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.

Closing the Gap: The Power of Agentic AI in the Persistent Value Pyramid

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Three years into the AI era, the market has advanced quickly, but the value pyramid remains bottom-heavy

Although the generative AI (GenAI) cycle began three years ago, the market is still translating that progress into broad-based economic value. Model performance has improved, infrastructure investment has accelerated, enterprise experimentation has broadened, and buyers are recognizing value from early deployments. This progress reinforces TBR’s early view that AI would reshape enterprise work over time through workforce augmentation, task automation and role redesign. Although the long-term promise of AI has not dimmed, realizing the technology’s potential will take longer than was initially expected. The issue is whether enough value can move into the software, services and workflow layers where enterprise productivity is realized. That is the central question about the next phase of the AI market: When — and how meaningfully — will the AI value pyramid begin to invert?
 

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AI infrastructure dominates the broader opportunity, with AI services representing only a small portion of the market today

The AI story remains trapped in a phase dominated by infrastructure investment, but early success in coding platforms offers a glimpse of how AI services revenue will scale over time. GPU and memory chip makers are capturing the largest revenue pools at the base of the pyramid, hyperscalers and neoclouds are monetizing compute delivery in the middle, and AI services providers are capturing a comparatively smaller share at the top. This imbalance is a sign that the cycle remains in its early stage. The first wave of monetization has accrued to the vendors supplying the capacity required to train, host and scale AI. The next wave will need to come from production usage, recurring inference consumption and AI services that deliver clear enterprise outcomes.
 
Over the long term, the AI value pyramid will need to invert. The market cannot remain structurally dependent on infrastructure build-out if AI is to become a durable enterprise productivity platform. Value will need to move closer to where work is executed, governed and measured. That is the future hyperscalers are investing in as large cloud commitments continue to flow from model developers such as Anthropic and OpenAI. The current distribution is rational for an early technology supercycle, but it cannot be the steady-state structure of the market indefinitely.

The AI value pyramid will need to invert over the long term

To put rough numbers to the AI value pyramid, across the three buckets of semiconductors, hyperscale delivery and AI services, semiconductor vendors are still capturing the lion’s share of the market. As noted in TBR’s 4Q25 AI Infrastructure Market Landscape, AI server and systems revenue reached an estimated $235 billion in 2025, inclusive of NVIDIA, AMD and hyperscalers. By comparison, TBR estimates 2025 AI cloud delivery and platforms revenue was under $50 billion, inclusive of Amazon Web Services (AWS), Microsoft, Google Cloud and ServiceNow, while AI services revenue was around $25 billion, inclusive of OpenAI, Anthropic, Microsoft, AWS, Google Cloud and ServiceNow.
 
Put another way, the foundational layer of the AI market saw nearly 10x the revenue opportunity of AI services providers in 2025. This gap is the clearest evidence that the AI market remains early in its value shift. Infrastructure monetized first because the market needed capacity before broad production usage could scale. Over the long term, however, this structure will need to change. If AI is to realize its full enterprise potential, the revenue generated via inference consumption on AI services, workflow management and agentic execution needs to increase.
 

2026 AI Revenue Pyramid (Source: TBR)


 

AI end-user perception remains positive, supporting future adoption

Upfront investment ahead of long-term opportunity is normal in a technology supercycle, but the AI services market needs to expand for the broader AI opportunity to be sustainable. Hyperscalers are investing for this future, and TBR’s customer research continues to point to an AI world where customer appetite is growing as early adopters recognize more value than expected from AI-related projects.
 
In TBR’s upcoming 2H25 AI Applications Customer Research, 55% of respondents are increasing their IT budgets to support AI adoption, up from 48% in 2H24, while 58% of respondents stated AI tools exceeded their expectations for value creation. With buyers seeing value and adjusting budgets accordingly, TBR continues to view end-user demand as robust. The demand side of the pyramid is not the main constraint, but it remains to be seen how quickly vendors can convert that appetite into production usage, recurring consumption and AI services revenue.

Agentic systems and usage-based revenue will accelerate revenue growth, helping close the value gap

The AI services market will not scale simply because the models improve. Instead, the market will scale when AI systems become embedded deeply enough in enterprise workflows to generate sustained inference demand. Agentic systems are the clearest path to that outcome. Unlike first-generation copilots, which primarily assist with discrete tasks, agents are designed to execute multistep work across applications, data sources and governance layers. That matters economically because every step in an agentic workflow creates additional model calls, tool calls and consumption events.
 
This shift gives AI services vendors a more credible path to monetization. Fixed-seat copilots can demonstrate productivity, but they disconnect usage from revenue and can pressure margins when consumption rises. Agentic systems practically require usage-based pricing because the work performed is more measurable and the consumption profile is more directly tied to business activity. In this model, AI revenue growth comes not from selling more experimental seats but from running more enterprise work through AI-enabled execution layers.
 
Infrastructure capacity is being built ahead of demand, but that investment will only be justified if inference workloads expand across real production environments. The vendors best positioned to capture that opportunity will be those that control where AI work is governed and managed. Model quality remains important, but workflow control is becoming the more durable source of value.

Code-generation tooling is becoming the first scaled, enterprise agentic AI market

This thesis is already playing out in software development, which is the first scaled example because it combines high labor cost, measurable productivity gains, structured workflows and clear willingness to pay. The adoption of AI tools has significantly increased software developers’ productivity, accelerating code generation and reshaping expectations around the cost of producing software. Executives from several notable software incumbents, including SAP and Salesforce, have boasted about their ability to limit hiring for new developers, with Salesforce leaders stating the company did not hire any in 2025. Software developer job postings on Indeed remain nearly 70% below their highs in 2022, reinforcing the view that the market for developer talent has shrunk significantly over the past three years, while revenues from leading AI code-generation tools, including Claude Code and OpenAI Codex, grow in the triple digits year-to-year.
 
The market for AI-powered development platforms is also moving toward agentic systems very quickly, trusting tools to leverage codebases, generate changes, test outputs, open pull requests and operate across parts of the development life cycle. As such, code generation is the first scaled enterprise proof point for agentic AI. It is a market where the productivity impact is measurable, user adoption is already material and monetization is scaling very rapidly. It also shows why execution control matters. GitHub has an advantage because it owns the repository, pull request, CI/CD (continuous integration and continuous delivery) and governance workflow. Cursor is gaining traction as an agent workspace. Anthropic and OpenAI are competing through model-native coding agents in Claude Code and Codex. The market is a contest not only between models but also across the workflow control layer, the agent workspace and the model-native execution layer.

Conclusion

The AI market is not failing to meet its promise, but it is still early in translating that promise into broad-based economic value. Three years into the GenAI cycle, the largest pools of revenue remain concentrated in the infrastructure layers required to train, deploy and scale frontier models. That concentration is rational given the scale of compute demand, but it is not a sustainable end state if AI is to become a true enterprise productivity platform.
 
The next phase of market development will depend on whether the AI value pyramid can begin to invert. Infrastructure investment has created the capacity, but inference demand will need to scale through production usage, workflow integration and consumption-based pricing. Agentic systems are the most credible path to that outcome because they increase AI usage, extend AI into multistep workflows and create a stronger foundation for usage-based monetization. Improvements in code generation show that the market is beginning to change, but broader enterprise adoption will require the same combination of measurable productivity, workflow integration, governance and pricing alignment.
 
The AI value pyramid should begin to shift over time, but not simply toward model developers. Durable value will accrue to the vendors that control execution. That includes model providers building agent platforms, hyperscalers hosting and metering workloads, SaaS vendors embedding agents into business processes, developer platforms controlling software workflows, and services firms operationalizing AI across complex enterprise environments. For model providers specifically, this means competitive positioning can no longer be measured by benchmark performance alone. Revenue mix, infrastructure access, enterprise adoption, partner alignment, agentic capabilities and workflow integration are becoming more important indicators of vendors’ positioning long-term.

Will C&SI Revenue Growth Accelerate Significantly Due to AI Adoption at Scale?

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In 2025 the total consulting and systems integration (C&SI) market reached $556 billion, growing 4.9% year-to-year, with the top five vendors contributing roughly a quarter of total market revenue. The C&SI market among the 18 vendors tracked in TBR’s Consulting & Systems Integration Market Forecast reached $247 billion, up 2.9% over the same period. The main growth drivers were technology adoption and optimization-related business transformation.
 
Although clients tightened budgets in 2025 and thus far in 2026, they engaged in smaller projects around technology adoption and implementation. In 2030 the total C&SI market is estimated to reach $739 billion, reflecting growth of 4.4% year-to-year ($302 billion for the tracked 18 vendors, up 4.3%), slower than 2025, as the nature of consulting projects will change. Underpinning consulting with technology solutions and tools will create new projects but reduce the value of contracts, resulting in slower market growth. At the same time, investment in talent will be needed to ensure vendors can execute on consulting-led projects related to advanced technology solutions.
 

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Consulting and systems integration market best-case scenario

Accelerated adoption of AI at scale by the companies included in this market forecast, combined with increasing demand from clients to map out, implement and support AI-enabled solutions, could cause C&SI revenue to grow much faster than the single-digit rate projected in TBR’s baseline forecast.
 
Nearly all IT services companies and consultancies have adopted a customer-zero approach to emerging technologies. With enough experience and scale, they should move into the latter half of the 2020s with compelling use cases for clients and opportunities to benefit from their internal AI adoption.
 
Maturing AI use cases and next-generation AI-enabled solutions, such as agentic AI, will likely be less horizontal in nature and much more industry-specific. With very few exceptions, IT services companies and consultancies orient themselves and their offerings around industries, making the AI play to their strengths. Not every IT services vendor or consultancy can lead in every industry, so separation may come as certain vendors capture increasingly larger shares of an industry’s AI-specific C&SI needs. Every vendor can serve the largest IT-buying industries like financial services and healthcare, making true differentiation more about subindustries and, perhaps, slower AI adopters.
 

C&SI Market Forecast for 2025 through 2030 (Source: TBR)

Growth leaders will guide technology adoption and management with a focus on delivering outcomes and creating value

2030 market leader expectations

Laggards in 2030 will move into positive revenue growth positioning, but their growth potential will be mixed. Two main market changes will influence the slower growth rates in 2030. First, the shift to outcomes-based and fixed-price contracts will reduce total contract value despite the expansion of technology projects, resulting in lower revenue growth tied to consulting and higher revenue growth from managed services. Second, more niche consulting firms continue to emerge in the market. Although these firms target smaller enterprises than the larger vendors, these niche consulting firms will challenge traditional vendors. Growth opportunities will be focused on technology-led transformation as well as business consulting to improve operations following larger-scale transformation projects. SI services will likely remain in-house for traditional IT services vendors, such as the India-centric firms, helping solidify the image of transformation partners.
 

C&SI Revenue Projection for 2030 (Source: TBR Estimates and Company Data)

AI adoption and outcomes-based commercial models will reshape the traditional C&SI space and push IT services companies and consultancies to identify new areas of value and differentiation

TBR’s Consulting & Systems Integration Market Forecast is a comprehensive view of vendor activity and investment across the market. The research includes estimates for total market size, growth and trajectory, and highlights best- and worst-case scenarios for the market.
 
The first publication of this new research explored key market insights such as:

  • Clients will increasingly require fixed-price and outcomes-based billing models. By 2030, 85% of engagements will feature new commercial models, and the remainder will be split between time & materials and software sales.
  • The cannibalization of the traditional consulting model due to AI will lower contract values, requiring vendors to find new methods of growth such as in managed services, security and operations.
  • Specialization strategies related to talent, business models and portfolios will define laggards and leaders. The need for blended technology and traditional consulting skills will accelerate.
  • Although it is not an immediate threat, the emergence of niche consultants could pressure vendors and their ability to charge high prices.

The first publication of this annual report is now available. Click here to Learn how you can access the full research and all supporting data.

AI Adoption Predictions: What Will Determine Vendor Success and Who Is Positioned to Win

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Specialization around partner technologies and platforms along with industry expertise will steer portfolio developments over the next five years. Although growing AI adoption will lead to greater demand for integration, orchestration and managed services, it will challenge the traditional labor-based revenue models and push vendors to decouple revenue growth from headcount.
 
The shift toward outcome- and platform-based delivery will accelerate vendor consolidation, concentrating market share among providers that can demonstrate measurable ROI and scalable IP-led offerings. These dynamics will be amplified by industry-specific demand, especially in the manufacturing, energy and public sector verticals, where transformation investment and regulatory pressures will outpace market growth and benefit vendors with deep domain expertise and digital engineering capabilities.
 

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The next wave of AI

Physical AI will emerge as the next AI frontier, driven by cost-optimization needs in the underperforming manufacturing and industrial sectors. Surging energy demand from AI data centers will create a parallel growth opportunity. The expansion of task- and industry-specific agentic AI in 2025 will enable broader orchestration as enterprises streamline fragmented deployments, generating greater demand for data orchestration to unify enterprise data and maximize AI performance.
 
As physical AI scales, tighter integration between OT and IT systems will become critical, creating opportunities for vendors with combined digital engineering, IoT and AI capabilities while increasing activities around cybersecurity and real-time data orchestration.

Governance and data accountability

As companies increasingly rely on advanced AI to inform decision making, CxOs are scrutinizing governance and the quality of underlying data. In the second half of 2026, TBR anticipates advanced AI spending will partially pivot to governance and accountability frameworks, accelerating engineering and IT consulting engagements to optimize and clean data.

Consulting rebound

Persistent changes to the supply chain due to new and ongoing conflicts in Europe and the Middle East as well as shifting tariffs, immigration issues and compliance requirements will lead to a rise in consulting, as clients realize geopolitical and macroeconomic uncertainty are longer-term than perhaps initially anticipated. IT services companies will continue to build out supply chain consulting, supply chain optimization and analytics capabilities.

Cybersecurity becomes an essential foundation for AI

Rising geopolitical tensions, which are driving European governments to adopt stricter cybersecurity policies and increase investment in sovereign solutions, are creating major short- to mid-term opportunities in infrastructure services and, subsequently, IT consulting. If these concerns persist over the long term, Europe-based enterprise clients may be less willing to work with companies outside the region.
 
Cybersecurity and sovereign solutions will support AI adoption, as secure data, applications and infrastructure emerge as key concerns for the public sector and, subsequently, for heavily regulated industries in Europe such as financial services. EMEA accounted for 34% of revenue in 2025 in TBR’s 4Q25 IT Services Vendor Benchmark, which tracks 30 leading IT services providers.

The rise of local market in APAC

TBR has noted an ongoing uptick in demand for digital transformation and IT modernization in APAC, which is expected to accelerate as internet adoption increases in Southeast Asia and India’s gross national income rises. India-centric vendors stand to gain from the increase in demand for these services due to their unique ability to compete on price and strong local presence.
 
Further, companies that have lost market share with the introduction of new technologies such as AI and advanced AI, such as Wipro, have the chance to capitalize on more traditional services.

Measurable outcomes become a necessity in the U.S. federal sector

Through the end of 2028, U.S. federal spending will be centered on national security and modernization, with demand for AI, cyber solutions and mission software. From 2026 to 2028, providing tangible results and adapting quickly to new efficiency initiatives will be necessary to secure new deals in the U.S. federal space.

Contracting market share of the top 5 IT services providers signals that scale alone is insufficient as AI enables smaller providers to compete in higher-value services

Although the top five IT services providers will continue to increase revenue, TBR estimates their combined market share will contract through 2030. This reflects intensifying competition from midtier, regional and specialized providers across the IT services landscape, particularly as AI lowers barriers to entry and enables more targeted outcome-based offerings. Leading IT services providers must rely on differentiation through AI-led platforms, large-scale transformation capabilities and ecosystem orchestration to defend their share against a long tail of increasingly capable competitors. As clients prioritize ROI and accountability, IT services providers must deliver tangible value from AI-led transformations to sustain revenue growth and retain market share.
 

Top 5 IT Services Vendor Market Share for 2025 (Source: TBR and Company Data)


 

Top 5 IT Services Vendor Market Share 2030 (Source: TBR and Company Data)

 

Although the IT services market will expand steadily, changing delivery models and client demand will redefine how value is created

TBR’s IT Services Market Forecast is a comprehensive, forward-looking analysis of the IT services market and includes analysis of the market share of leading IT services vendors and competitive and customer dynamics that are driving revenue growth.
 
This research includes estimates for the size and growth of the overall market and by two segmentations, Consulting & Systems Integration and Rest of IT Services, which includes business process outsourcing, IT outsourcing, applications outsourcing, and hardware support and maintenance services. Current-year market sizing and a five-year forecast across the two services segments are featured in each publication, as well as analysis of growth drivers, top trends and leading market players.
 
The first publication of this annual report is now available. Click here to Learn how you can access the full research and all supporting data.

TBR Launches Consulting & Systems Integration Market Forecast

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TBR’s Consulting & Systems Integration Market Forecast provides a comparison across tracked vendors, establishes best practices and identifies what will separate leaders from laggards over the next five years.

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TBR Launches IT Services Market Forecast

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TBR’s IT Services Market Forecast is a comprehensive, forward-looking analysis of the IT services market, including market share of leading IT services vendors and the competitive and customer dynamics driving revenue growth.

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The Fragmentation of AI Infrastructure: 3 Forces Reshaping the Market

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The AI infrastructure market is evolving rapidly across 3 core dimensions

It is increasingly clear that the AI infrastructure market is neither unified nor single-dimensional. What began as a relatively cohesive, GPU-driven infrastructure build-out is rapidly diverging along three structurally different yet interrelated axes:

  1. Training versus inference
  2. GPUs versus custom AI ASICs (application-specific integrated circuits)
  3. Cloud versus on-premises deployments

Together, these dynamics represent fundamental shifts in how AI servers and systems are architected, deployed and monetized, impacting the entire AI ecosystem.
 
As these divides deepen, the AI infrastructure market is effectively splitting into distinct segments with different buyers, economics and competitive dynamics. Vendors and customers that continue to treat AI infrastructure as a single, homogeneous market risk misallocating capital, overestimating growth opportunities and underestimating emerging competitive threats.
 

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Training versus inference

Perhaps the most important divide in the AI infrastructure market is between training and inference workloads. Training prioritizes flexibility, scalability and rapid iteration, reinforcing GPUs as the foundation for frontier model development. Inference, however, operates under different constraints, where cost efficiency, power consumption and throughput take precedence, especially for hyperscalers. Flexibility remains important across more variable enterprise and heterogeneous workloads. This inference dynamic is driving hyperscalers to increasingly deploy custom AI ASICs optimized for cost per inference and energy efficiency.
 
TBR sees this shift as a reflection of a broader change in where value is created rather than an indication of a transition from one architecture to another. While training has driven initial infrastructure build-outs, inference represents the larger long-term opportunity as AI adoption expands across industries. As a result, the center of gravity in AI infrastructure is shifting toward production inference workloads, where efficiency and scale define competitiveness at the hyperscaler level, even as flexibility remains a key requirement across the broader market.
 

TBR AI Server & Systems Market Forecast by Accelerator Type (Source: TBR Estimates)


 
TBR forecasts the total AI server and systems market will eclipse $300 billion in 2026, growing at a rate north of 30% year-to-year, driven primarily by large-scale service provider AI infrastructure build-outs.

GPUs versus ASICs

The divergence between GPUs and custom AI ASICs reflects how different ecosystem players are positioning to capture this growing inference opportunity. Hyperscalers are investing in custom silicon to optimize performance, reduce costs and align infrastructure with large-scale, stable workloads, while also abstracting infrastructure through managed services to consolidate control higher up the stack.
 
At the same time, merchant accelerator vendors, like Advanced Micro Devices (AMD) and NVIDIA, are not ceding the inference opportunity to hyperscalers and their custom AI ASICs. Instead, they are investing aggressively to bolster their platform-level capabilities through tightly integrated hardware and software stacks, managed infrastructure offerings and next-generation systems optimized for tokens-per-watt efficiency. Competition shifts from a hardware-centric model to a platform-level battle, where control over how AI infrastructure is delivered, consumed and monetized increasingly determines value capture and directly influences deployment models.

Cloud versus on-premises deployments

While hyperscalers remain the largest demand vector behind the growing AI infrastructure market, the idea that AI workloads will be fully centralized in the cloud is already beginning to break down. Enterprises are encountering practical constraints, including data sovereignty requirements, data gravity, latency sensitivity and cost considerations, that are driving a more distributed deployment model.
 
At the same time, many organizations continue to face challenges related to data readiness and infrastructure complexity, which slow large-scale enterprise AI adoption and reinforce the need for hybrid approaches. As a result, TBR sees organizations deploying AI infrastructure across a mix of cloud, on-premises and edge environments — with that mix dictated by industry group and company size — rather than converging on a single deployment model.
 
This dynamic echoes previous technology cycles, in which organizations choose hybrid cloud architectures rather than centralizing all workloads in public or private cloud environments. However, TBR believes AI will create an even more fragmented and distributed infrastructure landscape, where deployment decisions are more closely tied to workload-specific requirements.

Implications of AI infrastructure market fragmentation

As these structural divides take hold, vendors are already being forced to make strategic trade-offs.
 
OEM strategies, for example, are diverging between high-volume, lower-margin deals with services providers and more targeted, higher-margin enterprise opportunities that emphasize integrated solutions and services. TBR views this divergence as a reflection of the broader realities that there is no single, unified go-to-market strategy for AI infrastructure and that demand and adoption by customer group is uneven. As such, vendors must align their current portfolios and alliance and investment strategies with specific market segments to optimize value capture rather than attempting to compete across all fronts simultaneously.
 
Upstream of the OEMs, NVIDIA’s near-monopoly position in the AI infrastructure market is gradually receding as hyperscaler AI ASICs and other merchant accelerators vie for their place in the AI data center. AMD’s investments in developing rack-scale integrated systems and emphasis on ecosystem openness directly compete with NVIDIA in the merchant accelerator space, while hyperscaler AI ASICs pose an adjacent threat for share of the inference market. Peripherally, other vendors are also entering the merchant market with processors and systems architectures purpose-built for specific inference applications.

Winners will align to the right fragment — not the entire market

As fragmentation accelerates, competitive positioning will increasingly depend on market segment alignment.

  • Hyperscalers will continue investing in the consolidation of control through infrastructure abstraction and the deployment of custom AI ASIC-based servers and systems.
  • Merchant silicon vendors will reinforce their dominance in training and relevance in inference through platform- and ecosystem-level investments.
  • OEMs will increasingly lean into their respective services-led, enterprise-focused models as demand diversifies beyond services providers.
  • Enterprises will adopt hybrid AI strategies that balance cost, control and flexibility as a growing number of industry-specific use cases become better defined.

In this environment, there is no single winner across AI infrastructure. Instead, leadership will be defined within each segment, and success will depend on how effectively vendors align their strategies with the underlying structure of the rapidly evolving market.

Conclusion

Understanding how AI infrastructure is fragmenting — and where value is shifting as a result — is critical for forecasting demand, evaluating competitive positioning and aligning long-term strategy.
 
TBR’s AI Infrastructure Market Landscape provides a detailed analysis of these dynamics, including vendor performance, ecosystem developments and evolving market opportunities across the AI infrastructure stack. Preview the data and analysis in our latest AI Infrastructure Market Landscape.