Winners and Losers in a Fragmenting AI Infrastructure Market

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

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

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

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

 

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

Reinvention Services Marks the Beginning of Accenture 2.0

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

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

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

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

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

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

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

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

Reinvention Engines: The backbone of Accenture’s Reinvention Partners

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

Client use cases elevate the theoretical to the practical

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

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

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

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

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

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

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Strategy shifts in applications development accelerate and augment client outcomes

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

How will HCLTech mitigate clients’ rising concerns about ROI?

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

Where is HCLTech’s road map from here?

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

Why Informatica Matters More Than Ever to Salesforce

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Informatica becomes Salesforce’s trust engine

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

IDMC goes headless

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

Headless data management for the ecosystem

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

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

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

Conclusion

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