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.

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.

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.

Next 2026: Lakehouse and Agentic PaaS Push Google Cloud Closer to the Center of AI Value Creation

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All hyperscalers tout themselves as “full-stack” to a degree, but Google Cloud’s distinct advantage is that it owns a leading frontier model, Gemini. Having Gemini deeply embedded throughout the portfolio creates a powerful flywheel effect that lets Google Cloud monetize AI in ways others cannot. At the same time, the value is shifting from the AI models themselves to how those models work with a growing set of tools and data to create value. From a repackaged PaaS layer to a revamped data stack, announcements at Google Cloud Next 2026 reinforce that this will be the company’s next chapter.

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Comcast Business Advances its Enterprise Strategy Through AI-driven Innovation and Ecosystem Expansion

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2026 Comcast Business Analyst Conference, Philadelphia, April 15-16, 2026 — A select group of industry analysts gathered at the Comcast Center in Philadelphia to hear from Comcast Business leaders about the progress and success of the unit’s sales and go-to-market strategies. The event continued to center on its theme introduced at last year’s conference, “Everything, Everywhere, All at Once,” reflecting the increasingly complex operating environment customers face and Comcast Business’ role in helping them navigate change through integrated solutions. Building on this theme, Comcast Business emphasized the accelerating pace of innovation over the past year, underscoring advancements in AI and network capabilities as it aims to deliver solutions that keep pace with the speed of business transformation. The event was hosted by NBC News Business and Data Correspondent Brian Cheung and included a State of the Business session with Comcast Business President Edward Zimmermann, a Strategy & Vision session with Comcast Business Chief Product Officer Bob Victor, and an update on Comcast’s network from Chief Network Officer Elad Nafshi. The agenda also featured panel discussions with senior leadership, speaker sessions with Comcast Business customers, and fireside chats with high-profile thought leaders on AI development and trends.

TBR perspective

Since 2025, Comcast Business has accelerated its transition from a connectivity-led provider to a solutions- and platform-oriented partner for enterprise customers. The 2026 analyst conference highlighted the company’s focus on expanding share among global enterprises through continued investment in AI-enabled networking, cybersecurity and edge compute capabilities. This evolution reflects both opportunity and necessity. Enterprise growth is increasingly driving overall performance, while the SMB segment faces intensifying pricing pressure from fixed wireless access (FWA) and converged offerings.
 
At the same time, rapid advancements in AI are reshaping customer requirements, placing greater emphasis on low-latency connectivity, integrated security and real-time data processing. Comcast Business is positioning itself to capitalize on these trends by leveraging its network scale, partner ecosystem and managed services portfolio to deliver differentiated outcomes. However, success will depend on the company’s ability to execute, particularly whether it can monetize AI-driven capabilities and scale its global platform.

Impact and opportunities

Comcast Business drives revenue growth via enterprise expansion, while its SMB segment faces increasing headwinds

Comcast Business’ revenue performance remains relatively strong, generating over $10.2 billion in 2025, exceeding its long-term goal of reaching $10 billion in annual revenue. Growth is increasingly driven by the enterprise segment, which expanded 13.1% in 2025, supported by the integration of acquisitions, such as Masergy and Nitel. Additionally, the company now serves approximately 90% of Fortune 500 companies in some way. Comcast Business is also expanding its focus on multinational enterprises, leveraging partnerships with global operators across more than 130 countries.
 
Despite this momentum, the SMB segment — the company’s largest revenue contributor — is becoming increasingly challenging. Competition from FWA providers and converged offerings in the U.S. market is intensifying pricing pressure as small businesses gravitate toward lower-cost “good enough” connectivity solutions. These dynamics contributed to a net loss of 48,000 business customer relationships in 2025, compared to a net loss of 16,000 in 2024 and net additions of 17,000 in 2023. TBR believes the majority of these losses occurred within the SMB segment.
 
To offset customer losses, Comcast Business is increasing its focus on cross-selling value-added services to customers in areas such as mobility, SD-WAN, security and unified communications. For instance, Comcast Business reported that its enterprise customers are spending three times as much for value-added services as on core connectivity services compared to 2023. Comcast Business will also increase wireless revenue from larger businesses in 2026 through its new MVNO agreement with T-Mobile. The agreement covers up to 1,000 lines per account, which will enable Comcast to begin targeting the midmarket with wireless offerings, whereas its existing B2B MVNO agreement with Verizon is limited to 20 lines per account.

Comcast Business scales AI across its portfolio, network and operations

Comcast is expanding its use of AI from targeted, efficiency-driven applications to a more pervasive, embedded role across its network, solutions portfolio and customer engagement model. AI is now integrated across key areas, including network optimization, cybersecurity, sales enablement and customer experience, and is improving operational efficiency through internal use cases such as automated RFP development, deep research and meeting summarization. AI integration is enabling Comcast to automate over 99.7% of software changes across its network, supporting self-healing capabilities that can quickly resolve outages and, over time, help improve customer retention.
 
Comcast expects AI to not only enhance network and operational efficiencies but also create meaningful revenue-generation opportunities, though the company remains in the early stages of developing monetization strategies. For example, Comcast’s edge computing capabilities support ultra-low latency speeds of less than 1 millisecond for many customers, positioning the company to enable advanced AI-driven applications such as AR/VR, which are more dependent on low latency than text-based use cases. Comcast Business is also exploring customer-facing AI use cases, including small-business concierge agents designed to manage front-desk functions such as greeting customers, scheduling appointments and handling routine inquiries, highlighting the potential to extend AI-driven value beyond internal operations and into customer-facing revenue opportunities.

The launch of Comcast Business Innovation Labs will accelerate the development of enterprise solutions

The company is advancing its enterprise strategy through the formal launch of Comcast Business Innovation Labs, an initiative designed to codevelop and rapidly scale first-to-market solutions for midmarket and enterprise customers. The lab brings together Comcast Business, customers and a broad ecosystem of technology partners to address specific business challenges, reflecting a more demand-driven approach to innovation. A key focus for Comcast Innovation Labs is supporting edge and AI-driven use cases by leveraging Comcast’s network capabilities and partner ecosystem.
 
Initial programs launched under the Comcast Business Innovation Lab include a partnership with Dell Technologies to deliver managed edge compute for AI and real-time applications and partnering with Digital Realty to enable seamless hybrid and multicloud connectivity through data center fabric services. Comcast Business is also collaborating with Expedient to support three core capabilities: AI operations at scale via Expedient’s Secure AI CTRL services, private cloud as a cost-efficient environment for workloads, and managed disaster recovery to support mission-critical applications.
 
TBR believes Comcast Innovation Labs strengthens the company’s ability to differentiate through ecosystem-driven innovation and faster solution development cycles, particularly as enterprise customers seek more tailored outcome-based offerings. However, the long-term impact of the initiative will depend on Comcast Business’ ability to scale these solutions beyond pilot environments and integrate them effectively across its broader portfolio and go-to-market strategy.

Conclusion

The 2026 Comcast Business Analyst Conference highlighted the company’s evolution from a connectivity-focused provider to a solutions-oriented partner for enterprise customers. Comcast Business’ ability to surpass $10 billion in annual revenue and sustain double-digit enterprise growth underscores the effectiveness of its upmarket strategy, supported by acquisitions, global partnerships and an expanding portfolio of value-added services.
 
However, SMB, which accounts for the majority of Comcast Business’ revenue, is becoming increasingly challenging as FWA competition and macroeconomic pressures drive greater pricing sensitivity. These headwinds will require Comcast Business to further strengthen its value proposition to retain and grow its SMB base and combat competitive pressures in the market.

From Ecosystem to Execution, NVIDIA Shapes How AI Is Built and Run

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NVIDIA’s increasing emphasis on physical AI signals that the company’s ambitions extend well beyond digital workloads. By linking its agent software stack with simulation, robotics and autonomous systems, NVIDIA is positioning itself as the foundational platform for both virtual and real-world AI applications. GTC 2025 established the importance of inference, and GTC 2026 clarified that the next phase of AI will be defined by agents, and NVIDIA is building the infrastructure to power them from end to end

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KPMG Collaborates with Microsoft to Develop Governed Agent Operating Model at Scale

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KPMG and Microsoft and the next phase of partner-led AI transformation

KPMG is repositioning itself from a Microsoft Dynamics-centric systems integrator to a broader AI-led transformation partner that has extensive experience with Microsoft technologies. As KPMG’s Microsoft alliance moves toward an AI-era operating model measured by sustained adoption and governed outcomes, the strategic questions for partners and competitors are centered on whether KPMG’s governance-led, platform-enabled approach becomes a repeatable bet in regulated and board programs — and, if so, how quickly peers can counter with comparable operational frameworks and field-ready narratives.
 
Recently, TBR had a chance to hear directly from KPMG’s Microsoft alliance leaders including Cherie Gartner, Global Lead Partner for Microsoft, KPMG LLP; Marco Amoedo, Global Chief Technology Officer, Microsoft, KPMG International; and Sven Rohl, Global Microsoft AI Business Solutions Lead, KPMG International, about how KPMG’s 20-plus-year alliance with Microsoft has evolved into a 360-degree relationship, framed by four interrelated fields of play including Reimagining the Enterprise, Platforms with Purpose, Modernization at Scale, and Secure by Design Enterprise. The following analysis reflects on this discussion and TBR’s ongoing research on KPMG and the Big Four, including our semiannual Management Consulting Benchmark and ecosystem intelligence reports.

Trust beyond scale

Framing the alliance as a global 360-degree relationship is a familiar phrase in alliance management, but KPMG tries to use the phrase differently, beyond just a relationship and more as a structure that enables shared accountability. Building off a decade-plus-long collaboration around Microsoft Dynamics applications, KPMG has now made Azure consumption the center of gravity, and executives described the metric as “the underpinning layer.” Aligning its internal success criteria with how Microsoft measures platform expansion means KPMG is implicitly shifting what it asks clients to do: not just approve a program and go live, but rather adopt, consume, expand and operate.
 
In the AI era, that distinction can become decisive as all parties look for pilots to be projectized. With KPMG firms managing a pool of over 40,000 Microsoft-trained consultants, including over 6,800 Dynamics experts and more than 14,000 Microsoft certifications, the KPMG global organization reinforced the scale of and commitment to the relationship by reemphasizing a multiyear, deliberate diversification push to grow Azure-led work. Certifications and specializations are not only a proof point but also a metric within KPMG’s multibillion-dollar investment with Microsoft, where Azure consumption remains a KPI to measure success.
 
We see this as a subtle but important narrative blend where KPMG is not disowning the legacy but rather using it as proof of proximity to business systems while moving the growth story upstream into cloud, data, security and AI. We believe if KPMG is successful with its messaging, the result will be a partner story that is designed to meet the next procurement threshold: Show me you can run this safely and prove it is worth it.
 
Further, rather than organizing around discrete Microsoft products (e.g., Azure, Dynamics, M365), KPMG is also designing integrated offerings that map to the go-to-market motions of Microsoft’s solution areas including Cloud & AI platforms, AI business solutions, and Security. We view these offerings as an opportunity for KPMG to deploy its Powered Enterprise framework structured around transformation discussions that begin with business priorities and end with one or more technology solutions, rather than leading with technology.
 
Although this approach is not unique to KPMG, it allows the firm to lean on its value proposition, something partners appreciate as they look to avoid coopetition. With knowledge management also testing the trust and alignment among partners, solution architects within KPMG and Microsoft are helping the companies build a robust foundation. Growth acceleration will come from ensuring field sellers within both organizations are equally able to tell each partner’s story as well as their own.
 

Platform-enabled integrated offerings bring the partners closer together

As the AI market moves from proof of concept to a portfolio of agents embedded into core workflows, buyers’ concerns are changing. They are now seeking vendors that are less focused on deploying solutions and more on helping them address concerns around governance and data residency control without disrupting the architecture, cost calculations, or operating processes across a multivendor environment, all while avoiding turning the program into a fragile dependency chain. This is where most AI transformation narratives collapse.
 
Vendors typically over-index on capability demonstrations and under-invest in the operating model. That weakness is precisely where KPMG firms are investing. The firm’s broader operating model strategy — its efforts to standardize, consolidate and reduce fragmentation across its global organization of member firms — starts to matter even more in this context especially as AI at scale punishes decentralization. KPMG’s direction is designed to make “One KPMG” more real operationally as the firm recognizes that this is the only way to make agentic delivery repeatable as it seeks to act more like a platform-led business.

The core bet: productizing transformation and productizing trust

KPMG’s alliance partnership with Microsoft is built around two assets that function as the firm’s packaging layer for the AI era: KPMG Velocity and KPMG Workbench. KPMG Velocity provides AI-enabled products and services through a platform ecosystem that integrates KPMG’s insights, methods, expertise, capabilities and data with advanced technology. KPMG Workbench is the firm’s global AI platform, designed to scale global adoption and integration of AI and underpin KPMG client delivery solutions.
 
For KPMG, the key strategic point is not the existence of Velocity, as peers similarly package methods in platforms, but rather what Velocity is positioned to do: make Microsoft’s technology consumable in terms of measurable outcomes. The most important aspect of KPMG Workbench is not that it uses AI but rather the kinds of capabilities the platform offers that signal production readiness, especially those targeting skeptical buyers, as Workbench is designed to address key questions around trust and compliance, economics and sovereignty.
 
In summary, if KPMG Velocity is the packaging layer that makes transformation repeatable, KPMG Workbench is the operating layer that makes transformation defensible. KPMG now has the opportunity to use these platforms consistently across member firms, both with Microsoft and with other key partners.

The alliance motion that matters: Azure-central, multivendor, and designed for reality

Another strategic message KPMG’s Microsoft alliance leaders discussed is that they do not expect buyers to lean on a single vendor, even if Microsoft is the anchor. They repeatedly emphasized a multiparty ecosystem go-to-market strategy with Microsoft alongside SAP, ServiceNow and others. The pattern is consistent with the emergence of the multiparty alliance construct that TBR has observed within the past 18 to 24 months and has discussed at length within our Ecosystem Intelligence research stream.
 
Overall, KPMG treats Azure as the compute and platform foundation but acknowledges that the enterprise estate is triangulated by design. This is an important positioning, especially as the market has shifted from platform selection to platform negotiation where large enterprises will not rip and replace existing solutions but will look for vendors that can orchestrate their tech stack. Any vendor that assumes it can win by forcing a monoculture could lose relevance.
 
KPMG’s approach is therefore less Microsoft-only than it is Azure-central. It is a strategy built for deal reality and positions the firm to create outcomes without demanding architectural purity. This strategy could strengthen KPMG’s reputation with procurement departments, especially in regulated environments, where buyers must balance modernization with risk management and existing vendor commitments.

Reconciling outcome-based consulting with consumption-based cloud

During the briefing, KPMG highlighted two approaches that close the gap with its aspiration to drive outcome-based pricing at scale: where every solution must deliver a measurable return, and where Azure is the core growth metric of Microsoft’s consumption-based economics. This leads to a defensive dynamic where clients increasingly expect AI-enabled efficiencies to translate into lower costs and fewer people, forcing KPMG to justify its pricing by clearly demonstrating the value of its platforms and IP, and an offensive dynamic, in which the firm invests heavily in managed services and Consulting as a Service models that bundle AI-driven solutions with ongoing delivery, aligning more naturally with Microsoft’s consumption-led view.
 
These two shifts are reflected in KPMG’s expansion of subscription-based offerings, such as KPMG Clara, KPMG Digital Gateway for Tax and a KPMG Digital Gateway for Law, and sector-specific AI managed services (e.g., trade surveillance and fraud monitoring for banks) built on KPMG platforms and Azure. These efforts are supported by new go-to-market capabilities like partner and staff sales academies focused on solution and subscription selling and the introduction of dedicated technology sellers in some member firms — changes that mirror hyperscaler and ISV selling motions. For KPMG to succeed at scale, it will likely need to continue evolving culturally and operationally beyond its traditional alliance partnership model, a shift the firm has already begun to address.

Long-term structural changes can help KPMG stay relevant with Microsoft as a copilot

Over the next 12 to 24 months, we expect professional services vendors’ partner strategies and enterprise buying to reorganize around roles and operating standards. First, partner segmentation will harden. Enterprises will increasingly select multiple partners for distinct roles: scale operators to industrialize, governance lighthouses to de-risk production and specialists to provide domain depth. The “one partner does everything” narrative will weaken.
 
Second, consumption will be scrutinized through a value lens. Azure consumption will become less about quota and more about the quality of the investment. Workbench-style telemetry and metering are early indicators of where buyer expectations are heading: cost attribution and measurable value per agent, per workflow and per portfolio.
 
Third, commercial models will shift faster toward managed operations and subscription-like constructs. Although value and usage are measurable and continually optimized, time-and-materials commercial constructs will become harder to defend as the default model.
 
Beyond 24 months, the most consequential shift is that “assurance-grade” operations may become a prerequisite for transformation itself. As agents operate inside financial processes, HR, security and supply chains, buyers will demand auditability and governance in ways that resemble assurance disciplines. The linkage of KPMG Workbench across advisory and audit-adjacent contexts is not incidental and hints at how KPMG believes it can compete.
 
At the same time, sovereignty-by-design becomes procurement leverage. Data residency routing and region-specific controls are not just technical features, but they will become deal accelerants, particularly in Europe and regulated industries. Finally, the source of lock-in shifts. The sticky asset will not be the implementation project but rather the operating standard: governance models, telemetry dashboards, value measurement frameworks and agent life cycle processes. Whoever defines those standards will own the long-term relationship, even if the underlying technology is theoretically interchangeable.
 
KPMG is positioning its Microsoft alliance for the next phase of enterprise AI adoption, in which production readiness, governance and demonstrable value are expected to differentiate winners from laggards. KPMG Velocity is framed as the mechanism for making transformation repeatable, while KPMG Workbench is positioned as the means to make transformation governable and auditable. Within this narrative, Azure consumption functions as the key performance indicator aligning KPMG’s incentives with Microsoft’s platform economics, and a multivendor triangulation strategy aligns KPMG’s go-to-market approach with enterprise buyer realities.
 
If KPMG can convert these elements into field-ready plays and consistently repeatable client outcomes, it may emerge as a uniquely valuable Microsoft partner for regulated, board-visible programs, shifting competitive pressure onto peers to differentiate through operational trust rather than delivery scale. Overall, KPMG appears to be attempting to move the competitive battleground from implementation speed to governed operations. If the market evolves in this direction, partners and competitors will need to clarify their roles and demonstrate credibility in an environment where agentic transformation is treated less as a discrete project and more as a continuously governed system.

Moving from Use Cases to AI Value: Infosys Focuses on Portfolio, Partner and Talent Readiness

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U.S. Analyst and Advisor Meet 2026, New York City, March 5, 2026 — Infosys hosted industry analysts and advisers for an afternoon in the newly branded Infosys Theater within Madison Square Garden. Using client stories amplified through technology partner support to reinforce Infosys’ role in the IT services, cloud and enterprise AI market, company executives consistently noted that enterprise AI success depends on combining strong data foundations, responsible governance, talent transformation, domain-specific use cases and partner-led execution, which can help turn AI from isolated experimentation into measurable business value.  

 

Americas’ scale and market maturity present an opportunity for Infosys to treat AI as a horizontal capability rather than an ad hoc solution

Similar to previous meetings, the event began with an update on the company’s strategy and performance in the Americas region. Anant Adya, EVP and head of Americas Delivery, led the presentation, highlighting key elements of the company’s success in the region, such as its hub-first strategy, including the addition of a hub in Costa Rica. Adya described the hubs as serving four roles: innovation, client cocreation, centers of excellence, and training and enablement. He specifically mentioned the hub in Hartford, Conn., which focuses on insurance and healthcare domain solutions and client prototyping, as well as a ServiceNow Center of Excellence, which focuses on onboarding and training local university students and other talent.
 
A key nuance in Adya’s presentation at last year’s event was Infosys’ positioning of AI and moving from the earlier pentagon-shaped strategy to a new hexagon-shaped strategy centered on AI. The message was that AI is no longer a horizontal add-on but is now meant to reshape the full services value chain. He emphasized that enterprise data readiness is the critical prerequisite, with cloud already assumed and data becoming the true launchpad for AI at scale. He used several client examples to illustrate the strategy in action: AI-infused managed services for a packaging company, AI-first manufacturing operations for a semiconductor client, AI-led SAP transformation for a utility, and AI-enabled wealth operations for a financial services firm. Across those examples, the focus was on measurable business outcomes like productivity, manufacturing uptime, revenue impact, and value creation, not just IT efficiency.
 
TBR appreciated these examples as we see them as a bridge into opportunities for Infosys to drive awareness of its broader capabilities and strengthen relationships among enterprises. We believe that pivoting from driving AI conversations centered on efficiency improvement to discussing business growth at scale will pressure-test Infosys’ commercial model readiness, especially as the former leads buyers to expect perpetual cost savings, thus pressuring Infosys’ margins, while the latter provides a pathway for increasing volume, delivered by fewer employees.
 

India-centric Vendors Low-cost Headcount vs. Operating Margin (Source: TBR)


 
Infosys has maintained an operating margin within its guided range of 20% to 22% for a few years, yet headcount growth began to rebound in 4Q25, at 4.2% year-to-year, following several quarters of decline. Additionally, grouping partners into strategic foundation partners, conventional core partners, and innovation-edge AI partners highlighted Infosys’ recognition of the value of the ecosystem and the importance of where each party plays. Aligning to core strengths from both a capability and a messaging perspective can help Infosys and partners demonstrate depth and strengthen trust with buyers who seek mutual accountability and understanding of go-to-market priorities.

Crystalizing AI talent strategy will provide Infosys with the necessary support to drive business value at scale

Following Adya’s strategy update, Ranjana Joshi, AVP and HR leader, Americas, discussed how Infosys is redesigning its workforce model for an AI-first services business. Although TBR has previously heard some of the details Joshi outlined, it was apparent that Infosys has crystallized what comes next in developing an AI-ready workforce. Infosys is building the talent strategy around three pillars: a new talent operating model, which she described as an “ambidextrous” model; a new career model; and a new talent development model.
 
According to Joshi, these pillars are set to help Infosys do two things at once: “augment the whole organization with AI while also building deeper engineering and domain expertise to deliver AI-first services.” Under its new “ambidextrous” approach, Infosys is changing how it hires. The company is shifting toward recruiting more specialized talent, including specialist programmers from U.S. campuses, alongside continued hiring of full-stack engineers and domain experts.
 
Additionally, Infosys continues to invest in internal bridge programs and hands-on sandbox-based development paths to ensure it develops the right-skilled bench. Meanwhile, career paths are being redesigned away from the traditional linear ladder. Instead of the old one-dimensional path from engineer to executive, Infosys has set up a Y-shaped career structure. One track is for the broad workforce that is AI-augmented, and the other is a specialist stream for people with deep engineering, domain and functional expertise. Further, the company is adding distinguished specialists in roles such as AI strategist and responsible AI engineer. These professionals are meant to act as catalysts who accelerate innovation and create specialist ecosystems around them.
 
Although these are important investments, especially as Infosys looks to build messaging centered on AI-readiness at scale, peers often cite numbers that suggest the majority of their workforce is AI-ready, challenging it to differentiate. Infosys’ opportunity lies in its ability to develop an AI-ready sales bench, especially as the technology forces it to act more like a software company than a traditional services company. We view Infosys’ investments in forward-deployed engineers (FDEs) as a bridge into an AI-ready sales force. Striking the right balance between pushing AI-first sales and managing relationships with ecosystem partners will be key, as going too far into selling tech could sour relationships with partners that try to do the same.

Helping clients scale, orchestrate and operationalize AI for business value will test Infosys’ commercial and operating model readiness

Throughout the rest of the afternoon, Infosys’ executives, clients and partners continued their efforts to separate hype from reality regarding scaling enterprise AI adoption. Compared to previous events, Infosys’ emphasis on domain-specific use cases amplified through panel discussions across financial services, energy, insurance, consumer packaged goods and life sciences strengthened the company’s message and AI strategy.
 
Joydeep Mukherjee, EVP and global head, Data & Analytics, Digital & Creative Services; and Srinivas Gopal, VP and Global Business Head – Data, Analytics & AI, noted that the market has moved beyond AI experimentation and is now demanding measurable enterprise value. Mukherjee’s core point was that most companies still struggle to scale AI because they are held back by weak strategy, fragmented data, governance gaps, integration challenges, and immaturity in their operating models. Scaling AI requires a coordinated blueprint spanning value discovery, operating model design, technology readiness, performance management, responsible AI and talent transformation.
 
Gopal built upon that point by showing what the next stage looks like in practice: agentic AI applied to real business processes, where enterprises use data products, intelligence layers and orchestrated agents to improve decision making, accelerate workflows and create business outcomes faster. Across both sessions, the message was that enterprise value does not come from isolated pilots or stand-alone agents but rather from combining strong data foundations, contextual business knowledge, governance and production-scale execution. This message also highlighted the breadth of Infosys’ capabilities in helping customers make that shift with Infosys Topaz as the orchestration layer.
 
In TBR’s 4Q25 Infosys report, we wrote: “Infosys’ recent cluster of GenAI [generative AI] announcements signals a deliberate pivot from AI-enabled services toward an agent-first delivery platform strategy, with Topaz Fabric positioned as the control plane. By pairing Fabric with hyperscaler-native capabilities like Amazon Q Developer for IT modernization and productivity, while embracing emerging AI software engineer tooling through Cognition’s Devin and scaling vertical agents such as its energy-operations solution on the Microsoft stack, Infosys is effectively building a multipartner operating model that can meet customers where they are without ceding the orchestration layer.
 
For competitors, differentiation is shifting from headcount, pricing and even domain expertise toward agent operational maturity, where productizing services compresses delivery timelines and forces a margin reset. For alliance partners, the upside (and risk) is that fabric-owning SIs become high-leverage distribution for platforms and models, while increasingly establishing the integration patterns, governance standards and customer experience — shifting partnerships from joint marketing to control of the runtime and commercial attach adds value.”
 
According to TBR’s December 2025 Digital Transformation: Voice of the Customer Research report, the market is signaling that vendors are getting better at packaging and surfacing IP, but monetization is still anchored to services constructs, not product economics, especially as automation and AI start to do a greater share of the work.
 
Against that backdrop, Infosys’ recent AI investments look like an attempt to bridge this gap rather than leap over it. The inflection point to watch is whether Infosys can translate its agent-first strategy into commercial pioneers that buyers can procure — clear SKUs, governance guarantees, and outcome and/or consumption mechanisms that do not simply relabel labor. If Infosys can prove repeatable productivity and quality gains and then price around outcomes with credible controls, it can move from AI-enabled services to a platform-led model that aligns with where buyers say they want vendors to go but have not consistently shifted toward yet in terms of their procurement behavior.
 
Alternatively, Infosys’ AI announcements can be interpreted as commercial positioning ahead of change. Topaz Fabric, agents and FDEs absolutely help industrialize delivery, but they also create a convenient story in which Infosys can claim platform-led differentiation while still monetizing primarily through large, multiyear services contracts. The recent launch of AI-enabled global capability center (GCC) framing, in particular, can be read in one of two ways: Either it is a structured route to platformizing captive operations, or it is a mechanism to lock in demand and defend wallet share by embedding Infosys’ tooling, processes and governance so deeply that switching becomes hard without changing the pricing model as much as the marketing implies. The near-term expectation is that Infosys will capture value the way the industry typically does during transitions: sell transformation rhetoric, deliver productivity gains, and then negotiate hard to keep most of the savings — or recycle those savings into scope expansion rather than price reductions.

Domain-aligned services backed by relentless and quality service execution will help Infosys sustain trust with key buyers

Across the panel discussions, speakers noted that most organizations have already experimented with pilots and proofs of concept. The real challenge now is turning those isolated efforts into repeatable, enterprisewide capabilities. Panelists described this as an execution problem as much as a technology challenge, requiring alignment across strategy, operating model, governance, data and talent.
 
Within a financial services discussion, banking was presented as one of the sectors most ready to use AI because it can improve both growth and efficiency while also supporting resilience and compliance. The insurance panel showed how AI is being applied in a regulated industry through underwriting, pricing, unstructured data extraction, faster service and operational efficiency. The insurance leaders repeatedly stressed that in the human-plus-AI equation, especially in specialty insurance, AI can speed intake and improve accuracy, but the knowledge and judgment of experts still matter for decision making and evaluating risk.
 
The energy discussion centered on the growing complexity of energy and commodity markets and the role Infosys wants to play in that transformation. Speakers described energy trading and risk management as a stabilizing force in volatile markets, especially as geopolitical disruption, AI-driven power demand and shifting wholesale-retail dynamics make forecasting and execution more difficult. Infosys’ acquisition of MRE Consulting, which featured prominently during the panel, will play a key role in strengthening trust with existing buyers and expanding the company’s addressable market opportunity across EMEA and APAC. Infosys’ Energy, Utilities, Resources and Services (EURS) vertical share of revenue has hovered around 13% to 14% of Infosys’ total sales. We believe MRE Consulting, along with future similar investments in the vertical, will help boost that share to between 16% and 17%, similar to how manufacturing grew from around 10% of Infosys’ total sales in 2020 when it signed its megadeal with Daimler to now hovering close to 17%.

Balancing steady services-enabled revenue growth with new product sales channels will test Infosys’ otherwise strong culture

Over the next 12 to 18 months, we will keep a close eye on whether Infosys can convert AI breadth into depth, which will be evidenced by higher attach rates of Topaz Fabric in large programs, clearer packaging and commercial models for GCC platformization, and consistent outcome metrics (not just activity metrics) tied to modernization, data readiness and workflow redesign.
 
Infosys has an opportunity to stand out from its peer group if it can make Fabric the default operating layer for agent delivery and use FDEs to accelerate time to value, creating durable differentiation versus peers, as many of them are still largely focused on tooling plus services. Key risks include execution complexity, partner dependence blurring differentiation, and value-capture pressure if Infosys is forced to compete on price, relinquishing the value of the productivity gains it gives clients. Additionally, the use of FDEs, even on a smaller scale than Infosys’ traditional labor arbitrage pyramid, can raise questions about the company’s use of a time-and-materials model versus agent-based wrapped pricing.
 
Further, accelerating revenue from value-based selling would bolster profitability but compel the company to recalibrate its staffing pyramid across legacy and new areas, pressure-testing attrition levels. Lastly, quick-hit wins within the agentic AI space could entice Infosys’ leadership to pursue a more aggressive product sales strategy, disrupting its ongoing success with traditional services deals. In an intense and rapidly changing competitive market for AI-enabled IT services solutions, Infosys has a fighting chance to stand out. And we think the company’s leadership and recent success will help Infosys separate from peers over the next few years
 
TBR will continue to cover Infosys across the IT services, ecosystems, cloud, and digital transformation spaces, including publishing quarterly reports that assess Infosys’ financial model, go-to-market strategy, and alliances and acquisitions strategies. For a comparison with Infosys’ peers and other IT services vendors, TBR includes Infosys in our quarterly IT Services Vendor Benchmark, our semiannual Global Delivery Benchmark and Cloud Ecosystem Report, and our annual Adobe and Salesforce Ecosystem Report, SAP, Oracle and Workday Ecosystem Report, and ServiceNow Ecosystem Report. Click here to learn more about accessing the data and analysis within these reports.