‘AI can be a knowledge management accelerator, but only if well-fed by an enterprise’s own data’

In February TBR met with two AI leaders in Fujitsu’s European Platform Business to better understand the company’s approach to the AI market, its evolving AI capabilities and offerings, and what we can expect as 2025 unfolds. Maria Levina, AI Business analyst, and Karl Hausdorf, head of AI Business, gave a detailed presentation focused primarily on the European market. The following reflects both that briefing and TBR’s ongoing research and analysis of Fujitsu, the company’s partners and peers, and the overall AI landscape.
 
One highlight that illustrates many facets of Fujitsu’s approach to AI was Levina and Hausdorf’s description of Fujitsu’s customers’ choices between “bring your own data” and “bring your own AI.” The first allows more AI-mature customers to bring their data into a Fujitsu-provided on-premises solution with full support for scaling, maintaining hardware and software, and updating, as needed.
 
The second allows customers to run their own AI stack on a Fujitsu “validated and optimized” platform, developed and maintained by Fujitsu and select technology partners. Critical, in TBR’s view, is Fujitsu’s positioning of these options as responsive to clients’ needs, as determined by all AI stakeholders within an enterprise, including IT, AI and business leaders.
 
Levina and Hausdorf explained, “Together, with our ecosystem of partners, we’re committed to unlock the potential of generative AI for our clients” through “on-premises data sovereign, sustainable private GPT and AI solutions,” focused on “rapid ROI.” Fujitsu is not approaching clients with a technology solution, but rather with options on how to address and solve business problems. As the Fujitsu team said, “Understand the why, know the what, and co-create the how.” The Fujitsu team also noted that the company’s industry expertise resides in the processes and workflows unique and/or critical to an industry.
 

‘Maintaining control of data means owning your AI in the future’

Before diving into Fujitsu’s AI offerings, the Fujitsu team laid out their understanding of the European market, sharing data the company collected around AI adoption, use of AI platforms, and barriers to growth (in Fujitsu’s phrasing, “progress-limiting factors,” which is perhaps a more positive spin on the usual list of barriers and challenges). Fujitsu surveyed or spoke with 400 data and IT professionals across six European countries, and the results indicated that overcoming legacy mindsets continues to be a major impediment to adopting and harnessing the value of AI.
 
TBR’s November 2024 Voice of the Customer Research similarly noted challenges in Europe with “the lack of engagement from employees who are being asked to change.” The Fujitsu team noted that change management, therefore, had to involve all AI stakeholders, including “IT people, business people and AI people” within an enterprise.
 
In TBR’s experience, IT services companies and consultancies continue to find new constituents for change management at their clients as the promise — and disruption — of AI becomes more widespread, reinforcing Fujitsu’s strategy of bringing change management to all AI stakeholders. Lastly, the Fujitsu team noted that within European clients, expectations around AI have heightened, especially as AI initiatives have launched across multiple business units. Again, Fujitsu’s research and TBR’s Voice of the Customer Research align around ROI expectations as AI matures.
 
The Fujitsu team introduced their AI platform by delineating the key performance indicators they believe a successful platform must have: scaling, performance and speed, simplicity, energy efficiency, AI services in data centers, and GPUs.
 
Although TBR is not in a position to evaluate the technological strengths, completeness or complexity of Fujitsu’s platform, the expansive KPIs indicate Fujitsu has considered not only the IT needs behind an AI deployment but also the larger business factors, particularly the financial impacts. Levina and Hausdorf then dove into the details, including the two customer options described above (bring your own data and bring your own AI). They discussed how Fujitsu offers consulting around the technical and business implications of AI platforms and solutions, including an “AI Test Drive,” which allows clients to test AI solutions before investing in new technologies, large language models (LLMs) or other AI components.
 
Notably for TBR, Fujitsu’s presentation extensively highlighted the company’s AI alliance partners, including Intel, NVIDIA, AMD and NetApp, as well as a slew of LLM providers, demonstrating an appreciation for the collaborative and ecosystem-dependent nature of AI at the enterprise level. The Fujitsu team also stressed the European nature of its AI strategy and platform.
 
European clients, Fujitsu noted, had specific requirements related to the European Union’s (EU) General Data Protection Regulation and the EU AI Act, as well as a preference for on-premises solutions. Some of the use cases Levina and Hausdorf described included a law firm using Fujitsu-enabled AI solutions to analyze case data, contracts, corporate and public legal documents, and multiple deployments of Fujitsu-enabled private GPTs.

Additional observations

• Fujitsu remains focused on targeting customers already aligned with the company around AI, a strategy that TBR believes speeds ROI and increases client retention.
• In contrast to some peers in the IT services market, Fujitsu has capabilities across the entire AI technology stack — hardware, software and service — which Levina and Hausdorf called “highly appealing,” especially to European clients.
• Levina and Hausdorf made two comments that, in TBR’s view, neatly sum up AI at present: “AI can be a knowledge management accelerator, but only if well-fed by an enterprise’s own data” and “maintaining control of data means owning your AI in the future.”

Fujitsu’s AI prowess makes it an invaluable partner

TBR has reported extensively on Fujitsu’s evolving AI capabilities and offerings, noting in a special report in May 2024: “TBR appreciates that Fujitsu’s combination of compute power and proven AI expertise makes the company a significant competitor and/or alliance partner for nearly every player fighting to turn GenAI [generative AI] hype into revenue.
 
“Second, Fujitsu’s vision of ‘converging technologies’ aligns exceptionally well with the more tectonic trends TBR has been observing in the technology space, indicating that Fujitsu’s market positioning is more strategic than transactional or opportunistic.” Add in Fujitsu’s deepening experience in delivering AI solutions to AI clients, and TBR continues to see tremendous near-term opportunity and growth for Fujitsu and its ecosystem partners.

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.
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 global solutions — Connected, Powered, Trusted and Elevate — which resonate with clients and technology partners, have now been brought together into one transformation framework under KPMG Velocity, providing KPMG’s professionals with clear insight into the firm’s strengths and strategy, and underpinning, in the near future, all KPMG’s transformation engagements. KPMG Velocity’s 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 — which KPMG fully acknowledges and is why KPMG Velocity’s offering is a differentiator for the firm in the market. With KPMG Velocity, all of KPMG’s multidisciplinary and heritage risk and regulatory considerations have been embedded across each transformation journey to ensure clients can remain compliant and avoid the pitfalls that can often arise during transformation. ​
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” and for large, multi-year, geographically dispersed enterprise, function and foundational transformations.
 
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 7 sectors (with the desire to expand these to 14) will allow the firm to demonstrate value and support its evolving Collective Strategy to act as a globally integrated firm. Additionally, new offerings like KPMG 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 where there is a deep understanding of the client needs. 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, KPMG has developed Velocity, a knowledge platform, AI recommendation and support engine underpinned by one universal method that pulls together every capability, offering and resource across the firm for the KPMG workforce. 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 that matches their ambitions — whether it be Enterprise, Function or Foundation — 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 clients 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 is designed to add value to client engagements (including describing, calculating and being accountable). It will also 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. For example, KPMG’s AI Workbench underpins how it is bringing agents and AI-enabled services to its clients and its people. Velocity, then, is a KPMG offering where every step is focused on achieving client outcomes, which comes back to understanding clients’ key business issues, not simply their technology stack. ​

The launch of Velocity internally (starting in March 2025) into its largest member firms brings to life KPMG’s approach to AI. KPMG expects its member firms to be able to start unlocking the power of Velocity beginning in May, and will launch Velocity externally later in the year. 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 client 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 with its global solutions — Connected, Powered, Trusted and Elevate — and proven IP, methods and enablers coming together through Velocity. ​
 
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 global solutions — Connected, Powered, Trusted and Elevate — which resonate with clients and technology partners, have now been brought together into one transformation framework under KPMG Velocity, providing KPMG’s professionals with clear insight into the firm’s strengths and strategy, and underpinning, in the near future, all KPMG’s transformation engagements. KPMG Velocity’s 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 — which KPMG fully acknowledges and is why KPMG Velocity’s offering is a differentiator for the firm in the market. With KPMG Velocity, all of KPMG’s multidisciplinary and heritage risk and regulatory considerations have been embedded across each transformation journey to ensure clients can remain compliant and avoid the pitfalls that can often arise during transformation. ​

Special report contributors: Catie Merrill, Senior Analyst; Kelly Lesizcka, Senior Analyst; Alex Demeule, Analyst; Boz Hristov, Principal Analyst

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.
 

 
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.
 

 
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.

U.S. Analyst and Advisor Meet 2025, New York City, March 4, 2025 — Infosys hosted industry analysts and advisors for a packed afternoon in the company’s offices at One World Trade Center. Using client stories amplified through technology partner support to reinforce Infosys’ role in the IT services, cloud and enterprise AI market, company executives consistently returned to a few main themes, including delivering business outcomes, maintaining trusted relationships, and focusing on speed, agility and simplification.  

 

Infosys’ hub-first strategy in the Americas demonstrates the company’s success with coinnovation and pursuit of large deals

Similar to previous events, Infosys kicked off the event with an update on the company’s strategy and performance in the Americas region. Anant Adya, Infosys EVP and head of Americas Delivery, led the presentation, highlighting key elements of the company’s success in the region, including its hub-first strategy; investments in and expansion of local talent pools, including in the U.S., Canada, Mexico, Brazil and the rest of LATAM; and strategic bets that are centered on delivering business outcomes and enabled through portfolio offerings such as Infosys Cobalt, Infosys Topaz and Infosys Aster..
Infosys’ six Tech Hubs across the U.S. remain the backbone of the company’s hub-first strategy. Located in Phoenix; Richardson, Texas; Raleigh, N.C.; Indianapolis; Providence, R.I.; and Hartford, Conn., and collectively staffed with thousands of local hires, these centers are increasingly allowing Infosys to drive coinnovation with clients and partners and pursue new opportunities with a key focus on large deals (defined by Infosys as deals over $50 million in value) in areas including cloud, AI, data, the edge and cybersecurity. Infosys has been rebalancing its onshore-offshore effort over the last five years.
 
For example, onshore effort was 24% in 4Q24, down from 27.7% in 4Q19. Offshore effort was 76% and 72.3% in 4Q24 and 4Q19, respectively. The recalibration began during the pandemic, as Infosys began capitalizing on the increase in remote working. The current ratio is also helping the company demonstrate pricing agility when competing for service delivery transformation projects. At the same time, maintaining a steady flow of local hires could help Infosys weather any pushback from the Trump administration on its America First Investment Policy requirements. Although the administration has yet to impose tariffs on companies utilizing services from overseas, it would not be surprising for this to happen in the future. Investing in training programs and collaborating with local universities through the Infosys Foundation would not only create a strong PR framework but also help Infosys increase its recruiting opportunities. Meanwhile, expanding across Canada and key LATAM countries, including Mexico and Brazil, to support both nearshore and locally sourced deals allows Infosys to diversify its revenue stream while enhancing the value of its brand beyond the U.S.
 
As Infosys continues to execute on its well-oiled strategy, investing and expanding in the next growth areas across the company’s cloud and enterprise AI portfolio will largely be centered on calibrating its commercial model as client discussions evolve from transactions to outcomes. For example, to support these expansion efforts, Infosys’ work within the Infosys Cobalt portfolio has evolved from tech optimization and data center migration to developing and applying industry solutions, and now includes accounting for the role of AI.
 
Building out a fluid enterprise to derive greater value from AI has compelled Infosys to develop solutions with an eye toward being more digital, more composable and more autonomous. This solution framing is also helping the company drive next-gen conversations with its technology partners and with clients that are seeking to develop an intelligent enterprise enabled by AI.

Infosys’ pivot toward Outcome as a Service will test the company’s ability to drive change management at scale, starting with its own operations

Expanding on Infosys’ evolving go-to-market strategy, portfolio, talent and collaboration with partners, Infosys Chief Delivery Officer Dinesh Rao, along with a series of client and partner panels throughout the afternoon, not only brought to light the company’s aspirations around driving outcome services opportunities but also discussed at length the challenges stakeholders face, often revolving around change management. Rao’s presentations spanned client use cases, AI evolution, and Infosys’ portfolio adjustment as well as resource pyramid calibration to balance support opportunities in foundational and emerging areas. Three key areas stood out:

• Client examples: Amplifying value through innovation has helped Infosys capture and deliver services for global clients across manufacturing, retail and consumer packaged goods (CPG), among other verticals, while also positioning the company to test new commercial models. For example, in a multiyear, multibillion-dollar deal supporting a multinational communications provider, Infosys is deploying its Outcome as Service commercial framework, bundling software, hardware and third-party services on a single platform.
• AI: Infosys launched NVIDIA-enabled small language models (SLMs) for Infosys Topaz BankingSLM and Infosys Topaz ITOpsSLM, targeting clients through core industry and horizontal offerings and allowing them to use their own data on top of the prebuilt SLMs. Additionally, Infosys released the Finacle Data and AI Suite of solutions to support banking clients seeking to enhance IT systems and customer experience using AI. The solutions include Finacle Data Platform, Finacle AI Platform and Finacle Generative AI. Infosys’ investments in industry-centric SLMs, which the company positions with clients as either accelerators or platforms to drive targeted conversations using industry and/or functional enterprise data, closely align with the company’s playbook from a few years ago, when it began developing industry cloud solutions, both proprietary and partner enabled. Embedding generative AI (GenAI) as part of a deal, rather than using it as a lead-in, is a smart strategy as it allows Infosys to better appeal to price-conscious clients and steer conversations toward outcomes and the benefits of the engagement, rather than trying to convince clients to spend a premium on a technology that has yet to prove ROI at scale. We believe Infosys’ investment in agentic AI capabilities for Infosys Topaz, along with the ITOpsSLM, can also position the company to drive nonlinear, profitable engagements, especially with clients that are seeking to migrate and modernize their existing mainframe infrastructure but lack the necessary COBOL skills and understanding of the environment.
• Resource pyramid: Infosys’ three talent categories — traditional software engineers, digital specialists focused on digital transformation and ongoing support, and Power Programmers — allow the company to balance innovation and growth while calibrating its business and commercial models. The Power Programmers group consists of highly skilled professionals who are responsible for developing products and ensuring that the intellectual property they create and use meets the cost-saving requirements Infosys pitches to clients. Although the other two groups follow a traditional employee pyramid structure, the Power Programmers group is much leaner and resembles the business models that many vendors, including Infosys, may aspire to adopt in the future.

Rao also discussed Infosys’ approach to innovation. The company’s business incubator framework, backed by Infosys’ $500 million innovation fund and enabled through its network of Living Labs, has empowered the company’s employees to think creatively, thus helping Infosys solidify its culture of learning and collaboration. Gaining employee buy-in is a must, especially at a time when the company is pivoting its own operations toward outcome-based service delivery.

AI- and partner-led discussions will continue to guide Infosys’ efforts to solidify its position as a trusted solution broker

Sunil Senan, Infosys’ global head of Data and AI, provided an update on Infosys’ AI-first strategy and portfolio, which have allowed Infosys to stay competitive in a rapidly evolving AI market. Senan noted that the opportunities around agentic AI require a rigorous data and governance strategy — an acknowledgment that is not surprising given the company’s typically humble yet pragmatic approach to emerging growth opportunities.
 
Scaling AI adoption comes with implications and responsibilities, which Infosys is trying to address one use case at a time. For example, in October 2023 Infosys launched the Responsible AI Suite, which includes accelerators across three main areas: Scan (identifying AI risk), Shield (building technical guardrails) and Steer (providing AI governance consulting). These capabilities will help Infosys strengthen ecosystem trust via the Responsible AI Coalition.
 
Infosys also claimed it was the first IT services company globally to achieve the ISO 42001:2023 certification for the ethical and responsible use of AI. Infosys recognizes that AI adoption will come in waves. The first wave, which started in November 2022 and continued for the next 18 to 24 months, was dominated by pilot projects focused on productivity and software development. In the current second wave, clients are starting to pivot conversations toward improving IT operations, business processes, marketing and sales.
 
The real business value will come from the third wave, which will focus on improving processes and experiences and capitalizing on opportunities around design and implementation. Infosys believes the third wave will start in the next six to 12 months. Although this time frame might suit cloud- and data-mature clients, only a small percentage of the enterprise is AI ready across all components including data, governance, strategy, technology and talent. Thus, it might take a bit longer for AI adoption to scale.
 
But as Infosys continues to execute its pragmatic strategy, the company relies on customer success stories that will help it build momentum. And there was no shortage of examples throughout the afternoon, with Infosys clients across the spectrum — from just getting started to scaling hundreds of AI deployments — sharing their experiences with Infosys and within the broader ecosystem.
 
We believe as Infosys pivots toward an Outcome as a Service commercial model, opportunities to scale AI will stem from the company’s ability to demonstrate value. In a traditional transformation project, the company often deployed professionals to perform typical implementation work and then transferred them to another project; during an AI project, staff would need to stay at a client’s site for a longer period to ensure the technology delivers the value promised. Approaching AI opportunities with a similar focus will help Infosys justify its rates but also help the company calibrate its staffing pyramid.
 
Infosys’ long-term success also depends on the company’s relationship with technology partners. During previous iterations of the summit, Infosys has had separate alliance-led presentations, but this time around the company included the partner presentations, specifically SAP, in a client panel. SAP’s presentation discussed a successful, three-year SAP S/4HANA migration for a global manufacturing client. Although the three-year turnaround was impressive, what stood out was how much the SAP executive was part of the conversation with the client. Speaking with the client on behalf of Infosys demonstrated trust and the depth of the relationship between SAP and Infosys.
 
Throughout TBR’s Ecosystem Intelligence research, we have written extensively that partners speaking on behalf of partners is often the last mile and the biggest challenge for vendors to overcome when they try to differentiate. We understand that vendors, especially IT services vendors, try to maintain tech agnosticism during consulting workshops, but when it comes to the implementation part of the engagement, developing more exclusive messaging resonates with clients much better as it shows knowledge, accountability and trust between parties.

Product Engineering and Quality Engineering present a tale of two cities that can help Infosys deliver value with minimal disruption to its financial profile

As Infosys continues to balance foundational growth with pursuing opportunities in new areas, the company’s evolving portfolio allows it to deliver steady financial results. Executives from Infosys’ Engineering Services and Quality Engineering lines of business, along with clients, highlighted how these two areas are helping Infosys achieve just that. Ben Ko, CEO of Kaleidoscope, a company Infosys acquired in 2020, explained how his company and its portfolio of solutions and products allow Infosys to capture manufacturing and R&D budgets, a slice of the overall enterprise spend that was somewhat untapped prior to expanding in the product engineering space. Infosys Engineering Services remains among the fastest-growing units within the company as Infosys strives to get closer to product development and minimize GenAI-related disruption on its content distribution and support position..
 
Since the 2020 purchase of Kaleidoscope, which provided a much-needed boost for the company to infuse new skills and the IP needed to appeal to the OT buyer, Infosys has enhanced its value proposition to meet GenAI-infused demand. For example, Infosys has purchased India-based, 900-person semiconductor design services vendor InSemi, and Germany-headquartered engineering R&D services firm in-tech, which presents a use case where the company applied a measured risk approach to enhance its chip-to-cloud strategy.
 
The purchase of in-tech certainly accelerates these opportunities, bringing in strong relationships with OEM providers, which is a necessary stepping stone as Infosys tries to bridge IT and OT relationships.
 
Meanwhile, Venky Iyengar, Infosys VP and head of Quality Engineering, along with clients, discussed how the Infosys business is adjusting both its value proposition and staffing models to account for automation and AI and to continue to deliver value to clients with minimal disruption to Infosys’ financial profile.
 
While a degree of revenue cannibalization is inevitable in the long run, Infosys’ approach toward platform-enabled quality engineering services, along with its efforts to fold these offerings under broader transformation projects, will allow the company to pivot and develop its position as a solutions broker.

It is all about the margins, and Infosys has the right ingredients to keep shareholders happy

Infosys, like many of its peers, faces a new reality influenced heavily by AI and reshuffling in buyers’ IT spending priorities. With IT becoming a utility, we expect enterprises not to cut back on spending but rather to demand from third-party vendors such as Infosys to deliver more with less. AI- and automation-enabled service delivery presents Infosys with the right tool to execute on such expectations. And as long as Infosys allows buyers to see that the productivity improvements have driven greater volume, then Infosys will be able to maintain its operating margin. Otherwise, buyers might start pushing back and asking for savings on their contracts when Infosys pitches new work but uses fewer employees. It was evident from the sessions that Infosys, with its enterprise AI capabilities, is strongly positioned to help clients unlock business value and drive growth. This aligns with the broader industry trend of leveraging AI to meet evolving client demands.
 
We understand that Outcome as a Service is a long-term play that will test Infosys’ culture and ability to manage trust within the ecosystem. The last five years of steady financial performance and the expansion of Infosys’ large and mega deals roster have provided the company with a strong foundation to make that pivot. Many of Infosys’ alliance partners, both technology and services ones, that TBR has spoken with view Infosys as a top delivery partner, thus providing the ecosystem support needed for the company to navigate the evolving IT services market.
 
TBR will continue to cover Infosys within the IT services, ecosystems, cloud and digital transformation spaces, including publishing quarterly reports with assessments of Infosys’ financial model, go-to-market, and alliances and acquisitions strategies.
 
For a comparison with Infosys’ peers and other IT services vendors, TBR includes Infosys in our quarterly IT Services Vendor Benchmark, our semiannual Global Delivery Benchmark and Cloud Ecosystem Report, and our annual Adobe and Salesforce Ecosystem Report; SAP, Oracle and Workday Ecosystem Report; and upcoming ServiceNow Ecosystem Report. Access the data and analysis in each of these reports with a TBR Insight Center™ free trial. Sign up today!