Grant Thornton Expands Its Oracle Practice Through Leadership, Industry Expertise and AI

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TBR recently spoke with Brandon Johnson, Oracle Advisory Leader at Grant Thornton, about the firm’s growing Oracle practice, his experience prior to joining Grant Thornton, and his expectations for Oracle and Grant Thornton over the next few years. The following reflects that discussion as well as TBR’s ongoing analysis of both companies, their competitors and peers, and trends across the entire technology landscape.

 
TBR believes Grant Thornton has built a differentiated Oracle practice around senior leadership, direct field access, and focused industry investment, all centered on midmarket clients. Grant Thornton has Oracle breadth without bloat, connecting finance, human capital management (HCM) and supply chain with tax, risk, cyber and industry advisory while keeping experienced leaders close to the work.
 
Existing demand supports growth, and, notably, Oracle generates about half of Grant Thornton’s leads, while Grant Thornton creates the remaining pipeline internally, reflecting the depth of the two companies’ relationship. Scale remains the biggest challenge, with Johnson admitting the bench needs strategic growth and a better balance of offshore and nearshore talent.
 
Despite this constraint, TBR expects Grant Thornton’s annual Oracle practice revenue to grow in the high teens to low 20% in the near term, potentially higher if Grant Thornton institutionalizes more of a relationship-led practice through standardized delivery, a broader talent pipeline, and reusable AI offerings with defined governance and outcome measures.
 
In short, Grant Thornton recruited the right leadership and developed tight relationships, and now it must execute on Oracle’s platform changes plus client demand.

Leadership, industry focus and co-selling have deepened the Oracle alliance

Grant Thornton hired Johnson, a 25-plus-year Accenture veteran, where he had successfully built a substantial Oracle business and established strong relationships with Oracle’s sales, product and alliance teams. He has been joined by additional experienced professionals specializing in Oracle HCM, finance, supply chain and enterprise transformation.
 
In TBR’s assessment, Grant Thornton gained unusually broad and deep access to Oracle for a firm of its relative size, and its long-standing experience working with Oracle provides it with the discipline and confidence to treat Oracle’s consulting practice as a delivery partner rather than a competitor.
 
For decades, professional services firms, particularly the Big Four, have maintained strained — at best — relationships with the consulting arms of software giants such as SAP, Microsoft and Oracle. In contrast, according to Johnson and Grant Thornton Chief Marketing Officer David Clarke, Grant Thornton enjoys a more mature, cooperative understanding of different consulting opportunities and roles.
 
Grant Thornton, in their view, leads transformation and draws on Oracle for specialized infrastructure and product depth. Of course, the age-old challenge of converting personal access and relationships into consistent field enablement and referrals remains.
 
According to Johnson, Grant Thornton’s Oracle practice concentrates on financial services, healthcare, hospitality and travel, professional services, transportation and logistics, with new investments in energy, utilities, oil and gas, and consumer sectors.
 
TBR’s research has consistently shown that concentrated industry specialization, rather than serving all industries, yields faster, more sustained revenue growth. Technology vendors increasingly reward partners that combine industry talent, subindustry offerings and reusable IP; broad but shallow coverage carries less value than proven depth in a limited set of markets.
 
Like many consultancies, Grant Thornton creates demand for Oracle through a presales team that demonstrates the technology, joint go-to-market campaigns and introductions to existing Grant Thornton clients.
 
Not surprisingly, Oracle, according to Clarke, promotes Grant Thornton in partner forums. More surprising, Oracle generates about half of Grant Thornton’s Oracle pipeline, reinforcing a shift from simple referral flow to co-selling.
 
TBR believes a technology company’s ability to explain a consulting partner’s value and tell that consultancy’s story remains the most critical indicator of the current depth and long-term potential of a tech vendor-consultancy alliance. A tech vendor must be able to effectively communicate a consultancy’s story to generate significant pipeline, and clearly, Oracle is doing this well for Grant Thornton.
 
Further cementing the alliance, Grant Thornton’s relatively early participation in Oracle’s AI Agent Studio gives the firm a foothold in the transformation layer between applications and infrastructure, enabling the firm to use agents to connect data, automate processes and coordinate decisions across Oracle and non-Oracle systems.
 
Oracle’s Fusion Agentic Applications expand that opportunity rather than reduce services demand. Clients still need integration, controls, process redesign, data readiness and change management, and Grant Thornton should be able to tie each deployment to measurable gains in close cycles, workforce operations, procurement, service or supply chain.

Grant Thornton’s Oracle sweet spot: Complex midmarket transformations

Grant Thornton’s ideal clients share a few basic characteristics: large enough to have outgrown boutique consultancies and IT services companies; small enough to neither need nor want to pay the higher prices of a Big Four firm or global systems integrator; and experiencing rapid growth fueled by acquisitions, carve-outs and increased technology investments while contending with fragmented IT systems and business model disruptions.

How does Grant Thornton marry its client base and Oracle’s technology?

  • Integrated Fusion transformations: Grant Thornton positions HCM with finance and supply chain; it reported to TBR that roughly 70% of its Oracle pipeline includes HCM packaged into a broader bundle. Oracle’s common application and data architecture enables the firm to frame workforce, financial and operational change as a single program.
  • Private equity (PE) carve-outs and post-acquisition integrations: Grant Thornton combines PE relationships and preconfigured designs to establish finance, consolidation, HR and operational systems quickly, especially when a portfolio company must separate from a parent or prepare for another transaction. Undoubtedly, Grant Thornton brings its customer-zero experience to these opportunities.
  • Industry-specific transformations: In the healthcare, financial services, transportation, hospitality and energy verticals, Grant Thornton can combine Oracle with regulatory, tax, risk, cyber and operating expertise. Grant Thornton is not unique, but by staying focused on a handful of industries, the firm’s expertise sets it apart from its peers. TBR notes that even if the industry transformations are not unique among services companies, there is a level of differentiation among tech partners. In our opinion, Oracle is one of the very few SaaS vendors that offer fully prepackaged industry applications, giving firms like Grant Thornton opportunities to build around these applications, helping them lead with business discussions and work backward into horizontal finance and HR transformations.
  • AI-enabled finance and enterprise transformation: For Grant Thornton’s clients, Oracle agents can improve finance, HR, procurement, supply chain and service workflows, but only when Grant Thornton pairs the deployments with process redesign, governance, controls, data readiness and workforce change.
  • Alternatives to large integrators: According to Johnson and Clarke, Grant Thornton’s senior-level attention and practical delivery style appeal to buyers concerned about junior staffing, change orders or standardized playbooks. Sustaining that advantage may require Grant Thornton to make its capacity model as credible as its relationship model.

In TBR’s view, Grant Thornton will likely avoid infrastructure-heavy Oracle Cloud Infrastructure (OCI) programs, complex database modernization and very large global transformations until it builds deeper capabilities — though these may be necessary as OCI matures and continues to underpin the broader platform and applications portfolio.
 
OCI will also become more essential to the One Oracle positioning and sales strategy Oracle will increasingly emphasize with its partners. Until then, Grant Thornton can own the transformation agenda with its clients from the apps layer and continue developing its deep relationship with Oracle without overstating its full-stack depth.

Capacity, field awareness and technology depth will set the ceiling

Talent remains the most pressing constraint. With almost no Oracle bench, each large win for Grant Thornton creates staffing pressure, even as the firm continues building a talent base in India and recruiting top-flight nearshore talent as well as considers investing in Philippines-based capacity ahead of demand, all without weakening the firm’s promise to lead every delivery with senior, experienced professionals.
 
Compounding the talent shortage, Oracle field awareness of Grant Thornton’s strengths remains uneven. Executive access opens opportunities, but scale requires a formal enablement system that gives sellers concise guidance on target clients, industry plays, credentials, reference architectures, available capacity, and reasons to select Grant Thornton. The firm could also develop more depth across analytics, data and OCI.
 
Although becoming an infrastructure outsourcer would not fit Grant Thornton’s strategy or brand, strengthening OCI architecture, integration, security and data governance would prove more credible ownership of the transformation layer.
 
To further differentiate from peers, Grant Thornton could assign ownership, governance, version control and performance metrics to its own accelerators and link each asset to faster deployment or better client outcomes. Those metrics could also be a catalyst for disruption of the commercial model, should Grant Thornton and its clients together see benefits in fixed-fee and/or value-led commercial structures.
 
In TBR’s view, consulting commercial models have begun to evolve rapidly toward explicitly tying fees to adoption, productivity, transaction speed, working capital, workforce efficiency and/or financial close improvement. Grant Thornton’s Oracle practice could be an internal catalyst or accelerator for that change.

Productize, scale, prioritize, invest and attach

Leadership, client demand, alliance access and proven delivery competency give Grant Thornton the foundation to make its Oracle practice one of its fastest-growing technology businesses. With C-Suite-to-field-level relationships that run deeper than market awareness, the practice has room to take market share before larger competitors react.
 
Adding capacity, converting beta AI work into marketable solutions, proving industry plays, and expanding Oracle-originated opportunities could all help Grant Thornton when it comes to hiring, delivery, and field awareness.”
 
TBR believes Grant Thornton will likely accelerate its growth and increase its relevance to Oracle and shared clients by:

  • Productizing a small set of repeatable offerings in Oracle’s AI Agent Studio, including financial close, procurement, HR service delivery (HRSD), workforce planning and supply-chain, through ideally defined deployment methods and outcome metrics
  • Scaling through delivery pods, which will preserve senior client leadership while expanding offshore and nearshore leverage across the entire Oracle stack
  • Prioritizing sectors where Oracle commitment, Grant Thornton relationships and midmarket demand overlap, especially financial services, healthcare, energy, transportation and logistics, and hospitality
  • Investing in OCI architecture, Oracle Integration Cloud, data governance, security and cross-platform orchestration rather than commoditized infrastructure
  • Attaching optimization, managed services, agent governance and adoption support to implementations while providing recurring revenues

To bring the greatest value to its clients and to Oracle, Grant Thornton does not need scale parity with the largest Oracle partners — a point Johnson and Clarke made repeatedly to TBR. Instead, the firm will institutionalize its alliance access, focus its industry model and bring measurable results to its midmarket  clients. Capacity, repeatability and field execution will help Grant Thornton punch well above its weight.

Lenovo’s AI-native Support Services Transformation: From Reactive Support to Intelligent Service Orchestration

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Enterprises are under pressure to do more with less. Infrastructure estates are becoming more complex, user expectations are rising and support organizations are being asked to deliver faster, more personalized and more consistent support across varying geographies, device types and service channels. TBR’s 2026 Infrastructure Strategy survey reflects this operational burden, with 66% of respondents agreeing or strongly agreeing that most of their team’s time is spent trying to keep up with day-to-day tasks. Additionally, according to respondents, shortages of skilled IT staff remain a major compounding constraint. The result is a widening gap between what customers expect from support services and what traditional operating models can consistently deliver.

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The Nontraditional Growth Drivers Fueling Telecom Infrastructure Services Market Growth Through 2028

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The telecom infrastructure services (TIS) market returned to growth in 2025 after two years of substantial declines. TBR expects the market to grow from 2026 to 2028 due to several factors, before contracting in the lead-up to the 6G spend cycle.
 
TBR research shows growth catalysts through 2028 will include:

  • Communication service providers (CSPs), private equity firms and governments providing funding for fiber access
  • Satellite connectivity becoming increasingly positioned to address rural and remote coverage gaps at a fraction of the long-term capex required for new terrestrial builds
  • Digital transformation initiatives and the implementation of complex technologies, such as multivendor open vRAN, AI RAN and 6G, proliferating
  • Hyperscaler investment across multiple network domains intensifying and driving TIS growth for certain customer segments

In the on-demand webinar below, Senior Analyst Michael Soper provides an in-depth, exclusive review of TBR’s Telecom Infrastructure Services Global Market Forecast 2025-2030, including insights into:

  • Key growth drivers and detractors expected in the TIS market through 2030
  • Why the TIS market is becoming more dependent on hyperscalers and fiber technology deployments
  • Which vendors are best positioned to capitalize on trends in the TIS market


 
This TBR Insights Live session is available on demand on our YouTube channel. Visit this link to download the presentation’s slide deck.
 
If you’d like to further explore the data mentioned in this TBR Insights Live session, sign up for a free trial of TBR Insight Center™ today.
 
TBR Insights Live sessions are held typically on Thursdays at 1 p.m. ET and include a 15-minute Q&A session following the main presentation. Previous sessions can be viewed anytime on TBR’s Webinar Portal.

Sovereignty Strengthens AI Revenue Opportunity for Telcos, but Obstacles Threaten to Slow Value Capture

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Sovereignty is strengthening the new revenue opportunity for communication service providers (CSPs) stemming from AI, but sizeable obstacles are slowing their ability to capture value. One of the most significant emerging challenges facing telcos is the cost of AI. AI investment decisions are increasingly evaluated like other major capital and operating expenditures, with heightened focus on measurable returns, governance and cost discipline.

In the on-demand webinar below, Principal Analyst Chris Antlitz gives an update on the telecom AI market, including:

  • Why telcos are placing greater scrutiny on the ROI and economic sustainability of AI
  • How and why the sovereignty trend is expected to drive new revenue for telcos from AI
  • What the telco AI revenue opportunity looks like from a regional and country-level perspective


 
This TBR Insights Live session is available on demand on our YouTube channel. Visit this link to download the presentation’s slide deck.
 
If you’d like to further explore the data mentioned in this TBR Insights Live session, sign up for a free trial of TBR Insight Center™ today.
 
TBR Insights Live sessions are held typically on Thursdays at 1 p.m. ET and include a 15-minute Q&A session following the main presentation. Previous sessions can be viewed anytime on TBR’s Webinar Portal.

Convergence Takes Center Stage as U.S. Telecom Operators Pursue the Next Phase of Growth

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The U.S. telecom market is entering a new phase in which convergence, broadband scale, and higher-value connectivity services such as AI infrastructure connectivity are becoming increasingly important competitive differentiators.
 
TBR’s latest U.S. Mobile and Broadband Operator Benchmark estimates U.S. operator connectivity revenue increased 0.7% year-to-year to $136.9 billion in 1Q26 as wireless service, equipment and broadband growth offset continued declines in legacy wireline and video services.
 
Revenue expansion slowed from 1.7% in 1Q25 as wireless service growth moderated and broadband competition intensified, increasing the importance of broadband scale, mobile-broadband cross-sell, and higher-value connectivity services as sources of incremental growth.
 

U.S. Operator Connectivity Total Market (Source: TBR Estimates)

M&A accelerates the convergence race

M&A is reshaping operator portfolios around converged connectivity and broader ownership of both wireless and broadband assets. Verizon’s Frontier acquisition and AT&T’s purchase of Lumen’s Mass Markets fiber business deepen their fiber portfolios, while T-Mobile’s fiber joint ventures involving Metronet and Lumos expand its reach beyond a primarily wireless and FWA model.
 
The pending Charter-Cox merger similarly increases cable scale across broadband and mobile. Collectively, these moves give operators greater control of both wireless and broadband infrastructure and position them to pursue integrated mobile-broadband strategies over the long term.
 

Operator Convergence Strategies (Source: TBR Data)

Fiber and FWA intensify the battle for broadband share

Broadband competition is increasingly shifting toward fiber and FWA as telcos expand coverage, and cable operators face continued subscriber pressure. FWA remains a key source of organic broadband growth, while fiber expansion and acquisitions are giving AT&T, Verizon and T-Mobile greater scale to compete for broadband households and support converged mobile-broadband strategies.
 
As fiber and FWA capture a larger share of broadband growth, Comcast and Charter are responding with aggressive mobile pricing to strengthen customer retention and defend their broadband subscriber bases.
 

Broadband Connection Mix of Benchmarked Companies (Source: TBR Estimates and Company Data)

Wireline growth pivots toward next-generation connectivity

Wireline broadband revenue continues to expand for AT&T and Verizon as fiber connection growth more than offsets decreases in legacy broadband services such as DSL. T-Mobile is positioning for wireline broadband revenue growth through M&A and fiber expansion, broadening its presence beyond fixed wireless. In contrast, broadband revenue is declining at Comcast and Charter amid subscriber losses and intense competition from fiber and FWA. Other wireline connectivity revenue continues to decrease, driven by declines in mature services such as fixed voice, video and legacy data connectivity.
 
Over time, newer B2B solutions and advanced connectivity services will become increasingly important growth drivers. TBR sees AI infrastructure connectivity as one of the largest long-term wireline opportunities, driving demand for fiber, optical transport, data center interconnection, cloud on-ramps and edge computing. Cybersecurity, SD-WAN/SASE, managed networking and private cellular networks provide additional growth vectors.
 

Wireline Broadband Revenue and Other Wireline Connectivity Revenue (Source: TBR Estimates and Company Data)

Conclusion

The next phase of U.S. telecom growth will increasingly depend on how effectively operators combine wireless and broadband assets to deepen customer relationships and capture new revenue opportunities. M&A and fiber expansion are accelerating this shift, giving AT&T, Verizon, and T-Mobile greater broadband scale while prompting Comcast and Charter to compete more aggressively through mobile.
 
As legacy wireline services decline, operators will also need to expand into higher-value connectivity segments, including AI infrastructure connectivity, advanced enterprise networking and managed services. Ultimately, broader connectivity portfolios will only create value if operators can translate them into higher mobile-broadband penetration, lower churn and sustainable revenue growth.

A Winning Strategy in IT Services and Consulting

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We have voluminous data at TBR, 30-plus years of data and analysis on the largest and leading companies across the technology stack, from McKinsey & Co. to Wipro, from Amazon Web Services to Verizon. Because we publish data and analysis quarterly, looking back one year and forward two to five years, we don’t often take the longitudinal view. Looking at our research since October 2021 (18 months after the start of the pandemic) to today, what can we say about the strategies, business models and investments that have separated leaders from laggards, particularly in the IT services and consulting space?
 
The short answer is talent and training; technology partnerships; and platforms, reusable IP and automation. If you’ve been reading our reports, you already knew that.
 
Longer answer: The companies that grew fastest generally combined one inorganic lever with several organic ones. Acquisitions or mergers produced large, immediate revenue spikes and confidence in a company’s growth story. More durable growth came from converting acquired capabilities into recurring managed services, cloud and application modernization, data and AI services, cybersecurity, industry solutions and broader client relationships. That’s a near-exhaustive list, and TBR’s most consistent advice has been that companies should do what they do well and only what they do well. But a consistent pattern emerges from that list: acquire capabilities, package those capabilities into differentiated offerings, cross-sell through partners and existing accounts, and deliver through recurring or managed services models. Easy, right?

Which overall strategies produced growth over the last few years, especially in IT services and consulting?

Acquisitions generated the most visible short-term revenue increases when companies added one or more of four assets: an existing client base that could be cross-sold existing offerings; specialized capabilities in cloud, data, AI, IT engineering or cybersecurity; that magical blend of credible global reach and authentically local presence; and recurring managed services, business process or subscription revenues. No company acquires like Accenture, but that simply means the 800,000-pound gorilla exemplifies the serial-acquisition model. Accenture’s investment capacity allows it to spend ahead of peers, add capabilities continually and enter adjacent markets. When looking at Accenture and its peers, TBR’s analysis distinguishes between lower-risk consulting acquisitions, which usually center on relationships and near-term revenue, and higher-risk AI platform and data acquisitions, which promise differentiation but have less-certain returns. By the start of 2026, industry-specific AI, data engineering and AI infrastructure had become central acquisition priorities across IT services companies and consultancies. Notably, many people lump mergers and acquisitions together, despite the significant difference. In our analysis since late 2021, mergers produced some of the highest reported growth rates, although the first-year results were often more inorganic than operational. For example, NTT DATA’s addition and integration of NTT Ltd. materially boosted 2023 revenue and expanded NTT DATA’s ability to sell integrated networking, data center, infrastructure and IT services. By 2025, closer alignment with parent NTT was creating additional cross-selling opportunities, stronger financial backing and a more unified enterprise proposition.
 
New offerings provided strong organic growth, with the most successful new offerings addressing work that clients could not defer indefinitely. Long-duration application, infrastructure and business process contracts produced more predictable revenue than discretionary consulting. During the last few years, the Big Four significantly expanded managed services and transaction services to protect client relationships and compete beyond advisory work. Application management, hybrid cloud, SAP modernization and platform engineering also generated significant revenues even when IT directors complained about tight digital transformation budgets (see TBR’s Digital Transformation: Voice of the Customer Research for details). Of course, new AI capabilities generated new revenues, especially when IT services companies and consultancies connected AI to data modernization, operating efficiency and industry workflows, not just isolated proofs of concept. For example, HCLTech’s AI Factory, Physical AI and data intelligence offerings contributed to its strongest growth since 2023. In addition, cybersecurity — in which companies perennially underinvest — as well as sovereign cloud and localized delivery, shifted from optional growth areas to required capabilities, particularly in Europe and among regulated industries.
 
In addition, strategic investments didn’t generate revenue spikes but remained essential for sustained growth. Training employees in hyperscaler platforms, generative AI, data engineering and industry skills helped IT services companies and consultancies execute increasingly complex, AI-enabled programs. At the same time and, for the leading companies tightly correlated, structured and strategic relationships with the hyperscalers, software vendors and semiconductor companies expanded market access while reducing the cost of building capabilities internally. Over the last few years, Accenture, Capgemini, HCLTech, IBM Consulting and the Big Four increasingly used partnerships to create multiparty offerings. And AI-enabled delivery, agent frameworks and reusable assets improved productivity and allowed IT services companies and consultancies to package services more consistently. Notably, according to TBR’s analysis, AI adoption by IT services companies and consultancies continues to contribute more to productivity and scope expansion than to broad-based revenue acceleration.

The next winning strategy

Since October 2021, traditional global systems integrators have generally produced steady single-digit growth. Accenture, Cognizant, HCLTech, Infosys and Tata Consultancy Services have stayed in the top 10 for year-to-year revenue growth in TBR’s IT Services Vendor Benchmark as their large-deal pipelines, installed client bases, partner ecosystems and managed services exposure have provided greater revenue stability.
 
In the same period, IT services companies and consultancies have derived the fastest growth from changing the revenue mix, not simply adding more people: recurring services instead of projects, industry solutions instead of generic capabilities, platforms and AI-enabled delivery instead of labor-only delivery, and acquisitions that could be cross-sold rather than merely consolidated. That has been the winning strategy for the last few years.
 
Will it remain a winning strategy for 2027 and beyond? AI might upend significant parts of the technology stack and force disruption of every company’s business model, but playing to strengths, doing only what you do well, and staying flexible in your commercial model will likely remain core attributes of leading IT services companies and consultancies.

AI-wrapped IT Modernization Opportunities Will Serve as Both Marketing and Revenue-generating Engines as Infosys Builds Verticalized Use Cases Tied to Outcomes to Expand AI Revenue Share

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Infosys is using AI to sell modernization — not replacing modernization with AI

With front-office transformation remaining among the top AI use cases, Infosys is leaning on Infosys Aster to drive opportunities beyond campaign execution and into a broader platform for redesigning knowledge-intensive marketing and content workflows, evidenced by the deal with Handelsblatt Media Group and the launch of CMO AI hub in collaboration with ANA’s Global CMO Growth Council. These two use cases could allow Infosys to move from implementing marketing technology to advising CMOs on where AI should be deployed, but given the bespoke collaboration, the company will need to think more strategically to turn outcomes into repeatable IP and scale sales beyond one-off engagements.
 
We estimate Infosys’ Digital Marketing Services revenue was $1.8 billion in 2025 and will reach $3.1 billion by 2030, with the company largely leaning on its core application services capabilities to drive development and management of web, mobile and commerce offerings. Using Infosys Aster as the entry point to AI-led marketing transformation could help Infosys shift the revenue mix toward managed-services-led consulting work.
 
Infosys’ opportunity is not simply to sell more AI tools but also to use AI as the reason clients modernize applications, clean data, rethink workflows and consolidate vendors. That positioning matters because buyers remain practical: They want productivity, measurable outcomes and lower operating complexity, not another layer of technology hype.
 
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 a services provider that, 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 expectations for impatient shareholders will be key, especially as peers pursue similar strategies.

Capturing foundational revenue opportunities will remain essential as Infosys looks to balance driving AI-first and platform-enabled sales to build a beachhead for long-term opportunities across lines of business

As Infosys enhances and expands its AI-ready portfolio, the company understands the value of long-term IT modernization contracts, especially as AI services revenue is only 8.2% of its total revenue in 2Q26. Deal wins in 2Q26 with Truist Financial for Global Capability Center (GCC) setup and management, Sterling Bank for Finacle implementation, IHH Healthcare for AI-enabled ERP transformation, DNB Bank ASA for financial crime operations modernization, and Global Foundas for AI-enabled IT modernization and managed services, among other engagements, demonstrate Infosys’ commitment to securing its foundational revenues, especially after the company faced headwinds in renewing the entirety of its marquee deal with Mercedes-Benz in 1Q26.
 

Infosys Service Line Revenue Mix (Source: TBR)


 

Infosys Operating Margin and Human Intensity Reduction Index (Source: TBR)


 
Although we do not expect Infosys to slow its pursuit of such deals anytime soon, the company needs to balance AI labeling with real AI usage to avoid AI washing. Generating $1 billion of AI services revenue is a milestone worth recognizing, but pushing the narrative too much could increase the cannibalization of traditional revenue. While TBR will continue to monitor and estimate Infosys’ AI performance, we believe that the company will become less vocal about itemizing AI sales in the next two to three years.

The Paradox of AI Infrastructure: Hyperscalers’ Ambition Collides With the Power Grid and What This Means for Telcos

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The AI infrastructure supercycle is entering a new phase. The central question is no longer whether hyperscalers have the capital or appetite to build AI infrastructure but whether the power grid can support what they intend to build.
 
TBR’s latest Hyperscaler Capex Market Forecast 2025-2030 estimates that Meta, Alphabet, Microsoft, Amazon and Apple (MAMAA) will collectively spend approximately $743 billion in capex in 2026, up 70% year-to-year. Much of that investment is flowing into AI-related infrastructure, including data centers, servers, storage and networking.
 
But capital alone cannot build the AI economy. Increasingly, the constraint is energy.

Power is becoming the limiting factor

U.S. data center developers have requested 1,066 GW of electricity, equivalent to roughly 83% of existing U.S. utility-scale generation capacity. Yet an August 2026 Bloomberg study titled “Most Power Sought for US Data Centers Will Never Materialize” indicates utilities and grid operators can ultimately commit to only about 28% of that amount.
 
Some of that demand is undoubtedly inflated by duplicate or speculative applications. Even so, the underlying problem is clear: Generation capacity as well as grid infrastructure and interconnection timelines cannot expand at the pace AI investment currently requires.
 
Hyperscalers are already responding. TBR noted in the same forecast that Alphabet, Amazon, Microsoft and Meta are increasingly looking beyond conventional utility procurement and pursuing natural gas, renewables, energy storage, nuclear and geothermal resources.
 
That shift has significant competitive implications. Access to power is evolving from an operating requirement into a strategic differentiator. The companies that can secure reliable energy — and place compute where that energy is available — will be better positioned to translate AI capex into usable capacity.

For telecom operators, the collision between AI and energy creates both opportunity and strategic risk

The first wave of generative AI infrastructure has been dominated by centralized training clusters, helping to delay the long-anticipated shift toward more distributed edge cloud infrastructure. That dynamic could begin to change as AI moves increasingly from training toward inferencing.
 
Inference workloads that require lower latency, faster response times, or proximity to users and devices will create greater incentives to distribute compute closer to endpoints. At the same time, power constraints could push AI infrastructure into a wider range of locations based on not only proximity to demand but also access to available electricity. That combination could make the AI infrastructure footprint substantially more distributed.
 
For telecom operators, this creates an important opening. Hyperscalers will need greater network capacity to connect AI infrastructure spread across data centers, edge locations and new power-rich geographies. Telecom networks therefore become increasingly important not simply as pipes connecting compute but also as infrastructure that enables hyperscalers to place compute wherever power, economics and workload requirements are most favorable.
 
A more distributed inference architecture could also alleviate some pressure on large, centralized power hubs by spreading portions of AI compute across a broader infrastructure footprint. It will not solve the underlying energy shortage, but it could change how — and where — that constraint is managed.
 
TBR Graph: Hyperscaler Capex Market Forecast and Growth

Conclusion

The next infrastructure bottleneck for AI may not hinge solely on computational power but could be significantly influenced by energy availability.
 
As the demand for new AI capabilities rises, power constraints will likely reshape the geography of data centers and accelerate the distribution of inference workloads. Moreover, the strategic importance of telecom networks will increase as they connect an increasingly fragmented AI infrastructure landscape.
 
Consequently, the upcoming phase of the AI infrastructure supercycle could be characterized as much by access to megawatts and connectivity as it is by the availability of GPUs.

AMD Enters the Execution Phase of its Full-stack AI Strategy

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Advancing AI 2026 showed that AMD has assembled the architecture and road map required to compete at rack scale. Helios connects the company’s GPUs, CPUs, networking and software into a system-level platform, while expanding partnerships with Microsoft, OpenAI, Anthropic and Meta provide credible paths to large deployments. ROCm.ai gives AMD a new way to reduce the engineering effort required to bring up and optimize applications on Instinct GPUs. Agentic workloads also expand the AI-driven opportunity for EPYC beyond its role as a host CPU for accelerators, allowing AMD to address the agent sandboxes, applications, databases and data services surrounding GPU inference.

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Hidden Costs of AI Market Intelligence

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From AI assistants to enterprise intelligence infrastructure

For most organizations, the economics of enterprise AI begin with a familiar metric: the software subscription. Whether evaluating ChatGPT Enterprise, Microsoft 365 Copilot or another generative AI platform, procurement teams often focus on a straightforward calculation of monthly cost per employee.
 
That view captures only a fraction of the enterprise AI equation.
 
The larger expense emerges after deployment, as thousands of employees begin incorporating AI into their daily work. The ease of generating research, analysis, presentations and strategic content encourages broader adoption, more frequent interaction and increasingly sophisticated requests. Every prompt initiates inference. Every inference consumes compute. Every generated response requires some degree of human validation before it influences an important business decision.
 
As the cost of generating intelligence declines, the demand for intelligence expands.
 
This is a modern expression of Jevons Paradox. Throughout history, improvements in efficiency have rarely reduced overall resource consumption. Instead, they have lowered the barriers to use, encouraging dramatically higher levels of consumption. Steam power increased coal demand. Cloud computing reduced the cost of infrastructure while driving unprecedented growth in compute utilization.
 
Enterprise AI is following a similar trajectory, replacing physical infrastructure with machine cognition as the resource being consumed.
 
The implications become apparent when AI is deployed across an entire enterprise.
 
Sales organizations generate competitive battle cards. Product marketing develops positioning documents. Corporate strategy evaluates competitors and ecosystem partners. Customer success prepares executive briefings. Regional teams produce account-specific analyses. Communications teams draft white papers and case studies.
 
Viewed independently, each request appears inexpensive. But viewed collectively across thousands of employees, these activities often represent repeated attempts to answer substantially the same strategic questions. Similar prompts generate similar analyses, consuming new inference capacity each time while producing variations in language, emphasis and interpretation. The organization pays repeatedly to recreate knowledge that, in many cases, already exists somewhere within the enterprise.
 
The resulting cost extends beyond tokens or compute consumption. Enterprises also see duplicated validation efforts, fragmented institutional knowledge, inconsistent market messaging, and strategic drift across customer-facing materials. Two sales teams may describe the same competitor differently. Marketing may publish messaging that differs from corporate strategy. Product managers may rely on analyses that have already been superseded by more recent market developments.
 
The issue is not that AI generates poor content. The issue is that, without a common intelligence foundation, decentralized generation produces decentralized understanding.
 
As enterprises mature their AI strategies, many are beginning to rethink where AI should generate knowledge and where it should retrieve knowledge.
 
This distinction is becoming increasingly important. AI models excel at synthesizing information, adapting content to specific audiences, and producing incremental analysis. They are less effective as the sole source of authoritative market intelligence, particularly when that intelligence must remain consistent across thousands of employees and hundreds of customer interactions.
 
An emerging architectural pattern addresses this challenge by introducing a trusted intelligence layer between enterprise users and the language model. Rather than asking AI to reconstruct market intelligence from public information each time a question is asked, organizations provide AI with access to continuously maintained repositories of validated research, competitive analysis, benchmark data, and proprietary industry intelligence. The model retrieves trusted information first and then applies its reasoning capabilities to contextualize, summarize and personalize the response for the user’s specific objective.
 
The result is a shift from repeated generation to governed reuse.
 
This architecture changes the economics of enterprise AI in several important ways. Reusable intelligence reduces unnecessary inference, lowers duplicate validation efforts, accelerates response times, and creates a persistent body of institutional knowledge that improves with every update rather than disappearing at the end of each conversation. Equally important, it establishes a consistent strategic narrative across customer-facing deliverables.
 
For organizations competing in dynamic technology markets, narrative consistency has tangible business value. Battle cards, white papers, executive presentations, case studies, sales proposals, analyst briefings, and account strategies should reinforce a common view of competitors, ecosystem partners, market trends, and corporate differentiation. When every employee independently generates these materials through public AI models, subtle variations inevitably emerge. Over time, those variations dilute strategic messaging and increase governance risk.
 
Trusted third-party intelligence providers can play an important role in this architecture.
 
Organizations such as Technology Business Research, Inc. (TBR) continuously develop proprietary competitive intelligence, quantitative benchmarks, ecosystem analysis and market research that extend beyond the knowledge available in publicly trained language models. When this intelligence is licensed through APIs or securely incorporated into enterprise retrieval architectures, AI no longer begins each interaction from a blank slate. Instead, it reasons from an authoritative and continuously updated foundation.
 
And the value extends beyond only reducing inference costs. Trusted external intelligence provides an objective source of market truth that complements internal expertise while enabling consistent positioning across the enterprise. Marketing, product management, sales, strategy and executive leadership can draw from the same validated knowledge base while allowing AI to tailor outputs for different audiences without altering the underlying strategic narrative.
 
This represents a meaningful evolution in enterprise AI. The competitive advantage no longer comes solely from selecting the most capable language model. It comes from building an intelligence architecture that combines AI reasoning with governed, reusable and continuously refreshed knowledge.
 
Organizations that embrace this approach are likely to realize two complementary benefits: a reduction in the hidden costs associated with repeatedly generating the same intelligence and strengthened consistency and credibility of every market-facing interaction.
 
The enterprises that succeed will not be those that provide unrestricted AI access to every employee. The winners will build trusted intelligence architectures enabled with proprietary, non-public data and analysis that prevent organizations from paying thousands of times for the same expensive thought.
 
The future of enterprise AI is not simply broader access. It is governed intelligence aligned to enterprise strategy, with CFO approved efficiency. And increasingly, that efficiency may prove more valuable than the AI models themselves.