Telecom Infrastructure Services Market Expected to Become More Dependent on Hyperscalers and Fiber Technology Deployments

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Post-peak 5G investment by telcos in the largest markets is mitigated by broadband- and AI-related investments as well as rising spend on CSP digital transformation initiatives

TBR expects the telecom infrastructure services (TIS) market to grow from 2026 to 2028 due to several factors, before contracting in the lead-up to the 6G spend cycle.

Growth catalysts through 2028 include:

  • Communication service providers (CSPs), private equity firms and governments will provide funding for fiber access. Although this will spur growth, it will not be as intense as previously assumed, as fixed wireless access (FWA) is reducing the need for ubiquitous fiber to the premises (FTTP) deployment, while the Broadband Equity Access and Deployment Program’s (BEAD) shift toward technology-neutral funding materially changes broadband deployment assumptions by expanding the role of non-fiber access technologies. Satellite connectivity is also 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 and AI RAN, will proliferate, driving growth in the professional and managed services markets.
  • Hyperscaler investment across multiple network domains will intensify through the forecast period, both to support cloud and AI as well as for general connectivity, driving TIS spend growth for this customer segment.

Key trends for the telecom infrastructure services market

Influx of gear in the field delays impending decline in maintenance services spend

Maintenance services was the best-performing TIS segment in 2025, and the market will continue to grow through 2028 due to contracts to support gear in the field as CSPs maintain LTE networks, hyperscalers deploying more network infrastructure, and high-speed broadband networks proliferating.
 
By 2029 the maintenance services market will be challenged by consolidation among CSPs, AI technologies that drive more network automation, and commoditized hardware. Maintenance spend is unlikely to see a resurgence during the 6G era as this technology will be more software-centric than previous generations of cellular technology.
 

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

Join Senior Analyst Michael Soper Thursday, Aug. 6 for insights into key growth drivers and detractors expected in the telecom infrastructure services market through 2030 and the reason behind the TIS market’s increased dependency on hyperscalers and fiber technology deployments

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Hyperscalers, neoscalers, private equity firms and governments fund fiber-related TIS growth

Fiber access projects are driving a substantial portion of TIS market growth during the forecast period, supporting planning, design, network infrastructure integration, deployment and maintenance services. CSPs are deploying fiber not only for their consumer and enterprise connectivity businesses but also for hyperscaler AI workloads.
 
Hyperscalers will continue to increase fiber investment for use cases, including direct connections to cloud customers and within their data centers. Fiber-related TIS growth will be most apparent in the U.S., which is the largest country of spend for hyperscalers and neoscalers. The U.S. is seeing increased participation in the market from private equity firms and will be able to leverage government funds for closing the digital divide.

Level 4 autonomous networks has become more of a buzzword than an impactful trend to the TIS market

Enabling high autonomy of CSP networks will drive growing interest from CSPs in vendor managed and maintenance services as customers find increased value in use cases such as AI-based solutions for network maintenance, which will improve the efficiency of network operations. Vendors are bringing AI to managed services and optimization offerings in pursuit of Level 4 autonomous networking and have publicized commercial deployments of this technology, but deployments are occurring in niche areas of CSP networks and not at scale. Greater use of AI-driven networks will ultimately help reduce maintenance spend in the later years of the forecast period.

TBR’s Telecom Infrastructure Services Global Market Forecast

TBR’s Telecom Infrastructure Services Global Market Forecast tracks spend by CSPs, which includes telecom operators, cable operators and select hyperscalers, on infrastructure services. TBR categorizes infrastructure services into four distinct buckets: deployment services, maintenance services, professional services and managed services. This research includes current-year market sizing plus a five-year forecast across services segments and regions as well as examines growth drivers, top trends and leading market players. Vendor market share is also included.
 
Download a free preview of TBR’s latest telecom infrastructure services research: Subscribe to Insights Flight today!

HPE’s AI Infrastructure Strategy Takes Shape as Juniper Moves to the Center

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HPE’s Discover message was broad, but the structure was clear. The company is repositioning around AI infrastructure architecture, with networking serving as the foundation, GreenLake as the hybrid operating layer, Private Cloud AI as the governed agentic AI platform and partners as the scale mechanism to bring the combined HPE and Juniper portfolio to market. Discover announcements showed that HPE possesses the pieces needed to build an AI infrastructure and is beginning to connect them. The next question is whether HPE can turn that architecture into easier buying motions, faster deployments and measurable production outcomes for customers.

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Winners and Losers in a Fragmenting AI Infrastructure Market

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AI infrastructure growth is surging, but vendors must navigate fragmented demand and shifting ecosystems to find success

As AI infrastructure demand accelerates, vendors are being forced into increasingly difficult strategic trade-offs. The market is no longer moving in a single direction; it is fragmenting across customer segments, silicon strategies and deployment models.
 
Hyperscalers are driving growth on the ODM side of the market while investing in the development of AI services and custom AI ASICs to gain control over cost and performance. Merchant accelerator vendors, like NVIDIA and AMD, are doubling down on investments to influence the AI ecosystem beyond the infrastructure layer.
 
At the same time, OEMs such as Dell Technologies, Hewlett Packard Enterprise and Supermicro are diverging in how they pursue growth in the rapidly evolving AI infrastructure market, splitting between higher-margin enterprise opportunities and high-volume neocloud deals, where ecosystem relationships have become increasingly integral. Success now depends on aligning with the right market segment at the right time, while balancing long-term market share and profitability.

In the on-demand webinar below, Principal Analyst Angela Lambert and Senior Analyst Ben Carbonneau give an in-depth look at what TBR’s data and analysis show for market expectations into 2030, including:

  • How the AI infrastructure market will evolve over the next five years, with insights by customer group
  • Why the hyperscalers are NVIDIA’s biggest threat, despite being the company’s largest customers
  • How OEM strategies are diverging, including which strategies TBR sees as most beneficial, and the importance of ecosystem relationships

 

 
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.

Reinvention Services Marks the Beginning of Accenture 2.0

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Accenture employs a ‘disrupt yourself rather than being disrupted’ strategy as it gears up to transform its business model and capitalize on AI

In late April, Accenture hosted over 30 industry analysts and clients and included a large number of senior leaders, such as CEO Julie Sweet and Manish Sharma, Accenture chief strategy and services officer, for its first analyst event in seven years in Bengaluru, India. In the spirit of disruption over the last seven years — a pandemic, several geopolitical conflicts and the AI boom — Accenture executives shared how the IT services behemoth has embarked on its own disruption enabled by the recently launched Reinvention Services growth model. Clients’ stories reinforced the notion of disruption. Trust, transparency and simplification best describe Accenture’s reorganization of its services as the company looks to secure its foundational revenue base while pursuing new growth opportunities in areas such as products, among others. The balanced approach at scale has made Accenture successful over the past three decades — since the boom of outsourcing.
 
Executing against Reinvention Services’ priorities will test Accenture’s proven engagement and delivery capabilities, which have been further disrupted by all things AI. But as Accenture’s leadership discussed at length, enterprise AI is less about technology deployment and more about an operating model transformation. Turning this challenge into an opportunity for its own business model will shape the pace and scale of Reinvention Services’ success. In 2019 we wrote “Disrupting, but Not Disrupted: Accenture Pivoted to Become a Solutions Broker Through Innovation,” but this time the disruption has caught up to Accenture, and the humility executives demonstrated throughout the event — also emphasized during Sweet’s discussion of the new growth model — is what we believe will help Accenture navigate the current market environment and prepare the company for its next chapter.

Aligning Reinvention Partners to buyers’ personas will help Accenture deliver on outcomes better and faster at scale

Throughout the three-day event, leaders from Accenture’s seven Reinvention Partners (RPs) — Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent — presented why and how each of their areas aligns with the company’s go-to-market strategy and, more importantly, with clients’ pain points that are largely disrupted by AI. Although the company’s Market Units still oversee the P&L, the announced changes within Accenture’s go-to-market strategy under the Reinvention Services model will help the company demonstrate agility at scale, especially as AI expands the number of enterprise changes required to make the technology useful. Although AI may reduce the cost of some work, the technology increases the ambition and complexity of clients’ transformation agendas. Here lies Accenture’s biggest opportunity.

‘We have more tech fluency than most of their tech people’

While each RP serves as an important link in Accenture’s efforts to expand its addressable market, we believe Talent, in particular, carries a certain weight as it can help Accenture close buyers’ AI adoption gap by bringing organization and change capabilities to the forefront of transformation discussions and, more importantly, sustain these efforts beyond the initial introduction and into post-deployment and management services. In the Talent RP, Karalee Close, global lead for Talent & Organization, stated, “Change has to change … [it] can no longer be, ‘Here is a thing, and we are rolling it out.’” Accenture’s customer-zero use case — while well documented across ongoing TBR research — can play a crucial role here as the company has a massive opportunity to demonstrate its tech fluency at scale as talent is a large cost for most clients. Supporting the Talent RP, Accenture’s platform and services bet on enterprise reskilling for the AI era, enabled though LearnVantage, can act as the scalable engine for workforce transformation. The LearnVantage model is a combination of proprietary learning platforms, partner content, expert instructors and reusable modular assets, helping Accenture support clients, partners, academia and governments. For example, Accenture, through LearnVantage, is the exclusive partner for SAP’s in-person training.
 
Song, Accenture’s ever-evolving business, will remain critical to the company’s growth story, especially as most AI adoption use cases over the last two years have happened in the front office around transforming customer experience and sales automation. Accenture’s GrowthOS, which was recently enhanced through the company’s investments to use WEVO’s synthetic persona capabilities, will allow Accenture to offer more targeted solutions at speed as CMOs and growth leaders increasingly look for a holistic view of the customer rather than disconnected functions across brand, digital, sales, commerce and service. Importantly, this will be how Accenture approaches contract structures, especially as the engagement timeline shortens and clients look for promised outcomes. Focusing on using agentic AI and connected data to redesign the full customer life cycle will elevate Song’s profile, especially as many of Accenture’s peers lack the breadth and depth the RP carries.
 
Accenture’s cybersecurity story is strong and continues to evolve around the notion that the technology is a major AI-enabled growth area centered on speed, cost and productivity gains as the primary value drivers. Accenture’s consistent emphasis on industry-specific knowledge remains the cornerstone of the company’s cybersecurity capabilities as clients look for real use cases and industry context, rather than generic AI and cyber capabilities. Accenture’s Cyber.AI platform, backed by a network of ecosystem partners, will help Accenture test and deploy cyber response services at scale and introduce new commercial models that can help deliver AI-augmented managed services. According to TBR’s December 2025 Digital Transformation: Voice of the Customer Research, “Enterprise spending continues to emphasize technologies that address risk, automation and AI-driven productivity, with buying patterns reflecting both strategic priorities and market-driven pressure. Cybersecurity remains the top purchase category, driven by escalating regulatory and operational risk.”
 
Cyber and cloud have gone hand in hand in the buyer purchasing cycle, and we believe Accenture can extend that opportunity to position AI and analytics, along with the attached cyber services, as a more compelling value proposition, especially as all parties face a new reality in which robots, or generative AI (GenAI), are protecting themselves from other robots (cyberattacks). Balancing the development of AI security models with enabling workforce productivity through AI will help Accenture build strong use cases for navigating the complexities that have arisen from the growing need for AI security. Accenture can then bring these experiences into client discussions, as clients often face similar struggles as shadow AI becomes mainstream. Relying heavily on niche cyber partners will be key, especially as the cybersecurity segment ecosystem remains largely fragmented, and will help services vendors demonstrate depth. At the same time, building relationships with large AI-first vendors will support Accenture’s efforts to execute at scale and pressure-test services vendors’ pyramid evolution in a segment where trust remains the linchpin of success.

‘To make brownfields agentic takes a fundamental shift … getting clients to adopt agents is a step-change’

Accenture positioned Digital Core as the practical foundation for AI-led reinvention, rather than as a traditional IT modernization story, with the core message being that AI value is trapped inside legacy systems and processes, fragmented data, and brittle architecture, so clients cannot scale AI unless they modernize the underlying core first. Accenture’s framing around three priorities — foundational AI enablement; modernizing data, applications and infrastructure; and future-proofing through digital resilience —  allows the company to move up, down and sideways across clients’ tech stack, emphasizing process first, then people, then workbench (i.e., tools). With the strongest opportunity for Accenture existing within brownfield environments, especially among Global 2000 clients, Digital Core can become the bridge between the promise of AI and the reality of enterprise architectures (Digital Core Reinvention Partner Ajoy Menon’s quote above demonstrates that Accenture appreciates the challenges and opportunity in brownfield IT environments). Successful execution can shape Accenture’s and clients’ economic models, as the savings from modernization can fund growth initiatives with the desired end state of driving more nonlinear revenue growth. With Digital Core housing the largest talent pool of the seven RPs, the balance between traditional and new ways of doing business with clients will pressure-test Accenture’s business model, especially as the company is trying to shift the value story from cost takeout to reinvestment capacity.
 
We believe Digital Core’s message can resonate especially well with clients that are struggling with ERP modernization, data fragmentation, application complexity, resiliency requirements, cloud cost pressures and AI pilots that have not scaled. The biggest execution challenge for Accenture is to avoid using messaging that sounds like a rebranded infrastructure modernization pitch. We believe the Digital Core narrative works best when it is tied directly to measurable business outcomes, including the ability to run agentic AI safely at scale. Keeping it pragmatic — where Accenture leaders start with the process and architecture, accept partial autonomy, control the economics and use the modernization savings to fund reinvention — will help Accenture maintain its incumbent position.
 
Technology alliance partners remain critical to the success of Accenture’s RPs, and throughout each presentation company executives made sure to elevate the value of these relationships. As Accenture looks to grow its alliance-enabled revenue mix, we expect the next wave of opportunities will come from developing a multiparty Business Groups, which will test its orchestrating capabilities. In the meantime, Accenture’s growing relationships with AI-native companies such as Palantir will also pressure-test the alignment around portfolio, commercial and delivery models. Accenture’s Palantir relationship is aligned in three areas: Palantir’s ontology and AI operating layer, Accenture’s process and industry transformation capability, and Accenture’s ability to industrialize delivery. The opportunity for this relationship is significant, especially in regulated industries, sovereign AI, SAP modernization, workforce optimization, defense and public sector, and complex data-rich enterprises. Accenture must prove repeatable economics, avoid over-relying on expensive platform layers, and scale true Palantir engineering talent beyond a small core of experts to sustain trust as Palantir continues to build similar relationships with other services companies. (See TBR’s Ecosystem Intelligence research stream for additional details and analysis on Accenture’s relationships with alliance partners.)

Reinvention Engines: The backbone of Accenture’s Reinvention Partners

Serving as the enabling layer for Accenture’s evolving operating model, the Reinvention Engines (REs) — AI and Data, Industry and Process, and Technology — provide the connective tissue between functional expertise and technology capabilities, supporting the company’s shift from project-based delivery to outcome-led, AI-enabled enterprise reinvention. The goal is not to bolt AI onto existing processes but to redesign the work, workforce and workplace around AI-native capabilities. As Accenture shifts its value proposition from services to measurable outcomes, the REs are positioned around continuous value creation rather than isolated delivery or one-time implementations. Central to this shift is the Reinvention.AI platform, powered by the Intelligent Digital Brain, which codifies Accenture’s institutional knowledge, delivery patterns, industry experience and reusable assets so teams can apply AI more effectively across sales, solutioning and delivery, ultimately accelerating time to market.
 
Over time, we expect Reinvention.AI to become increasingly aligned with partner technology stacks, similar to how Accenture has connected prior platforms such as myConcerto. Accenture’s talent is another core pillar, with its resource model built around senior expertise, judgment and trust at the top; broad upskilling at scale in the middle; and AI-native talent by default at the entry level. The REs also serve as Accenture’s customer-zero proof point for Reinvention Services, supported by Reinvention Deployment Engineers (RDEs), in pod-based teams that combine architects, change experts, data specialists and AI-native full-stack engineers with partner ecosystem capabilities, industry depth, rapid delivery and reusable assets. Accenture’s biggest challenge will likely be evolving its commercial model for a token-based AI world while maintaining clear linkage to client outcomes and value realization.
 
Lan Guan, Accenture’s chief AI & Data officer and lead for AI and Data Reinvention Engine, discussed at length Accenture’s vision and strategy positioning the AI business around industrialized AI operations that include advisory, platform build, evaluation, tuning, managed services and ongoing optimization. In her presentation, Guan repeatedly argued that Accenture’s advantage is its ability to codify industry know-how into reusable assets, architectures, skills, ontologies and reasoning systems. For example, Accenture helped a life sciences client capture its domain experts’ knowledge from structured files such as Excel. This information was converted into machine-readable artifacts such as markdown-style skills, combined with deterministic adapters and data connectors, and used to power a pharmaceutical reasoning engine.
 
Although the industry context does give Accenture an advantage, we believe the true value will come more from delivering measurable business outcomes and less from cost-optimization, IT-centric service-level agreements. As Accenture looks to move its positioning from AI implementation partner to enterprise intelligence architect, the company’s Intelligent Digital Brain — an industry-specific architecture intended to give enterprises a reusable intelligence layer — will serve as the tech backbone helping Accenture to execute on its vision as the solution connects data foundations, knowledge engineering, models, agents, ontologies and business workflows into a more durable enterprise AI system. Ecosystem orchestration and delivery at scale will remain critical as Accenture expands its network of partners to include frontier AI labs, research institutions and academia within its ongoing relationship with hyperscalers and hardware providers.

Client use cases elevate the theoretical to the practical

Use cases presented throughout the event provided additional depth around the vision and execution of Accenture Reinvention Services, with clients bringing candor, transparency and expectations and raising the bar for Accenture as it looks to take on the risk to deliver outcomes through service quality and innovative commercial models.
 
For example, a telco executive discussed at length how they did not want pay for people and could not internally develop metrics around outcomes. The client “assessed that Accenture was the best [among global systems integrators] at AI” and created a joint venture (JV) with Accenture, with both companies fully invested in the outcomes over the next seven years. The transformation was repeatedly described as a whole-business reinvention, not a technology project. The telco executive emphasized that technology is “less than 50%” of the work, as stakeholders, processes, customers, reporting and operations are equally — or more — important. That same client has moved from experimentation to scaled AI with roughly 380 AI use cases, organizing them into eight overarching transformation priorities tied to company objectives related to oversight and measuring outcomes.
 
The same executive praised Accenture’s AI Refinery tools, control plane, talent and capabilities. His view was that Accenture’s key strengths are its capabilities; its tools that make reinvention faster, thinner and more disruptive; and its ability to remain objective and help clients avoid lock-in to hyperscalers, large language model (LLM) providers or software vendors. Overall, the conversation served as a strong proof point for Accenture’s AI reinvention narrative where AI at scale requires aligned commercial models, board-level commitment, data foundations, governance, architecture discipline, cost control, ecosystem orchestration and deep workforce change, not just tools or pilots.
 
In another presentation, a global Resources client positioned the relationship as a strong proof point for AI-led reinvention without traditional outsourcing. The use case was a corporate-function transformation in which Accenture is not managing the operations but instead is leading an agentic transformation with Microsoft and SAP in the ecosystem, with the commercial model structured around outcomes rather than people-based delivery. The client did not want a long discovery or workshop-heavy engagement, and the expectation was that Accenture already had enough data, pattern recognition and domain experience to offer some solutions. The client chose Accenture not only because of its thought leadership but also its execution capacity.
 
Additionally, the use case amplified Accenture’s role as an ecosystem orchestrator, as the engagement involved Accenture sitting with Microsoft and SAP to help architect the transformation. Overall, the conversation served a different purpose compared to the one with the telco client. The telco story was about establishing a strategic JV and AI at scale operating model, while the global Resources client use case focused on agentic, outcome-based, outsourcing-free corporate function reinvention, supporting Accenture’s argument throughout the event that AI can reshape commercial models, client delivery, ecosystem orchestration and enterprise operations when tied to measurable outcomes.
 
Last, for a global transportation client, the on-stage discussion was used as a proof point about Accenture’s ability to take a messy, high-scale operational problem; apply AI and process redesign; rebuild client trust — probably the hardest thing in any relationship — and expand from HR transformation into a broader, outcome-based enterprise relationship.

Products: A new (or semi-new) strategic bet Accenture views as the next growth frontier, beyond a pure financial boost

Expanding addressable market opportunities — usually through acquisitions (Accenture had a dedicated breakout session about its acquisition strategy) — has allowed Accenture to stay abreast of innovation and often turn itself into a market setter (think the launch and expansion of Accenture Interactive, now Song, in the last 10-plus years). This time will be no different. Accenture leadership outlined priority growth areas for the company, with Products piquing the most interest among analysts in both formal and informal discussions throughout the event. That is not surprising, as this bet represents a fundamental change in Accenture’s engagement and delivery models.
 
Expanding the Products portfolio — defended by three moats: data, domain and distribution — will provide a fresh boost of revenue and support the success of Reinvention Services. Growing the share of product sales will be a strategic pivot toward non-FTE, IP-led, subscription-style revenue. Although Products represents a rather enticing opportunity for Accenture, accounting for the dynamics of running a software organization including sales channels, the development life cycle and the all-important positioning against ecosystem partners offerings will be critical.
 
We believe the recent purchases of Faculty and Ookla will provide greater insights into Accenture’s products endeavors as the company looks to grow the share of nonlinear revenue-based sales. Faculty provides Accenture with AI-native talent, decision intelligence IP, AI safety credibility and product-led revenue opportunity. An important next step will be for Accenture to show that this can translate into viable, repeatable examples of AI changing the economics of core business processes, beyond compelling demos. Ookla arms Accenture with the IP that can help the products part of the business act as a data intelligence terminal for communications and telco clients.
 
TBR remains cautiously optimistic about Accenture’s pursuits in Products. We believe Accenture has an opportunity to use the bet to drive enough business that can boost short-term profitability and grow relationships with new personas — a strategy that historically has paid off for the company.
 
The rest of Accenture’s growth strategic bets include Capital Projects, Data Centers, LearnVantage, Cybersecurity, Agentic Commerce, New Ecosystem Partners, AI and Data Services. TBR’s ongoing coverage of Accenture provides deeper analysis on these areas.

Reinvention Services’ success goes through meticulous execution of Accenture’s AI strategy

As Accenture begins to execute on Reinvention Services’ agenda, the company’s AI strategy will be among the key pillars shaping the pace and scale of success. Based on the company’s track record, we are positive Reinvention Services will be a successful endeavor. Accenture’s AI strategy is becoming more coherent and more revealing, as the company still has to prove that the push into higher-growth, higher-margin assets and non-FTE revenue streams is more than a polished narrative wrapped around a familiar playbook. The company’s executives are saying all the right things: more outcome-based work, more proprietary platforms, more ecosystem leverage, more non-FTE revenue and, eventually, more software- and data-like economics. Accenture’s executives are also arguing that faster AI-enabled delivery will create more downstream work rather than compress the addressable market. That is possible, but it remains the classic incumbent-consulting answer to every automation wave. Yes, the old work gets faster, but somehow the pool of adjacent work gets even bigger. It remains to be seen whether Accenture is cannibalizing parts of its own labor-based model faster than it can replace them with scalable IP-led revenue.
 
We expect Accenture to keep winning business in the near term as enterprises still need a translator between frontier models, legacy estates and operating-model change, and Accenture remains one of the few firms with the ability to play that role at scale. But over the next 12 to 24 months, the burden of proof will increase as stakeholders demand clearer evidence that AI is producing differentiated commercial models, not just better human-based utilization. Further, a key indicator of Reinvention Services’ success will be Accenture’s ability to grow its operating margin faster than it has in the past. Accenture’s operating margin expanded from 13.1% in FY03 to 14.7% in FY25, reflecting the company’s consistent strategy rooted in service delivery. Accenture has an opportunity to increase the metric from the midteens to the low-20% range, provided it continues to rotate its workforce, prioritizing the hiring of AI-ready salespeople and reducing support staff where needed. Ramping up hiring of freshers will also help it maintain a steady financial profile as the company counts on graduates from the class of 2026 and subsequent years, who have had exposure to GenAI for most of their time in college, making it cheaper for Accenture to calibrate their AI training during the onboarding process.

HCLTech’s AI Strategy Signals the Future of Application Development & Modernization Services

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Strategy shifts in applications development accelerate and augment client outcomes

How will application services evolve in the era of AI? And how will clients maximize return on investment as they undergo application and IT modernization as well as digital transformation to facilitate AI adoption and push innovation? On May 6, TBR attended HCLTech’s inaugural Global Leadership Briefing for its Modern Application business, and HCLTech addressed the intersection of these two questions. HCLTech detailed clients’ current and future application needs and how its refreshed strategy enhances application capabilities and service quality.
 
HCLTech’s Modern Application business head, Padmaja Enjeti, explained the company’s view that “AI is not just accelerating application development, it’s basically redefining what applications are and how they’re engineered, and in what context.” AI is now embedded across workflows, architecture and design, rather than being simply an add-on. HCLTech defined how AI fundamentally has changed application development practice across its four pillars: AI-driven modernization, AI-native application development, intelligent quality engineering, and AI-driven integration. AI Force, HCLTech’s generative AI (GenAI) and agentic AI platform, is the company’s “execution backbone” at the core of its innovation efforts.
 
During the briefing the company detailed how AI Force is adapting each of these pillars. In this report, TBR will focus on discussions of specific AI Force solutions related to the AI-driven modernization, application development, and intelligent quality engineering pillars, as they are concrete examples of how the company is reshaping client outcomes. The cornerstone of the discussion about AI-driven modernization was AI Force.ATLAS, an agentic framework for modernization at scale. Vineet Gogia, HCLTech’s Modern Application practice director, provided an in-depth look at how the solution can reverse-engineer legacy code. AI Force.ATLAS generates a knowledge graph for dependency mapping, providing traceability and validation throughout the coding modernization process. The solution also has an interactive analyst-agent interface to answer client questions.
 
During the applications development discussion, global AI-native Application Development practice director, Venkatraman Natarajan, demonstrated AI Force.Agent Squad, an agent-powered framework for the autonomous software development life cycle. The solution offers an agent dashboard and control panel, supporting cohesive development workflows including feature development, bug fixing, re-architecture, PR review and security vulnerability remediation.
 
Charu Sharma, Integration practice director, demonstrated AI Force.QMetrix during the intelligent quality engineering portion of the briefing. AI Force.QMetrix is an interconnected quality engineering maturity assessment tool for an applications portfolio, including autonomous workflows and agents.
 
Together, these solutions enhance HCLTech’s value proposition and align with client demands for more trustworthy AI and less manual inputs, in TBR’s view. HCLTech places governance practices, such as human-in-the-loop and related transparent processes, as a key component across its AI Force solutions. As the company shifts toward agent-driven development, where applications are becoming more adaptive through AI Force, HCLTech becomes more agile.

How will HCLTech mitigate clients’ rising concerns about ROI?

Agile or not, IT services clients are demanding ROI across AI-related deals. According to TBR’s IT Services Market Forecast 2025-2030, clients are “prioritizing ROI, cost efficiency and accountability, increasingly favoring fixed-price and outcome-oriented engagements. Clients are also looking for measurable results and shorter payback periods and have lower tolerance for time-and-materials billing. This pushes vendors to absorb productivity gains internally instead of translating them into revenue through increased staffing levels. India-centric providers face the most immediate disruption due to their exposure to labor-intensive services.” Shortly after TBR published this report, HCLTech CEO C. Vijayakumar stated on the company’s 1Q26 earnings that HCLTech is experiencing a “deflation” around traditional IT services. In TBR’s view, HCLTech’s proactive approach positions the company well to engage with clients on newer areas, provided it can address their growing concerns around AI.
 
During the session, Enjeti touched on how the Application Development practice is evolving to accommodate clients’ rapidly changing demands. HCLTech is “focusing on building these new pricing models … along with our customers [and] delivery squads from an agentic squad construction perspective … where AI agents are amplifying human teams so we are able to commit to productivity at scale without linear headcount growth.” At the same time, the company is standardizing its factory model to drive repeatable outcomes. HCLTech is successfully leveraging AI Force solutions to deliver outcomes; however, these can vary dramatically. For example, AI Force.Agent Squad enabled a client to develop an application with 40% fewer defects and shorten the time to market by about 50%. With AI Force i-Catalogue, an AI-powered digital asset access and management solution part of the integration pillar, HCLTech helped a mining company modernize and standardize integration by implementing an Integration Competency Center. The solution improved automation efficiency by 25%, reduced onboarding time, and increased asset reuse by approximately 30%.
 
Meaningful outcomes from adopting advanced AI solutions can mean many things, such as reduced costs, enhanced service quality and decreased time to market. Amid rising ROI concerns, clients may have to decide which outcome(s) they are looking for. Sustaining advanced AI engagement momentum may depend on how well HCLTech can effectively communicate the importance of choosing a desired outcome and the right metric to measure its success. HCLTech needs to reassure clients that they can experience enterprisewide productivity improvements related to these AI engagements, but it will take time for the benefits to fully materialize.

Where is HCLTech’s road map from here?

Investments in platform-enabled solutions through AI Force, aligning with HCLTech’s engineering and software strengths, provide a strong near-term outlook, especially paired with the company’s industry-specific approach, which is becoming essential in the AI era. HCLTech is reorganizing its talent structure to be specialized, including dedicated teams — AI builders, AI super users and AI decision makers — which enhance task-specific expertise. Through the introduction of full-stack engineers, forward-deployed engineers and AI orchestrators, HCLTech is addressing new pricing needs.
 
Reengineering applications accelerates time to value and augments service quality; however, advanced AI capabilities evolve quickly. Over the next five years, HCLTech needs to protect margins, perhaps through fostering more value through IT consulting. The company also needs to protect itself from competition from other India-centric vendors, and TBR believes this will require persistent innovation and, most importantly, execution.

Why Informatica Matters More Than Ever to Salesforce

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Informatica becomes Salesforce’s trust engine

Before Salesforce acquired Informatica, the synergies were obvious. Informatica preps and governs data, while Salesforce, an applications system-of-record responsible for critical operational data, helps put the data in context. This is a compelling proposition in a market where agentic AI innovation far outpaces what customers can actually deploy due to generic agents and lack of trust.
 
Informatica rounds out three core pillars of the Data Cloud portfolio: Data 360 for connecting to cloud data lakes, MuleSoft as the integration layer and Tableau for analytics. Historically, there have been overlapping capabilities between Informatica and MuleSoft, but we believe MuleSoft takes on more of the heavy extract-transform-load work, leaving room for Informatica to focus on upstream governance, including data quality and cataloging. As the next wave of AI centers on trust, context and outcomes, this is where Informatica, and therefore Salesforce, want to be.
 
To be clear, some challenges remain. Despite the talk about “headless” and an impending future where everything is connected via Model Context Protocol (MCP), Informatica World 2026 did surprisingly little to address who is actually governing those MCP servers. Seeing headless workflows in action was certainly compelling, particularly when Informatica data lineage rules were applied to a prompt in Slackbot. It was a great way to showcase how to use Informatica to understand what is behind an AI output, but it also raised the question of who governs this new headless process. These challenges could present an early opportunity for Informatica, Salesforce and the ecosystem partners willing to address data implications ahead of AI.

IDMC goes headless

Interacting with customers and hearing about their growing use of CLAIRE agents for tasks such as data discovery was notable and aligns with TBR’s research indicating that data management is one of the leading use cases for generative AI (GenAI) in IT. At the event, Informatica announced new CLAIRE agents for Integration, Data Enrichment and Data Stewardship. Most will agree, though, that the launch of Intelligent Data Management Cloud (IDMC) as headless was the most notable announcement of the 2026 event.
 
As a reminder, Salesforce Headless 360 delivers the platform outside the core interface using various app infrastructure (e.g., MCPs, APIs). Other vendors are doing this as well, but Salesforce is far ahead in packaging and marketing. With the user interface (UI) as the metaphorical “head,” it is a great way for a SaaS vendor like Salesforce to disassociate with the UI and transition into a platform where agents can create workflows and drive business change. With Agentforce, Salesforce has been on this journey for quite some time, but now that the company has the ability to put trust in the data through Informatica, Salesforce is perhaps finally in a position where Agentforce can scale.
 
Aligning with Salesforce’s Headless 360 vision, Informatica is delivering IDMC as headless. This means customers can use Informatica’s data management features directly within the AI development platform of their choice, be it Slack, Claude, or even at the data infrastructure layer with nearly all Salesforce’s Data 360 partners.
 
For customers increasingly bogged down by platform sprawl, this is a notable development. Customers no longer need to go through the IDMC interface to use Informatica (though they still can, and we got a firsthand look at a completely revamped IDMC UI). Instead, they can use features from IDMC services, such as master data management (MDM) and cloud data governance catalog, alongside the tools they use in their everyday work.
 
This will boost Informatica’s exposure among not only developers — a previously under-tapped audience — but also everyday business users. For instance, as a hypothetical example, we were shown how, to increase trust in responses, a business professional can prompt Slackbot to show where data came from and provide the data quality score via Informatica if the professional is not confident in the original answer Slackbot gives to a question such as, “What were our sales in Q3?”
 
As previously mentioned, this type of headless workflow still raises some additional AI governance questions. But it is clear how Informatica fills a big trust gap in AI workflows that Salesforce was previously lacking. If the relationship is executed properly and alongside the right partners, Informatica and Salesforce have a big opportunity to actually change the conversation with AI decision makers, who have perhaps previously not treated data management as a first-class problem.

Headless data management for the ecosystem

It made sense that many of the headless IDMC demos were done via Slack, and for those who use Slack as one of their everyday work tools, it was probably the most relatable tool. But for those relying on MCP connections, IDMC is not constrained and can go as far down as the runtime via Salesforce Data 360.
 
Put simply, anyone who stores the data is in the best position to provide the AI with context. This included not only SaaS vendors responsible for operational data, like Salesforce, but also infrastructure vendors that actually store nonoperational, external data (e.g., hyperscalers, Snowflake, Databricks). To unlock data and bring it to their platforms, both vendor groups have come to rely on each other. It explains the heavy influx of data sharing — or “zero-copy” integration — activity we have seen since the dawn of ChatGPT, and Salesforce is no exception.
 
At the event, Informatica made it clear that IDMC headless will be available to this infrastructure ecosystem. This means customers can configure MCPs in Databricks or Snowflake and start cataloging that data without leaving that platform’s interface. Informatica has already worked with Snowflake and Databricks, so the partnership is not particularly new, but IDMC headless lets Informatica’s features integrate more natively into these platforms, bringing Informatica closer to these critical platforms where big data and AI workloads increasingly run.
 
What to watch for: Salesforce Data 360 may connect to the runtime, but Salesforce is not a runtime and still exists as an application. TBR’s customer conversations reveal that moving governance too far from the compute typically creates performance challenges, so Salesforce may need to monitor how Snowflake, Databricks or even the hyperscalers move further into upper-stack governance.

For GSIs that want to sell trust in AI, data governance is a must

The Salesforce-Informatica proposition is becoming centered on trusting data, and thus AI. This aligns with the global systems integrators (GSIs), which, above all else, sell on trust with their clients.
 
With Informatica, Salesforce-heavy SIs now have more opportunities at the data layer. MDM modernization aside (Informatica still has a large legacy component), SIs will be well positioned to govern a new context layer that emerges from Headless 360 and an overall changing SaaS landscape. But to be successful, SIs simply need to ensure they consider the data implications before AI.
 
Though constrained in its relationship with Salesforce, EY is a great example of a company that recognizes not every challenge can be solved with AI, and it often considers data implications first. If the context layer influences AI inference quality as we think it will, then GSIs will need to factor in data governance ahead of AI deployment.

Conclusion

The value Informatica’s portfolio offers Salesforce is clear, but Informatica World 2026 reinforced just how much Salesforce needs Informatica to scale Agentforce and reposition itself in the market.
 
Through the end of 2026, nearly every vendor will emphasize context as a key theme and differentiator. But long-term competitiveness will sit with not only those that own or store the enterprise data but also those that can put a trust wrapper around it.
 
With Informatica’s governance capabilities filling a key gap in the Salesforce data portfolio, the focus for Salesforce now becomes successfully selling Informatica as part of the broader platform strategy. It will be an interesting test case for whether every SaaS company could become a platform company.

In the Agentic Era, Teradata’s Hybrid Strategy Will Drive Its Success

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In 2026 operational scale becomes Teradata’s advantage

Today, just about everybody wants to be a data company. But the market rewards a few simple truths: If you store the data, control the compute runtime and can put AI in context, you are in a highly defensible position. This includes hyperscalers and data platforms like Teradata, Snowflake and Databricks, which are expanding their governance capabilities and growing their influence, opportunity and ability to disrupt.
 
But an inflection point is developing within this cohort of powerful data vendors. Over the last decade, Snowflake and Databricks, and in some cases hyperscaler platforms, were built to empower the modern data team through speed and experience. Put simply, these vendors won mindshare by making data engineering faster, easier and cloud-first.
 
AI changes the conversation. The question becomes less about “How fast can we build?” and more about “How can we operationalize what we build at scale?”
 
For a company like Teradata, which has decades of experience managing complex, operational workloads in a hybrid fashion, this shift matters. The opportunities for Snowflake and Databricks to paint vendors associated with on-premises compute as “legacy” are fading. Perhaps that’s because demand for on-premises AI deployments is growing, with many customers expanding data center spend, at least according to TBR research. As enterprises look to operationalize AI across distributed environments, scale becomes less about where the infrastructure sits and more about how effectively the underlying data can be governed, accessed and turned into value. Teradata’s ability to deliver AI to where customers’ data already lives — without the need to modernize their infrastructure — will become more relevant.

A new data category for the agentic era: autonomous knowledge

Teradata’s notion of contextualizing AI using the enterprise data it already manages — particularly as agentic systems become more common — has led to a new category: autonomous knowledge. Combining Teradata AI components like agents, workspaces and tools (the autonomy) with the data stored in Teradata (the knowledge) creates a clear path for Teradata to enable agent-based systems that can deliver insight and act.
 
With the Autonomous Knowledge Platform, announced May 7, Teradata is productizing this vision. At its core, the Autonomous Knowledge Platform consists primarily of existing capabilities — including tools to build, deploy and manage AI — repackaged in a single platform available via both Teradata Cloud and Teradata Factory (on premises). Those following the data landscape know that this strategy of unifying the AI and data layers under a single control plane has been one of the biggest overarching trends since ChatGPT. So, although the Autonomous Knowledge Platform is not net-new, it still marks an important shift for Teradata in not just protecting the install base but also driving growth from it.
 
We can draw parallels between autonomous knowledge and what Databricks did two years ago in pioneering the data intelligence concept and its subsequent rebrand as the Data Intelligence Platform. Although positioned differently, both strategies ultimately aim to solve the same challenge: get AI systems to understand the structure and context of enterprise data.
 
However, what stands out is how Teradata is positioning itself for the agentic era. If the knowledge in Teradata is strong enough, agents should not only be built faster but also be more capable of delivering the previously mentioned insights and actions. In today’s market, that’s a fair argument: The bottleneck to agentic AI is likely not building the agents, and the pace of innovation coming from various communities will ensure this process becomes increasingly seamless. The real challenge is making systems deliver these actions consistently within a large organization.

After action comes outcome, and SIs should take note

If autonomy plus knowledge equals action and insight, then the next phase becomes turning action into business outcomes. This has been a major focus for SIs in recent months, and the consulting business model will become even more influenced by outcome-driven engagements that deliver specialized services.
 
Recently, SIs have established more formal alliances with cloud-native platforms like Snowflake and Databricks as part of their broader data and AI practices. Although the nature of these relationships may be changing, they are still highly centered on migration and modernization. For both parties it’s a win-win relationship: Modernizing data warehouses may offer some more immediate integration and database administrator (DBA)-type opportunities with long-term AI potential for the SIs, while SIs give Snowflake and Databricks the enterprise C-Suite access they very much need.
 
In some ways, Teradata is already there and may emerge as an increasingly opportunistic technology partner. If hybrid environments become more common, as we expect them to, Teradata’s platform may unlock new opportunities for SIs to test outcome-based delivery.

IT Services Market Outlook: Forecasting Growth, AI Impact and Competitive Shifts Through 2030

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Now is a particularly fraught time for IT services, with talent-centric, human-delivered business models under pressure from AI and the labor arbitrage value proposition challenged to evolve faster than AI adoption. TBR experts are constantly fielding questions around which strategies IT services companies are using to retain clients, strengthen technology alliances and reinvent their businesses.
 
Forecasting the IT services market requires understanding the vastly different business models of the largest vendors and accounting for both macroeconomic and technology-driven shifts in IT services buyers’ behaviors. And market challenges seen now will only loom larger in the future.
 
Building on a 30-year data set of two-year company-centric forecasts, TBR’s Professional Services team has built out marketwide views for IT services and consulting through 2030. The IT services and consulting & systems integration forecasts include company-specific predictions as well as best-case, worst-case and wild card scenarios, all built from a foundational understanding of what is and will be shaping the market.

In the on-demand webinar below, Principal Analyst and Practice Manager Patrick Heffernan, Principal Analyst Bozhidar Hristov, Senior Analyst Kelly Lesiczka and Analyst Jill Cookinham share insights into:

  • How, why and to what extent the IT services market will grow between 2026 and 2030
  • Which IT services companies are best positioned to grow fastest and/or strengthen their market share among the leading 30 companies
  • What factors could accelerate IT services revenue growth, and what disruptions could bend the growth curve into negative territory

Additionally, the team shares an exclusive look at TBR’s newest forecast research, the IT Services Market Forecast and the Consulting & Systems Integration Market Forecast.
 

 
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.

Dell Technologies World 2026 Highlights Dell’s Growing Leadership in Enterprise AI Infrastructure

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Dell Technologies World 2026 reinforced the success of the company’s long-term AI strategy. While Dell Technologies (Dell) has spent the last three years aggressively ramping production to meet intense demand for infrastructure to support model training, the company has also been preparing for the coming inference-heavy phase of AI, which will create a significant opportunity with its enterprise customers. Dell is staying true to its roots as a hardware company by reinforcing that the brand of hardware that organizations select to support their most critical initiatives matters more now than ever.

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Extreme Connect 2026 Showcases Coherent AI-centric Vision, but Long-term Differentiation Will Depend on Sustained Execution

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TBR perspective

Extreme Platform ONE and, more specifically, Agent ONE represent one of the more cohesive AI visions in the networking space to date, and Agent ONE’s emphasis on human-in-the-loop aims to address key customer fears about trust while leveraging AI in more value-added ways. Extreme Networks has strong full-stack offerings and emphasizes the power of partnership, which is paramount for a smaller player in a large market. This is particularly true in network security, where Extreme Networks looks to partners to fill gaps that larger providers can fill by leveraging their broader portfolios.
 
Though limited in its security portfolio, Extreme offers universal Zero Trust Network Access as part of Extreme Platform ONE; its in-house security offerings focus on network access control and segmentation, and it looks to partners for capabilities such as Next-generation Firewall. However, Extreme Networks competes with stalwarts in the networking industry, such as Cisco and Hewlett Packard Enterprise (HPE) (which completed its acquisition of Juniper Networks in July).
 
Although the competitors’ entrenched solutions make it harder for Extreme Networks to capture share, the company can overcome this challenge with a differentiated vision. Offerings such as Third-Party Management Engine (TPME) give Extreme Networks an advantage, even with its smaller size in a market with few large competitors more palatable. Extreme Networks has strong footholds in key markets including state and local government and education (SLED), stadiums, and large venues. Five consecutive quarters of double-digit growth and achievements, like its Electronic Product Environmental Assessment Tool (EPEAT) certification, reflect its strong execution, but larger networking vendors are pursuing similar goals, particularly around AI and sustainability, meaning current differentiators are likely to stand out less over time.

Key announcements

  • At Extreme Connect 2026, a series of AI-centric enhancements to Extreme Platform ONE were unveiled, including:
    • Agent ONE Coworker (general availability in summer 2026)
    • Agent ONE Operator (general availability by the end of 2026)
    • Extreme Exchange (time frame TBD)
  • Wi-Fi 7 announcements position Extreme Networks’ new AP4020, AP4060 and AP5020 access points as AI-driven infrastructure built for high-density enterprise and public environments.
  • In addition to a cohesive vertical AI stack built on Platform ONE, which is a key differentiator in the market, Extreme Networks also sets itself apart through its tangible commitment to sustainability by receiving its EPEAT certification.

Extreme Platform ONE gains AI features and remains purpose-built to keep humans in the loop

Extreme Connect 2026 announcements centered on new AI features, which are becoming table stakes. The intentionality of the AI capabilities and the vision that underpins both Agent ONE Coworker and Agent ONE Operator differentiated Extreme Networks’ story from many other AI networking announcements. Although these solutions are not yet generally available, Extreme Networks has announced them ahead of its industry peers.
 
Underpinning much of what Extreme Networks presented at Extreme Connect 2026 was Extreme Fabric, which is designed to deliver the high-performance, low-latency connectivity needed to support modern AI and distributed enterprise workloads. This fabric connects networking, AI operations and infrastructure management into a more unified operational model. Through partnerships, Extreme Networks is also extending the fabric’s role in scalable AI environments, where efficient resource utilization and real-time performance are necessary.

Agent ONE Coworker

Extreme introduced Agent ONE Coworker, which will become available within Extreme Platform ONE this summer. Agent ONE Coworker is designed to collaborate with users as a human coworker would, right down to a nudge feature that informs and reminds users about tasks worth investigating. This feature reinforces Extreme Networks’ approach to AI as a companion rather than a replacement for human work. The design of Agent ONE Coworker is more advanced than that of industry-standard chatbots, and its integration with Platform ONE positions Extreme Networks to maximize the platform’s value to customers. However, Agent ONE Coworker’s use is limited to Platform ONE customers, giving it a narrow audience. Driving customers toward Extreme Platform ONE with these added features is likely part of Extreme Networks’ goal, as the company’s vision centers on Platform ONE.

Agent ONE Operator

Agent ONE Operator is the second mode of Agent ONE and is scheduled to become available by the end of 2026. During the event, Extreme Networks’ executives placed substantial emphasis on the notion of the AI harness, a layer in the training of Agent ONE Operator designed to keep humans in the loop while also providing a degree of autonomy. Operator can only perform the tasks the user allows, and it remains restricted to preset constraints. This design is intended to foster trust by the human user to permit an additional amount of defined autonomy for the offering. Agent ONE Operator is designed to provide the user with a recap of what occurred after the user logged out. The purpose of these built-in safeguards and reporting measures is to ensure there are no surprises with the solution, which will foster trust in the technology.
 
Within Agent ONE, Extreme Exchange will enable users to either custom build or adopt prebuilt skills to train Agent ONE Operator to make the experience tailored to the end-user environment, similar to how a teammate would be onboarded. With Extreme Exchange, IT teams will be able to add new AI-driven capabilities through a no-code environment as well as learn from peers, partners, and Extreme Networks’ best practices and ideas around how to enable Agent ONE Operator to best serve its end users.

The power of partners to round out a vision is emphasized with LIQID

Reinforcing the AI vision, Extreme Networks’ leaders highlighted the company’s partnership with LIQID, which aims to address how to run large-scale AI workloads on premises. The combination of Extreme Networks’ fabric networking with LIQID’s composable GPU, memory and storage platform creates a solution for enterprises to scale AI inference workloads more predictably. As a smaller vendor in a consolidating market with multiple behemoth competitors, strategic partnerships will play a key role in providing Extreme Networks with a competitive edge.

Extreme Networks unveiled Wi-Fi 7 portfolio additions targeted at customers in high-density environments

Extreme Networks announced additions to its Wi-Fi 7 portfolio with three new access points designed for different enterprise environments. The AP4020 and AP4060 access points support flexible indoor and outdoor deployments, and the AP5020 targets dense environments where performance and reliability are critical. Operational capabilities like dual IoT radios, dedicated security sensors, PoE (power over Ethernet) failover, always-on encryption and AI-driven management through Platform ONE are key highlighted features with these new access points, which are targeted at stadiums, hospitals, universities and large public venues — markets in which Extreme Networks already has a strong presence. Extreme Networks also rolled out wired solutions, including ruggedized options, to complement its Wi-Fi 7 announcements.

Extreme Networks’ sustainability efforts were understated but impressive

Though considerably understated at the event, Extreme Networks’ sustainability efforts can be viewed as a differentiator in the networking space. Setting and publishing progress toward sustainability goals are the industry standard in the modern era, but the volume and variety of certifications Extreme Networks holds are noteworthy. Specifically, its EPEAT certification, which it earned March 19, makes it one of few vendors in enterprise networking space with this achievement. The certification creates a valued differentiator, particularly in Europe, that is increasingly becoming table stakes for long-term success.

Conclusion

Extreme Connect 2026 demonstrated that Extreme Networks is evolving toward a more AI-centric operational platform strategy centered on Platform ONE and its underlying fabric architecture. The company presented a comparatively cohesive vision while also showing tangible execution momentum through continued revenue growth and accelerated feature delivery. However, it is worth noting that the general availability of Agent ONE Operator is more than six months away and has yet to be announced for Extreme Exchange. This is a long time to wait for actionable customer proof points, especially in the AI market. Although these new solutions were shown as demos and not presented as slides — suggesting the capabilities exist —  the time-to-market gap is notable.
 
Additionally, fear about the use of AI is likely to remain a key inhibitor to adoption, as end users are afraid that AI adoption means increased security risks and a lack of visibility. This stands as a significant barrier to progress , regardless of the quality of AI solutions coming to market, and requires a level of mindset shift that cannot be achieved piecemeal. Extreme Networks’ emphasis on human-in-the-loop governance and controlled autonomy may help address enterprise concerns around trust and AI adoption, but the long-term success of the strategy will depend less on vision and more on the company’s ability to operationalize these capabilities in production environments faster and more effectively than larger competitors that have broader market reach and larger captive market shares.