Infosys Moves From AI Experimentation to Enterprise Orchestration as Infosys Topaz Takes Center Stage in the Company’s Next Chapter

Infosys hosted more than 200 clients, partners, analysts and advisers during its flagship event, Infosys Americas Confluence 2026. The packed agenda included Partner Day and Analyst Day tracks, client stories, partner presentations and Living Labs demos of both Infosys- and partner-developed solutions and services. AI dominated the agenda, but the conversations were notably different than those at Confluence 2025. Last year, Infosys and its clients largely focused on the prerequisites for enterprise AI adoption, including data readiness, governance, architecture and talent. This year the discussion shifted toward what happens when enterprises actually begin scaling AI; the need to address rising complexity, agent governance, token economics, cybersecurity and model choice; and the ever-difficult task of proving business value. Infosys used the event to reinforce its ambition to become a leading enterprise AI services provider, positioning Infosys Topaz — and increasingly Infosys Topaz Fabric — as the enabling layer connecting models, applications, data business processes and partner technologies.

Infosys Topaz is evolving from an AI portfolio into an orchestration layer, putting Infosys’ integration strengths back at the center of the AI opportunity

Infosys’ AI strategy has matured considerably since Confluence 2025 and even more significantly compared to two years ago. In 2024, Infosys was positioning Infosys Topaz as a collection of AI services and solutions that could sit on top of the company’s Cobalt cloud foundations, as the company was taking a more pragmatic approach to increase its value with clients, using the idea that a client could not meaningfully scale generative AI (GenAI) without first addressing data, governance, architecture and workforce readiness.
 
In 2026 these prerequisites remain, but Infosys is now trying to operationalize them through a more coherent AI architecture. Infosys’ AI-first value framework spans areas including strategy and engineering, data, process AI, agentic legacy modernization, physical AI and AI trust, with the company tying these capabilities to three broader market shifts: automated intelligence, AI-augmented workforce and AI-powered predictive agility. Infosys Topaz Fabric provides a common layer through which the company can expose models, tools and AI assets while protecting client data and managing the economics of model consumption.
 
Supporting both open and closed public models through Infosys Topaz Fabric will give the company the flexibility to strengthen its position as an AI orchestrator. We believe supporting both models is table stakes for the services provider, which, for years, has played the technology-agnostic game with its alliance partners. The true value will come from Infosys’ ability to deploy its frontier engineers at speed and capture the right balance of AI-first and AI-infused revenue opportunities, as the former will help it drive higher pricing and the latter will increase client stickiness. Managing the expectations of impatient shareholders will be key, especially as peers pursue similar strategies.
 
An event session with an energy client brought some of that strategy to life. Infosys argued that the only part of the AI stack the enterprise truly owns is the context, including processes, institutional knowledge, operating history and domain expertise. The model can be rented or swapped, but context can not. The client described context not as a static database but as a supply chain containing years of engineering knowledge, operational data, procedures, decisions and best practices.
 
This framing aligns with Infosys’ strategy as the company does not need to own the client’s context but rather make the context usable across applications, data and business workflows. Understanding the context is key to Infosys’ value proposition. The client described Infosys connecting those dots while leaving ownership of the underlying knowledge to the client. Executing well against the strategy could help Infosys elevate in the context-orchestration assurance value chain, a step above its traditional implementation services realm, but it could also fall short if the company fails to remain proactive in the “what’s next phase” of the client’s business growth conversations.

AI-centric alliances allow Infosys to build its position as a tech orchestrator, but differentiation will require agility as the tech requires a multiparty setup

Although alliance partners remain central to Infosys’ go-to-market strategy, AI is changing their function. In a traditional cloud or enterprise application relationship, partners often provided the technology platform while Infosys supplied the integration, implementation and managed services. AI, however, makes the relationship a bit more fluid as clients may use several model providers, hyperscalers, SaaS platform and AI-native tools simultaneously. This will test Infosys’ traditional vendor-agnostic stance as the ability to move between technologies and govern them consistently becomes as important as expertise in any one platform.
 
During the event, Infosys’ executives including company’s CEO Salil Parekh highlighted partnerships with cloud, SaaS and frontier AI companies while noting that Infosys Topaz Fabric can select the appropriate model for the task and cost requirements. Meanwhile, the company continues to use its large install capabilities around vendors such as SAP and Microsoft serve as the foundation for AI-enabled transformation, particularly as those vendors increasingly embed AI into their own platforms. The 360-degree arrangements of some of these relationships has historically benefited Infosys, as clients often view Infosys as a trusted execution provider.
 
AI has now raised the stakes, and Infosys has an opportunity to elevate that trust further and become as independent orchestrator across a fragmented AI stack. Over the last six months, Infosys’ alliances with Anthropic, OpenAI, Intel, Harness and Cursor suggest the company is trying to capture the emerging opportunity between AI creation and enterprise execution. Anthropic gives Infosys access to frontier reasoning and agent design for complex, regulated workflows, which help Infosys move upstream into operating-model redesign rather than be confined to downstream implementation.
 
Through its relationship with OpenAI, Infosys is combining Infosys Topaz Fabric and OpenAI Codex to jointly drive AI adoption across enterprises, with a focus on transforming software development and modernization. We see the relationship with OpenAI as both an opportunity and a threat for Infosys, highlighting leadership’s willingness to disrupt the company rather than be disrupted. By tying Infosys Topaz Fabric to Intel Xeon, Gaudi and AI PC environments, and by emphasizing rightsized architectures, benchmarking and total cost of ownership, Infosys is positioned to shape enterprises’ infrastructure decisions before they select hyperscaler-led architectures. Harness then tackles the production bottleneck that follows AI-enabled code generation by standardizing the path from code to governed deployment across hybrid and multicloud estates.
 
Cursor adds another layer of significance because it pushes Infosys deeper into the developer workbench. If Infosys can standardize these partner technologies across its own delivery base, it could convert internal productivity gains into a proprietary delivery advantage by using its workforce as both a proving ground and a distribution channel. With Infosys’ ©Human Intensity Reduction Index (HIRI) trending toward 0% in the last few quarters, the company has a long way to go before nonlinear growth materializes. We believe the best path forward for Infosys will be less about monetizing agents through direct sales and more about bundling them in fixed-priced-wrapped contracts that can help it generate higher volume, but only after it pivots from IT-based to business-centric outcomes.

Client use cases demonstrated that Infosys’ AI value proposition centers on embedding AI into transformations already understood

Client examples throughout the event were more mature than the productivity pilots that dominated early GenAI discussions, with some of the more concrete examples revolving around embedding AI into broader modernization, engineering and operating model changes.
 
For an energy client, Infosys built an operations assistant that began as a conversational interface for engineering procedures but evolved into a tool that helps engineers aggregate data, conduct analysis, and move from the client reactively addressing issues toward more proactive operations. The client linked the approach to improved maintenance and reliability; lower operating costs; and extended useful life of assets, potentially by several years. Infosys is also codeveloping the client’s enterprise AI platform, where multiple agents support both revenue-generating and cost-cutting use cases.
 
A financial services client offered a different modernization example. Instead of approaching legacy IT environments as systems that must be replaced wholesale, the company described using AI to automate regression testing, document legacy applications and incrementally modernize older code. This use case shed light on the shift AI is creating around cost versus value, potentially making incremental renovations more attractive than large-scale replacement programs, especially as the overuse of tokens can backfire and slow progress. For Infosys, this could expand its application modernization opportunity, even as automating coding reduces the labor needed for individual tasks.
 
A personal-care provider illustrated the relationship between platform implications and AI readiness. Using the Infosys Equinox commerce platform to support its IT transformation initiative, the company is in the process of consolidating seven disparate commerce platforms into two. The client emphasized that fragmented technology and data environments become even more problematic as AI scales, as they amplify wherever complexity already exists, further reinforcing Infosys’ position that AI does not replace the need for modernization but makes it more urgent.
 
Other examples showed similar progression from isolated pilots to scale programs. One client said it had moved roughly 24 AI use cases into production within several months, with another 27 ready to follow, and described Infosys Topaz as helping turn large volumes of operational events into actionable outcomes. Infosys is also extending Infosys Topaz Fabric into developer environments and operations tools, including lightweight embedded apps and purpose-built applications for modernization, software development life cycle, data engineering and process engineering.
 
Protecting relevance while creating a path toward higher-value orchestration work will allow Infosys to maintain client stickiness as the company continues to use AI to deepen and accelerate transformation programs across technology estates it is already familiar with. Although one can argue that forums such as Confluence often have a predetermined agenda and talking points, TBR’s attendance at the conference the last several years and ongoing coverage of the company for the better part of the last two decades have allowed us to see and hear clients’ firsthand accounts and consistent feedback about the company’s culture built around humility. Many of the clients presenting on stage are not just frequent presenters but also expand on the relationship story and discuss what’s new and what’s changing in their work together.

Governance is moving upstream as agentic AI expands both the opportunity and the complexity of enterprise transformation

One of the consistent themes of the event was that AI becomes more complicated as it scales. Enterprises are discovering that moving several controlled pilots to hundreds or thousands of agents changes the governance problem significantly. Data lineage, agent identity, model selection, security, token consumption and accountability must increasingly be considered at the architecture and operating-model levels rather than handled on an ad hoc project basis.
 
Infosys’ clients, both on and off stage, repeatedly emphasized the importance of addressing governance earlier. For example, an energy equipment and manufacturing client described an approach in which AI ideas move through the governance process that asks questions about ROI, subject matter expert (SME) availability and data readiness before development begins.
 
Meanwhile, as agents proliferate across the enterprise and governance complexity increases the need for better cybersecurity, Infosys will create further openings if the company manages to combine capabilities that clients previously bought separately. As AI trust, cybersecurity, data architecture, application modernization and managed services increasingly intersect around the same workflows, the company could use Infosys Topaz Fabric as the connective layer while monetizing governance and assurance through recurring services. With both Infosys’ cybersecurity and Infosys Consulting capabilities cutting horizontally across the company’s entire portfolio, Infosys needs to ensure the internal governance hierarchy does not create a hurdle of greater integration, impacting client relationships.

AI will compress labor intensity faster than it eliminates Infosys’ talent pyramid, increasing pressure on the company to monetize productivity gains

Infosys is redesigning its workforce model for an AI-first services business. The company is building a talent strategy around three pillars: a new talent operating model, a new career model and a new talent development model. According to Infosys executives, these pillars will help the company “augment the whole organization with AI while also building deeper engineering and domain expertise to deliver AI-first services.”
 
We see these pillars as an evolution rather than a complete delivery model transformation as the company tries to minimize the disruption of its ongoing contracts and broader stakeholder ecosystem. We do not expect Infosys to ever fully change its delivery model and develop a HIRI that resembles that of a software company. However, the company’s workforce redesign will give it the necessary insights into how to position the newly developed skills with clients and tech partners. Executives noted that the company has trained more than 270,000 employees on AI, with a significant number already using AI-powered coding tools.
 
In addition, Infosys plans to build a pool of 6,000 frontier engineers over the next several years. This is in line with many of its peers, which are looking to pursue similar routes to elevate the value of their offerings. The challenge ahead for both Infosys and its peers is centered on balancing the scale and skills of this bench and on managing the influence of this bench on their staffing pyramids. The Palantir-developed forward-deployed engineer (FDE) model relies on fewer people and more platform-based service delivery centered on outcomes.
 
Moving away from the mindset and messaging around scale of this bench could be a double-edged sword unless Infosys transforms its commercial model at scale. The current use of fixed-price engagements creates a buffer against potential pushback from shareholders about revenue visibility. Using fixed-price contracts as a wrapper for outcome-based services will likely be the next phase but will compel Infosys to better align its expectations with tech partners.

Leadership continuity limits transition risk but could make it harder for Infosys move up the value chain

Confluence also served as an important marker in Infosys’ leadership transition. Parekh introduced company veteran Ashiss Dash, who was recently appointed by the board as the CEO Designate until April 2027, when he will become the company’s next CEO. Dash used his initial remarks to emphasize client centricity, continuous learning and the company’s long history of reinventing itself through successive technology disruptions. Although we do not expect Dash to make any drastic changes to Infosys’ operating model, we believe he will likely try to put a stamp on his tenure, just like every CEO, by pursuing further industry specialization and deeper alliance alignment.
 
Leaning on a strong foundation enabled through a pragmatic approach to enhance rather than fully transform its operating and delivery models will allow Infosys to focus on preparing its commercial and partner models to scale AI sales. Initial results suggest Infosys has the proper ingredients to execute on the task. Accelerating that evolution will test Dash’s ability to double the company’s revenue, as Parekh has done during his tenure, with Infosys growing from $10 billion in 2018 to over $20 billion in 2025. The stakes are high for Dash and his team, especially as AI is as much of a revenue tailwind worth messaging as it is a compressor of core areas. Expanding AI-first and AI-infused revenue while maintaining service quality execution will also test Dash’s readiness as the company looks to meet expectations within its stakeholder ecosystem.

Infosys’ next challenge is no longer proving it can build AI but whether it can shape what clients do with the technology

Infosys’ share of AI revenue grew from 5.5% in 1Q26 to 8.2% of total sales in 2Q26, suggesting that the company’s formula is working for now. Generating over $1 billion in AI services revenue on an annual basis is a notable milestone, but pushing the narrative too much could increase the cannibalization of traditional revenue. TBR will continue to monitor and estimate Infosys’ AI performance, especially as AI-related sales and marketing expenses create new margin headwinds. We believe that in two to three years, the company will be less vocal about itemizing AI sales. Sustaining momentum will be measured not just by sales but also by profitable growth, something Dash will have to navigate.
 
Infosys has a credible opportunity to separate from peers if it can make Infosys Topaz Fabric the operating layer for governed agent delivery while using FDEs to shorten the time to value and embed AI into clients’ operating models. That would strengthen Infosys’ differentiation at a time when many competitors continue to go to market leaning on AI tools plus their services. However, this shift will pressure Infosys’ commercial model. Clients are already expecting AI productivity benefits to flow back to them, and competitive intensity is increasing, raising the risk that Infosys gives away too much value through pricing concessions.
 
The company will need to carefully balance its use of time-and-materials (T&M)-based, fixed-price and agent-based pricing models, ensuring productivity gains are captured through outcome-based constructs rather than diluted through discounting. Execution complexity, partner dependence, offshore-led margin protection, and the need to recalibrate the staffing pyramid around FDEs and other AI-native roles remain the main risks to watch.

Gemini for Industries: Context, Governance and the Changing Role of SI Partners

Throughout 2026, Google’s Gemini for business has evolved from a set of components to a unified solution for practical AI. Today, it is used by 90% of the Fortune 100 and has become the fastest-growing product in Google Cloud history. Two complementary pieces exist: the application — which is designed to be the “front door” for the workplace, drawing on context from Workspace as well as other productivity apps (e.g., M365, Slack) — and the platform for building, customizing and operating AI. New FinOps capabilities and the inclusion of the Antigravity harness in all  subscriptions are recent examples of how Google Cloud is addressing both sides of the portfolio, unlocking a strategic flywheel where Gemini creates demand and fuels usage of the Gemini platform. If Google Cloud can effectively deliver both as part of a unified solution, helping customers combat agent sprawl while staying true to its roots of openness and flexibility, it will take a big step to address one of AI’s central challenges: how to translate capable AI models into something that can scale at enterprise levels. 

Reshaping context

Context holds the power to shift the market from AI that assists to AI that acts by connecting to enterprise data and systems. This helps explain why nearly all vendors —SaaS, PaaS and IaaS players alike — are touting themselves as the context layer for agent systems. But what many vendors have yet to address is that context is more than a technical integration and will rarely be confined to a single stack.
 
With Gemini for Industries currently in preview for legal and financial services, Google Cloud is changing the narrative of what context really means. It views context as more than just a set of Model Context Protocol (MCP) servers connecting to trusted data — though those secure MCP connectors are critical for ensuring that agents respect existing entitlements, keeping licensed data licensed and permissioned data permissioned. True context is also access to a broader set of methods, rules and skills relevant for a unique set of workflows.
 
With the Gemini architecture, Google Cloud can deliver this context in a unified way, letting developers build agents that extend to the business through the application. If partner execution is successful, combining industry knowledge with governance access to enterprises systems could offer a confidence boost to enterprises looking to deploy AI agents in production.

The role of partners

Gemini for Industries revolves around three core principles: skills, security and governance, and partners. The skills side of the equation is supported by forward-deployed engineers (FDEs) that cobuild alongside clients. For instance, in legal, elite law firms are moving beyond merely adopting software to actively co-building these solutions as seen in Google Cloud’s newly announced collaboration with Cooley to develop capabilities for complex litigation. It is also being supported by a growing set of out-of-the-box capabilities like the Financial Research agent that offers full explainability and over 50 foundational skills.
 
Meanwhile, security and governance stem from secure MCP integrations with more than a dozen enterprise platforms and industry data sources (e.g., Moody’s, S&P Global).  Successful partners will bring both together and make Gemini deployable.
 
These vendors may claim different levels of industry expertise that complement Gemini, but their commitment to the platform is better reflected in internal adoption. Google reports the number of seats adopted by services partners grew 9x year-to-year. This suggests there is strong app adoption among partners and an overall willingness to experiment with agents, but the real test will be which SIs can rally around the platform, building and governing in ways that elevate client value at a time when agent stacks slowly absorb traditional SI-type work.
 

Graph: Google Cloud Practice Derived Revenue and Revenue Growth_4Q25 TBR

4Q25 Google Cloud Practice Derived Revenue and Revenue Growth (Source: TBR)

Industry is the accelerator, but governance is the differentiator

Deep industry expertise is essential to delivering client results, but it is becoming less of a point of differentiation among SIs. Gemini for Industries will test partners’ ability to deploy the right skills and, more broadly, will also test how effectively they can extend unique governance frameworks through their use of Gemini internally to clients.
 
More specifically, with agent identity (e.g., Gemini’s new “co-worker Workspace profile), SIs’ role in governance becomes increasingly real and more urgent as they now need to manage how these autonomous identities interact with business workflows, and do so securely.
 
More broadly, as federated retrieval, industry connectors and out-of-the-box agents, the governance opportunity for SIs starts to shift from governing each source to governing how the context across these sources is combined and used.

Systems integration or systems configuration?

By delivering a common platform for operating agents post-deployment, Google Cloud may help guide the shift to where many SIs are already heading: using implementation and integration as a loss leader with the end goal of driving more managed services.
 
Highlighting the urgency of the shift, a use case was just announced at Gemini at Work. It highlighted how On used Gemini to migrate 24 microservices; an external vendor reportedly quoted $500,000 for a component of the work, and On was able to complete the project without outside implementation resources.
 
With scenarios such as these, it remains to be seen how much foundational services revenues SIs can retain, but prebuilt solutions enabled by agents may be a catalyst for managed services with human in the loop for last-mile, high-value tasks.

Trust at Speed: EY’s Mobility Practice Is a Catalyst for AI-era Workforce Transformation

Earlier this year EY hosted more than 250 people from over 115 companies and 20 countries at the EY Global Workforce Conference in Rome. Over three days, TBR spoke with EY leaders such as Gerard Osei-Bonsu, Global People Advisory Services Tax leader; Maureen Flood, Global People Advisory Services Tax partner; and David Rooney, EY Germany partner, as well as human resources leaders from large and small companies and representatives from EY’s technology partners. The following includes details from the event as well as TBR’s ongoing analysis of EY, people advisory services, and consultancies.

Speed and trust anchor EY’s Mobility growth opportunity

Speed and trust equal momentum. From the opening presentation through every main-stage speech, panel discussion and breakout, EY leaders repeatedly came back to this formulation to reinforce the event’s core message: In an AI age, trust separates good from bad, success from failure, adoption from resistance. EY’s own research supports this formulation. The firm’s 2026 Mobility Reimagined Survey found that “high-trust mobility functions are 1.9 times more likely to report speed in deploying to new markets, 1.7 times more likely to report speed in policy decision-making, and 1.6 times more likely to get significantly positive ROI from their mobility investments.”
 
Gerard Osei-Bonsu, Global People Advisory Services Tax leader, described speed as quicker answers and faster decisions allowing changes at speed, although many companies remain more reactive than proactive. He said building trust requires strategic integration, operational effectiveness and satisfying employee experiences, all rooted in reliable data and technology. And critically, “speed without trust fails.”
 
Osei-Bonsu’s framing of speed and trust informed seemingly every other EY professional’s presentation and resonated with the clients TBR spoke with during the event (as well as EY’s leaders’ remarks during the firm’s Global Analyst Summit, which was also held in May). A few EY leaders and EY clients described a plethora of unreliable technologies and vendors in the Mobility and People Advisory Services space that are generating uncertainty around policies, risks and AI.
 
This counterpoint to EY’s trust narrative creates, in TBR’s view, an excellent environment for EY’s global workforce strategy, connecting mobility, payroll, immigration, rewards, workforce planning and technology, to continue growing. As the trusted adviser bringing speed, reliability and governance, the practice should experience ongoing revenue acceleration and remain among the strongest components of EY’s tax, audit and consulting portfolios.

Microsoft remains the technology backbone of EY’s Mobility strategy

Echoing last year’s event in Barcelona, Spain, EY’s strategic alliance with Microsoft permeated many of the presentations, use cases and discussions, with EY professionals reinforcing that the firm’s Mobility solutions are, almost exclusively, built on Microsoft’s platform. EY’s mobility, technology and industry expertise, orchestrated on Microsoft’s platform, provides EY’s clients with improved employee experiences across a range of mobility issues and journeys.
 
Critically, according to multiple EY partners, the firm’s tight alliance with Microsoft enables faster innovation of both EY’s proprietary solutions and Microsoft’s platform. A Microsoft leader attending the conference summed it up as “cocreated transformation, built-on experience and developed trust.” An EY leader noted that the firm delivers services around data and AI “at speed and with trust” baked in, lending EY “credibility, global scale and proven outcomes” that peers and competitors would struggle to replicate. Osei-Bonsu noted that “none of us have the blueprints” for everything, making technology alliances critical to EY’s success. He added that within the Mobility space a diverse set of players and ecosystem partners rely on EY and Microsoft to provide a kind of “centrifugal, gravitational” force powered by EY’s combination of workforce expertise, data, governance, technology and AI-enabled delivery.
 
In TBR’s write-up of the event last year, our assessment of Microsoft as EY’s most-strategic partner included a hedge that “EY noted expectations that other technology alliance partners, including ServiceNow, will become increasingly strategic to Mobility.” Discussions in Rome show Microsoft remains EY’s predominant technology alliance partner, perhaps even more so than a year ago, leading TBR to believe the EY-Microsoft partnership continues to underpin Mobility and provides a client-zero use case EY can share with clients as well as an example, within EY, of the advantages of partnering tightly and nearly exclusively with a technology provider like Microsoft.
 
TBR continues to see Mobility and People Advisory Services as catalysts for change within EY, further evidenced by Microsoft’s role in the Mobility practice. (Note: In May EY and Microsoft announced a $1 billion-plus, five-year initiative, including integrated EY teams and Microsoft forward-deployed engineers focused on scaling enterprise AI.)

EY brings CHROs deeper into M&A by connecting people, tax and technology

In an M&A-focused breakout session, Flood and EY colleagues discussed challenges around including talent management within a typical M&A process. Flood commented that human resources leaders often get left out of the loop during due diligence, potentially lowering the value of an acquisition (by increasing onboarding and assimilation expenses or failing to see red flags), echoing a sentiment TBR has heard from chief technology officers and IT directors lamenting the costs needed to fit an acquired assets technology into an existing environment.
 
On a positive note, Flood said the chief human resources officers (CHROs) at EY’s clients more frequently have a seat at the M&A table for workforce value creation and workforce integration, although typically only at companies already focused on changes to their people and organization, not just their tech. In TBR’s view, EY has a well-established brand around M&A and, notably, divestitures, and can leverage that experience and credibility to consult around the people aspects, not simply the technology and financials.
 
The firm’s HR Mobility Tax practice, for example, can play a more significant role in transaction evaluation and due diligence, provided EY’s clients fully appreciate the breadth of EY’s services beyond traditional M&A. In TBR’s view, greater adoption of AI by EY and the firm’s clients will, because of the transparency-enhancing nature of AI, accelerate broader consideration of human resources challenges and opportunities during M&A engagements, boost exposure and boost revenue for EY’s Mobility practice. CHROs will not make the final purchasing decisions, but EY will help them to increasingly be expected at the table.

Payroll software: EY’s Trojan horse for scaling IP

In a special session with TBR, Sheri Sullivan, Global Payroll Operate leader, and EY professionals discussed EY’s bet on developing a software platform designed to “demystify” payroll. Sullivan explained that humans frequently serve as the bottleneck in payroll processes, creating an opening for a trusted, auditable AI-enabled solution. With multiple trusted and accurate data sources, AI can generate useful payroll analysis and manage payroll systems.
 
As customer zero for the software, EY has been learning the limitations and seeing the benefits, all of which Sullivan and her team have begun to bring to clients. As of the Rome conference, EY’s client list for this AI-enabled product remains small, and the firm continues to test different commercial models, with the expectation scaling would begin in earnest before the end of 2026. In TBR’s view, even a relatively small client base and modest revenue stream from a payroll services platform could be significantly disruptive within the firm and the market.
 
While learning to perfect selling software from within a professional services firm, the payroll practice could be demonstrating to other parts of EY how to adopt, transform and monetize IP. TBR recognizes EY has been in the software-selling business for a couple of decades, but not at scale and not in the AI age. If that were to change, payroll services would be a likely catalyst. Across the broader market, every single company runs payroll. A trusted services provider, with a legacy brand around audit, tax, governance and risk, and enabled by proven AI, could displace smaller competitors, particularly among large clients that already work with EY. In short, TBR looks forward to hearing soon that EY’s global payroll services practice has become the firm’s fastest-growing revenue line.
 
One critical aspect of this event, as noted by TBR in previous years, has been EY’s near refusal to sell itself or ask attendees to explicitly describe their experiences and relationships with EY.
 
But EY did not present this capability as a customer-zero use case that clients could buy. Instead, EY partners stressed that the firm focused on Mobility as having evolved beyond logistics to include talent development, upskilling and adjusting the entire firm to new skills demands: Mobility within EY as part of improving the firm (and quietly conveying to clients “You could do this, too.”).

EY turns trust into an advantage in an uncertain workforce technology market

EY’s 2025 Barcelona message aged well, but in TBR’s view, mobility has progressed from being human-centric and tech-enabled to trust-dependent, AI-scaled and demonstrably business-critical. EY can increasingly combine employee experience, managed services, regulatory expertise, and its deepening alliance with Microsoft to demonstrate that trusted data and AI can deliver measurable outcomes in this space.

TBR walked away from the event with five themes:

  1. The need to connect Mobility within a company to the company’s business overall echoed a decade-plus of consulting advice to CTOs and IT directors to be part of the business and not simply a cost center. EY has been hammering home the lessons learned from those technology consulting engagements and helping CHRO and tax executive clients become better internal business partners.
  2. AI is a tool, not an answer. The week prior to the Rome conference, TBR attended EY’s Global Analyst Summit in New York and heard repeatedly that AI serves a purpose in an organization but cannot be the answer to every business problem, despite the relentless hype since the release of ChatGPT. As a firm, EY has a compelling and mature AI message.
  3. Unreliable technology, too many vendors, and growing uncertainty in the Mobility and broader People Advisory Services space open tremendous opportunities for a trusted, proven Mobility provider, such as EY. And in Rome, it was not just EY leaders making the case but also clients. During one breakout session poll, only 47% of respondents said their technology platforms are “reliable.” The opening for EY is clear.
  4. An EY software solution for payroll could significantly disrupt the payroll services market while helping EY overall become more adept at running a software practice within a professional services firm. TBR has seen some of EY’s peers fail at precisely that challenge, but EY’s leaders in this effort spoke knowingly about the mines scattered across the space while also bursting with passion for solving payroll problems with an EY software tool. Watch this space.
  5. Immigration issues show no signs of abating in 2026 or beyond, providing more opportunities for EY to bring a mix of tax and consulting services to a growing number of clients. Several conference sessions highlighted immigration’s role in enabling access to talent, supporting workforce strategy and navigating future workforce challenges. As AI threatens to upend employment across a number of tech jobs typically associated with low-cost locations at the same time enterprises realize they lack much needed in-house AI talent, immigration challenges may morph in new ways, boosting demand for a trusted adviser.

EY remains ambitious about its revenue targets for People Advisory Services, and TBR’s assessment from 2025 still rings true: “Companies keep recruiting, hiring, paying, rewarding, moving, repatriating, retiring and hiring in an endless loop … [so] absent the worst possible global political and economic scenarios, including a drastic curtailment of global mobility, TBR believes that ambition is perhaps a bit too modest.”
 
Technology and macroeconomic trends favor EY’s Mobility practice. Perhaps just as significantly, so do trends across professional services, including AI adoption, business model disruption, commercial model shifts, and an all-around recognition that the largest consultancies need to change to meet clients’ changing demands and AI-enabled expectations. For the last three years, TBR has heard from both EY leaders and EY clients that the firm’s Mobility practice is already a catalyst for that change.

5G’s Next Act Is About Monetization, While 6G Builds on the 5G Foundation

5G moves from coverage to economic discipline

The telecom industry is entering a more disciplined phase of mobile network investment. Much of the early 5G buildout in developed markets is complete, and operators are now prioritizing capacity, efficiency and monetization over nationwide coverage. TBR expects global communication service provider (CSP) investment in 5G infrastructure to grow only incrementally, driven primarily by fixed wireless access (FWA), 5G-Advanced and 5G standalone (SA), while initial commercial 6G deployments could begin as soon as 2029. The transition is an evolution as operators seek to extract more value from existing 5G assets while preparing those same networks to support 6G.

FWA leads, but 5G SA remains the monetization bottleneck

FWA remains the clearest source of net-new 5G revenue. According to Ericsson’s June 2026 Mobility Report, there are 175 service providers globally offering 5G FWA, and TBR projects more than 400 million residential and commercial premises will be covered by 5G FWA by the end of 2029. FWA subscriber growth is prompting operators to add dedicated capacity, spectrum and network optimization as the excess mobile capacity that supported early FWA deployments is consumed.
 
The larger challenge is 5G SA. Of approximately 390 CSPs with commercial 5G, only about 90, or 23%, had launched 5G SA as of June 2026. This slow migration limits operators’ ability to monetize new capabilities such as network slicing, RedCap (Reduced Capability) and ultra-low-latency services. Commercial slicing is expanding into premium mobile, public safety, FWA, transportation and live events, but direct revenue remains minimal as most services are bundled into higher-tier plans.

5G-Advanced and AI-RAN create a bridge to 6G

Rather than triggering another broad hardware replacement cycle, 5G-Advanced will emphasize software, Massive MIMO and AI-enabled optimization. For example, AI for RAN is emerging as the first commercial AI-RAN opportunity, using AI to improve channel estimation, beam management, traffic forecasting and energy efficiency. Most deployments remain proofs of concept or field trials in 2026, with initial commercial deployments expected in 2027 and more meaningful vendor revenue beginning in 2028 and beyond. Nokia estimates AI-RAN could improve spectral efficiency by more than 2x by 2028.
 
These investments also establish the technical foundation for 6G. 3GPP (3rd Generation Partnership Project) Release 20 standards, expected in 2027, will continue 5G-Advanced development while studying 6G architecture, spectrum, AI integration and integrated sensing and communication (ISAC). 3GPP Release 21 is expected to establish the first formal 6G specifications, with final protocols in March 2029. TBR expects 6G to rely heavily on software upgrades to existing 5G radio and core infrastructure, supplemented by new spectrum, radios, accelerated computing and edge infrastructure.

6G broadens the operator opportunity beyond connectivity

The most important 6G shift will be commercial, not generational. ISAC can turn communications networks into sensing platforms capable of identifying location, velocity, elevation and moving objects, creating opportunities in drone detection, border security, transportation, industrial automation and emergency response. Importantly, operators do not need to wait for 6G as usable ISAC solutions can leverage 5G-Advanced and 5G core, with trials already underway and commercial deployments expected to begin in 2027.
 
National security could become an especially important catalyst for 6G as governments seek better ways to monitor lower airspace, where drones can evade traditional radar systems. ISAC can reuse much of the existing cellular infrastructure, including sites, spectrum, transport and some radios, but applications such as drone detection will require better elevation-domain coverage and investments in beamforming, sensing software, signal processing and edge computing. This creates incremental RAN investment opportunities while potentially allowing operators to monetize sensing data alongside distributed AI inference, network intelligence and APIs.
 
The commercial model remains uncertain. CSPs could operate sensing services themselves, while governments and militaries may deploy private ISAC systems that are at least partly co-located with commercial networks. Either model expands telecom infrastructure beyond carrying traffic toward networks that can also observe and interpret the physical environment.

TBR perspective: 6G will reward operators that finish the 5G job

TBR believes the path to 6G runs directly through unfinished 5G work. Operators that expand 5G SA, improve spectrum efficiency, commercialize differentiated connectivity and build programmable, AI-ready networks will be better positioned for an economical 6G migration. FWA will remain the near-term revenue anchor, but longer-term value will increasingly depend on whether CSPs can turn AI, sensing and network capabilities into standardized services that enterprises and governments can easily buy. The transition to 6G is about proving that operators can finally monetize the capabilities 5G was designed to enable.

Exploring KPMG’s Role in Quantum

In July KPMG hosted its 9th annual Tech and Innovation Symposium in Deer Valley, Utah, attended by more than 100 technology and business professionals, politicians, academics, KPMG technology alliance partners, KPMG professionals and a handful of analysts, including TBR. Three days of discussions, breakout sessions and informal chats covered AI, the space economy, quantum, cybersecurity, workforce transitions, the NBA, geopolitics and more. This is the second in a series of blogs focusing on specific elements, discussions and/or insights TBR noted during the event.

The urgency around quantum computing

“So right now, our quantum computers work … they do all the stuff that a regular computer does. They even show potential in many areas where we can demonstrate speed up, but we cannot justify an ROI with the existing quantum computers, and no one else can.” — Subodh Kulkarni, President & Chief Executive Officer, Rigetti Computing, during a panel discussion at the KPMG Tech and Innovation Symposium
 
If the hype around AI was a bit subdued this summer compared to last year’s event (see the first blog in this series), the urgency around quantum computing more than filled the gap. KPMG US CEO Tim Walsh’s initial question after the first expert speaker related to quantum. The opening breakout session for industry analysts, including TBR, focused on quantum, led by KPMG’s Aaron Kemp, senior director, Quantum Research, Enterprise Innovation. TBR found KPMG’s quantum message compelling, and maybe a touch frightening across three specific areas: post-quantum cryptography (PQC); quantum advisory; and quantum today, which, as the quote above makes clear, is here now.

Quantum’s disruption potential

“We received an RFI that really caught us caught us off guard about five years ago, asking a lot of deep questions about the coming PQC migration, what it was going to take for an organization to do that. We started looking at how are we going to answer those questions for our clients. PQC is obviously extremely complicated.” — Aaron Kemp, KPMG Senior Director, Quantum Research, Enterprise Innovation, during a panel session
 
In a discussion with TBR and during a couple of panels, Kemp stressed the massive disruptive potential of PQC. Likening the challenge to Y2K, he said IT services companies, consultancies and niche technology vendors would likely see huge opportunities in preparing for and then mitigating against the fallout from post-Q-day cybersecurity challenges. In TBR’s ongoing research and analysis of the U.S. federal sector IT services players, IBM, General Dynamics Information Technology, Booz Allen Hamilton, Accenture Federal Services and Leidos have been concentrating heavily on post-quantum cryptography, cryptographic agility, inventory and remediation services and quantum-safe networking. Even with those efforts focused on government buyers, the activities and investments by companies with significant commercial sector clients (such as Accenture and IBM) prove smart money has positioned for PQC opportunities.
 
During a panel discussion, Barak Bussel, co-founder of 7i Capital and board chair of the UCLA Center for Quantum Science and Engineering (CQSE), said, “We no longer can rely on the old kind of cryptography and the old encryption paradigms. We chatted about this before. We don’t use Enigma anymore. I mean, it’s a different world, and now it’s time to kind of migrate away from, you know, problems of factoring large numbers to a new kind of encryption methodologies that will take us to the next step.” Every panelist, and maybe every attendee at the Symposium, seemed certain that PQC and all that comes with it are frighteningly close.
 
“It’s estimated there’s 16,500 quantum researchers on the planet. When we separate those into the organizations, where does that talent come from, and how do you stay strategically aligned with quantum, the GPUs, AI, as we kind of move into this new paradigm of compute?” — Aaron Kemp, KPMG Senior Director, Quantum Research, Enterprise Innovation, during a panel session
 
According to Kemp, KPMG’s Quantum Advisory practice will focus on Fortune 200 companies, which will likely invest in their own quantum computing capabilities. In discussing this strategy with TBR and other analysts, Kemp noted the dearth of quantum talent, an assessment he echoed the following day during a panel discussion. To address that challenge, KPMG has been training professionals, particularly in the cybersecurity practice, on quantum capabilities and KPMG’s offerings.
 
The firm has also been advising clients on the near-term steps they can make to prepare for Q-day as well as for the business opportunities quantum computing will create in other sectors, such as academic research, government and life sciences. Kemp described a coming “massive opportunity” to retire technical debt, get rid of duplicate software licenses, clean up data, and streamline IT and cybersecurity within an enterprise — efforts KPMG can address with expertise and experience. In TBR’s view, KPMG’s quantum advisory story meets the moment: urgent, practical and preparing for an uncertain, but unquestionably disrupted, future. One nagging question remains for TBR: Is the rest of KPMG ready for quantum?

An analyst’s perspective: the calm before the storm

From my colleague, Principal Analyst Boz Hristov:
 
“PQC is a must-have discussion and investment, given the move to all-things-are-digital and especially the rise of digital currencies. We know blockchain solves for some of the cryptographies, but there are some strong, powerful quantum computers that could reverse the public keys to steal private ones and really defy the idea of blockchain. Given KPMG’s role in the financial services world — across its entire stack of offerings (tax, audit, consulting) — the firm needs to think about how to be part of the solution. Possibly the PQC play, which, in a perfect world, could battle bad actors and help blockchain-based crypto exchanges survive. Lots of GRC [governance, risk and compliance] opportunity here, no doubt. The challenge with PQC is that it can be expensive and very data heavy, making blockchain ledgers slow down and ultimately raise transaction fees. Migration hurdles can be another challenge as users would need to more often move to quantum-safe addresses.
 
“What is the expected play? The good-old hybrid playbook usually helps: develop and run PQC standards that run on classical computers but resist quantum decryption. Although this may not be the state KPMG’s quantum professionals want to operate in, it would be the cheaper option when it comes to scaling. Quantum is exciting, and there is huge potential, but firms like KPMG need to avoid turning it into another blockchain where a technology was looking for a problem to solve. TBR’s December 2025 Digital Transformation: Voice of the Customer Research ​noted 15% of buyers procured quantum to support their digital transformation [DT] programs in 2025, down from 18% in 2024. Since we started conducting the survey seven years ago, quantum has continued to be in the bottom half of the tech enabling buyers’ DT initiatives: not disappearing but also not top of mind. The complexities of the tech might make current buyers even more uneasy, and I think generative AI has given them plenty of change management challenges. Perhaps the generational shift at the buyer side really needs to happen before quantum picks up steam. In the meantime, KPMG has an opportunity to educate and prepare its clients. May have to do some pro bono work. But if the timing is right, it could be KPMG’s Y2K moment in terms of its relationship with IT buyers.”
 

Technologies Purchased for Central Digital Transformation Initiatives

Technologies Purchased for Central Digital Transformation Initiative (Source: TBR)

Be curious

“Quantum’s been five years away for 30 years. Quantum is here. We’re actually working in it. So start looking into where it’s applicable in your organization. What’s going to drive for mandatory migrations, governance, but also what you do with it? And then be curious, which we’ve talked about this whole event. We don’t have a lot of algorithms yet. We don’t have a lot of ideas of what we can do with these. We talked yesterday about we’re going to give these to 18-year-olds with no biases of how computers work, and we’re going to see things we’ve never seen. So inspire your people to poke at it, to understand what’s going on. Get on, play with the free minutes that everybody can get, and accept that this is coming and is going to be part of our strategies going forward.” — Aaron Kemp, KPMG Senior Director, Quantum Research, Enterprise Innovation, during a panel session
 
During the discussion with analysts and on various panels, multiple quantum professionals extolled the idea of quantum computing operating alongside traditional and high-performance computing (HPC), allowing enterprise clients and others to select the best compute power and engine for the task. With TBR, Kemp mentioned IBM’s new data center combining HPC, Watson AI, and quantum computing as a model for helping clients use a “what fits, rather than a one-size-fits-all” approach. Kemp also said (maybe lamented?) that “quantum-enhanced solutions” are possible today, but the focus among quantum practitioners remains the hard problems only quantum may be able to solve, instead of the near-term practical uses.
 
During the larger panel discussion, as reflected in the quotes, the panelists described the current state as progressing faster than expected and delivering value, potentially, when paired with traditional computing capabilities. Pulling all the threads together, KPMG has positioned itself as ready to help clients prepare for when quantum shifts from an expensive idea to an immediate opportunity and threat. Can the firm create near-term revenue as PQC meets its Y2K moment, provide reliable and useful services through hybrid computing, and manage the ecosystem and orchestration layer in quantum? Maybe. At scale with clients? Likely. Are KPMG’s clients really ready for that? Are all of KPMG’s partners, principals and directors? Let’s see.
 
TBR doesn’t have quantum experts, and we don’t pretend to understand the physics. But we do understand business models and consulting. And KPMG appears to be in the right place to bring quantum value to its clients while expanding the firm’s reach, revenues and retention.

Holiday Shopping Is Becoming an AI Story: How Value-conscious Consumers, Rising Memory Costs and Connectivity Are Reshaping Retail

Consumers are spending, but value is winning

The 2026 holiday season is shaping up to be strong on paper. Mastercard Economics Institute expects U.S. retail sales, excluding automobiles and gas, to grow 5.5% year-to-year from Nov. 1 through Dec. 24, while Bain forecasts holiday sales will exceed $1 trillion for the first time. Yet the headline growth masks a more cautious consumer. Forbes reports that 31% of shoppers expect to increase their holiday budgets, but 53% of those spending more say that is because everything costs more, not because they have greater purchasing power. Bain similarly points to inflation, low savings and elevated credit card delinquencies as pressure points influencing spending decisions this holiday season. The result is a shopper willing to spend but increasingly focused on value.

AI is the new holiday shopping assistant

The search for value is accelerating consumer adoption of AI, as AI tools are seen as a means to more effectively locate and pursue greater value with purchasing decisions. Forbes reports that nearly two-thirds of U.S. shoppers expect AI to influence at least 30% of their holiday spending, with deal-finding, product comparisons and retailer selection among the leading use cases.
 
Bain found that 24% of online shoppers expect to begin their holiday search on generative AI platforms, up from 17% in 2025, while another 13% plan to use retailer-owned AI shopping agents. This demonstrates that consumers are lowering their caution around AI usage, as previously skeptical users are more willing to experiment with AI tools in the name of cost savings.
 
Additionally, Mastercard found that AI users tend to shop across a broader set of merchants and spend earlier in the holiday season. AI is therefore becoming an intermediary between consumers and brands. Though humans remain involved as the decision makers, AI enhances and extends the shopping experience to new brands, new shopping platforms and potentially new products.

The hidden cost of the AI shopping boom

The irony is that the same AI adoption helping consumers find value is increasing costs in consumer products such as electronics. Hyperscalers, neoscalers and neoclouds are accelerating AI infrastructure investment, increasing demand for ever-scarcer key memory and components, thus driving up these costs. Suppliers are prioritizing scarce capacity for server DRAM, high-bandwidth memory and enterprise storage, tightening supply for consumer applications. This dynamic will keep memory prices elevated and will continue to result in higher component costs for consumer electronics.
 
For retailers, AI can improve personalization and customer service, but the infrastructure required to deliver those capabilities comes with a rising cost.

Higher device costs add another holiday spending pressure point

The AI infrastructure boom could also reach holiday shoppers through higher smartphone costs. AI server demand is contributing to higher DRAM and NAND prices, and smartphone manufacturers have little appetite to absorb those increases, potentially pushing more costs to consumers at a time when value remains a priority. This creates a challenge for U.S. mobile network operators, which rely heavily on device promotions to drive upgrades and switching. The carriers still offer substantial smartphone discounts, but many of the largest offers require trade-ins, qualifying plans or new lines, with savings delivered through multiyear bill credits.
 
As device prices rise, holiday shoppers may delay upgrades or depend more heavily on financing and promotions, forcing operators to balance consumer affordability with the cost of more expensive device subsidies. The holiday quarter remains an important device sales period for U.S. operators. Consumers that are stretching an inflation-affected budget for the 2026 holiday season may find themselves delaying upgrades, especially if device prices increase further due to component cost growth.
 
Additionally, higher component costs will likely limit U.S. operators’ ability to discount as heavily as they have in years past.

Connectivity turns AI investment into a retail experience

As retailers invest more heavily in AI to capture increasingly value-conscious holiday shoppers, the effectiveness of those investments will depend on the infrastructure supporting them. AI recommendations, computer vision, mobile point-of-sale, real-time inventory and in-store personalization only create value when data can move reliably between devices, stores, edge systems and cloud platforms.
 
A 2026 Verizon Business study found that AI and machine learning applications are driving 67% of retailer network upgrades, while only 39% of retailers are satisfied with their edge computing support. Verizon is positioning 5G Business Internet and private networks for these workloads to address the gap in satisfaction.
 
AT&T’s Connected Spaces combines connectivity, edge infrastructure and Azure AI for environments including retail, and its collaboration with AWS connects fiber and 5G directly to cloud environments for business AI workloads.
 
T-Mobile similarly positions 5G, IoT and advanced network solutions for inventory management, mobile point-of-sale and computer vision. In this sense, connectivity becomes part of the same holiday spending story as retailers may compete through AI-enabled experiences, but networks ultimately determine how reliably those experiences reach consumers.

The holiday shopping cart now reaches back to the data center

Holiday shopping is becoming a useful snapshot of a much larger technology cycle. Consumers, who have been continuously squeezed by inflation for years, want better prices and more personalized experiences. Retailers are turning to AI to deliver both, and AI is driving investment in compute, memory, edge infrastructure and networks to support this shift in demand.
 
The connection from a shopper asking an AI assistant to find the best gift to a retailer’s cloud infrastructure, memory procurement and store connectivity may not be visible, but it is increasingly direct and essential as customers have higher expectations for seamless and positive customer experiences. For telecom operators, that makes retail AI more than an application trend. It is another reason reliable fiber, 5G, edge computing and resilient enterprise connectivity are becoming part of the commercial infrastructure behind everyday consumer experiences.

AI Is Changing Enterprise Networking, but Not All Disruption Looks the Same

AI has become a nearly universal component in enterprise networking product road maps

TBR’s latest research on AI disruption among enterprise network vendors addresses the gap in measuring how deeply AI is changing how these vendors operate by providing a quantitative assessment of Cisco, Hewlett Packard Enterprise (HPE) and Extreme Networks using TBR’s ©Human Intensity Reduction Index (HIRI). HIRI is a proprietary metric that provides a direct measure of a company’s AI, human capital, utilization, offshore leverage and delivery model evolution on its operating margin. The metric helps distinguish genuine business-model disruption from window-dressing AI announcements.
 
Our initial results show an industry in transition. HIRI and operating margins are improving across the peer group, but AI is only one part of the story. Restructuring efforts, revenue growth, subscription adoption and portfolio consolidation continue to strongly influence workforce productivity. Additionally, customer demand for AI-based solutions to support their own AI transformations boosts financial performance independent of the vendors’ internal AI investments.

HIRI helps separate AI-related activity from routine business operations changes

The three vendors illustrate different approaches to achieving the same fundamental goal. Cisco demonstrates the clearest evidence of AI directly handling work, including 145,000 customer support cases resolved without human intervention. Cisco has also publicly touted that AI agents are now available to all remaining employees following its latest round of layoffs. HPE is leveraging AI enhancements along with the Juniper integration, creating opportunities to reuse development resources across a much larger networking portfolio. Extreme Networks is taking a product-led approach, using Agent ONE and Platform ONE to advance autonomous networking and accelerate its transition toward a more SaaS-oriented business model.
 
HIRI is only part of the story, however. Through our research, we also examine how AI is disrupting vendors’ operating, commercial, delivery and partner models, layering a qualitative lens over quantitative metrics. This analysis makes it clear that AI is only part of the broader efficiency story for these vendors. One of the most important emerging AI-centric themes is the progression from AI assistants toward agentic and autonomous systems that can increasingly investigate, troubleshoot and execute network operations with less human intervention. These vendors are both producers and consumers of this technology, placing them all squarely at the center of this transformation. There are also very real, nontechnological roadblocks to this progress, namely FOBO (fear of becoming obsolete) and the underlying hesitation among end users to adopt and embrace AI, a challenge companies continue to seek to overcome.
 
TBR’s AI disruption research will continue to examine whether enterprise network vendors’ investments in AI are materially changing how they operate, generate revenue, deliver technology and engage their ecosystems. Access the current enterprise network vendor AI disruption data and analysis with a TBR Insight Center™ free trial.

Exploring KPMG’s Role in AI

In July KPMG hosted its 9th annual Tech and Innovation Symposium in Deer Valley, Utah, attended by more than 100 technology and business professionals, politicians, academics, KPMG technology alliance partners, KPMG professionals and a handful of analysts, including TBR. Three days of discussions, breakout sessions and informal chats covered AI, the space economy, quantum, cybersecurity, workforce transitions, the NBA, geopolitics and more. This is the first in a series of blogs focusing on specific elements, discussions and/or insights TBR noted during the event.
 
In 2026 KPMG’s message around business outcomes is landing differently than it did just a year ago, when AI hype possibly reached its peak. Now the firm and its clients talk less about productivity, cost cutting, or even AI’s potential to increase revenues and growth. Technology has taken a backseat to change as a business outcome. It is a subtle shift, perhaps, but in contrast to hype about AI’s unrivaled promise, the joys of vibe coding, and AI as the answer to every question, a grounded, restrained, business-first approach — one could even say “vibe” — provided a convincing foundation for the wary optimism that permeated the KPMG event.
 
Two specific recurring themes capture the difference between 2025 AI talk and 2026 AI reality. First, speakers, including but not exclusively KPMG professionals, stressed that enterprises cannot overspend on governance, risk and compliance. Understanding the inputs, the outputs, and what led from one to the other will be critical to leveraging AI at scale. Absent trusted and auditable AI, there will be chaos (apres moi, le deluge, perhaps). Second, using the right type of AI matters more than using AI. To paraphrase Ted Lasso, don’t be a gravedigger using a spoon.
 
Additional insights into KPMG’s AI initiatives based on discussions and presentations at the Utah event:

  • TBR noted a disconnect between AI as it is reported in major news outlets and AI-centric news and analysis feeds and AI as discussed by the speakers, KPMG professionals and attendees. IPOs, circular financing among chipmakers and frontier model companies, acquisitions, and AI personalities barely registered. Regulatory changes effecting enterprises’ business decisions around AI, practical applications of AI-enabled solutions, cost considerations when choosing different AI models, and the twin challenges of change management and trust featured heavily. In short, the Symposium participants seemingly ignored AI drama to focus on AI practicalities.
  • During a question-and-answer session, KPMG US CEO Tim Walsh provided a fairly compact set of steps for enterprises embarking on an AI initiative: know yourself, know your data and operations, know your resilience, and bring in an outsider to help. In TBR’s view, perhaps KPMG would prefer the last step to come first, but as a way for KPMG’s clients to think about scaled AI adoption within an enterprise, those steps are necessary for success, starting and ending with trust. At last year’s Symposium, a KPMG speaker said success depends on leaders, labs and the masses, a formula TBR brought up with clients during this event, testing its continued relevance. Next year, TBR will ask if outside help still rings true.
  • In a session with TBR, KPMG leaders noted that the firm has encouraged every professional across the firm to use AI agents, even if only at a rudimentary level. They also stressed KPMG’s AI role lies between the AI model layer and the data layer, providing intelligence and orchestration based on KPMG’s understanding of how businesses actually run. In TBR’s view, this intentional positioning in the middle of the AI value chain (in addition, of course, to AI advisory services at the front end of consulting) reflects the firm’s broader strategy of becoming an even more critical ecosystem partner to technology providers. When a KPMG leader talked about the firm’s OpenAI + Oracle + KPMG customer-zero story, TBR heard how OpenAI and Oracle can now better explain, to their clients, KPMG’s value and KPMG’s orchestration and business operations layer.

At the start of 2026, “simplicity” seemed to be the word of the year. Clients clamored for simplicity in AI deployments and their technology spend. Consultancies and IT services companies promised simplicity in pricing, implementation and operations. By July, trust replaced simplicity: trust at scale, trust through auditable AI, and trust enabled by Responsible AI.
 
What struck TBR most about KPMG’s frequent evocations of trust was the humility that went with it. Knowing trust has to be earned, maintained and renewed seemed to inform how KPMG leaders spoke about the firm’s capabilities and relationships. And in casual discussions with KPMG clients at the Utah event, trust came up repeatedly as a key reason they were there.

Nokia Aims to Use Current Tech Transition to Strengthen Fixed Networks, Enter New Markets and Add AI Capabilities to its Core Technology

2026 Nokia Fixed Networks Industry Analyst Event, Yokohama, Japan, Sept. 15-17, 2026 — A select group of industry analysts gathered at The Westin in Yokohama, Japan, to hear from Nokia leaders, partners and customers about the company’s Fixed Networks business unit’s strategies, capabilities and opportunities. AI, hardware upgrades and the pivot toward hyperscaler opportunities were among the themes across the various executive presentations.

TBR perspective

The fixed networks market is at an inflection point, with three major technology transitions underway across access, Wi-Fi and network management. TR-069* (technical report 069) is evolving to TR-369 (technical report 369), the first major hardware refresh toward Wi-Fi 7 is underway, and the migration from GPON to XGS-PON, and selectively to 25G and 50G PON, is progressing as these technologies advance.
 
These shifts create a favorable window for Nokia to capture demand because hardware replacement is a logical time to pull through software modernization. Operators are simultaneously reconsidering how they manage the home, automate access networks and prepare fiber infrastructure for changing traffic patterns caused by AI adoption. Nokia is approaching this wave of progress with clearly defined road maps across relevant portfolio areas (e.g., Altiplano, Corteca, Quillion, PON) and is leveraging its hardware roots as the foundation for its AI story.
 
Nokia is positioning AI as another layer of value across its Fixed Networks portfolio, which spans access infrastructure, network control and home broadband. This approach is intentionally pragmatic, focused on applying AI where it can improve customer care, network operations, rollout efficiency and user experience. The strategy is less likely to produce a constant stream of attention-grabbing but relatively empty AI announcements. Instead, it gives Nokia a more credible path to converting AI from a feature into an operating model as customers become comfortable moving from AI-assisted workflows toward more autonomous operations.
 
Communication service providers (CSPs) remain the foundation of Nokia’s Fixed Networks customer base, but the company is increasingly applying PON economics and operational advantages to adjacent non-CSP environments, particularly hyperscalers. Out-of-band management (OOBM) is the clearest near-term example, with Nokia using co-creation and a highly configurable portfolio to establish credibility with hyperscalers before extending more standardized capabilities to neoscalers and others in the enterprise space.
 
In parallel, customer examples show that growth in the broader fixed networks market requires a variety of nuanced approaches. In some markets, Nokia can help customers modernize existing infrastructure, while in others, the opportunity depends on operators finding ways to make connectivity affordable and deployable for their customers through unconventional models.
 
Nokia is becoming more selective about the Fixed Networks opportunities it pursues, particularly in the hardware business, as it shifts away from deals where competitive pricing would push margins below targets. Rising component costs are also affecting customer purchasing decisions, constraining unit volumes or altering purchase timing. These factors resulted in top-line year-to-year declines for the Fixed Networks business in 2Q26.

Hyperscaler success first; neoscaler opportunity will build

Nokia is entering the hyperscaler space with PON capabilities for data center OOBM, using a co-creation approach and customer choice to establish itself in this market, leading with flexibility as its differentiator. The value proposition centers on characteristics that translate well from access networks into data centers, including density, space efficiency, lower cabling requirements, energy efficiency, and architectural flexibility. Nokia is also building choice into elements such as mount location, port count and interface mix, reflecting the highly customized demands of hyperscalers.
 
This opportunity is strategically meaningful as hyperscaler wins can provide a healthy financial complement and diversify the customer base, but CSPs will remain the core of the business. The more consequential long-term opportunity will be productizing what Nokia learns from hyperscaler engagements and carrying it into the broader data center market.
 
The flexibility Nokia is emphasizing for hyperscalers and neoscalers is well suited to these buyers, which have clearly defined architectural requirements. For many CSPs, excessive choice can create complexity when they look to Nokia for guidance on technology evolution, monetization and operations. As Nokia expands across customer types, its ability to package the right amount of flexibility for each segment will matter almost as much as the breadth of the portfolio itself.

AI road map emphasizes right-fit AI over AI-first positioning

Nokia is approaching AI from the broader customer-improvement perspective, layering capabilities within Altiplano and Corteca onto its existing hardware and software foundation to address operational pain points as customers modernize, leading with a platform-centric story. The road map spans two complementary domains, with Altiplano applying AI to network control, analysis and operations, and Corteca applying it to device management, customer care and the broadband experience.
 
Altiplano illustrates Nokia’s deliberate progression toward autonomy. Predictive AI and machine learning already support functions such as network analysis, capacity planning, churn reduction and KPI prediction. Agentic capabilities add a conversational interface and another set of virtual hands for network engineers. The longer-term direction moves toward AI completing complex end-to-end tasks under human supervision. Nokia characterized itself as between the human-in-the-loop and human-on-the-loop stages. This is an important distinction because it suggests the near-term objective is controlled operational augmentation, not unsupervised autonomy.
 
Nokia’s AI vision favors domain-grounded and deterministic approaches where possible, including smaller specialized models when they can provide better accuracy and token economics than larger general-purpose models. Model and deployment flexibility remain key to Nokia’s AI vision, enabling operators to use different models and combine cloud and on-premises inferences without requiring its users to conform to a single AI stack.
 
Corteca follows a similarly staged path in the home. Nokia is currently using AI to assist customer-care personnel, with the road map moving toward advanced analytics and quality-of-experience optimization. Home broadband challenges are not simply about faster Wi-Fi. Operators need visibility into what is occurring inside the home to manage Wi-Fi 7 experiences effectively, and the transition from TR-069 to TR-369 gives Nokia an opportunity to make Corteca a more central data and management layer as devices are refreshed, creating a natural opportunity to adopt modern software capabilities in kind.
 
For Corteca, this progression is about improving the broadband customer experience in a quantifiable way, such as net promoter score improvements, using greater visibility and AI-driven intelligence to help operators identify customers experiencing service issues faster and determine the appropriate action before those problems translate into customer dissatisfaction.
 
Taken together, Nokia’s approach to AI is appropriately conservative for a network domain where reliability and trust matter. The company seeks to make AI consumable, measurable and progressively more autonomous without separating it from the underlying network expertise. The risk is that a measured road map can appear less differentiated because, while AI has become pervasive in competitors’ fixed networks messaging, the level of substance behind that messaging varies considerably, with some announcements still emphasizing broad AI visions without clearly defining deployment maturity or measurable outcomes.
 
Nokia’s AI road map asserts that domain expertise, trustworthy data and deterministic execution will ultimately matter more than model size or the volume and frequency of AI messaging. Nokia also touts gradual operational evolution, with defined use cases to substantiate its AI vision.

Customer examples highlight need for creativity to expand the fixed networks market

Many of Nokia’s core fixed network markets are mature from a regional perspective, meaning growth from existing customers increasingly depends on upgrades, share shifts and expansion into adjacent use cases. The event’s customer examples reinforced that there is no universal path to additional fixed networks growth. In highly saturated markets, customers are selectively modernizing where fiber creates incremental value. Elsewhere, the central challenge is not technology performance but whether operators can make broadband economically accessible to households that remain unconnected.
 
Developing markets require a different kind of creativity. Customer discussions related to emerging markets highlighted affordability, geography, payment behavior and infrastructure resilience as constraints that hardware modernization cannot solve alone. Operators are experimenting with prepaid broadband, short billing periods, rapid fulfillment, alternative distribution and localized network designs to expand the potential customer base by designing the service around how people actually live, earn and pay.
 
Country- and city-specific variables such as weather, housing, population density, affordability and existing infrastructure determine whether the business case works. Nokia’s opportunity is therefore not only to provide more capable access technology, but to help operators combine technology, deployment architecture and business models in ways that make connectivity viable in places where a conventional fixed-broadband model may not. Multi-PON flexibility lets operators delay or stage technology choices, Corteca and Altiplano can improve the economics of operating and supporting networks, and automation can reduce the human effort required for deployment and assurance. The common thread is flexibility.

Conclusion

Nokia’s Fixed Networks strategy is increasingly defined by disciplined expansion. The company is using simultaneous technology transitions of access, Wi-Fi and network management to deepen its position with CSPs, applying PON expertise to hyperscaler and neoscaler data centers, and building AI into Altiplano and Corteca through a staged path toward greater autonomy. Across each initiative, the consistent message is flexibility. Multiple PON generations, multiple AI models and deployment options, and different architectures for different customer economics all reinforce this message.
 
Hyperscalers can absorb customization, neoscalers can standardize it, CSPs often need a more prescriptive road map, and emerging-market operators may need entirely new commercial models to make connectivity viable in their footprints. The next phase of Nokia’s Fixed Networks growth will therefore depend as much on understanding how each customer creates value as it does on advancing the underlying technology itself.

Setting the Pace: What Accenture, Deloitte and Infosys Signal for Professional Services in 2027

Across the professional services spectrum, from strategy consulting through systems integration and managed IT services, competitors frequently emulate the approaches and investment decisions of three stand-out companies: Accenture, Deloitte and Infosys. That is not marketing spin on their behalf but rather a reflection of the questions we field from our clients, including the cloud, software, hardware and AI partners of these companies and their peers.
 
As part of TBR’s ongoing research on and analysis of consultancies and IT services companies, our quarterly and semiannual reports include scenarios that expand on the market trends, business model shifts, investments, and portfolio and leadership changes that underpin the performance data and shed light on what is coming next. This special report looks at trends across Accenture, Deloitte and Infosys over the last year and highlights commonalities, positioning for 2027, and outliers that may indicate the direction of these companies and their peers.
 
First, the basics: In 2Q26 Accenture generated $18.72 billion in quarterly revenue, up 5.6% year-to-year, covering its fiscal third-quarter results. TBR estimates Deloitte’s FY26 annual services revenue reached an estimated $46.9 billion, up 6.4%, reflecting just the services business covered by TBR, not Deloitte’s total global revenue. Infosys generated $5.1 billion in 2Q26 revenue, up 2.9%.
 
TBR’s individual company reports include detailed data and estimates across service lines, geographies and industries, and TBR’s benchmarks use proprietary data and taxonomies to allow for direct comparisons between more than 30 IT services companies and management consultancies.

Show me some ROI: Growing pressure to turn AI investment into measurable operational results

In TBR’s research, IT services and consulting buyers want AI to be integrated into business workflows and expect they will be able to demonstrate returns on their investment while sustaining AI adoption beyond a pilot project. These are not new or unreasonable demands, but the pressure has been mounting after a couple years of AI hype.

  • Accenture addresses this through governed agentic AI and physical AI, using manufacturing and infrastructure engagements to demonstrate improvements in downtime, quality and operational efficiency. In TBR’s view, Accenture is the best positioned vendor to capture the broadest opportunity as it combines business transformation capabilities with industrial execution, ecosystem reach and proven delivery at scale.
  • Deloitte approaches the same demand through cybersecurity and its emerging physical AI portfolio, where trust, resilience and operational safety create opportunities for its consulting expertise.
  • Infosys embeds AI into modernization and managed services engagements, while acquisitions such as Optimum Healthcare IT and Stratus strengthen its ability to address industry-specific requirements. In TBR’s view, Infosys’ push to help clients fund AI through modernization budgets provides a useful near-term revenue boost.

Enterprise buyers have tired of technology promises. That fatigue appeared before 4Q22 (when ChatGPT arrived) but was tamped down a bit in the frenzy around generative AI (GenAI) and the FOMO (fear of missing out) associated with AI adoption in 2023 and 2024. Spiraling costs and ill-defined ROI have cooled enterprise buyers’ enthusiasm and focused their buying decisions. Accenture, Deloitte and Infosys appear to have recalibrated how they position their AI offerings to reflect that new buyer mood.

The robots have not taken over, yet: AI-enabled delivery and commercial models still depend on humans

AI promises productivity improvements by off-loading repeatable, mundane tasks to algorithms and robots. If an IT services company or consultancy can do things faster with AI and automation, clients can reasonably expect their time-and-materials bill will decline. Accenture, Deloitte, Infosys and all their peers now find themselves — or, really, have found themselves since at least mid-2023 — challenged with managing the cannibalization that their AI investments may be bringing to their traditional IT services. What to do?

  • Accenture increasingly invests in proprietary assets and uses Reinvention.AI to make institutional knowledge reusable across delivery. Notably, Accenture has progressed from uncertainty about its appetite for software sales in 4Q25 toward a clearer emphasis on asset ownership and revenue growth without proportional headcount expansion.
  • Deloitte uses Operate and cybersecurity to explore recurring services and risk-sharing arrangements, including multiple delivery options for its Anthropic-enabled secure software platform.
  • Infosys pursues similar economics through Topaz Fabric, AI-enabled global capability centers and frontier engineers, but its unchanged 54% fixed-price revenue share and declining ©Human Intensity Reduction Index (HIRI) in 2Q26 indicate that commercial and workforce transformations remain gradual.

TBR’s HIRI proves useful in comparing these three companies on the effects of internal AI adoption on their performance, specifically as it relates to productivity and generating profitable dollars with the same or fewer people. In TBR’s research, Accenture’s simultaneous improvement in HIRI and operating margin provide an encouraging early signal that the company will lead in pivoting toward the right commercial model to meet the AI age.
 
Deloitte has a particularly promising opportunity within cybersecurity, where continuous, measurable work can support new pricing structures if the firm controls customization and contractual risk (notably, TBR has not yet published a HIRI for Deloitte and its Big Four peers). TBR’s analysis of Deloitte’s resource management strategy and HIRI calculations suggest the firm’s strategy is shifting toward restructuring the talent pyramid for an AI-enabled, Operate-led consulting model.
 
Deloitte is moving in two directions simultaneously: tightening the traditional consulting workforce in slower markets while expanding lower-cost and tech-heavy delivery capacity in growth hubs. If Deloitte can industrialize AI-enabled delivery, offshore talent, job-family specialization and Operate capacity, it can protect margins while winning larger, outcome-based transformation deals. If not, Deloitte risks drifting toward a labor-leverage model where more consultants, fewer partner touch points and pricing pressure make it look less like a differentiated strategy adviser and more like a scaled implementation and managed services provider. With Deloitte’s HIRI hovering around 0%, the firm appears to still lean more on a labor-arbitrage delivery model.
 
Although Infosys’ HIRI has improved since the dawn of ChatGPT, the metric has continued to slide since its peak in late 2024, declining 30 basis points sequentially in 2Q26. The biggest challenge for Infosys will be maintaining service quality and creating enough volume even if its net headcount addition remains flat rather than in decline due to AI, as many expect. With Infosys’ HIRI staying relatively flat over the last couple of quarters and revenue growth decelerating, delivering or exceeding operating margin in line with its guidance will remain the key way to satisfy impatient shareholders.

The ecosystem challenge: Who controls AI deployment and who owns the client?

Every IT services company and consultancy needs a well-curated ecosystem of hyperscalers, model providers and enterprise software vendors, but those partners increasingly offer capabilities that overlap with services work. This age-old threat, which TBR covered in detail in a separate special report, has always been more about opportunistic wins by technology companies and offerings that stayed close to the box (i.e., highly dependent on the technology itself).
 
AI may change the calculus by allowing technology companies to profitably offer services, especially as AI-enabled solutions reduce the need for humans (see previous section). Currently, TBR sees this threat as marginal, not existential, but that could change as soon as the end of 2027.

  • Accenture has responded to this growing threat by combining extensive partnerships with greater ownership of domain assets, data and orchestration capabilities, including offerings for sovereign and regulated environments.
  • Deloitte largely pursues an asset-light approach through initiatives such as Silicon2Service, Palantir-enabled EOS and Zora AI on Oracle infrastructure, creating consulting and Operate opportunities while potentially leaving Deloitte exposed when partners incorporate similar functionality into their platforms.
  • Infosys positions Topaz Fabric as the coordination layer across models and technologies, seeking to retain responsibility for integration, governance and execution.

Accenture owns assets that complement its partners’ breadth, Infosys could make its Topaz Fabric essential to clients’ operations, and Deloitte must provide differentiated value  to keep the hyperscalers and others at bay. To be clear, TBR believes the technology providers will not significantly erode these three companies’ IT services and consulting revenues, but as AI increasingly changes the overall market, IT services companies and consultancies that have not followed strategies laid down by Accenture, Infosys and Deloitte will likely be the first to see opportunities shift to their technology partners.

Space: Is it a cliché to say ‘the next frontier’?

Deloitte’s expansion into space technology through Project Constellation and the Deloitte-2 and Deloitte-3 satellites creates opportunities in on-orbit cybersecurity, AI and space data that sit beyond the enterprise AI opportunities. But these openings will come at a cost and with potential margin headwinds, as these pursuits are capital-intensive, demonstrating a pivot away from Deloitte’s asset-light model. Accelerating investments in Project Constellation (the firm has plans to launch nine satellite in total) will also come with additional opportunities and challenges.
 
Doubling down on operating its own satellites could provide Deloitte with an opportunity to demonstrate payload, edge and software-defined upgrades, helping the firm build credibility against engineering-heavy competitors, including the aerospace contracts. In other words, if the firm is successful, Deloitte could treat its space tech investments as a lab, leveraging the knowledge gained to on-Earth OT environments. The challenges will likely center on commercialization, as space projects remain heavily government-led with long procurement cycle.
 
Although Deloitte is well positioned to win advisory and transformation work, converting opportunities in emerging areas such as in-space manufacturing into managed services will likely take years. At the same time, the Trump administration’s recent launch of the United States Space Academy suggests Deloitte is making a long-term commitment to space technology while continuing to advance its Operate-to-Transform strategy on Earth over the short to mid-term.
 
Across all these elements of running a highly successful consultancy and/or IT services company, Accenture, Deloitte and Infosys continue to provide growth blueprints for their peers, as well as some, perhaps unintended, advice on strategies and steps to avoid.