Platform strategy emerges as critical differentiator in agentic AI era

Every company claims to be a data company, every tech company wants to be an AI company, and every smart company has become a platform company. For companies in the technology ecosystem, including consultancies like McKinsey & Co. and EY, OEMs like Dell Technologies (Dell) and Hitachi, connectivity specialists like Nokia and Ericsson, and IT services generalists like Accenture and IBM, finding the right platform partner might be the most strategic decision they need to make before 2026.

The speed of technology change and challenges adapting to business model changes drive the importance of platforms. The rapid developments in AI, most recently with agentic solutions, illustrate how being a platform company enables vendors to maintain a solutioning role even as technology evolves, and new participants become critical in the eyes of customers. Agentic solutions have also created myriad ways for companies to sustain their business models, acting as a conduit between end customers and the changing vendor landscape.

Ecosystem partnerships evolve as agnosticism fades, fast followers rise and multiparty alliances drive wins

Partnering across the technology ecosystem will be different going forward in three specific ways that will make early-2020s alliances seem as wildly dated as the metaverse. First, agnosticism will enter its death throes. Companies, starting with consulting firms, can no longer simply let the customer decide which components of the tech stack will come from which companies. Having no preference no longer implies flexibility to work with anyone and everyone because every company now works with anyone and everyone. Having no preference now implies having no technology innovation or, critically, no commercial relationship to bring to bear on the client’s business and technology needs. Clients have grown accustomed to tech agnosticism and moved beyond it. They want to know which companies have special relationships that bring innovative ideas, the latest solutions and the best commercial terms. Agnosticism is not special at all.

Second, 2026 will be the year when fast followers start following in earnest. TBR has seen industry-leading consultancies, IT services companies, hyperscalers and software vendors align their go-to-market motions, sales and leadership teams, and even cross-training and knowledge management initiatives to build a pipeline and accelerate revenue for all parties. According to data and insights from TBR’s Ecosystem Research, the most profitable and fastest-growing companies have been leveraging their alliances in new and profoundly meaningful ways.

For example, in TBR’s September 2024 Adobe and Salesforce Ecosystem Report, we estimate that IBM, currently among the top four largest by revenue, will have the third-fastest Salesforce-related revenue growth from 2023 to 2028 as “the two will integrate IBM watsonx platform and IBM Granite series models with the Salesforce Einstein 1 Platform. IBM Consulting is creating industry-specific prompt templates and copilot actions for Salesforce applications.” TBR anticipates more companies across the technology ecosystem will emulate the leading companies’ alliance strategies in 2026. Success begets imitation.

Third, transparency wrought by AI adoption will compel companies across the technology ecosystem to partner differently. As agnosticism fades and strategies converged around the best practices, differentiation will come from companies’ abilities to engage in multiparty alliances. In March, we highlighted Informatica’s realization that although going to market with one partner led to a win rate of 47%, compared to only 17% when going alone, going to market with more than one partner led to an 83% win rate. A couple of months later, Salesforce acquired Informatica. Without question, Salesforce understands the lessons Informatica learned about the power of multiparty alliances.

Reflecting this understanding, Salesforce has quickly embraced a multiparty ethos, prioritizing third-party integrations within its broader AI platform strategy. Its Agentforce Partner Network already includes Amazon Web Services (AWS), IBM, Workday, Google Cloud, and dozens of SI and ISV partners building reusable agent actions and templates into a shared agent ecosystem rather than separate point products. At the same time, Salesforce has expanded its alliance with Google so that customers can build agents using Gemini models while running core Salesforce workloads on Google Cloud infrastructure, although that capability is not yet generally available. On the services side, partners will lead deployment, governance, and change management. For instance, Cognizant recently announced an expanded partnership to help clients deploy, scale, and govern enterprise agents within the Salesforce ecosystem.

Like services vendors, Salesforce is moving away from an agnostic approach in favor of prioritization. Partner performance KPIs are closely scrutinized, and ecosystem participants showing the most momentum in scaling Agentforce adoption are receiving the bulk of co-marketing and co-innovation resources. From Salesforce’s perspective, it matters little whether a service vendor is an upstart; in fact, the company has seen several emerging partners rise quickly through the ecosystem ranks due to their adaptability and speed amid Salesforce’s growing focus on data and AI. Regardless of which vendors carry the Agentforce torch, Salesforce is making clear distinctions and taking note. By becoming more targeted, the company is better positioned to build structured multiparty alliance frameworks with selected vendors—an approach that will help Salesforce achieve the win rates cited by Informatica.

Next year, companies across the technology ecosystem will begin doing the same, slowly. We are under no illusions that multiparty alliances will become easier beginning Jan. 1, 2026. Commercial arrangements, sales incentives, and selecting the right mix in a multiparty alliance will still require investment, leadership and time. The fastest-growing companies in the technology ecosystem will commit to making their multiparty alliance strategy work, and we expect they will start to see those win rates above 80%.


In this changed environment, TBR will be more closely tracking the companies and firms most likely to be disruptive in the near term. Our current Ecosystem Research stream covers 15-plus global systems integrators’ and consultancies’ relationships with nine cloud companies and software vendors. Of those companies, we expect 2026 to be particularly exciting, perhaps even momentous or strategic, for Deloitte, Salesforce, Dell and IBM. In TBR’s quarterly coverage of those four companies, subscribers will learn about strategies, trends and performances that reflect how ecosystem participants expect the agentic AI age to shape the market.

Now is a good time to be in the database business

Since relational databases first entered the market in the 1970s, a lot has changed. Even as non-relational systems entered the scene to help overcome the scale constraints of their SQL-based counterparts, technological revolutions — from the internet to cloud computing — have ultimately revealed the database’s true purpose: a transactional system for storing data. But generative AI (GenAI) is changing markets in ways we never expected, and databases are gaining renewed importance and being seen as more of a strategic piece of the AI strategy. That is because large language models (LLMs), while revolutionary in their ability to generate content and provide reasoning, do not have the underlying memory and ability to execute against the private data that agents thrive on. Customers are now realizing that if they want to bring the next wave of AI applications — AI agents — into production, they need to consider the role of the database.

If it is any indication of the opportunity, almost every cloud vendor now wants to be a data company. Even established data companies are moving down to the OLTP layer, recognizing that while AI models may train well alongside analytics systems (OLAP), AI agents, which thrive on enterprise context, are best built closest to where that enterprise data resides. But even as new analytics vendors vie for a piece of the TBR-estimated $71 billion cloud operational database market, the few companies that have been in the business for more than five decades, including MongoDB, will have some clear advantages when addressing customers’ needs around AI, especially as customers are averse to using net-new databases for vector search.

In today’s market, capabilities like vector and retrieval-augmented generation (RAG) have become a prerequisite for any modern, AI-ready database. While MongoDB is an early supporter of delivering vector search natively in-database, specifically with MongoDB Atlas, it is the company’s 50 years of experience with document architecture and JSON — which powers LLMs and agentic protocols like MCP (Model Context Protocol) — that make MongoDB well suited to address the AI opportunity once customers overcome legacy application modernization hurdles.

Modernization precedes AI

Although the fact that 30% of Atlas’ annual recurring revenue (ARR) comes from customers using at least one feature, such as vector search, suggests early momentum, AI is not yet having a significant impact on MongoDB’s growth. The question then becomes, when will this opportunity manifest? While AI startups and digital natives are ready to adopt scalable databases, most enterprises with bigger budgets are not prepared to make the shift. The reality is that these enterprises, although eager to leverage GenAI and agentic AI, have accumulated decades of technical debt and are often ill-prepared to handle the AI innovations the market is introducing.

Enterprises need a way to justify not only the cost of upgrading but also re-architecting their core infrastructure software to take advantage of AI. This is where the innovation announced at the September MongoDB.local NYC event was focused. In some ways, a lot of MongoDB’s AI work has already been accomplished by default due to its JSON-native architecture and early support for vector search in the cloud. The company is now focusing on preparing customers to take advantage of AI.

“At some point Oracle lost the competitive advantage because it was everything for everybody, and as a result of that, the code base become very voluminous and bloated, and that simply wasn’t scalable and agile enough to go around and develop [on] … Oracle Database is still the best on-premises relational system. But when people talk about specifically for GenAI, most likely the GenAI decision makers are not going to be the old-world relational database experts.”

— Managing Director (Firmwide) and Chief Data Architect, Financial Services

Announcing AMP

MongoDB believes that legacy relational databases are ill-equipped to support the needs of AI. However, this is not a unique point of view, as TBR has heard the same thing during its discussions with enterprise decision makers. Of course, Oracle and other vendors will make it very attractive for customers to upgrade to their AI databases, saving them the cost and risk of migrating to a different data model such as MongoDB’s, while often offering steep discounts.

Even so, there will always be a market for customers that want to reduce their dependency on the likes of Oracle and Microsoft SQL Server, not just for scalability reasons as they consider AI but also to reduce the high degree of lock-in these vendors create. That is where Application Modernization Platform (AMP) comes into play. While MongoDB announced new feature updates you would expect at a database conference, such as support for in-query encryption with MongoDB v. 8.2, AMP was the company’s big strategic announcement at MongoDB.local NYC.

At its core, AMP brings together the various tools, processes and best practices MongoDB has used over the years to help customers transition from a relational schema to a document schema. As described to participants at the event, AMP follows “three T’s,” Tools, Techniques and Talent. To be clear, MongoDB has always believed these three attributes are key to a successful migration, and AMP provides the company with an opportunity for some fresh messaging. That said, unlike some of MongoDB’s existing capabilities, such as Modernization Factory and Relational Migrator, AMP leverages GenAI and LLMs to actually convert code, making the platform more than a rebranding or repackaging of existing capabilities, which could help MongoDB expand beyond its core developer audience and engage in more senior-level conversations.

Tools: GenAI for code conversion

Although we have not yet seen significant traction for code conversion as a GenAI use case, customers seem willing to experiment in the hope of lowering the cost and time of migration. This is particularly true as AI puts pressure on businesses to move faster, which is challenging for enterprises that are still spending a significant portion of their IT budgets on maintaining legacy systems. There is no shortage of GenAI-powered code conversion tools in the market to help solve migration challenges, from Microsoft’s GitHub Copilot to Amazon Q Developer: Transform, and AMP acts in a similar capacity, relying on LLMs to analyze the existing code base, test the code and convert it to newer frameworks.

It is too soon to tell if AMP will drive revenue growth for MongoDB, but early use cases are promising. For example, we heard from Geneva-based asset management firm Lombard Odier, which already uses MongoDB as its core database engine and is now using AMP to modernize and migrate 250 applications. We also see an opportunity for MongoDB to leverage its existing hyperscaler partners, which collectively support MongoDB Atlas across 120 cloud regions, to integrate the platform with one of these partners’ existing code conversion capabilities.

Techniques & Talent and the role of services partners

Any GenAI tool on its own is unlikely to solve the modernization problem, and the other attributes of AMP — Techniques and Talent — are where MongoDB adds value. As previously mentioned, MongoDB has been in the business of transitioning customers away from the relational model for decades, and techniques like the App Transformation Framework (ATF) and MongoDB’s engineers, who are equipped to help with validation and deployment, are where MongoDB can add its unique perspective and leverage its code conversion to win these workloads.

That said, we believe MongoDB will still need to consider the role of its services partners, which can provide MongoDB with the enterprise access it needs as it begins to look upmarket and branch outside its core developer audience. For many data ISVs, gaining the attention of global systems integrators (GSIs) is not always easy, as GSIs go where the revenue opportunities are, such as large-scale IaaS migrations or upper-stack areas like analytics that they can build around. With GenAI, however, we see GSIs paying more attention to the data infrastructure layer.

For instance, when we surveyed alliance decision makers at professional services companies, 55% of respondents said data strategy & management represented the most opportunity for partner-led growth over the next two years, up from 42% a year ago. Moreover, GSIs are refocusing on managed services, and a technology partner that can create an enabling layer for cheap and fast migration — leading to customers spending less money on implementation services and more on managed services —  represents an emerging opportunity within the ecosystem. In many ways, this shift is being driven by GenAI’s disruption of the traditional services model, but many of MongoDB’s SI partners that were represented at MongoDB.local NYC, including Accenture, Infosys and Capgemini, have also demonstrated success using code conversion tools from other vendors to hasten migrations for clients and scale their own modernization practices.


The same opportunity exists for MongoDB and SIs, provided the company puts the right partner model in place around AMP. At the end of the day, we believe that while MongoDB will be successful in using AMP to transform applications stemming from the data tier, GSIs will play a critical role in extending AMP to the application layer, optimizing that application and, most importantly, tying it to a business outcome. This will be especially true if MongoDB further integrates with its hyperscaler partners, which are perhaps best equipped to bring the GSI to the table and act as an orchestrator of a MongoDB-GSI relationship.

The opportunity is there; success will boil down to execution

Agentic AI has customers and ecosystem participants reconsidering the role of the database. MongoDB is well positioned to capitalize on the AI opportunity, partly due to its JSON-native document database, as well as its early support for native vector search in the cloud. But for customers using legacy systems, there is still much work to be done in preparing for AI, and this is where MongoDB’s investment priorities lie. GenAI provides opportunities to accelerate migrations in ways that customers, which have been running their legacy databases for decades, have never experienced. AMP, which uses LLMs to evaluate and convert code to modern frameworks, combined with MongoDB’s extensive experience in helping customers transition away from the relational model, could be a compelling way to help customers shed their legacy tech debt. Strategically, this could serve as a stepping stone for MongoDB to become a more relevant piece of the enterprise AI strategy. With this opportunity, much of MongoDB’s near-term success will hinge on execution, including a willingness to proactively collaborate with partners in new ways.

2025 Ericsson Industry Analyst Event, Boston, Sept. 11, 2025 — A select group of industry analysts gathered at Convene in Boston to hear from Ericsson leaders, partners and customers about the company’s Enterprise business unit’s strategies, capabilities and opportunities in domains such as private cellular networks (PCNs) and network APIs, with AI and 5G monetization serving as themes that ran across the various executive presentations.

TBR perspective

Ericsson struck a cautiously optimistic tone at its annual industry analyst event, which focused on the Enterprise segment. The company acknowledged struggles and highlighted learnings and adaptations, especially pertaining to Vonage and the new Aduna joint venture for network APIs, which will set the stage for better outcomes moving forward. Ericsson is uniquely positioned to capitalize on the still-nascent PCN opportunity that is developing globally, but the vendor’s go-to-market encumbrances continue to constrain its growth prospects.

Specifically, the range and nature of Ericsson’s partnerships with the broader PCN ecosystem remain relatively limited compared to other players in the domain, especially frontrunner Nokia. Ericsson’s go-to-market channel beyond CSPs for enterprise growth areas remains limited relative to competitors such as Nokia, particularly in PCN. Nokia decided several years ago to reduce its reliance on CSPs and made a concerted effort to sell its PCN solutions directly to enterprises and through a robust roster of channel partners, including global systems integrators (GSIs), niche systems integrators (SIs), VARs and communication service providers (CSPs).

Ericsson has little control over one of its biggest challenges: CSPs are difficult to deal with, hesitant to work together for competitive reasons, and move slowly. Compounding this, CSPs are Ericsson’s largest customer cohort and partner channel, and TBR estimates more than 97% of Ericsson’s total company revenue through direct and indirect means stemmed from CSPs in 2024. These are key reasons why TBR expects Ericsson’s enterprise revenue will lag its potential in areas such as PCN, network APIs and communication applications.

Impact and Opportunities

FWA has significantly more room to run

Ericsson estimates that approximately 25% of all mobile broadband traffic globally is fixed wireless access (FWA) now and that 18% of premises globally will utilize FWA within five years, representing unprecedented growth considering FWA only began to take off in 2020. These statistics align with what TBR has been saying for several years: The market opportunity for FWA is much larger and more vibrant than the industry originally thought.

For example, TBR estimates FWA is technologically and economically feasible to support up to 50% of residential premises in the U.S. This opportunity is helped by the rapid time to deployment and strong value proposition the technology provides end users, especially when compared to other broadband access mediums like fiber-to-the-premises (FTTP), which is laborious and expensive to build out and has higher service costs for the end user. Business premises can also be strong candidates for adopting FWA. Ericsson is arguably the largest beneficiary of the FWA movement on a global basis in terms of revenue generated by selling FWA enablement products and services to CSPs (infrastructure only, not including customer premises equipment [CPE]).

Awareness gap in market slows adoption

On several occasions at the event, Ericsson leaders mentioned that there is an “awareness gap” in terms of the efficacy and outcomes that 5G-enabled technologies are achieving, especially as it pertains to PCN for businesses and the public sector. For example, Newmont, a Tier 1 mining company that adopted a private 5G network from Ericsson to remotely control its dump trucks at the mine, has realized a tenfold increase in coverage and a significant corresponding boost in productivity.

Such outcomes are compelling to enterprises looking to transform their operations to drive more revenue growth and or reduce costs. Though word is gradually getting out that early adopter enterprises are achieving strong results from new technology solutions, more needs to be done. Greater emphasis on partnerships with companies that have the ears of senior management at enterprises, most notably global systems integrators (GSIs), are a key way for Ericsson to promote the benefits of these solutions.

Differentiation, both in technology and marketing, needs to be addressed

Ericsson needs to do more to differentiate its technology solutions, and this includes ensuring that differentiation is well messaged to the market. Though some consolidation has occurred in the PCN, network API and communications applications domains, there remains significant competition and fragmentation, with many vendors playing in the market that are not well differentiated. This lack of differentiation is likely another reason Ericsson is struggling to achieve outsized growth in these nascent market areas. TBR did not hear a compelling narrative as to how Ericsson is differentiating itself in terms of technology and partnerships in these key, high-growth market areas. It remains to be seen if Ericsson’s NetCloud AI-powered cloud management and orchestration solution for PCN becomes a key differentiator once it is scaled up to large deployments of the Ericsson Private 5G solution.

Private cellular networks channel remains underdeveloped

Ericsson’s PCN revenue is growing at a relatively strong double-digit rate, but the size of the business is less than 60% the size of frontrunner (outside of China) Nokia’s PCN revenue, according to TBR’s Private Cellular Networks Vendor Benchmark. This differential in revenues is primarily due to Ericsson’s underdeveloped go-to-market approach and channel relative to Nokia. This is an issue TBR has identified and written about for the past few years, but Ericsson seems to have made minimal progress.

Ericsson is partnering extensively on PCN, but according to TBR’s research, most of that activity is driven by CSPs — even though enterprises and the public sector primarily work with GSIs, niche SIs, VARs and government contractors on digital transformation-related initiatives. Ericsson is also partnering with non-CSPs, including a new agreement with NTT DATA that reflects the kind of deeper, broader partnerships TBR believes Ericsson should pursue. Surface-level partnerships are essentially reseller arrangements, whereas some vendors have robust engagements with key GSIs and other types of partners. Nokia’s relationship with EY and Kyndryl are two such examples.

Connected laptops is a niche market, not a mass market opportunity

Ericsson has jumped on the bandwagon of 5G-connected PCs, and representatives from T-Mobile and HP Inc. spoke at the event about why this is a unique market opportunity. Though 5G-connected PCs sounds like a great feature that end users will utilize, TBR believes the additional cost required to embed the 5G modem into the PC, plus the subscription fees that would need to be paid to the service provider, broadly limits the scope of who would actually find enough value in the product and corresponding service to actually pay for it, especially when most people have smartphones and those smartphones have mobile hotspot tethering, which essentially makes the computer a cellular-connected device at no additional cost.

To be sure, 5G-connected PCs offer a unique capability that the mass market would value, but once extra cost is involved, only a small fraction of that market is likely to pay for the experience. Some enterprises and select types of SMBs (e.g., construction firms) and small office/home office (SOHO) workers (e.g., real estate agents and other road warrior workers) would be unique candidates for 5G-connected PCs, but this would be more of a niche market than a mass market opportunity. As such, TBR suggests network and PC vendors reassess their addressable market projections to be more aligned with observed user behavior and price-for-value considerations.

Conclusion

Ericsson has competitive technology, but its overreliance on CSPs to purchase that technology and/or scale it into end markets remains a weakness that will continue to hamper the company’s ability to participate more significantly in key growth domains, such as PCN. On the network API and communications application side, progress is being made and some scale is occurring, but Ericsson and its CSP partners are up against relatively fast-moving, well-resourced and more specialized entities, most notably hyperscalers and other digital-native players. Addressing the telecom industry’s weaknesses and shortcomings in these market areas will require more investment in channel development and more robust strategic partnerships with entities such as government contractors, GSIs and niche, domain-specific SIs.


Ericsson’s dependence on slow-moving CSPs is a risky proposition, especially when it comes to driving growth in key areas, including PCN, network APIs and communication applications. Ericsson should take a page out of Nokia’s playbook for aligning its opportunity areas with non-CSP players to accelerate growth. Specifically, Ericsson should take a closer look at how Nokia structured its PCN business, especially its channel ecosystem, to reduce its reliance on CSPs. This has proved to be the most optimal way to participate in opportunities arising in the enterprise end markets Ericsson is targeting.

On June 5 and 6, 2025, Fujitsu hosted around 25 analysts and advisers for an Executive Analyst Day event in Santa Clara, Calif. Fujitsu leaders from across the globe spoke on various parts of the company’s business and Fujitsu subject matter experts demonstrated solutions currently in research development or recently piloted with a few Fujitsu clients. The following includes insights from the event and analysis based on TBR’s ongoing research around Fujitsu and the broader IT services sector.

Fujitsu is putting the pieces together

The Fujitsu story keeps getting better. A year ago, TBR was struck by Fujitsu’s grounded and comprehensive approach to artificial intelligence. Later in 2024, Fujitsu’s Uvance initiative led TBR to conclude that the company had “the right vision, strategy and approach. We will continue to monitor the company’s ability to execute.” And this June Fujitsu extended a few more pieces, continuing to demonstrate capabilities, vision and a grounded approach that should garner steadily improving results, especially in the Americas. Those pieces? An enhanced alliances strategy and organization, ambitious and well-supported acquisition and investment capabilities, and an improving story around Uvance. Key components continue to evolve, such as Fujitsu’s AI offerings and global delivery operations, both of which support success in the Americas and contribute to a strengthening brand. In TBR’s view, Fujitsu has positioned itself, perhaps quietly compared to larger peers in the Americas, as a potentially disruptive force in those arenas where it chooses to compete.

Fujitsu positions its Americas practice and startup strategy as growth pillars

Speaking specifically about the Americas practice, Fujitsu leaders noted a strong performance in 2024 (a 7% profit, the highest among Fujitsu’s international regions), intentions to partner and acquire more aggressively, and a sustained focus on seven countries and specific U.S. states. Asked how Fujitsu Americas keeps up with quickly changing market conditions, Fujitsu leaders mentioned quantum capabilities, innovation, and partnering with ServiceNow while keeping the company focused entirely on services.

Breaking down various revenue streams, Fujitsu leaders said roughly 40% of revenue comes from Application Development Management and Application Solutions; 30% from partnering with SAP, ServiceNow and Oracle; 20% from workplace solutions and cloud migrations; and 10% from data and AI. In TBR’s view, Fujitsu Americas has built the solid foundation needed to outpace the overall company in terms of growth, provided the Wayfinders initiative gains traction and Fujitsu Americas stays focused on core industries in which Fujitsu has permission to play. Acquisitions and increased investments in alliance partnerships should be fuel added to a well-built fire.

Fujitsu executives highlighted the company’s startup strategy and some recent success stories, including a Japan-based AI biopharma company and an Italy-based AI governance company. Overall, Fujitsu’s startup strategy follows one of two paths: Either Fujitsu takes its own IP and licenses it to a startup (or invests in a startup rather than collecting a licensing fee), allowing the startup to test the market, likely reaching clients not typically within Fujitsu’s target market; or Fujitsu partners with a startup, providing funding, tools, platforms and entrée to Fujitsu’s client base. The second approach, according to Fujitsu, is utilized when the company comes across a technology and thinks, “Fujitsu would not have thought of this.”

In TBR’s view, both approaches reflect Fujitsu’s starting point with startups: R&D. In contrast to peers, which attempt to read market trends, capture emerging customer sentiment or conduct intensive gap analysis, Fujitsu relies on its deep R&D experience and extensive research and technology network to uncover startups well-suited and complementary to Fujitsu’s offerings or doing something unique and worthy of Fujitsu’s investment. Or, one suspects, both.

Advise, design, implement, support

First, the hard truth: In TBR’s view, Fujitsu needs a cleaner, more compelling Uvance story, at least in the Americas. Fujitsu executives said the Uvance percentage of the company’s Service Solution business increased from 10% to 30% from 2022 to 2025, so Uvance is resonating in the market and is increasingly a critical part of Fujitsu’s overall business. Challenging more accelerated growth in the Americas, in TBR’s view, is the mixed messaging about what Uvance is and what it brings Fujitsu’s clients. At various times over two days, Uvance was described as:

• “solving cross-industry issues, filling in the white spaces”;
• a “product and an accelerator” taking “proof of concepts to commercialization”;
• as a means to “address social challenges” not just “solving business problems”; and
• and “the center of our business.”

Yes, Uvance can be all those things, but the message then gets muddled.

Second, the rest of the truth: In TBR’s view, Fujitsu’s strategy around Uvance, Wayfinders and consulting writ large positions the company well, given Fujitsu’s strengths, accelerating change in the overall consulting business model, and market opportunities. Fujitsu positions Uvance Wayfinders as engaging clients “before implementation,” reflecting the company’s technology-centric value proposition. Fujitsu is not proposing business strategies or solving business problems but is instead applying its core technology strengths to a defined client technology environment and bringing efficiencies and improvements. As one Fujitsu executive said, “Like Accenture, not McKinsey.”

In TBR’s research, clients appreciate IT services companies and consultancies that stay within their own lane, doing what they do well and not persistently looking to upsell or increase their footprint. As management consultancies face the existential disruption of AI on their business models, Fujitsu’s Uvance neatly complements the company’s extensive AI-enabled offerings and capabilities. Fujitsu can advise — and, more importantly, implement — based on experience and talent at scale, harnessing AI disruption rather than being upended by it. Lastly, Fujitsu’s focus on three specific industries in the Americas  — public sector, manufacturing and retail — helps tremendously. No company has deep expertise in every industry, so delivering a message that Fujitsu excels in three specific areas builds credibility in a market where Fujitsu does not have a large-scale footprint or presence. In TBR’s view, this strategic decision to stay focused will be critical to Fujitsu’s continued growth in the Americas.

Deepening ServiceNow partnership

In a session dedicated to Fujitsu’s global alliances, the company brought in an executive from ServiceNow, complementing the Fujitsu presentation with specific examples of the two companies’ strategies and partnership. Fujitsu’s global alliances lead, Fleur Copping, noted that technology alliance partners provide entrée to new customers or position Fujitsu differently in the market, essentially amplifying the Fujitsu brand. Notably, every IT services company partners with nearly every technology partner, and differences in technology capabilities barely register, compelling Fujitsu — like other IT services companies — to differentiate in how they partner.

Copping said Fujitsu had professionalized its alliances organization and go-to-market efforts in recognition of the criticality of alliances in the emerging IT services ecosystem. Fujitsu has designated strategic partners —Amazon Web Services (AWS), SAP, ServiceNow, Microsoft and Salesforce — with an intent to develop and deploy scale around all five without becoming siloed around a specific partner. Copping adding that co-selling with these partners means being “coherent in front of the clients.” In TBR’s view, keeping the alliance focus on clients and their outcomes, rather than the commercial models or specific go-to-market motions, is an ideal strategy but is difficult to execute, in part because it is a break from previous practice.

The last two years have seen a “massive uptick in how Fujitsu shows up in the ecosystem,” according to the ServiceNow executive. A few highlights:

• Fujitsu acquired a ServiceNow boutique consultancy in the APAC region.
• ServiceNow’s “mature rigor” in its process of evaluating partners and offering expanded incentives to drive partner behavior spotlight Fujitsu’s higher ranking across the pre-sales to post-sales spectrum.
• Fujitsu has been an early participant in ServiceNow’s Enterprise Training Agreement program.

Fujitsu and ServiceNow have invested in training their sellers on each other’s portfolios.

Highlighting the third bullet, the ServiceNow executive said, “What does good look like in building talent [around ServiceNow’s portfolio]? Fujitsu is the partner I point to.” In TBR’s view, having a clearly defined and easily understood differentiating quality — that is clearly tied to the partnership and not an attribute applicable to the company as a whole — is critical to helping technology alliance partners and clients understand what added value an IT services company brings. TBR also noted that ServiceNow uses net-new annual contract value as an incentive for sales staff; in contrast, Fujitsu’s revenues depend on client retention and managed services contracts. Innovation, according to both companies, serves as bridge between the divergent sales incentive structures. TBR notes that innovation remains difficult to measure, but we see the evident alignment of ServiceNow’s and Fujitsu’s strategy and leadership approaches as perhaps more consequential for the long-term alliance. In sum, Fujitsu and ServiceNow presented a compelling partnership story — one that bears close scrutiny as the broader technology ecosystem evolves.



“In terms of the services, kind of scoping and bundling, that was typically a black-box process. So, we bring each other in at the right times, but they [services partners] never included us in terms of the actual kind of creation of the services offerings and the margins and the profitability, etc. So, we were only kind of aware of that, on a very macro level. It was just the solutioning part of it, saying, ‘OK, we know that this is the top-line business initiative that we’re solving for. And then we need to create the underlying products and services around that to create the recording of the product.’ So that’s the way that we typically work with them.” — Senior Manager of Global Channels and Alliances, Cloud

What peers can learn from Fujitsu

Competitors and technology alliance partners should keep an eye on Fujitsu’s acquisitions, investments in startups and new partnerships. Fujitsu’s investment portfolio has a mandate to invest for accelerated growth, has leadership support at the highest level, and has experience that has been built over the last few years. In TBR’s view, all three of those elements — a mission, leadership, and the muscle memory around acquiring, investing and partnering — are essential. Further, when a Fujitsu executive said the company’s CEO starts every acquisition discussion with questions around the technology and the customers, TBR saw a perfect reflection of the company’s overall culture and value proposition: take technology and solve customers’ (and societal) problems. Fujitsu executives did note concern that the market has been changing so rapidly that acquisitions made today may become less valuable by the time they are absorbed into the company. Overall, TBR believes Fujitsu will invest more in partnering (traditional alliances and corporate venture capital) more because of the current market dynamics and not because of a lack of appetite for acquisitions. Circling back to the competitive and alliances implications: be wary of — or partner with — a company that knows how to execute on M&A.

Final thoughts

So, what comes next? In TBR’s view, Fujitsu has the opportunity to expand its market presence and success in the Americas, particularly if the company can leverage three advantages and strengths it enjoys right now. First, technology alliance partners, such as ServiceNow, Salesforce and AWS, are aggressively partnering with IT services companies and consultancies that can show flexible commercial arrangements, bring new clients and coinnovate. Second, Fujitsu has the resources and resolve to aggressively acquire, partner or invest. TBR has not seen an IT services company or consultancy grow without attitude and resources. Third, across all the company’s recent analyst events and briefings, Fujitsu’s core culture has remained focused on bringing technology solutions to clients.


Focus and staying within a company’s strengths, in TBR’s view, separate well-run, high-performing consultancies and IT services vendors from peers. TBR does not anticipate that Fujitsu will wander from its path but does expect that analyst events in 2026 will bring additional Fujitsu strengths to the forefront.

All eyes were again trained on San Jose, Calif., during AMD Advancing AI 2025, held on June 12, just three months after NVIDIA GTC 2025. The event centered on AMD’s bold AI strategy that, in contrast to that of its top competitor, emphasizes an open ecosystem approach to appeal to developers and organizations alike. The entire industry seeks increased competition and accelerated innovation in AI, and AMD plans to fill this void in the market.

Catching up with NVIDIA — can AMD achieve the seemingly impossible?

AMD’s Advancing AI 2025 event presented an opportunity for CEO Lisa Su to outline how AMD’s investments, both organic and inorganic, position the company to challenge NVIDIA’s dominant position in the market. During the event’s keynote address, Su announced new Instinct GPUs, the company’s first rack scale solution, and the debut of ROCm 7.0, the next generation of the company’s open-source AI software platform. She also detailed the company’s hardware road map and highlighted strategic partnerships that underscore the increasing viability of the company’s AI technology.

However, NVIDIA’s dominance in the market cannot be understated, and the AI incumbent’s first-mover advantage has created massive barriers of entry to the space that AMD will tactfully need to invest in overcoming. For instance, TBR estimates NVIDIA derived more than 25 times the revenue AMD did from the sale of data center GPUs in 2024. Nonetheless, AMD is committed to the endeavor, and the company’s overall AI strategy is clear: deliver competitive hardware and leverage ecosystem openness and cost competitiveness to drive platform differentiation and gain share in the market.

Acquired assets pave the way for Helios

Su’s keynote address began with the launch of AMD’s Instinct 350 Series GPUs, comprised of the Instinct MI350X and MI355X. The Instinct MI355X outperforms the MI350X but also requires liquid cooling, whereas the MI350X can be air cooled. As such, the Instinct MI355X offers maximum inferencing throughput and is specifically designed to be integrated into high-density racks while the MI350X targets mixed training and inference workloads and is ideal for standard rack configurations. Both GPUs pack 288GB of HBM3e memory capacity — significantly more than the 192GB offered by NVIDIA’s B200 GPUs.

The denser memory architecture of the AMD Instinct 350 Series is a key enabler of the chip’s comparable performance to NVIDIA’s B200 where AMD claims to deliver equivalent to approximately twice the compute performance of Blackwell, depending on the floating-point precision of the model being run. However, even more noteworthy was AMD’s introduction of its open rack scale AI infrastructure, which was made possible by the company’s 2022 acquisition of Pensando Systems.

Along with the acquired company’s software stack, Pensando added a high-performance data processing unit (DPU) to AMD’s portfolio. By leveraging this technology and integrating Pensando’s team into the company, AMD unveiled the industry’s first Ultra Ethernet Consortium (UEC)-compliant network interface card (NIC) for AI, dubbed AMD Pensando Pollara 400 AI NIC, in 4Q24, highlighting the company’s support of open standards.

At Advancing AI 2025, Su formally announced the integration of Pollara 400 AI NIC with the company’s MI350 Series GPU and fifth-generation EPYC CPU to create the company’s first AI rack solution architecture, configurable as an air-cooled variant featuring 64 MI350X GPUs or a liquid-cooled variant featuring up to 128 MI355X GPUs. The development of AMD’s rack scale solution architecture comes in response to the release of NVIDIA’s GB200 NVL72 rack scale solution, with both racks being Open Compute Platform (OCP)-compliant to ensure interoperability and simplified integration with existing OCP-compliant infrastructure.

Going a step further, at the event Su introduced AMD’s next-generation GPU — the Instinct MI400 series — alongside the company’s next-generation rack scale solution, both of which are expected to be made available in 2026. The Instinct MI400 series is slated to deliver roughly twice the peak performance of the MI355X, while Helios — AMD’s next-generation rack scale solution — will leverage 72 MI400 series GPUs in combination with next-generation EPYC Venice CPUs and Pensando Vulcano network adapters. Unsurprisingly, Helios will adhere to OCP standards and support both Ultra Accelerator Link (UALink) and UEC standards for GPU-to-GPU interconnection and rack-to-rack connectivity.

In comparison to the prerelease specs of NVIDIA’s upcoming Vera Rubin NVL72 solution, which is also scheduled to be released in 2026, Helios is expected to deliver the same scale-up bandwidth and similar FP4 and FP8 performance with 50% greater HBM4 memory capacity, memory bandwidth and scale-out bandwidth. However, with AMD GPUs delivering higher memory capacity and bandwidth than equivalent NVIDIA GPUs, this begs the question: Why do NVIDIA GPUs dominate the market?

Developers, developers, developers

At NVIDIA GTC 2025, CEO Jensen Huang said, “Software is the most important feature of NVIDIA GPUs,” and this statement could not be more true. While NVIDIA has benefited from first-mover advantage in the GPU space, currently the company’s GPU release cycle is only slightly ahead of AMD’s in terms of delivering roughly equivalent silicon to market from a compute performance perspective. However, AMD has a leg up when it comes to GPU memory capacity, which helps to drive inference efficiency.

Where NVIDIA’s first-mover advantage really benefits the company is on the software side of the accelerated computing equation. In 2006 NVIDIA introduced CUDA (Compute Unified Device Architecture), a coding language and framework purpose-built to enable the acceleration of workloads beyond just graphics on the GPU. Since then, CUDA has amassed a developer base nearing 6 million, boasting more than 300 libraries and 600 AI models, all while garnering over 48 million downloads. Importantly, CUDA is proprietary, designed and optimized to exclusively support NVIDIA GPUs, resulting in strong vendor lock-in.

Conversely, AMD’s ROCm is open source and relies heavily on community contributions to drive the development of applications. Recognizing the inertia behind CUDA and the legacy applications built and optimized on the platform, ROCm leverages HIP (Heterogenous-computing Interface for Portability) to allow for the porting of CUDA-based code, simplifying code migration. However, certain CUDA-based applications — especially those that are more complex — do not run with the same performance on AMD GPUs after being ported due to NVIDIA software optimizations that have not yet been replicated.

Recognizing the critical importance of the ecosystem to the company’s broader success, AMD continues to invest in enhancing its ROCm platform to appeal to more developers. At Advancing AI 2025, the company introduced ROCm 7, which promises to deliver stronger inference throughput and training performance compared to ROCm 6. Additionally, AMD announced that ROCm 7 supports distributed inference, which decouples the prefill and decode phases of inferencing to vastly reduce the cost of token generation, especially when applied to AI reasoning models. Minimizing the cost of token generation is key to maximizing customers’ revenue opportunity, especially those running high-volume workloads such as service providers.

In addition to distributed inference capabilities similar to those offered by NVIDIA Dynamo, AMD announced ROCm Enterprise AI, a machine learning operations (MLOps) and cluster management platform designed to support enterprise adoption of Instinct GPUs. ROCm Enterprise AI includes tools for model fine-tuning, Kubernetes integration and workflow management. The platform will rely heavily on software partnerships with companies like Red Hat and VMware to support the development of new, use-case- and industry-specific AI applications, and in stark contrast to NVIDIA AI Enterprise, ROCm Enterprise AI will be available free of charge. This pricing strategy is key in driving the development of ROCm applications and the adoption of the platform. However, customers may continue to be willing to pay for the maturity and breadth of NVIDIA AI Enterprise, especially as NVIDIA continues to invest in the expansion of its capabilities.

Partners advocate for the viability of AMD in the AI data center

Key strategic partners, including executives from Meta, Oracle and xAI, joined Su on stage during the event’s keynote, endorsing the company’s AI platforms. All three companies have deployed AMD Instinct GPUs and intend to deploy more as time goes on. These are effectively some of the largest players in the AI space, and their words underscore the value they see in AMD and the company’s approach of driving a more competitive ecosystem to accelerate AI innovation and reduce single-vendor lock-in.

However, perhaps the most noteworthy endorsement came from OpenAI CEO Sam Altman, who discussed how his company is working alongside AMD to design AMD’s next-generation GPUs, which will ultimately be employed to help support OpenAI’s infrastructure. While on stage, Altman also underscored the growing AI market with arguably the most ambitious, albeit somewhat self-serving, statement of the entire keynote: “Theoretically, at some point, you can see that a significant fraction of the power on Earth should be spent running AI compute.” It is safe to say that AMD would be pleased if this ends up being the case; however, for now, AMD is projecting the data center AI accelerator total addressable market will grow to greater than $500 billion by 2028, with inference representing a strong majority of AI workloads.

AMD has become the clear No. 2 leader in AI data center and is well positioned to take share from NVIDIA

AMD’s Advancing AI 2025 event served as a testament, reaffirming the company’s open-ecosystem-driven and cost-competitive AI strategy while also highlighting how far the company’s AI hardware portfolio has come over the last few years. However, while AMD’s commitment to an open software ecosystem and open industry standards is a strong differentiator for the company, it is also a major risk as it makes AMD’s success dependent on the performance of partners and consortium members. Nonetheless, TBR sees the reputation of AMD GPUs becoming more positive, but NVIDIA’s massive installed base and developer ecosystem make competing with the industry giant a significant feat.