post-training scaling and test-time scaling. As their names suggest, model pre-training and post-training are on the AI model training side of the equation. However, test-time scaling takes place during inference, allocating more computational resources during the inference phase to allow a model to reason through several potential responses before outputting the best answer.
 
Traditional AI models operate quickly, generating hundreds of tokens to output a response. However, with test-time scaling, reasoning models generate thousands or even tens of thousands of thinking tokens before outputting an answer. As such, NVIDIA expects the new world of AI reasoning to drive more than 100 times the token generation, equating to more than 100 times the revenue opportunity for AI factories.
 
During an exclusive session with industry analysts, Huang said, “Inference is the hardest computing at scale problem [the world has ever seen],” dispelling the misnomer that inference is somehow easier and demands fewer resources than training while also indirectly supporting Huang’s belief that the transformation of traditional data centers into AI factories will drive total data center capital expenditures (capex) to $1 trillion or more by 2028.
 

Graph: NVIDIA Revenue, Growth and Projections (Source: TBR)

NVIDIA Revenue, Growth and Projections (Source: TBR) — If you believe you have access to TBR’s NVIDIA research via your employer’s enterprise license or would like to learn how to access the full research, click here.


 
While on the surface, $1 trillion in data center capex by 2028 sounds like a lofty threshold, TBR believes the capex amount and timeline are feasible considering NVIDIA’s estimate that 2024 data center capex was around $400 billion.
 
Additionally, during 1Q25, announcements centered on investment commitments to build out data centers have become increasingly common, and TBR expects this trend to only accelerate over the next few years. For example, in January the Trump administration announced the Stargate Project with the intent to invest $500 billion over the next four years to build new AI infrastructure in the United States.
 
However, it is worth noting that Stargate’s $500 billion figure represents more than just AI servers; it includes other items such as the construction of new energy infrastructure to power data centers. TBR believes the same holds true for NVIDIA’s $1 trillion figure, especially when considering TBR’s 2024 total AI server market estimate of $39 billion.

The more you buy, the more you make: NVIDIA innovates to maximize potential AI factory revenue

To support the burgeoning demands of AI, NVIDIA is staying true to the playbook through which it has already derived so much success — investing in platform innovation and the support of its growing partner ecosystem to drive the adoption of AI technology across all industries.

AI factory revenue relies on user productivity

Reasoning capabilities allow models to meet the demands of a wider range of increasingly complex AI use cases. Although the revenue opportunity of AI factories increases as AI reasoning drives an exponential rise in token generation, expanding token generation also creates bottlenecks within AI factories and inevitably there is a tradeoff. To maximize revenue potential, AI factories must optimize the balance between token volume and cost per token.
 
From the perspective of an AI inference service user, experience comes down to the speed at which answers are generated and the accuracy of those answers. Accuracy is tied directly to the underlying AI model(s) powering the service and can be thought of as a constant variable in this scenario, while the speed at which answers are generated for a single user is dictated by the rate of output token generation for that specific user. Having more GPUs dedicated to serving a single user results in an increased rate of output token generation for that user and is something that users are typically willing to pay a premium for.
 
However, in general, as more GPUs are dedicated to serving a single user, the overall output token generation of the AI factory falls. On the opposite end of the spectrum, an AI factory can maximize its overall output token generation by changing GPU resource allocations to serve a greater number of users at the same time; however, this has a negative impact on the rate of output tokens generated per user, increasing request latency and thereby detracting from the user’s experience.
 
As NVIDIA noted during the event, to maximize revenue, AI factories must optimize the balance of total factory output token generation and the rate of output token generation per user. However, once the optimal allocation of GPU resources is determined, revenue opportunity hits a threshold. As such, to increase the productivity and revenue opportunity of AI factories, NVIDIA supports the AI ecosystem with its investments in the development of increasingly performant GPUs, allowing for greater total factory output token generation as well as increased rates of output token generation per user.
 
During his keynote address, Huang laid out NVIDIA’s four-year GPU road map, detailing the upcoming Blackwell Ultra as well as the NVIDIA GB300 NVL72 rack, which leverages Blackwell Ultra and features an updated NVL72 design for improved energy efficiency and serviceability. Additionally, he discussed the company’s Vera Rubin architecture, which is set for release in late 2026 and marks the shift from HBM3/HBM3e to HBM4 memory, as well as Vera Rubin Ultra, which is expected in 2027 and will leverage HBM4e memory to deliver higher memory bandwidth. To round out NVIDIA’s four-year road map, Huang announced the company’s Feynman GPU architecture, which is slated for release in 2028.

Scale up before you scale out, but NVIDIA supports both

In combination with NVIDIA’s updated GPU architecture road map, Huang revealed preliminary technical specifications for the Vera Rubin NVL144 and Rubin Ultra NVL576 racks, with each system being built on iterative generations of the company’s ConnectX SuperNIC and NVLink technologies, promising stronger networking performance with respect to increased bandwidth and higher throughput. NVIDIA’s growing focus on NVL rack systems underscores Huang’s philosophy that organizations should scale up before they scale out, prioritizing the deployment of fewer densely configured AI systems compared to a greater number of less powerful systems to drive simplicity and workload efficiency.
 

Graph: 2024 Data Center GPU Market Share (Source: TBR)

2024 Data Center GPU Market Share (Source: TBR) — If you believe you have access to TBR’s NVIDIA research via your employer’s enterprise license or would like to learn how to access the full research, click here.


 
Networking has and continues to become more integral to NVIDIA’s business as the company’s industry-leading advancements in accelerated compute have necessitated full-stack AI infrastructure innovation. While NVIDIA drives accelerated computing efficiency on and close to the motherboard through the design of increasingly high-performance GPUs and CPUs and its ongoing investments in ConnectX and NVLink, the company is also heavily invested in driving AI infrastructure efficiency through its networking platform investments in Quantum-X InfiniBand and Spectrum-X Ethernet.
 
Although copper is well suited for short-distance data transmissions, fiber optics is more effective over long distances. As such, the scale-out of AI factories requires an incredible number of optical transceivers to connect every NIC (network interface card) to every switch, representing the single largest hardware component in a typical AI data center. NVIDIA estimates that optical transceivers consume approximately 10% of total computing power in most AI data centers. During his keynote address, Huang announced NVIDIA Photonics — what the company describes as a coinvention across an ecosystem of copacked optics partners — to reduce power consumption and the number of discrete components in an AI data center.
 
Leveraging components from partners, including TSMC, Sumitomo and Corning, NVIDIA Photonics allows NVIDIA to replace pluggable optical transceivers with optical engines that are copackaged with the switch ASIC. This allows optical fibers to plug directly into the switch with the onboard optical engine processing and converting incoming data — in the form of optical signals — into electrical signals that can then be immediately processed by the switch. Liquid-cooled Quantum-X Photonic switch systems are expected to become available later this year ahead of the Spectrum-X Photonic switch systems that are coming in 2026. NVIDIA claims that the new systems improve power efficiency by 3.5x while also delivering 10x higher resiliency and 1.3x faster time to deploy compared to traditional AI data center architectures leveraging pluggable optical transceivers.

Securing the developer base

Adjacent to what the company is doing in the data center, NVIDIA announced other, more accessible Blackwell-based hardware platforms, including RTX PRO Series GPUs, DGX Spark and DGX Station, at GTC 2025. At CES (Consumer Electronics Show) 2025 in January, NVIDIA made two major announcements: Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students with access to the Grace Blackwell platform; and the next-generation GeForce RTX 50 Series of consumer desktop and laptop GPUs for gamers, creators and developers.
 
Building on these announcements, at GTC 2025 NVIDIA introduced DGX Spark, the new name of the previously announced Project DIGITS, leveraging NVIDIA GB10 Grace Blackwell Superchip and ConnectX-7 to deliver 1,000 AI TFLOPS (tera floating-point operations per second) performance in an energy-efficient and compact form factor. DGX Spark will come pre-installed with the NVIDIA AI software stack to support local prototyping, fine-tuning and inferencing of models with up to 200 billion parameters, and NVIDIA OEM partners ASUS, Dell Technologies, HP Inc. and Lenovo are already building their own branded versions.
 
To complement its recently unveiled GeForce RTX 50 Series, NVIDIA announced a comprehensive lineup of RTX PRO Series GPUs for laptops, desktops and servers with “PRO” denoting the solutions’ intent to support enterprise applications. At the top end of the lineup, RTX PRO 6000 will deliver up to 4,000 AI TFLOPS performance, making it the most powerful discrete desktop GPU ever created. While DGX Spark systems will be available beginning in July, DGX Station is expected to be released toward the end of the year. DGX Station promises to be the highest-performing desktop AI supercomputer, featuring the GB300 Grace Blackwell Ultra Desktop Superchip and ConnectX-8, with OEM partners, including ASUS, Box, Dell Technologies, HP Inc., Lambda and Supermicro, building systems. Together, these announcements highlight NVIDIA’s commitment to democratizing AI and supporting developers.

Software is the most important feature of NVIDIA GPUs

In TBR’s 1Q24 Semiconductor Market Landscape, NVIDIA led all vendors in terms of trailing-12 month (TTM) corporate revenue growth, with hardware revenue accounting for an estimated 88.9% of the company’s TTM top line. However, while NVIDIA’s industry-leading top-line growth continues to be driven primarily by increasing GPU and AI infrastructure systems sales, the reason customers choose NVIDIA hardware ultimately boils down to two interrelated factors: the company’s developer ecosystem, and its AI platform strategy.

The CUDA advantage

In 2006 NVIDIA introduced CUDA (Compute Unified Device Architecture), a coding language and framework purpose-built to enable the acceleration of workloads beyond graphics. With CUDA, developers gained the ability to code applications optimized to run on NVIDIA GPUs. Since CUDA’s inception, NVIDIA has relentlessly invested in strengthening CUDA, supporting backward compatibility, publishing new CUDA libraries, and giving developers new resources to optimize the performance and simplify the building of applications.
 
As such, many legacy AI applications and libraries are rooted in CUDA, whose documentation is light years ahead of competing platforms, such as AMD ROCm. With respect to driving AI efficiency, several NVIDIA executives and spokespeople at GTC 2025 circled back to the notion that, when it comes to enabling the most complex AI workloads of today and tomorrow, software optimization is as important as, if not more important than, infrastructure innovation and optimization, underscoring the unique value behind NVIDIA’s CUDA-optimized GPUs. In short, at the heart of NVIDIA’s comprehensive AI stack and competitive advantage is CUDA, and as Huang emphasized to the attending industry analysts, “Software is the most important feature of NVIDIA GPUs.”

A new framework for AI inference

As the AI inference boom materializes, NVIDIA has leveraged the programmability of its GPUs to optimize the performance of reasoning models at scale, with Huang introducing NVIDIA Dynamo at GTC 2025. Dynamo is an open-source modular inference framework that was designed to serve GenAI models in multinode distributed environments and specifically developed for accelerating and scaling AI reasoning models to maximize token revenue generation.
 
The framework leverages a technique called “disaggregated serving,” which separates the processing of input tokens in the prefill phase of inference from the processing of output tokens in the decode phase. Traditional large language model (LLM) deployments leverage a single GPU or GPU node for both the prefill and decode phases, but each phase has different resource requirements, with prefill being compute-bound and decode being memory-bound. As NVIDIA’s VP of Accelerated Computing Ian Buck put it, “Dynamo is the Kubernetes of GPU orchestration.”
 
To optimize the utilization of GPU resources for distributed inference, Dynamo’s Planner feature continuously monitors GPU capacity metrics in distributed inference environments to make real-time decisions on whether to serve incoming user requests using disaggregated or aggregated serving while also selecting and dynamically shifting GPU resources to serve prefill or decode inference phases.
 
To further drive inference efficiencies by reducing request latency and time to first token, Dynamo has a Smart Router feature to minimize key value (KV) cache re-computation. KV cache can be thought of as the model’s contextual understanding of a user’s input. As the size of the input increases, KV cache computation increases quadratically, and if the same request is frequently executed, this can lead to excessive KV cache re-computation, reducing inference efficiency. Dynamo Smart Router works by assigning an overlap score to each new inference request as it arrives and then using that overlap score to intelligently route the request to the best-suited resource — i.e., whichever available resource has the highest overlap score between its KV cache and the user’s request — minimizing KV cache recomputation and freeing up GPU resources.
 
Additionally, Dynamo leans on its Distributed KV Cache Manager feature to support both distributed and disaggregated inference serving and to offer hierarchical caching capabilities. Calculating KV cache is resource intensive, but as AI demand increases, so does the volume of KV cache that must be stored to minimize KV cache recomputation. Dynamo Distributed KV Cache Manager leverages advanced caching policies to prioritize the placement of frequently accessed data closer to the GPU, with less accessed data being offloaded farther from the GPU.
 
As such, the hottest KV cache data is stored on GPU memory with progressively colder data being offloaded to shared CPU host memory, solid-state drives (SSDs) or networked object storage. Leveraging these key features, NVIDIA claims Dynamo maximizes resource utilization, yielding up to 30 times higher performance for AI factories running reasoning models like DeepSeek-R1 on NVIDIA Blackwell. Additionally, NVIDIA leaders state that while designed specifically for the inference of AI reasoning models, Dynamo can double token generation when applied to traditional knowledge-based LLMs on NVIDIA Hopper.
 

The Super Bowl but everybody wins

NVIDIA’s astronomical revenue growth and relentless innovation road map aside, perhaps nothing emphasizes the degree of importance the company holds over the future of the entire AI market more than the number of partners that are clamoring to gain a foothold using NVIDIA as a launching point. The San Jose McEnery Convention Center was filled with nearly 400 exhibitors showcasing how NVIDIA’s AI and accelerated computing platforms are driving innovation across all industries. NVIDIA GTC is no longer a conference highlighting the innovations of a single company; it is the epicenter of showcasing AI opportunity, and every company that wishes to play a role in the market was in attendance.
 
The broad swath of NVIDIA’s partner ecosystem was represented. Infrastructure OEMs and ODMs displayed systems built on NVIDIA reference architectures, while NVIDIA inception startups highlighted their own diverse codeveloped AI solutions. However, perhaps the most compelling and largest-scale example of NVIDIA relying on its partners to deliver AI solutions to end customers came from the company’s global systems integrator (GSI) partners.

NVIDIA provides the platform; partners provide the solution

The world’s leading GSIs, including Accenture, Deloitte, EY, Infosys and Tata Consultancy Services (TCS), all showcased how they are leveraging NVIDIA’s AI Enterprise software platform — comprising NIMs, NeMo and Blueprints — to help customers build and deploy their own customized AI solutions with a heavy emphasis on agentic AI. While some of the largest enterprises in the world have the talent required to build bespoke AI solutions, many other organizations rely on NVIDIA-certified GSI partners with training and expertise in NVIDIA’s AI technologies to develop and deploy AI solutions.
 
Agentic AI has emerged as the next frontier of AI, using reasoning and iterative planning to solve complex, multistep problems autonomously, leading to enhanced productivity and user experiences. NVIDIA AI Enterprise’s tools help make this possible, and at GTC 2025, NVIDIA business leaders shed light on three overarching reasons why NVIDIA AI Enterprise has resonated with end customers and NVIDIA partners alike.
 
First, NVIDIA AI Enterprise builds on CUDA to deliver software-optimized full-stack acceleration, much like other NVIDIA AI platforms. Business leaders essentially explained NIMs — the building blocks of AI Enterprise — as an opinionated way of running a GenAI model on a GPU in the most efficient way possible.
 
Second, NVIDIA AI Enterprise is enterprise grade, meaning that the thousands of first- and third-party libraries constituting the platform are constantly maintained with AI copilots scanning for security threats and AI agents patching software autonomously. Additionally, enterprises demand commitments to maintenance and standard APIs that are not going to change, and NVIDIA AI Enterprise ticks these boxes while also offering tiered levels of support services on top of the platform.
 
Finally, because NIMs are containerized, based on Kubernetes, AI Enterprise is extremely portable, allowing the platform to deliver a consistent experience across a variety of environments.

Autonomous vehicles are the tip of the physical AI iceberg

Several of NVIDIA’s automotive partners also attended GTC 2025, displaying their vehicles inside and outside the convention center. These partners all leverage at least one of NVIDIA’s three computing platforms comprising the company’s end-to-end solutions for autonomous vehicles, with several partners leveraging NVIDIA’s entire platform — including General Motors (GM), whose adoption of NVIDIA AI, simulation and accelerated compute was announced by Huang during the GTC 2025 keynote address.
 
While autonomous vehicles are perhaps the most tangible example, NVIDIA’s three computer systems can be used to build robots of all kinds, ranging from industrial robots used on manufacturing lines to surgical robots supporting the healthcare industry. The three computers required to build physical AI include NVIDIA DGX, which is leveraged for model pre-training and post-training; NVIDIA OVX, which is leveraged for simulation to further train, test and validate physical AI models; and NVIDIA AGX, which acts as the robot runtime and is used to safely deploy distilled physical AI models in the real world.
 
Following the emergence of agentic AI, NVIDIA sees physical AI as the next wave of artificial intelligence, and the company has already codeveloped foundation models and simulation frameworks to support advancements in the field with industry-leading partners, such as Disney Research and Google DeepMind.

Conclusion

The sheer scale of NVIDIA GTC 2025 reaffirmed NVIDIA’s position at the epicenter of the AI revolution, with Huang’s keynote address filling all the available seating in the SAP Center. Born from Huang’s long-standing vision of accelerating workloads by applying parallel processing, NVIDIA’s relentless investments in the R&D of the entire AI stack — from GPUs to interconnect and software platforms to developer resources — remains the driving force behind the AI giant’s success and seemingly insurmountable lead over competitors.
 
NVIDIA’s first-mover advantage in accelerated computing was predicated on the company’s CUDA platform and its ability to allow developers to optimize applications running on NVIDIA GPUs. Nearly 20 years later, NVIDIA continues to leverage CUDA and its robust ecosystem of developers to create innovative AI platforms, such as Omniverse and AI Enterprise, that attract partners from every corner of the technology ecosystem. By swimming in its own lane and relying on its growing NVIDIA Partner Network to deliver AI systems and solutions to end customers, NVIDIA has built an unrivaled ecosystem of partners whose actions on the front lines with end customers facilitate the near-infinite gravity behind the company’s AI platforms.

Infosys, Cognizant, TCS and Wipro ITS Double Down on Competitive Pricing Strategy While Trying to Enhance Client Engagement 

TBR FourCast is a quarterly blog series examining and comparing the performance, strategies and industry standing of four IT services companies. The series also highlights standouts and laggards, according to TBR’s quarterly revenue projections. This quarter, we look at four India-centric vendors — Infosys, Cognizant, Wipro IT Services (ITS) and Tata Consultancy Services (TCS) — and analyze how investments in portfolios, training and innovation are positioning them for growth.

 
Although vendors experienced a small rise in discretionary spending among financial services clients in 4Q24, smaller deal wins, particularly those in consulting, remain infrequent, leading IT service vendors to reprioritize resources to align with market demand and invest in innovative and emerging technologies. India-centric vendors are leveraging competitive pricing enabled by largely offshore delivery, capitalizing on clients’ demand for cost optimization and operational efficiency.
 
According to TBR’s 4Q24 Cognizant report, “Amid an unfavorable sales environment, in which the procurement process is prolonged as IT buyers grapple with smaller budgets and additional layers of executive approval, Cognizant has increasingly relied on its legacy DNA as a low-cost IT services provider to compete on price. This approach is far from exclusive to Cognizant, as other India-centric peers such as Wipro ITS also compete on price. As part of this strategy, Cognizant has been increasingly engaging on a fixed-price basis, relying on automation to maintain margins on net-new accounts.” At the same time, many vendors outside India are executing on localization strategies to enhance client engagement and build signings.
 

TBR Fourcast: Insights into Accenture, Deloitte, IBM Consulting and Infosys, including Accenture’s extensive investment in GenAI and IBM Consulting’s and Infosys’ risk of falling into a downward trajectory

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A well-balanced portfolio with industry-specific solutions is key to enhancing client engagement

In addition to leveraging their low-cost pricing models, India-centric vendors will need to focus on deepening relations with clients to grow revenue. Applying emerging technology, such as AI and generative AI, to industry-specific solutions is allowing vendors to remain competitive by demonstrating competency as well as an in-depth understanding of clients’ needs. As localization strategies become more common, leveraging industry-specific solutions will be important for the India-centric vendors’ revenue growth.
 
All India-centric vendors are forming and expanding partnerships to accelerate the adoption of AI technology and expand client reach within and across verticals. For example, Infosys and NVIDIA released small language models (SLMs) for Infosys Topaz BankingSLM and Infosys Topaz ITOpsSLM, enabling enterprise data to be used over prebuilt SLMs, which help to facilitate the development of industry-specific use cases.
 
Similarly, TCS established the AI.Cloud business unit, highlighting the importance of integrated solutions that combine AI and cloud capabilities to maximize client value. This is further demonstrated by the creation of a dedicated NVIDIA unit within AI.Cloud to accelerate AI adoption across industries through tailored solutions leveraging NVIDIA’s technology.
 

IT Services Revenue and Headcount for Cognizant, Infosys, TCS and Wipro, 2024 (Source: TBR)


 
Although partners are key to a strong go-to-market strategy, especially when introducing emerging technologies and deepening client reach, it is important for the India-centric vendors to differentiate their offerings. Partnering with leaders in AI and cloud, such as NVIDIA and ServiceNow, is vital to remaining relevant; however, developing proprietary solutions internally will be important to distinguish the India-centric vendors from each other. Infosys’ recent portfolio additions will support the company’s revenue growth.
 
For example, the company launched the Finacle Data and AI Suite for banking clients to use AI to improve the customer experience and IT systems. Since July, Cognizant has been diligently building out its Neuro suite, which supports the adoption of automation and AI. Cognizant launched Neuro edge, which is an update to Neuro AI and includes Cognizant Neuro Cybersecurity and a multi-orchestration agent; Neuro Stores 360; and a Neuro AI Multi-Agent Accelerator and Multi-Agent Service Suite.
 
Similarly, TCS is focusing on internal development alongside strategic partnerships, notably establishing the TCS GoZero Hub, a center researching net-zero carbon emissions solutions for Australian clients, and the TCS Responsible AI Framework. Proprietary portfolio offerings around in-demand technology, namely AI, cloud and security, provide vendors with credibility among buyers seeking a third party that can solve their current and future problems.

Investments around innovation and training development build credibility and attract more clients

Cognizant, Infosys and TCS have been investing in building portfolios that balance partner-enabled and proprietary solutions. Meanwhile, Wipro ITS has relied heavily on partnerships to build its portfolio. Despite ongoing restructuring efforts, Wipro ITS continues to lag behind its India-centric peers in delivering in-demand and innovative solutions, especially those related to IoT and digital. Ensuring strong proprietary solutions requires vendors to continually invest in training development and innovation. Wipro ITS is investing in talent development to enhance its AI and digital skills and is increasingly hiring staff with skills in emerging technology. TBR believes these investments will boost Wipro ITS’ revenue performance but does not expect Wipro ITS’ performance to exceed that of Infosys and TCS.
 
Of the four vendors, Infosys provides perhaps the best example of how to invest in talent and innovation. The company is establishing a center in Kolkata, India, and will staff it with employees who have skills in cloud, AI and digital across industries. Beyond training, Infosys has expanded its innovative efforts by establishing an incubator and encouraging employees to bring forward ideas. Infosys’ training and incubator efforts will help propel the company’s growth. Likewise, TCS added freshers to the company in 2024 and will continue to do so in 2025, while remaining vigilant about the company’s cloud and AI training.
 
Given Infosys’ and TCS’ linear revenue growth models, their future performance continues to rely heavily on how well hiring, training and reskilling initiatives are executed, particularly around cloud and AI. The companies that remain more focused on employees and innovative capabilities can ensure that quality services are delivered to clients, allowing them to stay competitive and expand revenue share. In contrast to Infosys and TCS, Cognizant is ramping up its efforts to retain employees more reactively through rehiring former employees and increasing wages. Cognizant’s less pervasive training and innovation efforts could hurt long-term revenue growth, thereby aiding Infosys in its efforts to surpass Cognizant in revenue size, even with Cognizant’s recent acquisition of Belcan.

Improving revenue performance will depend on proactive go-to-market and resource management strategies

Ongoing and proactive investments in innovation, training around in-demand technology, and a balanced portfolio are key to competing for market share. Further, providing clients with industry-specific solutions from internal developments and having an in-depth understanding of partner-enabled and emerging technologies are vital to fueling revenue growth. In 4Q24 Infosys had the highest revenue per employee of the India-centric vendors, at $59,856, followed by Cognizant at $57,867, TCS at $49,600 and Wipro ITS at $45,812.
 
Wipro ITS could elevate its standing with investments in innovation and AI, enabling the company to develop more offerings internally and potentially secure more large deal wins. Cognizant and Wipro ITS could continue to trail behind Infosys and TCS in performance if they let service quality slip or do not continue to train employees on relevant skills. In addition to investing in innovation, training and portfolios, in the long term the four vendors would benefit from leveraging heavy India-based resources to help diversify revenue opportunities with local clients.
 

IT Services Revenue Forecast for Cognizant, Infosys, TCS and Wipro, 2020-2029 (Source: TBR)

 

5 Key Questions on Big Four Evolution and Strategy

Amid ongoing organizational shifts at the Big Four, 5 key questions are consistently heard among their employees, clients and ecosystem partners

The Big Four professional services firms — Deloitte, EY, KPMG and PwC — have all been undergoing organizational changes in the last couple years. TBR regularly hears five questions about how these firms manage themselves, grow and change. Taking a longitudinal view allows TBR to see that recent restructurings, layoffs and offerings all reflect how these firms are trying to address the following: who gets the best talent, who decides what’s next, who sells, how everyone in a firm knows what everyone else does, and what role will managed services play.
 
At any given moment, one or more of these firms may have solid answers, a consistent strategy and a fit-for-purpose organizational structure. Eventually, all that changes. TBR keeps these five questions in mind as we cover the Big Four, and in this blog we’re unpacking each question and why it matters.
 

Find out what’s in store for IT services vendors and consultancies in 2025 in terms of strategy consulting, generative AI (GenAI) and ecosystem intelligence.
 
Download TBR’s 2025 Digital Transformation Predictions special report today!


 

Question 1: Who gets the best talent?

The Big Four firms have some intellectual property, well-established brands and continually evolving alliances with technology providers, but their core asset is simply people. The firms bring clients talented people to solve business problems, provide assurance and/or implement a technology, leveraging a people arbitrage business model: I’ll supply the talent when you need it and for as long as you need it, then I’ll take that talent back. The catch? Within the firm, who decides which clients get the best talent? When capabilities around a particular technology or offering are in short supply, who decides how to allocate limited resources? Local office leaders, country leaders, regional leaders, global leaders, industry leaders, lead client service partners, technology alliance leaders?
 
Managing these competing demands for resources requires exceptional leadership and, as we’ve seen through the years, sometimes means upending the organizational structure to better suit a new way of deciding who gets the best talent.

Question 2: Who decides what’s next?

Over the last decade, the Big Four firms have launched practices in areas such as blockchain, cloud, AI, people advisory, a collection of SaaS offerings, and cybersecurity, to name a handful. Some of those practices have grown, some have disappeared and some just continue to be. In every case, a collection of partners made a business case to the partnership as a whole, getting enough consensus to invest people and money into building something new. At the same time, every Big Four firm has tweaked how it makes those decisions: which partners lead on new offerings, how consensus is built and how new offerings are evaluated over time — essentially, what’s next. If this seems like an inside-the-firm small consideration, look back at the list of practices — at least three generate significant revenue streams for each firm. With hindsight, maybe those new practices and offerings appear to be no-brainers, but some group of partners in each firm still had to make the case, pull together resources and convince the firm to bet on something new. Being late to market changes the way this question gets answered. So does the fear of being too entrenched in selling today to see what’s going to sell tomorrow.

Question 3: Who sells?

Speaking of selling, the Big Four have traditionally eschewed traditional sales teams, relying on every partner (and wannabe partner) to be responsible for landing new clients and expanding footprints within existing client bases. For generations, that worked well enough; however, as all of the Big Four firms’ offerings have become increasingly infused with technology, two developments have forced some changes.
 
First, highly skilled technology-focused talent didn’t always have the skills needed to sell and so couldn’t be evaluated, promoted and compensated in the traditional partner model. The Big Four all adjusted, creating new career paths for the valuable but not-partner-track professionals. Second, the technology-dependent offerings themselves became too complex for most partners to understand and sell on their own, creating an opening for professionals who combined tech skills and sales savvy. The ongoing challenges? Who decides which partners sell to clients? Software is fundamentally different from services, so if a firm experiments with selling software, is a dedicated software sales team necessary? How can partners in one service line sell clients on tech-heavy, specialized offerings from another service line? The Big Four firms continue bending the traditional selling model, and nearly every organizational change includes some element of tackling these “Who sells?” questions.

Question 4: How does everyone in a firm knows what everyone else does?

No one has conquered knowledge management — in any company, anywhere. The Big Four firms have repeatedly launched initiatives, platforms, and evaluation and compensation metrics trying to ensure that tax partners understand all the consulting offerings and that audit partners know when and how to bring in transaction partners. TBR believes the digital transformation and innovation centers that all four firms launched in the mid-2010s (e.g., PwC’s Experience Centers) were intended, as a side benefit, to enhance internal knowledge sharing. Big Four leaders have told TBR that their fellow partners across all service lines learned something new about their own firm every time they attended a client session at one of the centers.
 
Today, all four firms are leveraging AI-enabled platforms to enhance internal knowledge management and will likely see significant improvements. But the challenge will persist, as new offerings, capabilities, use cases, learnings and people constantly refresh the pool of knowledge, which needs to be shared for the Big Four to bring their entire selves to clients. Further, forming regional super-partnerships and reducing 100-plus member firms to a few dozen reflect the need for operational efficiencies, better service for international clients, and, yes, enhanced knowledge management so that everyone in the U.K. knows what everyone in Singapore is doing.

Question 5: What role will managed services play?

If the previous four questions have been perennial challenges for the Big Four firms, shaping organizational structure and leadership priorities, this last one has been a slow-building, nearly existential question. How will professional services firms built on a prestigious reputation and elevated fees staff, price, manage and even grow managed services practices that are, by nature, more transactional than the traditional client relationship model? Each of the Big Four firms has taken a different path (which is one reason TBR sees these firms as concurrently so similar and so different), and each firm has adjusted its vision for managed services at least once in the last four years.
 
And, once again, one common element among all the reorganizations and restructurings has been competing views — within the firms themselves as well as among the partners — about what happens next with managed services. In TBR’s view, the role of managed services may prove to be the biggest differentiator among these four firms over the next five years, even as managing talent in a generative AI (GenAI) world and keeping pace with technology partners’ shifting demands challenge Big Four leadership.
 

Watch Now: 2025 Predictions for Ecosystems & Alliances

Conclusion: What it all means for clients, partners and the ecosystem as a whole

If you’re reading news on the Big Four, keeping these five questions in mind can be useful to understand the “why” behind the “what.”
 
If you’re a client, other questions matter more. (Are they solving your problem or not? Do you trust them? Do they bring you people you like? Nothing else matters.)
 
If you’re a technology partner in the Big Four ecosystem, these five questions are critical to understanding where your alliance is headed. Does your Big Four partner have the right talent to dedicate to your shared clients? Are they innovating with you, and who is leading that effort? Can they capably sell your technology? Do you understand what they bring to the table and what differentiates them from the other Big Four firms and the sea of IT services companies? Are they investing for the long haul or for a quick consulting gig?
 
Circling back, if you’re running a Big Four firm, how you’re addressing these questions helps determine your internal organizational structure and your strategy for the next five years. And how you explain it all to your ecosystem partners may determine how fast you grow alongside them.

The Emerging Data Ecosystem: ISVs, Hyperscalers and Global Systems Integrators

Register for The Emerging Data Ecosystem:
ISVs, Hyperscalers and Global Systems Integrators

 

There is no GenAI strategy without a data strategy

Realizing the long-promised ROI of generative AI (GenAI) will require customers look for ways to better access, integrate, manage and govern large amounts of unstructured data. Data-native ISVs, hyperscalers and global systems integrators (GSIs) are evolving their critical ecosystems of solutions to deliver on the commitments of GenAI for enterprise. As such, roles within the cloud ecosystem are shifting, and the increase in open APIs and architectures will have lasting impacts on many data cloud ISVs and GSIs, including how they partner with one another as they race to gain AI workloads.
 
Join TBR Principal Analyst Boz Hristov and Senior Analyst Catie Merrill Thursday, May 1, 2025, for a live discussion and Q&A on insights into data cloud ISVs’ and hyperscalers’ strategies and their professional services partners, as well as how forming a triparty alliance structure at the data layer will help partners pursue higher-value GenAI opportunities.
 
Additionally, the pair will share an exclusive look at TBR’s revamped Cloud Data & Analytics Market Landscape, which provides insight into enterprises’ data strategies, vendor analysis by workload, and where the market is headed through 2025 and beyond. TBR’s Cloud Data & Analytics research stream tracks all hyperscalers; SaaS vendors such as SAP and Salesforce; and data cloud ISVs including Boomi, Confluent, Cloudera, Databricks, Informatica, MongoDB and Snowflake. The research also looks at the overarching layers of the data cloud stack, from storage and querying to business intelligence.

In this free session on the emerging data ecosystem you’ll learn:

  • The data cloud ISVs that have demonstrated success in alliance strategies
  • Ecosystem best practices of data cloud ISVs
  • C-Suite priorities regarding data management and GenAI
  • How hyperscalers are adjusting their partnering strategies to improve the flow of data and win new GenAI workloads
  • Why vendors are positioning around data intelligence, and the components necessary to succeed in this space

Register Now

 
TBR Insights Live sessions are held typically on Thursdays at 1 p.m. ET and include a 15-minute Q&A session following the main presentation. Previous sessions can be viewed anytime on TBR’s Webinar Portal.
 
TBR Insights Live: The Emerging Data Ecosystem: ISVS, Hyperscalers and Global Systems Integrators

Cloud Opportunity Expected to Increase Once DOGE Disruption Subsides

The U.S. federal government will need modern cloud services to be most efficient, regardless of DOGE-driven changes

Rolling pockets of chaos and an overall cloud of uncertainty may be the best way to describe the first two months of the new Trump administration. One upside to federal contracts is that they tend to be long-term in nature, which provides some stability for all types of vendors with existing contracts. However, the current transition has been rocky, to say the least, as contracts are getting canceled, agency staffing is reduced, and the existence of entire agencies is called into question.
 
Beyond the distinct financial impacts that are occurring to many federal systems integrators (FSIs) and IT vendors, the overall uncertainty about future changes has complicated government contractors’ ability to conduct business as usual. Short-term uncertainty will likely persist, but eventually we will see a silver lining for the ecosystem of IT providers catering to the needs of the U.S. federal government. The government may become a more streamlined entity, in all respects, but IT will need to remain at the forefront of U.S. government operations.
 
Differences of opinion on optimal levels of funding will persist, but most people concur that the IT infrastructure supporting many core government agencies is antiquated and long overdue for upgrade. After the Department of Government Efficiency (DOGE) completes its cost-cutting and agency reorganizations, the overall approach to modernizing those systems will come into greater clarity, but third parties including FSIs and IT vendors like Amazon Web Services (AWS), Microsoft, Google and Oracle will all likely be a part of the solution enabling the reformed federal government to modernize and play an ongoing role eliminating waste, fraud and abuse using a refreshed IT infrastructure environment.
 

Explore the expected impact of DOGE on federal systems integrators and how it could shape the technology landscape


 

Vendors hope federal spending materializes after the fog of dismantling and reducing headcount dissipates

Reducing the size of the federal workforce was an immediate focus for DOGE. With the “Fork in the Road” email sent by the Office of Personnel Management to encourage staff resignations and the nonvoluntary firing of workers across civilian agencies, the total number of employees shed from the federal workforce is estimated to have surpassed 100,000 in the first two months of the Trump presidency.
 
The entire federal workforce still totals more than 3 million, excluding 1.3 million active military personnel, and additional cuts are a certainty. Early in the formation of DOGE, the idea of cutting up to 75% of federal workers was floated, which could be far-fetched in reality. Regardless, it is clear the workforce-reduction efforts will continue to be a focus as DOGE expands its reach to additional government agencies and pushes further than just the probationary employees that made up the bulk of early reductions.
 
As headcount reductions continue, cloud and software vendors could assist the administration with those cuts while, at the same time, be impacted by the fallout of those cuts. On Workday’s FY4Q25 earnings call, CEO Carl Eisenbach painted the impact of DOGE in an opportunistic light, stating: “In fact, the majority of them [federal IT systems] are still on-premise, which means they’re inefficient. And as we think about DOGE and what that could potentially do going forward, if you want to drive efficiency in the government, you have to upgrade your systems. And we find that as a really rich opportunity.”
 
If, in the era of DOGE, government agencies undertake new, or continue existing, efforts to modernize IT systems and adopt cloud-enabled solutions, it would certainly be a big opportunity not just for Workday, but for the entire federal IT contractor market. The certainty of that opportunity is still questionable, however, given the rapidity with which major changes to how government operates are occurring. Any technology opportunities with USAID (United States Agency for International Development), for instance, are now dubious given the speed with which the agency has been dissolved, even as legal challenges abound.
 
Additional rapid changes will occur with the Department of Education given President Trump’s clear directive to new Secretary of Education Linda McMahon to dismantle the agency. On ServiceNow’s 4Q24 earnings call, CFO Gina Mastantuono noted some of this uncertainty while also remaining optimistic about the federal opportunity, stating the company’s guidance reflects a stronger U.S. federal performance in the back half of 2025, given changes brought on by the administration.

A build-it-yourself approach could challenge packaged IT solutions

DOGE head Elon Musk has clearly employed many of the same techniques and strategies he has used in the past, such as sending a “Fork in the Road” email to Twitter employees and requiring them to send a weekly email of their accomplishments after he purchased Twitter (now called X). With that in mind, it is relevant to think about the approaches to IT that Musk has used as CEO of Tesla and SpaceX for clues about what might occur in the U.S. federal space.
 
For some of the most important mission-critical IT and software decisions at Tesla and SpaceX, Musk deployed a proprietary software package that is shared by the companies to manage core manufacturing and sales, CRM and financial processes. Instead of utilizing a prebuilt solution from the likes of SAP or Oracle, internal teams at SpaceX and Tesla built, customized and manage their own ERP solution named WARPDRIVE. Musk could very well encourage a similar approach in federal agencies, either by licensing WARPDRIVE to those agencies or by directing more proprietary programs to be custom-built to reduce expenditures and theoretically achieve a superior technological solution. Either option would be challenging to implement but remains within the realm of possibility and would effectively reduce the addressable market for third-party IT solutions.
 

Watch Now: Deep Dive into Generative AI’s Impact on the Cloud Market in 2025

Scaling back new and existing awards will stifle revenue for cloud vendors in the short term

In the U.S. federal sector, SIs are a key conduit for how cloud and software companies capture opportunities. The opportunity pipeline and associated timeline for deals is notoriously long for federal spending, but the total opportunity has already decreased in size based on the cuts made by DOGE. Some of the strategies and actions recently used by leading SIs in the federal space are discussed in TBR’s special report, Leading Federal Systems Integrators React to U.S. Department of Government Efficiency. As outlined in the special report, all 12 of the leading federal SIs are looking to reduce expenses and prepare for a slowing of revenue streams in the near term. After a period of federal investment and expansion, this certainly is a change in trajectory for their businesses. In addition to making similar cost reductions, all 12 vendors are also doubling down on their competitive differentiation to secure growth moving forward. All of the recent market shifts, including security, AI and digital transformation, have led FSIs to reinvest in capabilities that provide the best opportunities for long-term expansion.
 
In the short term, even existing contracts with the federal government are subject to reductions or termination, which impacts not only the SI but also the IT vendors that have secured subawards to provide their technology as part of the overall engagement. One example TBR cited in the special report was the $1.5 billion award Leidos has with the Social Security Administration (SSA), which includes subawards for Pegasystems, AWS and multiple other IT vendors. The Leidos deal was scaled back by DOGE, marking the beginning of the disruption to awards with SIs and subawards with IT vendors. SSA represents a small portion of the federal budget, so when DOGE looks to larger agencies such as the Department of Health and Human Services for cost reductions and efficiencies, the impact on the federal SIs and supporting IT vendors will be even greater.
 
In terms of the scale of revenue at stake, AWS alone has won close to $500 million in subaward contracts in the last three fiscal years. That does not directly translate into revenue, however, as the money still needs to be outlaid, a process that is even more tenuous given the current spending environment and actions taken by the DOGE team. In addition to deals tied to FSIs, cloud vendors and software vendors also have direct deals/prime awards with federal agencies that are at greater risk. AWS, for instance, has won a total of $445 million in prime award contracts over the past three fiscal years.
 
Most of those awards are multiyear contracts that are not guaranteed, and the revenue could be reduced or not disbursed. In fact, only $104 million of those awards to AWS have been outlaid, meaning the balance, more than $340 million, could be impacted. It is also important to note these figures only reflect past deals; we anticipate the new federal deal pipeline for vendors like AWS to shrink due to uncertainty and the administration’s focus on cost reductions.

Big cloud deals such as JWCC and Stargate are expected to proceed without significant funding impacts

The impacts of DOGE should be widespread throughout the government, but we expect the top federal IT opportunities, the Stargate Project and the Joint Warfighting Cloud Capability (JWCC) contract vehicle, to avoid major funding challenges. Though both projects are in the early stages and still subject to competitive jockeying between technology providers to secure task orders, we expect the funding to remain available even amid broader spending reductions.
 
The JWCC was announced in 2022 with a total of $9 billion in funding available to Oracle, Microsoft, AWS and Google Cloud. Oracle has been a leading provider under the contract to date. Roughly $2.5 billion has been awarded to the five vendors thus far in the contract, leaving more than $6 billion in additional task orders in the entire project. The spending bill passed in mid-March to avoid a federal shutdown illustrates the appetite to sustain, if not increase, defense spending. All the participants in JWCC have donated to and publicly supported the administration, which could solidify the longevity of the engagement.
 
Stargate was introduced by President Trump in the early days of his presidency, indicating that the project is likely to proceed in some fashion regardless of any budgetary pressure. The project will be a joint venture with OpenAI, SoftBank and Oracle to initially build a $100 billion data center in Texas. Over the next four years, the project aims to build additional large-scale data centers, with a total of $500 billion in funding, making it the largest centralized data center investment in history. The funding includes significant financial backing from the U.S. government, with contributions from SoftBank, a firm known for its long-term investment strategies. OpenAI, SoftBank, Oracle and MGX are the initial equity investors, while Arm, Microsoft, NVIDIA and OpenAI have been named as technology partners and will have some involvement in the project.

Modern cloud IT solutions should have an elevated role in the restructured federal government

The headcount reductions, eliminations of agencies, and overall uncertainty will disrupt business as usual in the U.S. federal sector at least through the end of 2Q25. Once the new, smaller and streamlined structure emerges, we expect the value of modern IT solutions to be recognized and spending to resume and even increase compared with the prior trajectory. Having fewer human resources, likely fewer skilled IT professionals, and an altered view of budgeting and ROI for all initiatives, IT included, all amplify the value that can be added by modernizing the infrastructure and solutions that support the mission of government agencies.
 
Across fragmented environments, many of which are still traditional on premises and based on aging technology, consolidation and use of government-grade cloud delivery can improve performance and reduce the total cost to deliver even over a relatively short three-to-five-year time frame. On the commercial side, many of the organizations we speak with note that the simplification of their IT environments is one of the strongest drivers of cloud adoption. AI and generative AI capabilities add to the benefits that can now be enabled. And for government agencies, preexisting data protocols and procedures increase their readiness to apply next-generation data analysis and AI. We see the business use cases for AI becoming more compelling on the commercial side, which bodes well for adding real value in the U.S. federal sector as it adapts to a more streamlined way of operations.

2024-2029 Devices Market Forecast

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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.
 

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

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.
 

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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

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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.
 

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

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.”
 

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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.