Agentic AI, Sovereignty, Resiliency, Trust and Governance Permeate Mobile World Congress 2026

/by

TBR perspective

Though AI was the overarching topic of discussion at Mobile World Congress 2026 (MWC26), sovereignty, resiliency, trust and governance also permeated conversations throughout the event. The rapid acceleration of AI — combined with mounting geopolitical uncertainty — is forcing governments and enterprises to reassess long-standing assumptions about global supply chains, labor markets, education systems and technological dependencies. As a result, sovereignty is emerging as a defining strategic priority.
 
While definitions of sovereignty vary, a common theme emerged throughout MWC: Nations increasingly want to own, operate or meaningfully influence critical components of the digital stack that underpin economic stability and national security, including connectivity infrastructure, data platforms, cloud environments and, increasingly, AI capabilities.
 
For telecom operators, this shift could represent one of the most significant structural growth opportunities for the industry in years. Telcos sit at the intersection of domestic technology infrastructure and government policy, positioning them as natural partners in sovereignty-driven initiatives such as secure networks, domestic data hosting, cloud services, resilient communications infrastructure and trusted AI deployment.
 
Governments are increasingly looking for national or regional champions that can anchor sovereign digital ecosystems, and telecom operators — given their existing infrastructure, regulatory relationships and role in critical communications — are well positioned to play that role. As sovereignty agendas translate into funding programs, regulatory support and public-private partnerships, telecom providers that align their strategies with national priorities could unlock new revenue streams while reinforcing their roles as strategic infrastructure providers in the AI era.
 

MWC26: Are Telecom Operators Embracing or Resisting AI?

Principal Analyst Chris Antlitz shares his top takeaways from Mobile World Congress 2026 and examines how emerging opportunities are likely to drive disruptions in technology and business models and impact markets.

Watch on Demand

Impact and Opportunities

Sovereignty and resiliency may be the telecom industry’s next growth catalyst

Governments are beginning to put real action behind years of rhetoric around digital sovereignty, largely under the banner of national security. More than 90 countries now have formal sovereignty statements, signaling sustained policy momentum rather than a passing trend. Current geopolitical conflicts, including the war in the Middle East, are reinforcing the urgency of this shift. TBR expects governments with the financial capacity to allocate substantial funding and regulatory support as well as provide other favorable market conditions to domestic champions that can help advance sovereignty objectives. Telecom operators and their critical vendor ecosystems are particularly well positioned to play a central role.
 
Specifically, TBR expects telecom operators to receive unprecedented government backing to expand their roles across sovereign digital infrastructure. This includes owning and operating data centers, supporting data residency requirements, and investing in greater network resiliency through cybersecurity, redundant facilities and diversified backhaul routes. These investments are aimed at strengthening national security, protecting sensitive data and reducing reliance on non-domestic providers wherever possible. Telecom operators are also well positioned to integrate connectivity infrastructure and data centers with AI models and applications in ways that comply with domestic regulations. Canada, Europe, the developed Middle East and parts of Asia and Oceania are likely to lead this sovereignty push.

Governance becomes a prerequisite for success with AI

Governance was a recurring theme across content sessions and executive meetings at MWC26. As telecom operators move from experimentation to operational in AI, creating a corporatewide, centralized framework for data management, model oversight and regulatory compliance is becoming essential. Without clear governance, AI initiatives often remain fragmented across business units, leading to inconsistent outcomes, duplicated efforts and limited enterprise impact.
 
The challenge is that most telecom operators still lack a horizontal governance model for both AI and data. Data ownership is often siloed, policies vary by department and there is limited visibility into how models are trained, deployed and monitored. This fragmentation makes it difficult to scale AI beyond isolated pilots and increases operational, regulatory and reputational risk.
 
Telecom operators with strong C-suite sponsorship are best positioned to overcome these challenges. Executive backing helps enforce common standards, prioritize enterprisewide data initiatives and ensure AI programs are aligned with broader digital transformation objectives. Without this level of leadership support, governance efforts often stall as organizational silos resist change.
 
Leading telcos are beginning to formalize governance by creating centralized data offices and appointing chief data officers responsible for enterprisewide data strategy and governance. In practice, robust governance is quickly becoming a prerequisite for AI. Organizations that establish clear frameworks for data quality, access, security and accountability will be far better positioned to operationalize AI at scale and consistently generate business value.

Trust can be a CSP differentiator and revenue driver for telecom operators

Trust emerged as a central theme at MWC26, with many industry leaders warning that confidence in the digital ecosystem is eroding as scams and fraud proliferate across networks. Estimates suggest that roughly $500 billion is lost each year globally to fraud and scams, underscoring the magnitude of the challenge. With networks acting as a critical conduit — and often a chokepoint — for malicious actors, many speakers emphasized that telecom operators must play a more proactive role in addressing the issue. The industry issued a clear call to action for operators, technology vendors, regulators and other ecosystem participants to collaborate more aggressively to combat fraud and restore confidence in the digital world.
 
Several discussions at MWC26 framed trust as both a responsibility and a potential competitive advantage for telecom operators. Telcos sit at the center of digital connectivity and increasingly function as a protective layer for the broader digital economy. However, while technology innovation is accelerating rapidly, the mechanisms that ensure trust — security, verification, governance and consumer protections — are not evolving at the same pace. Fraud and scams not only cause financial harm but also risk eroding public confidence in digital networks and services. Because trust is fragile and can be quickly lost, operators must treat it as a strategic asset that requires ongoing investment and careful stewardship.
 
Some industry leaders suggested that trust could become a differentiator for telecom operators relative to hyperscalers and other digital-native companies. Customers often view telecom providers as more regulated and infrastructure-centric, and therefore inherently more accountable than large technology platforms. The open question for the industry is whether this trust advantage can be monetized, either through stronger customer loyalty, increased market share or the development of new trusted digital services. However, the inverse is also true: If trust erodes further, consumers and enterprises may alter their behavior, potentially bypassing traditional telecom channels.
 
Bharti Group Chairman Sunil Mittal pointed to roaming as a model for industry collaboration. Over the past two decades, operators have worked collectively to dramatically reduce the cost and friction associated with international roaming. Mittal suggested that a similar level of global coordination among operators, technology providers and regulators could be applied to tackling scams and fraud. In doing so, telecom operators could help society regain greater control over the digital environment — in addition to reinforcing their role as a trusted foundation of the global communications ecosystem.

The initial focus for AI-RAN is AI for RAN

Most of the AI-RAN-related announcements and discussions at MWC26 centered on AI for RAN, which entails applying AI models to improve the performance, efficiency and automation of radio networks. Several vendors demonstrated how specialized GPUs and AI accelerators can be integrated into the baseband to process RAN workloads more efficiently than traditional hardware, enabling operators to apply machine learning models to tasks such as network optimization, traffic management, and increasingly, radio signal processing. Key use cases include channel estimation, traffic prediction and beamforming optimization, each of which can help improve spectral efficiency and throughput while lowering power consumption. The first commercially available AI-RAN products will come to market in 2027, with 2026 as a development and proof-of-concept year. RAN vendors can expect to start realizing meaningful revenue growth from AI-RAN in 2028.

Agentic AI holds promise, but telco adoption remains slow

Agentic AI — systems capable of autonomously planning and executing complex tasks — featured prominently in discussions across MWC26, but adoption among telecom operators remains limited. While vendors and technology firms are aggressively advancing agent-based architectures, most telecom operators remain focused on foundational generative AI deployments such as copilots, knowledge assistants, customer service automation and language translation (T-Mobile’s Live Translation service is an example of an initial network use case for agentic AI). Moving from these assistive use cases to fully autonomous agents requires significantly higher levels of data quality, governance and system integration than most telecom operators currently possess.
 
Data fragmentation and the lack of enterprisewide governance frameworks are major barriers to scaling agentic AI initiatives for telecom operators. Telecom operators typically operate across dozens of siloed operational and business support systems, making it difficult for autonomous agents to reliably access and act on enterprise data. As a result, most agentic AI activity among telcos remains confined to pilot programs and proofs of concept, often focused on narrow operational workflows such as network troubleshooting or IT automation. Until operators establish stronger data governance models and centralized AI strategies, agentic AI will likely remain an experimental capability rather than a widely deployed operational tool.

AI agent marketplaces hint at the emergence of a digital labor market

One of the more intriguing ideas circulating at MWC26 was the notion of job postings for AI agents and the emergence of marketplaces where organizations can source them. As agentic AI evolves and becomes intertwined with the labor market, enterprises may increasingly seek ways to “hire” AI agents capable of executing discrete tasks or workflows.
 
Conceptually, this could resemble a new class of digital labor market in which organizations post tasks or roles that can be fulfilled by autonomous agents. Over time, this dynamic could give rise to a broader ecosystem of AI agents hosted on marketplaces that are effectively “looking for work” — something akin to a hybrid between hyperscalers’ cloud marketplaces and platforms such as Indeed.
 
Early examples of agentic AI are already demonstrating this model’s potential. Initial AI agents introduced by companies such as Anthropic are capable of autonomously handling tasks ranging from legal research to creative work, often with minimal human intervention. As these systems become more sophisticated and capable of executing multistep workflows, it is likely that enterprises will increasingly source specialized agents rather than build every capability internally. This shift could drive the emergence of marketplaces where companies — and eventually consumers — discover, benchmark and deploy AI agents that meet specific operational needs.
 
In practice, enterprises are unlikely to post job listings through traditional HR processes. Instead, AI agents will likely be sourced and orchestrated through software platforms and workflow systems that dynamically assign tasks to the most appropriate agents. This creates the foundation for a new digital economy centered on AI labor, with new markets emerging around agent distribution, infrastructure and trust. As organizations begin deploying agents in critical workflows, marketplaces will likely evolve to include performance benchmarking, compliance certifications and reputation systems that help enterprises determine which agents are trustworthy, secure and effective.

ISAC is here now

The mobile industry may not need to wait for 6G for integrated sensing and communications (ISAC) capabilities to begin emerging. While ISAC is widely viewed as a core feature of future 6G networks, several vendors are already demonstrating how sensing functionality can be enabled using existing LTE and 5G infrastructure. Startups such as Tiami Networks are developing solutions that leverage existing radio signals and machine learning algorithms to transform cellular networks into wide-area sensing systems capable of detecting objects such as drones and vehicles or human movement.
 
These early implementations sit largely outside formal 3GPP ISAC specifications, relying instead on clever signal processing, software and edge compute to extract sensing insights from existing RAN transmissions. Although still in the early stages of commercialization, these systems are already being tested and deployed in niche environments such as defense, critical infrastructure protection and public safety. As standards bodies continue developing native ISAC capabilities for 6G, these early deployments provide a glimpse into how mobile networks may increasingly double as large-scale sensing platforms.

Conclusion

MWC26 made clear that the telecom industry is entering a new phase shaped by the convergence of AI, geopolitics and digital infrastructure. While AI dominated the conversation, the broader narrative that emerged centered on control, resilience and trust in an increasingly complex digital environment.
 
Sovereignty initiatives are pushing governments to rethink how critical infrastructure is owned and operated. At the same time, telecom operators are grappling with the organizational and technical prerequisites needed to operationalize AI at scale, including governance, data management and system integration. Together, these forces are redefining the role telecom operators play in the global technology stack — not simply as connectivity providers, but as strategic infrastructure partners in the digital economy.
 
Ultimately, sovereignty, governance, trust and AI are deeply interconnected. Governments want trusted domestic infrastructure. Enterprises want secure and reliable AI-enabled services. And consumers increasingly expect digital environments that are safe and resilient. Telecom operators sit at the center of all three dynamics. If telecom operators can successfully align AI innovation with strong governance frameworks and trusted infrastructure, they have an opportunity to move beyond the traditional connectivity business and play a far more central role in shaping the next phase of the digital economy.

Execution Over Hype: Fujitsu’s AI Strategy Takes Shape

/by

On Jan. 22, 2026, Fujitsu Americas hosted more than a dozen industry analysts in Toronto for a day of briefings on the company’s progress, specifically in the Americas. Speakers included Asif Poonja, EVP, corporate executive officer & CEO, Americas Region; John Slaytor, VP, head of Strategy & Uvance; Durga Kota, CTO, head of Data x AI, Fujitsu North Americas; Nicholas Lee, executive director & head of Fujitsu Intelligence; Sudhir Nair, CEO & co-head, managing partner, Uvance Wayfinders Americas; and Fleur Copping, VP, Strategic Alliances Unit, Regions. The following day, TBR recorded an episode of TBR Talks with Poonja in Fujitsu’s Toronto office, which went live March 6 on all streaming platforms. This special report highlights the information shared during analyst day, the TBR Talks conversation with Poonja, and TBR’s ongoing analysis of Fujitsu, both in the Americas and globally.

Fujitsu’s technology strategy

Artificial intelligence was emphasized during Kota’s discussion of the company’s technology strategy. He noted that Fujitsu’s top priority remains developing new intellectual property, with an emphasis on cocreating with clients. Fujitsu’s technology development teams also help accelerate the company’s overall portfolio transformation.
 
While this is inherently more of an internal strategy and less client-driven, Kota stressed that clients will eventually benefit from Fujitsu’s innovation, based on the company’s impressive track record to date. Kota noted that the company intends to be an intelligence integrator, bringing together all the technology in alignment with client needs.
 

In TBR’s view, the overall technology strategy — innovate with clients, invest in R&D and advancing technology, and bring it all together in a consistent manner — is not groundbreaking, but it fits well with Fujitsu’s culture, history and market position. Fujitsu has been a reliable innovator and has robust experience translating lab work into technology used by clients. In a market swamped with AI and tech hype, a sensible, practical tech strategy stands out for being reasonable, straightforward and executable.
 
Kota also commented on the well-known reality that clients have lots of data but do not know how to use it (noted in every edition of TBR’s Digital Transformation: Voice of the Customer Report since 2019). Fujitsu tackles the problems in part by examining different client personas:

  • Chief operating officers want to take advantage of data, so Fujitsu brings operational efficiency and optimization.
  • CTOs spend lots of money on technology but need to modernize, maintain and manage. Conveniently, CTOs can outsource all of that to Fujitsu.
    CFOs want to use AI to make changes to the business through scenarios, ontology-based decisions and automation, all of which are increasingly in Fujitsu’s wheelhouse, especially with the evolution of Uvance and Wayfinders.

In Kota’s and other Fujitsu leaders’ telling, the company’s differentiation comes from explainability (around AI), multiagent orchestration and governance. Fujitsu’s approach is to solve the biggest business problem that can feasibly be solved and then use the savings from that to fund work on smaller problems. By deploying at the client site, deepening relationships and moving faster, Fujitsu can sell clients on the target 5x savings, equivalent to 5x the price of Fujitsu services and solutions.
 
In TBR’s view, Fujitsu’s approach, with an emphasis on understanding personas and targeting savings, may not be markedly different from peers’, but the company remains focused on clients and outcomes, rather than internal metrics of IT services greatness. In TBR’s research, clients care about what you can do for them, not how you are different from peers. Fujitsu has the right focus and now needs to execute.

Uvance and Wayfinders

Fujitsu continues to develop its stories around Uvance and Wayfinders, including more tightly focused messaging that shows both differentiation from peers and a clear value proposition for Fujitsu’s clients and technology partners. Given the inherent challenges in repositioning Fujitsu in the market, TBR believes the company’s emphasis on developing the Uvance and Wayfinder stories is critical during 2026.
 
Slaytor discussed Fujitsu’s Uvance strategy, and Nair explained how Wayfinders will fit into Fujitsu’s shifting market position, noting that Wayfinders remains a global organization that works locally but is managed globally. Nair said Wayfinders brings global reach and an “end-to-end” understanding of Fujitsu’s solutions and services. Many of the Wayfinder professionals have been recruited directly from IT leadership roles within enterprises, bringing deep industry experience, or from leading consultancies.
 
Wayfinders serve as the “linchpin,” according to Nair, and benefit from the “tremendous technology, IP, capabilities, solutions and credentials” of Fujitsu as a whole. While leaning into an engineering mindset that remains foundational to Fujitsu, Wayfinder consultants can “broaden the conversation” to help clients “get to transformation.”
 
In TBR’s view, focusing on manufacturing clients, hiring directly from industry, and positioning Wayfinders as part of, but uniquely separated from, Fujitsu all makes sense … for now.
 
Building a consultancy requires continually solving two challenges: people and permission. Technology-centric IT services companies that have sought to build management consultancies in-house have typically struggled with a few of the following challenges, all of which tie back to people and permission: 1) scale; 2) brand, both internally and externally; 3) talent acquisition and retention; 4) over-indexing on lessons learned from a small sample of early engagements; and 5) culture, which changes slowly, if at all.

Can Fujitsu succeed in 2026?

In terms of success in 2026, a few factors weigh heavily in Fujitsu’s favor, including leadership commitment, a tightly proscribed value proposition, and excellent technology chops at a time when every consulting engagement is a technology engagement.
 
Perhaps the biggest challenge will be Fujitsu’s ability to convince clients and technology partners that the company’s consulting capabilities are on par with competitors’ and of the quality that everyone has come to expect from Fujitsu. The Wayfinders do not need to convince “the market” that they are bringing value; they just need to convince the right selection of Fujitsu’s clients — and Fujitsu’s leadership. 2026 should be a banner year for Uvance and Wayfinders.

Alliances

Fujitsu has become something of a TBR favorite when discussing technology alliances strategies. The company’s relationship with ServiceNow is a case study in aligning sales teams, and Fujitsu has consistently demonstrated leadership commitment to alliances across the ecosystem.
 
In Toronto, Copping continued to build on an impressive alliances story by detailing Fujitsu’s relationships with the top six strategic partners — Microsoft, Amazon Web Services (AWS), Oracle, SAP, ServiceNow and Salesforce — as well as developing alliances, such as Fujitsu’s investment in partnering more closely with Dynatrace. Copping explained precisely what Fujitsu looks for with its technology partners (echoing sentiments shared with TBR in early 2025): invest both financial resources and technology skills; help Fujitsu de-risk delivery of a partner’s technology; and provide access to innovation and the newest technology developments.
 
Illustrating execution of Fujitsu’s alliances strategy, Copping described two Oracle engagements, which both started as a client faced a messy, complex IT environment, saddled with technology that had outlived its usefulness, as well as a telecom engagement that showed how technology can propel and paralyze decision making. The three examples underscored a sentiment TBR has heard repeatedly since mid-2025: “I’m not sure every customer is ready for [the AI solutions] the tech companies are pushing.”
 
In wrapping up the alliance discussion and cementing Fujitsu’s place as a leading player in ecosystem management, Copping noted that Fujitsu’s 1,400 cybersecurity professionals were being cross-trained on ServiceNow and Fujitsu’s ServiceNow-related capabilities. Rather than train ServiceNow experts on cyber, Fujitsu’s focus has been to broaden ServiceNow skills — most critically, understanding — across an existing and profitable practice. TBR is frequently asked, “What separates the best ecosystem players from their peers?” This ServiceNow approach will now be part of the answer.

What’s next?

TBR will incorporate the numbers, data, details, strategies and case studies Fujitsu shared during its Toronto presentations into ongoing analysis, including a deeper examination of Uvance, the Wayfinders and Fujitsu’s AI differentiation.
 
One element that struck TBR was the intentional and stark difference between the company’s U.S. and Canada practices. This was evident during the event and made clearer by physically being in the Toronto office, and is also an aspect of Fujitsu’s overall North Americas strategy. Although Fujitsu is one company unquestionably working together and supported as a single region by the company’s global parent in Japan, the U.S. business is Manufacturing and the Canada business is Public Sector, not exclusively, but Fujitsu has — wisely, in TBR’s view — played to its strengths in Canada and Japan and avoided the temptation to make managing easier by harmonizing target client sets, go-to-market motions and marketing. Even as the Uvance and Wayfinder efforts catalyze change across the overall company, TBR anticipates the well-led and distinctly focused Canada and U.S. practices will continue to differentiate Fujitsu in the Americas.

Will AI be the Death of SaaS in 2026?

/by

TBR Insights Live session: How PaaS revenue will outpace SaaS revenue among cloud software vendors, why SaaS incumbents will position SLMs as a foundational part of their AI strategies, and whether the question, “Will AI be the death of SaaS?” will remain relevant

Read more

Skills Shortage Will Challenge the Scaling of Sovereign AI in 2026

/by

AI-related skills will remain scarce across both buyers and ecosystem partners as the rapid pace of innovation and the technical complexity required to enable sovereign AI continue to hinder adoption. These challenges, combined with a lack of clearly defined and compliant use cases among sovereign customers, gaps in sovereign cloud infrastructure availability and steep AI learning curve faced by ecosystem partners, will constrain meaningful investment and implementation of sovereign AI throughout 2026.

Sovereign AI momentum will build through partnerships in 2026, but meaningful financial impact remains a longer-term prospect

Sovereign AI will undoubtedly mature more quickly than the sovereign cloud market, but it is still too early to expect a noticeable financial impact from those capabilities in 2026. While Sovereign cloud did not develop until more than a decade after the general cloud trend was underway, it remains very nascent from an adoption and market development perspective. Widespread adoption of sovereign AI depends on deliverable sovereign cloud capabilities, among other requirements. Further complicating adoption is the advent of agentic AI, which introduces new risks by leveraging data that can be sovereign and sensitive and by taking action on the intelligence produced.
 

Despite the challenges to widespread adoption of sovereign AI in 2026, we expect vendors across the spectrum of business models to aggressively partner and invest to capitalize on the opportunity in this emerging segment. Partnership activities will center on the strongest sovereign AI providers and the most well-established sovereign cloud regions, as would be expected. Amazon Web Services (AWS) and Microsoft are the clear leaders in sovereign cloud delivery capabilities, and their geographic focus will remain the U.S. and Europe, particularly Germany. The U.K. should also see concentrated investment and lead in early adoption.
 

Watch now: 2026 Predictions for Cloud & Software, featuring Senior Analyst Alex Demeule

 

In some ways, the development of sovereign AI will look much like the Industrial Revolution, which disproportionately benefited the developed countries that had access to resources and oil to fund the new economic model. Microsoft and AWS have already announced specialty partner programs for sovereign AI and big-name alliances with the likes of Accenture and SAP. We expect those alliances and ecosystems to become more AI-focused in 2026, providing tighter integration between cloud providers, model providers, SIs and ISVs that will form the foundation for sovereign AI growth in 2027 and beyond.
Explore more SaaS predictions for 2026 in our special report Will AI be the Death of SaaS in 2026?

PaaS Revenue Will Outpace SaaS Revenue for Cloud Software Vendors

/by

Enterprise customers are prioritizing the modernization of their existing SaaS estates rather than adding new applications, driven by market saturation, accumulated technical debt, and a growing imperative to become AI-ready. As IT buyers shift their focus toward modern platforms, traditional SaaS leaders should expect their PaaS segments to continue significantly outperforming their core SaaS businesses in revenue growth.

A clear inflection in SaaS momentum emerges

SAP’s trajectory is tied to Business Technology Platform (BTP) becoming the architectural anchor of RISE programs. BTP is no longer an optional add-on but rather the control plane for process mining, metadata management and event-driven extensions. Attach rates above 80% in RISE cycles reflect SAP’s ability to position BTP as mandatory for modernization rather than discretionary middleware. The addition of Signavio and LeanIX broadened the portfolio, giving SAP a modern platform that starts with process intelligence and ends in a coherent data and extension strategy.
 

Salesforce is following a data-first path. Data Cloud has become the centerpiece of modernization discussions as the company works to consolidate fragmented CRM data models and unify cross-cloud metadata.
 

MuleSoft remains critical in stitching legacy systems into Salesforce’s AI-ready architecture, and early Data Cloud wins indicate customers view it as the foundation for copilots, agentic workflows and future small language model integration.
 

Both vendors benefit from a status as a highly trusted incumbent and deep process ownership, enabling them to sell platform capabilities not as adjacent tools but as prerequisites for becoming AI-ready.
 

SAP & Salesforce PaaS Revenue (Source: TBR)


 

Explore more SaaS predictions for 2026 in our special report Will AI be the Death of SaaS in 2026?

Supply Chain Threatens the Rise of AI PC in 2026

/by

AI PC Ambitions Face an Unforgiving Reality of Memory Constraints and Budget Pressure

For the PC industry, 2025 was the year that the end of Windows 10 support would drive a massive PC refresh cycle. As part of this refresh, AI PCs, devices with neural processing units (NPUs) designed to execute AI and machine learning tasks, were expected to rise in popularity as businesses prepared their workforce for a new era of AI-infused productivity. The PC refresh cycle of 2025 did materialize to some extent, with the market growing about 5.3% year-to-year according to TBR’s estimates, but sellers ran into a snag in the commercial PC market: Many buyers were unwilling to pay 10% to 20% more for an AI PC that could future-proof their PC fleet but offer minimal added value today. This sentiment has driven ecosystem players such as Intel and Microsoft to invest in making the AI PC more compelling to and affordable for the commercial PC market. The beginning of 2026 ushers in the latest generation of AI PC processors from Intel, AMD and Qualcomm that is poised to attract customers with performance, efficiency and security enhancements. In many ways, 2026 is a critical year for the success of the AI PC due to the significant investment in the platforms that have been years in the making.
 
When vendors made these long-term investments in next-generation AI PCs, no one knew that these new models would launch just as insatiable demand in the AI server market caused an unprecedented shortage of memory. The shortage, which in TBR’s opinion could easily last for two to three years, has already caused significant price hikes in the PC market — with more on the way — and will result in longer lead times and lower availability. As such, PC market leaders’ desire to usher in a new era of more expensive AI computing now sits in direct odds with unavoidable component price increases and tight customer budgets.
 

For a deeper analysis of the AI PC market, see TBR’s 4Q25 AI PC Market Landscape, which features market projections for PC OEMs and PC silicon vendors as well as a more technical dive into the rapidly evolving landscape of the memory market and its implications on AI PC adoption. Additionally, the report discusses how the Windows PC ecosystem is investing in driving the adoption of AI PCs and Windows on Arm.

Explore PC Research

Pressure to refresh drove 2025 growth, with the intrinsic value of AI PCs yet to be defined

In 2025 AI PCs capable of meeting Microsoft’s Copilot+ requirements began to hit the market in earnest, following their initial debut in the summer of 2024. On paper, the timing looked favorable: A Windows 11 refresh cycle was underway, and AI PCs were positioned to ride that momentum and steadily grow share within the broader commercial PC market. In practice, however, the refresh materialized unevenly and ultimately fell short of expectations. Although the market experienced solid growth, expanding an estimated 5.3% year-to-year in 2025, multiple structural and behavioral hurdles slowed adoption of AI PCs, preventing vendors from translating technical readiness into meaningful volume growth.
 
Pricing pressures emerged as a central inhibitor. Tariffs complicated OEM pricing strategies, and the incremental cost of NPU-enabled processors introduced a noticeable price increase that many commercial buyers were unprepared to absorb. This sticker shock was exacerbated by the lack of a compelling near-term value proposition for AI PCs. Vendors leaned heavily on the concept of future-proofing device fleets, a message that resonated poorly with budget-constrained IT buyers prioritizing immediate, tangible ROI. At the same time, commercial purchasing behavior has yet to normalize post-COVID. Some organizations are still digesting inventory acquired during earlier buying surges while others have permanently shifted from predictable refresh cycles to more opportunistic, as-needed purchases.
 
In response, OEMs and key ecosystem partners — notably Microsoft, Intel and AMD — adjusted their go-to-market strategies to reduce friction and keep AI PCs moving into commercial environments. OEMs refined system configurations where possible to lower bill-of-materials costs without undermining Copilot+ eligibility. Silicon providers increased front-end discounting, rebates and comarketing investments to narrow the price gap between traditional PCs and AI-capable systems. Together, these efforts drove down pricing of AI PCs in the commercial space and helped some customers make the transition to NPU-enabled silicon during their device refreshes.
"<yoastmark

Ecosystem investments years in the making underpin the future success of AI PCs

Despite increasing competition from AMD and Qualcomm, Intel remains the PC silicon market share leader due in large part to the robustness of the company’s partner and incentive programs. PC OEMs and the broader industry supplying PCs rely heavily on Intel to influence market direction and demand. Understanding that the AI PC market is somewhat nebulous due to ill-defined use cases, Intel has invested significantly to make its latest chips — Core Ultra Series 3 — a success, leaning into its heritage as an integrated device manufacturer and shifting manufacturing of the compute tile on the new system on a chip (SoC) in-house rather than outsourcing to Taiwan Semiconductor Manufacturing Co. as it did with the Core Ultra Series 2. Although the investment to bring 18A capacity online has been massive, in the long run Intel could benefit from a cost of sales perspective with the manufacturing based in the U.S. and improved yields as the process matures. In contrast, as fabless companies, AMD and Qualcomm rely solely on third-party manufacturing, which presents fewer risks but could prove more costly in the long run.
 
Silicon vendors’ investments in driving AI PC adoption have looked similar from company to company, with AMD, Intel and Qualcomm providing developer resources such as samples, guidance and reference applications to drive NPU-enabled application development. However, although AMD’s and Qualcomm’s resources are limited to software developer kits and other software stacks directly targeting the NPU, Intel’s play also leans into its reputation as a market maker with its AI PC Acceleration Program, incentivizing collaboration between ISVs and independent hardware vendors (IHVs) to create features that leverage the NPU. Additionally, with the engineering staff available through Intel Open Labs serving as a differentiator, Intel arguably provides more design resources, sales support and go-to-market funding than AMD and Qualcomm, echoing the company’s long-running strategy that has positioned it atop the Windows PC silicon market. Similar to what silicon vendors are providing, Microsoft has launched tools that will help ISVs refine their Windows-based apps to make the best use of CPU, GPU and NPU.
 
Qualcomm, more so than Intel and AMD, needs to prove itself in the commercial market and demonstrate not only that Arm can outperform x86 in commercial uses but also that the platform is a trustworthy investment. However, much of this weight falls on Microsoft’s shoulders as Windows on Arm is inherently its own initiative. Perhaps more important than its advances in performance in TOPS (trillions of operations per second), Qualcomm’s newly launched Snapdragon X2 Plus platform offers a competitive answer to Intel’s vPro platform with Snapdragon Guardian, which allows for advanced remote management and security features. However, Intel vPro’s Active Management Technology, which allows for out-of-band remote management, remains core to the company’s value proposition, is still unmatched by Qualcomm and AMD, and is a key reason noncloud-first organizations tend to choose Intel over its competition.

2025 Windows AI PC SoC Revenue by Covered Vendor [Intel, AMD, Qualcomm] (Source: TBR)

Massive scale AI infrastructure deployments are driving unprecedented fluctuations in memory costs, negatively impacting supply for the PC market

The PC market has weathered many component shortages that have had short-term impacts on product availability and pricing. In recent history, shortages have been caused by factors ranging from pandemic-induced buying changes and natural disasters to manufacturing industry consolidation and delays in next-generation technology rollouts. However, the current memory component shortage is unique due to its ties to structural AI demand versus a temporary shock to the market. Over the past two years, demand for AI infrastructure has shifted the slow-growing server market into hypergrowth, with TBR estimating that server spend nearly doubled from 2023 to 2025. Not only has the demand risen dramatically, but the AI server silicon, whether GPUs or AI ASICs, also requires significant amounts of high-bandwidth memory (HBM), unlike their traditional server counterparts.
 
As such, hyperscalers and neoclouds are absorbing a disproportionate share of DRAM and HBM as they scale AI training and inference infrastructure, and the same suppliers sell both HBM and conventional DRAM. As these memory suppliers prioritize higher-margin HBM, commodity DRAM output growth tightens as investments in new capacity are allocated more heavily toward supporting AI infrastructure rather than client devices.
 
This trend of elevated server demand combined with disproportionate consumption of high-performance memory will not abate anytime soon. OEMs’ total server order backlogs remain in the billions exiting 2025, and during AMD’s 4Q25 earnings call CEO Lisa Su reported the company expanded manufacturing capacity for server CPUs, confirming that component suppliers continue to shift their manufacturing lines in favor of servers rather than PCs.
 
As a result, PC DRAM supply has not grown enough to support increasing demand. The DRAM found in thin-and-light PCs is the same DRAM found in most smartphones, and the replaceable DRAM typically found in desktops and workstations is also used in traditional enterprise servers. Due to this supply-and-demand imbalance, prices for PC DRAM have skyrocketed and are likely to remain elevated through at least 2026 and into 2027. Already, contract prices for the DRAM found in desktops and workstations have increased between 10% and 30% year-to-year going into 2026 with the DRAM found in thin-and-light PCs increasing at an estimated rate of between 20% and 40%.
 

OEM Server Revenue in $USD Billions for 2020 through 2025 (Source: TBR)

Memory costs and longer lead times put AI PC momentum at risk

TBR expects the memory shortage will impact commercial PC buyers in three ways: pricing, availability and product assortment. Channel partners in contact with TBR have reported PC OEM prices have increased twice since the beginning of 2026, with more price hikes planned. Although price increases to date have been estimated between 10% and 20%, a partner noted a customer whose specific PC configuration price had increased by 40% since December 2025. Channel partners have also noted that delivery times have already become longer, meaning customers will have to accept longer lead times going forward.
 
These dynamics put AI PC models with higher-priced silicon in a precarious position. Customers with finite spend are already facing significant price increases on memory that offers no additional value. Many customers will face a choice of further delaying PC refresh, reallocating budget from other IT projects, or seeking watered-down configurations to reduce costs.
 
OEMs will need to maintain tight cost controls as much as possible through supply chain management and focus on product assortment. For example, OEMs will likely push more models with 16GB of RAM, which is the minimum requirement for Copilot+ PCs, over those with 32GB or 64GB. Ultimately, with longer lead times and a tight product assortment a likely outcome, TBR expects OEMs will focus on configurations and segments where they can maintain margins and best serve the customers that are willing to pay.
 
The cost burden will not fall solely on the OEMs. With all three AI PC silicon vendors launching new chips at CES in January, these vendors are motivated to make the new platforms a success. OEMs and silicon vendors already had to adjust pricing on NPU-enabled silicon in 2025 to reach a point where customers were willing to upgrade. TBR expects OEMs will pressure Intel, AMD and Qualcomm to provide additional discounting to ensure the success of the latest generation of chips.

AI PC Revenue by Big Three Windows OEMs [Dell Technologies, HP Inc., Lenovo] for 2025 and 2026 (Source: TBR)

2026 outlook: Increasing prices will stifle overall market growth and slow AI PC mix shift

Despite significant pricing impacts, TBR expects the PC market will grow in 2026. In fact, the early part of the year may perform better than expected if customers pull in buying to avoid additional price increases coming down the road.
 
Although the PC ecosystem has been investing in making NPU-enabled silicon more useful, the use cases for most AI PC buyers remain thin and focused on future innovation. The AI PC mix will continue to increase, but TBR believes the growth rate will be lower than we predicted in 2025. The increase in mix will be based more on the availability of chips and manufacturers’ emphasis on AI PCs than on organic customer demand.
 
Although TBR estimates the overall value of the PC market will grow 1.7% year-to-year in 2026, the increase will be driven by rising selling prices, not shipments, which are expected to decline as many buyers delay PC refreshes unless absolutely necessary. Meanwhile, TBR forecasts the overall value of the AI PC market will expand 33.4% in 2026 due in large part to a relatively small compare and PC OEMs’ (excluding Apple) growing AI PC revenue mix, which TBR predicts will increase from 23.9% in 2025 to 37.9% in 2026, a difference of 1,400 basis points. Although this mix shift is pronounced, TBR firmly believes that had PC DRAM prices remained the same, the increase would be more robust.
 

TBR’s AI PC Market Forecast for 2025 through 2030 (Source: TBR)

Conclusion

Rising demand for the types of DRAM found in PCs, in combination with memory suppliers focusing production capacity build-out on supporting massive-scale AI infrastructure deployments, has led to a supply-and-demand imbalance in the market, causing memory manufacturers and suppliers to rapidly increase PC DRAM prices. With margins already thin, PC OEMs are passing their rising build costs on to buyers in an attempt to protect profitability. AI PC prices were already higher than traditional PC prices, and with AI PCs typically configured with more memory, on average, AI PC prices are poised to continue increasing more than traditional PC prices, making the future-proofing-led sales proposition less compelling. Not only will these rising prices result in slower-than-anticipated adoption of AI PCs, but they will also cause more organizations to delay their PC refreshes. However, once PC DRAM prices stabilize and begin to fall, pent-up demand from delayed refreshes could yield a prosperous cycle for PC OEMs and silicon vendors alike.
 
Fortunately, although the rate at which memory prices are increasing is unprecedented, PC OEMs have weathered component shortages in the past. While increasing PC prices will slow the market’s transition to AI PCs in the near term, the proliferation of the AI PC over the next five years is almost guaranteed due to the alignment of ecosystem players, including PC silicon vendors, PC OEMs and Microsoft, which owns the operating system. As such, increasing memory costs and PC prices are less of a roadblock to AI PC adoption and more a speed bump impacting the momentum of adoption.

Alliances Will Extend Beyond Core Offerings as AI-driven Sales and Marketing Reshape Ecosystems

/by

Traditional one-plus-one alliances are evolving into multiparty alliances, unlocking new growth opportunities across the technology ecosystem. This shift is being accelerated by AI adoption — particularly in sales and marketing — which lowers the cost of expanding alliances, introducing new portfolio offerings and reaching new clients. As a result, tighter alignment among ecosystem participants is raising client expectations for more seamless integrations and more favorable commercial terms.

IT services firms will push beyond traditional alliances in 2026

Early adopters of generative AI (GenAI) frequently cited sales and marketing efficiency improvements as relatively easy use cases for proving the technology’s value and ROI. As those AI-enabled solutions matured, IT services companies sought to leverage productivity savings into expanded offerings, reflecting both the success of their customer-zero use cases and the opportunities to serve as data and AI orchestrators.
 

Qualities of a Successful Alliance (Source: TBR)


 

But IT services companies have their limits, and clients have preferred technology vendors, leading IT services companies to look to alliances to drive new growth. We have seen this pattern before, but in 2026 we will see IT services companies extend those alliances into devices, connectivity and even silicon, requiring a multiparty alliance approach that will strain commercial models, sales strategies and alliance leaders across the ecosystem.
 

In 2025, TBR’s ecosystem intelligence research repeatedly showed that companies across the technology ecosystem that developed multiparty go-to-market strategies and sought to leverage alliance partners beyond their traditional pairings experienced steadier revenue growth. One roadblock stood out: IT services companies rarely partnered with OEMs, primarily due to misalignment around sales, client base and brand.
 

TBR expects a substantial shift in 2026 as IT services companies more readily embrace partnerships with chip, edge, connectivity hardware and OT providers to extend IT services companies’ reach into clients’ technology environments and to fully exploit AI’s possibilities.
 

A partner at a leading consultancy with a substantial IT services practice once told TBR that even if an OEM gave him a gold brick, he would not try to sell it to his customers, in part to protect his brand from being associated with selling a physical product. This sentiment will not last through 2026.
 

Explore more 2026 predictions for alliances and partnerships by downloading our special report 2026 Will be a Pivotal Year as AI Momentum Drives Deeper Ecosystem Alliances.

KPMG-Salesforce Partnership: Evolving from Implementation to Agentic Outcome

/by

A strategic fit that can test innovation and outcome delivery readiness

KPMG’s alliance with Salesforce has moved from a high-growth implementation practice into a relationship increasingly defined by enterprise trust, measurable outcomes and the ability to operationalize agentic AI. Since entering Salesforce’s ecosystem in late 2019, KPMG has scaled the alliance to over 1,300 practitioners across more than 30 countries and is now repositioning the relationship around Agentforce-led transformation, AI-ready data foundations, and run/optimize operating models that sustain adoption. This shift mirrors a broader ecosystem trend: Leading platforms are prioritizing depth with a smaller set of preferred partners and evaluating alliances on their ability to drive usage, value realization and governance — not simply project throughput.
 
On Jan. 20, TBR spoke with KPMG’s Matthew Fidler, Global Salesforce Platform leader, U.S. Salesforce Platform & Alliance leader; and Andrew Dunn, Global Salesforce Alliance director, about the evolving relationship between KPMG and Salesforce. The following analysis reflects on this discussion and TBR’s ongoing research around KPMG and the Big Four, including our semiannual Management Consulting Benchmark and ecosystem intelligence reports.
 

Prediction: AI Momentum Forces Deeper Alliances in 2026

2026 will be a transitional year defined by technology ecosystem expansions — multiparty alliances spanning IT, OT, devices, edge and silicon; industrial/physical AI acceleration, especially at the edge and in manufacturing; and strategic bottlenecks as skill shortages and infrastructure gaps slow sovereign AI adoption. TBR expects significant changes in how technology vendors collaborate and compete, which lays the groundwork for broader, more integrated AI ecosystems.

Download Report

 

Client stickiness will depend on KPMG’s ability to demonstrate depth around Salesforce capabilities

KPMG entered the Salesforce partner ecosystem at the end of 2019 and quickly scaled the relationship through a combination of organic capability build-outs and targeted inorganic investments. The alliance has been characterized by aggressive certification growth, expansion into core CRM clouds and industry clouds, and an increasing focus on AI-related capabilities. KPMG also extended its footprint in EMEA through the acquisition of iCom in France to accelerate regional delivery capacity.
 
From an ecosystem standpoint, KPMG’s positioning emphasizes depth over breadth. Rather than pursuing every Salesforce product adjacency, the firm prioritizes sectors where its brand and domain expertise offer an advantage (as confirmed by Salesforce feedback), especially in financial services, healthcare and life sciences, and the public sector. In addition, KPMG frames Salesforce programs as enterprise transformations, not stand-alone technology implementations, a setup that is not unique to KPMG but gives the firm an opportunity to act as more of a business integrator.
 
According to TBR’s October 2025 Adobe and Salesforce Ecosystem report, partner success is increasingly anchored in trust and clarity as platforms are narrowing their focus on fewer strategic partners while expecting those partners to lead with a unified narrative and deliver measurable outcomes. Two implications of this trend matter most for the KPMG-Salesforce partnership.
 
First, generative AI (GenAI) and agentic delivery are creating a managed services battleground. Buyers are less willing to treat AI as a phase two add-on; they expect continuous optimization, governance and data foundation work to be integrated into the program from day one. This behavior favors partners that can industrialize repeatable assets (agent templates, reference architectures and data-readiness checklists) and run them operationally after the go-live.
 
Second, misalignment between vendor and partner messaging is becoming a risk. Inconsistent positioning across the field confuses clients and erodes confidence in the ecosystem. For KPMG and Salesforce, this elevates the importance of two-way enablement: shared account planning, consistent value narratives, and clear role delineation for strategy, implementation and post-go-live operations.
 
In short, the partnership’s differentiation will be judged less by breadth of services and more by whether KPMG can act as an outcome operator for Salesforce’s Agentforce and data platform agenda — improving adoption, consumption and governance.

Salesforce’s Agentforce provides a new path for KPMG to grow the business but only if commercials are aligned

Salesforce’s Agentforce push is reducing the time between platform adoption and measurable business outcomes, but it is also raising the bar for data readiness, integration and governance. The hard part of CRM transformation is both upstream (data harmonization, integration, identity, policy) and downstream (change management, adoption analytics, continuous optimization). For Salesforce, the upstream challenge is particularly relevant. Years of expansion into adjacent workflows, via both acquisition and internal development, along with less focus on tying the whole portfolio together, have made data harmonization a serious barrier. The acquisition of Informatica was driven in part by the need to offer a native platform that could overcome this hurdle, but Salesforce has not necessarily solved the upstream challenge yet. Given AI’s dependence on quality data and cross-workflow capability, this will be a critical obstacle Salesforce must overcome as it works to scale Agentforce adoption. In this environment, partners must treat AI as an operating model program, not a feature deployment.
 
In parallel, Salesforce’s partner model is shifting toward consumption-led measurements and AI-era enterprise license constructs, increasing the importance of adoption and sustained usage. This will create a commercial and governance inflection point. GenAI improves transparency around productivity and cycle-time benefits. At the same time, customers are increasingly questioning traditional time-and-materials billing models for agent-led work. For KPMG, alliance success will increasingly depend on pairing implementation excellence with credible activation motions (value instrumentation, managed services and consumption-aligned commercial models) and tight joint enablement so the field sellers tell one consistent story.

Partner-told use cases pave the way for future right-to-win areas

To build stakeholder trust, KPMG has begun translating Agentforce into client stories and Salesforce use cases as the firm looks to win new buyer personas. For example, a joint Agentforce-driven onboarding approach for a healthcare client compressed a process from 17 days to under five minutes on average, with the majority of cases requiring zero human touch. The use case is notable because it couples orchestration with risk and decision logic and demonstrates rapid time to value. Additionally, KPMG’s work with a global consumer brand illustrates how Agentforce can augment contact center operations, including scoring 100% of interactions against quality rubrics, automating returns workflows and reducing seasonal labor needs during peak periods. KPMG is also acting as an SI to Salesforce as the latter applies agents to its security review and controls workflows, reducing review cycle time from hours to minutes. This reinforces KPMG’s credibility in regulated workflows and creates reusable assets for clients. Last, following Salesforce’s acquisition of Informatica, KPMG reorganized internally to align Salesforce, MuleSoft and Informatica under one practice and is seeing increased field attention for master data management and data foundation projects — conditions that are increasingly prerequisites for reliable agent deployments.
 
Two of the aforementioned use cases stand out: KPMG acting as an SI for Salesforce; and KPMG’s internal reorganization to align its Salesforce, MuleSoft and Informatica capabilities. The first example reflects a customer-zero approach, which is among the most widely used approaches for consultancies to demonstrate value and trust and scale through the 360-degree relationship. Although this method is not unique to KPMG, it resonates with clients seeking reassurance that third-party providers trust each other.
 
The second example extends beyond operational efficiencies as realignment allows for a better multiparty alliance construct, which can lead to higher win rates. In our special report Informatica’s Alliance Strategy: Powering GSIs, Scaling AI and Strengthening the Data Ecosystem TBR analysts spoke with Richard Ganley, senior vice president, Global Partners, who shared the following: “We looked at basically all the opportunities that we’d had in our system, which we’d either won or we’d lost over the past two years. And we found if we didn’t work with a partner, our win rate was around 17%. If we worked with one partner, it went up to 47%, which kind of makes sense because we’ve got somebody in there speaking up for us, recommending us. But if we worked with two partners, and by two we mean one from the GSI and one from the ecosystem … the win rate goes up to 83%.” With Informatica among KPMG’s top partners, KPMG has an opportunity to build a deeper moat within the data management space, which we believe will remain the battleground for client share in the short to midterm.
 
As enterprise AI projects grow more complex and interdependent, multiparty alliances are becoming a preferred delivery mode. Salesforce is responding by forming deeper relationships with hyperscalers and global systems integrators, and by shifting from referral-based partnerships toward integrated frameworks that combine infrastructure, data and services into unified operating motions. This creates an opportunity for KPMG to build reusable components and industry-specific agent templates while also taking responsibility for trust, risk management and compliance to align its data and AI strategy with Salesforce’s and to make  multiparty alliances more natively integrated and programmatic.

Doubling down on agentic AI will test KPMG’s culture and readiness to transform its operating and commercial model

As the firm looks to secure its long-term play given the rapidly evolving nature of agentic AI, in the past year KPMG has accelerated its portfolio and go-to-market transformation by embedding AI across internal operations and client services, fueled by a large amount of capex investment from within KPMG’s partner ecosystem. We believe deepening its relationship with Salesforce and integrating Agentforce into the firm’s daily operations will enable consultants to accelerate speed to value. The use of Agentforce in an era led by Microsoft Copilot and OpenAI hints at the rise of hybrid AI and how vendors are preparing for buyers to use several AI platforms rather than settling for just one.
 
We believe KPMG’s trust, risk, compliance and industry operating-model capabilities map well to these requirements, particularly in regulated environments. The firm’s cocreation mechanisms (incubator-style engagements and Ignition Center experiences) can shorten the time from pilot to production, but differentiation will depend on repeatability and operationalization: packaged agent patterns, reference architectures and managed services that demonstrate sustained KPI movement. Equally important, the partnership should treat messaging alignment as an operational discipline — shared plays, account governance and value narratives that are consistent across Salesforce sellers, KPMG teams and client stakeholders. Last, using new commercial models where KPMG can leverage Salesforce-ready solutions as a loss leader to get paid on a per-usage basis can help the firm demonstrate pricing agility and expand its addressable opportunities with more price-sensitive midsize clients.
 
According to TBR’s October 2025 Adobe and Salesforce Ecosystem report: “As both tech and services partners look to diversify sales channels, the opportunity SMB clients present will lead to new alliance partner relationships, especially with startups and/or specialized vendors, thus challenging the status quo. Services vendors have an opportunity to maintain trust by using tech vendors’ marketplaces to drive portfolio awareness. Tech vendors need to maintain commercial agility as SIs are getting smarter about building their own off-the-shelf solutions and focusing on contextualization.”
 
KPMG’s ambition to establish a stronger foothold in the GenAI space — and to use the technology to deepen client relationships — could, in time, place an additional strain on the firm’s operating model. Building, selling and managing software-like solutions introduces new requirements around talent, product management and pricing that differ from traditional consulting motions. That said, KPMG appears to be taking steps to address some of these potential challenges, which may help reduce internal friction as the shift takes hold. In parallel, the growing appeal of managed services may continue to offer a relatively straightforward path to near-term revenue, but it does not necessarily have to come at the expense of GenAI momentum. If KPMG can align incentives and capacity planning with its GenAI priorities, partners may be less likely to default to “quicker win” engagements — and the firm may be better positioned to reinforce, rather than dilute, its consulting value proposition.

Accelerating stakeholder trust remains KPMG’s top priority as the firm leans on Salesforce amid ongoing market challenges

Deepening relationships with strategic alliance partners such as Salesforce will bolster KPMG’s trust within the ecosystem, helping it increase client stickiness as clients more often look for depth than scale in the AI era. Although KPMG has a tall order in business model transformation, the firm is not alone. In the next two years, AI-ready platforms such as KPMG Velocity and KPMG Workbench, backed by common governance and an ongoing commitment to funding and positioning with clients, will accelerate KPMG’s efforts to move beyond the traditional time-and-materials operating model. Although it can be challenging to address change at scale, getting the right framework in place, including establishing common KPIs and deeper collaboration with Salesforce, will give KPMG the necessary building blocks to move away from its historically risk-averse culture rooted in audit and assurance.
 
The best way for KPMG to differentiate will be to package reusable assets, generate outcomes (adoption and consumption) and establish joint field enablement so the KPMG-Salesforce story is consistent from boardroom strategy through post-go-live operations. If KPMG executes on all of these efforts, the firm can become one of Salesforce’s most strategic partners in AI- and data-intensive sectors, helping move deals from “CRM projects” to enterprise transformations measured in data leverage, AI impact, and realized business outcomes and, in turn, driving durable platform consumption.

 

Consulting Will Rebound in 2026

/by

After a period of relative softness, consulting revenues are expected to rebound to high-single- or low-double-digit growth as pervasive uncertainty pushes enterprises to seek external guidance. Demand will be particularly strong around risk mitigation, strategic planning and AI adoption, positioning forward-deployed engineers (FDEs), supply chain management (SCM) and people advisory services as leading revenue drivers in 2026.

Managed services shifts from delivery model to growth engine

AI-driven complexity will accelerate demand for consulting, particularly around data modernization, transparency, and helping enterprises understand the organizational impact of new software and AI capabilities. Popularized by Palantir, the FDE model will continue to permeate across IT services companies and consultancies in 2026, primarily as a marketing term but also backed by actual organizational changes, new hires, and adjusted ways of delivering value to clients.
 

AI integration work is increasingly performed by FDEs — engineers positioned close to clients who translate AI systems into business outcomes. Demand for FDEs is exploding, and hyperscalers and GSIs are building these roles, with Infosys viewed as an early leader and McKinsey & Co., Boston Consulting Group (BCG) and KPMG expected to position FDEs as premium integration talent. FDEs will likely follow the pattern of data scientists and other specialized roles within IT services companies and consultancies, responding to market demand and providing companies and firms with a new way to describe their AI-enabled offerings and solutions.
 

Listen now: 2026 Predictions for Managed Services, featuring Principal Analyst Bozhidar Hristov

 

Higher consulting demand will also come in an increasingly unstable geopolitical and economic world. Although uncertainty fueled by immigration issues, tariffs, fluctuating interest rates, and political instability dampened IT services and consulting growth in 2025, TBR expects an upturn in consulting revenues in 2026 even as those underlying conditions worsen. Navigating stormy seas demands a proven crew and trusted advisers; anyone can sail a ship in calm waters. TBR has seen a steady rise in investments into supply chain consulting, including increasing capabilities around the underlying technologies, such as blockchain and analytics. Combined with increasing investments in cybersecurity, TBR anticipates the three fastest-growing areas in consulting in 2026 will be risk mitigation (SCM and cyber, especially), strategy, and AI adoption.
 

But keep an eye on human capital management consulting. The IT services companies and consultancies will spend the next few years sorting out their own staffing models, adjusting the traditional apprentice-model pyramids to reflect AI-enabled roles and responsibilities changes (here come the obelisks). And these companies and firms will bring their lessons learned to enterprise clients, particularly around building highly functioning human-plus-robot teams, adjusting promotion and compensation packages, and budgeting for the expected higher costs of adopting AI at scale.
 

In all this consulting growth, TBR expects the leaders will be those IT services companies and consultancies that have refined and scaled their managed services offerings. This may come as a surprise to the longtime strategy consultants in leadership positions at many of these firms, but TBR’s research indicates revenue and growth at leading IT services companies and consultancies have been increasingly tapping into consulting opportunities identified through ongoing managed services engagements. Who knows more about underlying problems than the people working day-to-day with the client in their environment? TBR anticipates greater investment in managed services offerings and increased leadership attention paid to how those engagements underpin sustained consulting revenue growth. Managed services is no longer your mess for less but a Trojan horse for higher-margin consulting.
 

Explore more 2026 predictions for managed services in our special report In 2026, Managed Services Shifts from Delivery Model to Growth Engine.

The AI Alliance Shift: How 2026 Will Reshape Partner Ecosystems

/by

TBR Insights Live session: TBR’s ecosystem intelligence experts discuss how AI-driven sales and marketing will reshape ecosystems, how AI alliances will impact operational technology and what will challenge the scaling of sovereign AI.

Read more