TBR perspective

Disparate and siloed data, specialized software tools and interrelated processes challenge enterprises to gain real value from AI-enabled solutions. HCLTech’s AI Force platform provides visibility into data streams and interdependencies across the software development and operations life cycles — requiring minimal change management but no replacement of existing technology and greatly enhancing an enterprise’s existing IT environment. In short, AI Force is a nondisruptive force multiplier of customers’ technology investments.

In late September, TBR met with executives from HCLTech to discuss the company’s AI Force platform, overall business model, and strategies around AI and generative AI (GenAI). The HCLTech team included Apoorv Iyer, EVP and Global Lead, Generative AI Practice; Gopal Ratnam, Vice President, Product Management, Generative AI Products & Platforms; Alan Flower, EVP and Global Head, AI & Cloud Native Labs; and Rohan Kurian Varghese, Senior Vice President, Marketing. This special report reflects that discussion as well as TBR’s ongoing research on and analysis of HCLTech.

AI Force is a GenAI-powered platform that infuses intelligence across every phase of the dev and ops life cycles

HCLTech had an early start in AI, setting up a research team in 2016 and building out its AI engineering strengths around AI silicon; the development of AI-led IP solutions like DRYiCE, iAutomate and SDLC (Software Development Life Cycle), which was a precursor to AI Force; and its strong heritage in Data & AI with strategic acquisition like Actian, Starschema and, most recently, Zeenea. This has ingrained AI across HCLTech’s portfolio and underpinned transformation projects, allowing customers to seamlessly manage IT and cloud environments. Leveraging this heritage, HCLTech developed AI Force with responsible AI spanning built-in use cases that are scalable and modular and cover the entire software and operations life cycle, such as requirements and analysis (e.g., user story generation, change impact analysis), development (e.g., code generation, code refactoring), triage (e.g., duplicate defect detection), and technical support.

Through AI Force, HCLTech provides clients with a platform that supports not only software development life cycle, reducing the lift on manual tasks and shortening overall development time, but also the operations life cycle, enhancing overall efficiency and accelerating technology value across an enterprise by reducing accrued technical debt and producing better quality code. As one HCLTech leader described it, AI Force allows an enterprise to “stitch everything [in the IT environment] together and figure out where the issues are.”

Notably, AI Force has been on the market for over a year, is live with more than 25 of HCLTech’s enterprise clients, and serves the broader IT ecosystem within an enterprise, beyond just application development and maintenance teams. An HCLTech leader noted that the AI Force platform “reduces the lift of manual tasks and accelerates the overall service delivery time,” a clear operational and financial benefit for any enterprise and clearly more than simply a collection of software tools. Enterprises can now take intelligent decisions by harnessing data, leading to the accelerated development of products and applications, along with significant cost savings and improved efficiencies.

Before diving into specifics around AI Force, HCLTech’s leaders described some of the challenges enterprises face across the software development and operations life cycles, highlighting the complexities inherent in having multiple personas, disconnected processes, siloed data, disparate systems and specialized tools.

According to HCLTech, this landscape is missing a digital thread or intelligence hub capable of understanding the entire process end to end, including the data sets generated by specialized tools, and then further unlocking the relationships between the data sets. HCLTech’s AI Force can integrate existing tools — not replace them — and bring data sets together, create a knowledge graph of the relationships between the data sets, and conduct comprehensive root cause analysis.

AI Force’s key characteristics and advantages

In the discussion with TBR and during HCLTech’s presentation of AI Force’s capabilities, HCLTech’s AI leaders walked through AI Force’s go-to-market approach, characteristics, architecture, advantages and use cases. HCLTech conducted a demonstration of AI Force in action before turning to the synergies between AI Force and the company’s global network of AI and Cloud Labs.

At its core, HCLTech’s AI Force features extensibility, modularity and flexibility. It can integrate smoothly with existing IT environments, be leveraged for a large variety of use cases within an enterprise, and be deployed, consumed and priced in different ways that are suitable to an individual customer’s business needs.

In describing HCLTech’s go-to-market strategy, the AI leaders stressed three points:

1. HCLTech will continue to enhance large-scale engagements with the capabilities and benefits of AI Force from the start, affording the client immediate cost savings.
2. In other situations, HCLTech will assist clients in deploying AI Force as a platform within the client’s enterprise IT environment.
3. For clients already engaging HCLTech for managed IT services, AI Force can be deployed to gain cost savings and efficiencies, directly complementing existing managed services. This last approach, in TBR’s view, reinforces HCLTech’s value proposition around offering innovation, even in established managed services engagements, and expands its remit within the enterprise, from simply IT services to more consultative, business-outcomes-driven and AI-enabled solutions. As part of this consultative approach, HCLTech undertakes value stream mapping in the discovery process for deploying AI Force, including a detailed as-is picture, to-be picture, and the true impact at scale. Through this due diligence, HCLTech helps customers select the right projects that can benefit from AI Force.

Appealing broadly across the enterprise and embedding customer context

Recognizing that peers such as Infosys and EY have similarly developed suites of AI-enabled and AI-forward solutions, HCLTech leaders highlighted some aspects they believe distinguish the company’s capabilities, particularly AI Force.

First, the solution can be deployed on the cloud, on premises or even in edge-enabled devices, depending on a client’s needs and circumstances. The leaders described this aspect as appealing to HCLTech’s ecosystem partners, which include Microsoft, Amazon Web Services (AWS), SAP and IBM, further noting the already established integration with Microsoft’s GitHub Copilot and being offered as a certified extension.

Second, the HCLTech executives noted AI Force is valuable to more than just coders and enterprise professionals looking for AI-enabled cost- and time-saving assistance. Being extensible and working with multiple large language models (LLMs) made AI Force flexible enough for a broader enterprise workforce audience.

Third, the inclusion of a customer context using enterprise data makes the solution more than simply an addition to an existing LLM accelerator. HCLTech’s leaders emphasized the value of customer context inherent to the platform, noting that HCLTech will train AI models on customer-specific data.

On a related note, the HCLTech executives described the underlying AI architecture as “comprehensive, but not complex; unified” and “holistic, therefore not a point solution.” According to HCLTech, AI Force has been granted 18 patents, and its batch processing mode reduces the strain on the underlying cognitive infrastructure, leading to reduced energy consumption. In TBR’s view, the characteristics and architecture likely resonate with IT professionals and particularly software engineers, while the flexibility and customer context significantly enhance the business value of AI Force.

Building on key characteristics, the HCLTech AI leaders walked through AI Force’s overall advantages, including a single, unified platform, rather than hundreds of solutions; simplified management and budget; built-in use case prioritization, allowing decision-makers and IT support to focus on the use cases that would lead to business transformation; inherently enabled customer context, greatly enhancing the stickiness of AI Force within an enterprise; and built-in data ingestion and storage, significantly diminishing the likelihood of disjointed or counteracting results.

In TBR’s view, AI Force’s advantages play well for different buying and decision-making personas. Procurement, IT operations and even the CFO can appreciate a single solution with simplified management. Business unit leaders can find and deploy uses cases suitable to their specific needs. And the inherent stickiness of AI Force can appeal to executives looking to gain advantages from deploying AI-enhanced solutions and not simply paying for another round of new technologies.

Applying GenAI only when and where it is needed

Not every business problem is best solved by deploying GenAI-enabled solutions. HCLTech leaders emphasized that some customer problems can be handled by simple automation, some with traditional AI, and only a niche set through GenAI-enabled solutions.

In TBR’s view, HCLTech’s strategic decision to recognize that customers can solve problems with existing technologies and do not always need GenAI-enabled solutions plays well, given enterprise buyers’ fatigue around the constant carousel of emerging technologies and ever-increasing IT budgets. Simply showing customers that AI Force will help identify where GenAI is best suited and where it is not should resonate with IT decision makers and their C-Suite bosses, all of whom are looking for tangible returns on technology investments. If HCLTech can help get more from existing technologies, AI Force is an immediate value-add.

 
Notably, HCLTech works with a wide variety of models and is model agnostic. The choice of model depends on a client’s business problem and the context of the client’s own data. Rather than recommending a model based on technical specifications or a familiarity with a particular model, HCLTech centers the decision on the client’s specific business problem.

Four ways to consume, determined by the customer’s business problem

HCLTech’s customers can take advantage of the AI Force platform in whichever deployment and consumption model fits their needs. HCLTech offer the platform as a stand-alone deployment, embedded into the client’s IT environment, through APIs (which one HCLTech leader described as “headless … behind the scenes”), or on the edge through AI-enabled PCs.

Critically, HCLTech leaders assured TBR that the customer’s consumption model of choice made “no difference in how the customer pays for AI Force.” As for decision making around the consumption model, HCLTech leaders said the company advises customers based on the business problem the customer is trying to solve.

On this point, TBR believes HCLTech has, itself, made a strategic decision: allow the customer’s environment, needs and business problems to determine the best commercial and technological fit for HCLTech’s platform, rather than HCLTech’s business and commercial needs dictating deployment terms.

The discussion included detailed accounts of two deployments at different types of companies. First, to accelerate a legacy IT modernization effort at a financial institution, HCLTech used AI Force to map, migrate and test more than 200 legacy applications.

Second, at a massive global technology company, HCLTech used AI Force to radically reduce marketing spend through a what an HCLTech leader referred to as “marketing ops transformation from manual-driven content development by a third-party vendor to GenAI-automated content generation.” TBR has been briefed on similar marketing operations improvements through GenAI automation, but none at the same scale or with comparable cost savings as those described by HCLTech.

HCLTech leaders also described the company’s recently announced partnership extension with Xerox. The company will leverage automation, product and sustenance engineering, and process operations services — including order to cash, sales and marketing operations, and supply chain and procurement — along with AI Force, to deliver a unified interface that transforms the way employees and clients engage with Xerox.

HCLTech describes other AI Force use cases on its website.

Minimal change management and increased visibility provide immediate value

In TBR’s research, GenAI adoption has benefited enterprises with well-managed and orchestrated data, even if that data exists in silos. In contrast, enterprises with little visibility into their data have been challenged to see meaningful returns on their GenAI investments, in part because of a challenge HCLTech identified above: People within an enterprise typically like the specialized software tools they are already using and want to keep using them.

HCLTech’s AI Force does not ask for change from multiple personas across an enterprise or for adoption of a new set of tools; it instead provides greater visibility into everyone’s processes, software usage and IT environment and demonstrates how one person, process or tool can affect another. By providing visibility without demanding replacement and adoption, HCLTech’s AI Force can deliver value with minimal change management.

AI Force may be what helps HCLTech survive the coming IT services business model upheaval

As HCLTech’s leaders noted to TBR, HCLTech is not new to AI, as the company had been investing in AI, training its workforce around AI principles and deployments, working with chip manufactures, and developing and selling software all before GenAI emerged. As one slide in HCLTech’s presentation noted, the company has been “Building and deploying AI solutions since 2016.”

Legacy — and maybe more accurately, proven — skills and capabilities lend immediate credibility to what HCLTech brings to clients and partners with AI Force. Further, a significant part of what separates HCLTech from immediate peers is the company’s IP-driven services model, a strategic difference that becomes increasingly relevant as clients ask for more GenAI-enabled services and less labor-dependent services. HCLTech’s business model is not simply enhanced by AI Force and other IP-driven solutions; it might actually be saved by those capabilities as the entire IT services business model undergoes significant, AI-induced change.

TBR will be watching as HCLTech develops additional platforms, brings agentic AI solutions to discussions with clients, and enables fully autonomous AI deployments, all built on a solid foundation of expertise, experience and ever-increasing capabilities around artificial intelligence.

On Oct. 1, TBR attended Fujitsu’s Executive Analyst Day in Santa Clara, Calif., and engaged with Fujitsu leaders, including Tim White, chief strategy officer; Ted Okada, SVP and head of Technology; Ted Nakahara, SVP and Head of Strategic Alliances; Fleur Copping, VP of Strategic Alliances in Regions; and Asif Poonja, EVP and CEO of Fujitsu Americas. The following reflects main stage presentations, breakout sessions and one-on-one discussions, as well as TBR’s ongoing analysis of Fujitsu’s business model, strategy and performance.

Fujitsu in Transition, with Clear Direction and Intent, Playing to Strengths

Three things about Fujitsu stand out in a crowded IT services and consulting market. First, the company is in the middle of an organizational evolution, changing its business model to fit emerging client demands and orienting its go-to-market strategy around Uvance. Second, Fujitsu’s commitment to change in the Americas has completely remade the company around IT services and consulting, with aspirations to become a technology consulting leader. And third, Fujitsu’s alliances strategy, while still dependent on labor-intensive relationships and persistent account-level management, includes all the best practices TBR has seen from larger competitors, with at least one unique twist. In short, Fujitsu’s evolution will likely make the company a highly capable contender as the IT services and consulting market changes.

At the start of the analyst event, Tim White, chief strategy officer, explained that Fujitsu’s transition has been underway for a few years and has included allowing the Americas business to shed everything except services. As part of the overall transition, Fujitsu committed to expanding consulting while continuing to deliver on core IT services and modernizations. White noted that Fujitsu is roughly halfway through a three-year plan to grow services and the Americas region has already surpassed targets for 2024. For example, Uvance accounts for 37% of Fujitsu Americas’ business, above the 30% goal.

Critically, according to White, Fujitsu has not lost a step on technology advances or quality of services delivered, so clients and alliance partners continue to be well served. The change — the evolution — is primarily in how Fujitsu sees itself and its future. And that future is Uvance.
 

In TBR’s view, understanding Fujitsu’s existing and evolving business model, strategy and performance requires, perhaps surprisingly, a certain separation from the typical analysis, if only because of Fujitsu’s current transition.
 
While there is perhaps some uncertainty among analysts around Fujitsu’s brand, specific offerings and organizational structure, TBR sees no evidence that Fujitsu’s clients and technology alliance partners lack the clarity required to make decisions about Fujitsu’s capabilities, scale and skills.
 
Undoubtedly, Fujitsu’s brand in the Americas could use a significant boost — without which a ceiling could remain for the company’s growth — but the importance of marketwide brand recognition pales in comparison to a successful track record of delivering IT services and consulting, providing innovative solutions, and leveraging the latest technologies to solve clients’ problems.

Uvance Is “the Future State of Fujitsu’s Portfolio”

Fujitsu’s leaders stressed the centrality of Uvance in the company’s strategy and vision for IT services, consulting and technology. White described Uvance as “the future state of Fujitsu’s portfolio.” Asif Poonja, CEO of Americas, said, “Uvance is the center of our strategy.” At the center of Uvance is consulting. Fujitsu announced a goal to hire 10,000 consultants, but White and others explained that Fujitsu’s focus is not the number but the portfolio shift toward consulting while still serving clients who need core IT services and modernization.
 
Poonja noted that Fujitsu will focus on technology consulting, rather than McKinsey-style business consulting, playing to Fujitsu’s legacy technology strengths. In TBR’s view, technology-led consulting reflects the current demand among enterprise consulting buyers to infuse every consulting engagement with technology, a trend well underway before the hype began around generative AI (GenAI). Fujitsu’s leaders added that Uvance Wayfinders — essentially business and technology consultants — are able to pull together all of Fujitsu’s capabilities and offerings.

In TBR’s view, Uvance is the framework around the company’s “SaaS-like” business model, with the leaders using the term “SaaS-like” but recognizing the phrasing may need further refinement and/or explanation. Fujitsu will use platform-enabled services to drive higher-value conversations and engagements, led by the consultants the company is planning to hire and/or acquire. Fujitsu will sell IP when needed and drive managed services through its delivery capabilities. The shift in the Americas toward becoming an asset-light organization is the first step, and the second step is expanding consulting capabilities and scale. The third step is organizing delivery under a globally run P&L (which Fujitsu may have already begun).
 
Meanwhile, modernization services — moving from mainframe to cloud — remains the engine that keeps Fujitsu running. The company still has its own data centers outside the U.S. and also still has plenty of clients running on mainframe, especially in their core verticals, like public services. For TBR, Uvance’s success may depend on broader adoption of the asset-light Americas strategy, albeit at a pace that does not compromise quality or lose clients in core markets. Again, Uvance is the future state of Fujitsu’s portfolio.

Fujitsu Americas: “Leveraging Global Pillars to Grow”

As described by Poonja and White, Fujitsu in the Americas has persistently pared down its offerings to focus only on IT services and technology consulting, playing to Fujitsu’s strengths and concentrating on industries in which the company has proven capabilities, well-established relationships with clients and differentiated offerings.

Poonja added that, although Fujitsu Americas earned a small percentage of Fujitsu’s overall revenues, corporate leadership in Japan recognize the importance of the Americas market and understand the challenges of building a more widely known brand. Poonja stressed that Fujitsu Americas would continue “leveraging global pillars to grow” while staying focused on regional strengths, specifically in government, manufacturing and AI.

In TBR’s view, Fujitsu Americas’ current state and trajectory align well with Fujitsu’s overall corporate strategy. The business aspires to be a top technology consulting company and appreciates the difference between being skilled at technologies and being able to make the business case for Fujitsu’s solutions. As an integral part of its strategy, Fujitsu Americas consistently pulls in the global company’s broader strengths and capabilities.

The use cases that Fujitsu’s leaders shared during the event highlighted the company’s technology, such as 5G and AI, and its deployable, offshore scale. Overall, Fujitsu Americas’ leadership presented a compelling story of evolution, strategic focus, early positive results and appreciation for current weaknesses. In contrast to analyst events dominated by marketing messages, Fujitsu maintained a substantive and clear-eyed atmosphere, with discussions centered on realistic expectations for Fujitsu Americas’ changing position in the IT services and consulting market.

Fujitsu’s Alliances: Doing the Hard Work While Taking Customer Zero to Another Level

In both the formal presentations and the informal discussions, Fujitsu’s leaders impressed TBR with the fullness and maturity of the company’s alliances strategy. The ecosystem has changed substantially in recent years, forcing companies to rethink their partnering strategies and more closely examine the best practices of peers, competitors and alliance partners. This shift has been an ongoing focus of TBR’s research, which has increasingly been used by alliance leaders at global technology companies as they undergo this transformation.

As part of this research, TBR has analyzed a wide range of alliance strategies and activities, from inadequate and underfunded to strategically thoughtful and exceptionally well managed. Fujitsu Americas, in TBR’s assessment, lands solidly in the latter category, based on the full range of investments and activities that Fujitsu’s leaders described with respect to their five strategic partners: Amazon Web Services (AWS), SAP, Microsoft, Salesforce and ServiceNow. (Note: See TBR’s ecosystem reports for more information.)

According to Fujitsu’s leaders, the next strategic partner will be determined by Uvance’s business strategy and continued evolution in the technology space, particularly AI. Keeping perspective on the challenges of managing technology partners, Fleur Copping, VP of Strategic Alliances in Regions, noted that every alliance relationship requires constant attention and, often, engagement-by-engagement reinforcement around Fujitsu’s offerings, capabilities and value proposition. Copping further acknowledged that Fujitsu needs to strengthen partner cosell activities. In other words, even when executing on all the best practices, alliance management remains a hard slog.

During the event, TBR noted two additional points on alliances — areas that are perhaps unique to Fujitsu. First, TBR has consistently heard that the customer zero approach to new technologies and offerings resonates with clients by bringing credibility and assurance. IT services companies, consultancies and their technology partners have also told TBR that the customer zero approach helps solidify alliances and can lead to innovations and new solutions. Fujitsu appears to be taking customer zero to the next level. For example, Copping described how Fujitsu brought its internal human resource management professionals to a client meeting about a joint Fujitsu-ServiceNow opportunity. The Fujitsu professionals told the client about their own experiences using the ServiceNow solution. This more personal touch resonated with the client and demonstrated the fullness of Fujitsu’s capabilities to alliance partner ServiceNow.

Second, Copping noted that because many of Fujitsu’s customers “don’t have as much of a voice” with the cloud vendors and software giants as the largest enterprises, Fujitsu can be an advocate for these small and midsize enterprises, amplifying their concerns and needs to the likes of Microsoft and SAP. TBR has not heard Fujitsu’s peers explicitly state this marketing message. As a matter of positioning, particularly with technology partners, Fujitsu’s message could be another way of gaining mindshare and differentiating from IT services and consulting competitors.

Consulting Is Harder Than It Looks; Fujitsu Has a Good Plan

White “unabashedly” characterized Fujitsu as a technology company, but emphasized using technology as a means to deliver services rather than making technology a commodity play. In the Americas in particular, Fujitsu would not “move away from our heritage as a technology company” but would more fully embrace consulting and the future portfolio of Uvance.

In TBR’s view, keeping Fujitsu’s heralded research, innovation and technology capabilities as foundational strengths makes strategic sense while leaving open questions around consulting. For example, one Fujitsu leader outlined the company’s AI sales approach in four basic steps:

1. Get the client interested in Fujitsu’s technology
2. Do a proof of concept with Fujitsu’s AI platform
3. Allow the client to use a precommercial instance of the platform
4. Bring in Uvance to develop a full solution, highly customized to the client

The fourth step, at a minimum, requires consulting skills, business knowledge and industry expertise, although many of Fujitsu’s peers include those elements throughout the sales and delivery process. Recruiting (or acquiring), retaining and managing consulting talent could affect Fujitsu’s corporate culture and undoubtedly will challenge Fujitsu’s leadership.

Further, and perhaps the most significant obstacle for Fujitsu in the Americas, will be gaining permission from clients to deliver consulting. By narrowing its scope to technology consulting — not the broad swath of strategy and operations consulting — Fujitsu plays to its own strengths, lessens the marketing load, and likely does not give up market share as the company is unlikely to displace firms like McKinsey & Co. or Boston Consulting Group (BCG).

Part of gaining permission, in TBR’s view, will be positioning Fujitsu differently with its current clients, particularly with respect to the key personas interacting with Fujitsu professionals. During the event, one Fujitsu leader described current clients’ struggles to adopt GenAI as a combination of an inability to do the basic work of making their data usable, the uncertainty around return on investment, and a fear of running afoul of the law as new regulations come into effect.

Yes, Fujitsu can address all of these concerns, but these hurdles impact and reflect the responsibilities of three different personas within an enterprise. Fujitsu’s challenge will be to become the preferred technology consulting provider for all three personas. In short, consulting is harder than it looks, and TBR believes Fujitsu has the right vision, strategy and approach. We will continue to monitor the company’s ability to execute.

TBR’s ongoing coverage of Fujitsu includes dedicated quarterly reports and inclusion in appropriate benchmarks, market landscapes and ecosystem reports. Log in to TBR Insight Center™ to view all current research.

Strong Services Execution, Enabled Through Infosys Cobalt and Focused on Outcomes, Provides Foundation Upon Which Infosys Can Build AI Strategy

The steady performance of Infosys’ cloud business highlights the company’s pragmatic approach to its portfolio and go-to-market efforts, largely enabled by Infosys Cobalt.

Building on Infosys Cobalt’s success, the company now has an opportunity to steer client conversations toward AI and is positioning Infosys Topaz as the suite of services and solutions that can bring it all together. Agentic AI (i.e., autonomous AI) is the newest set of capabilities dominating client and partner conversations. Scaling AI adoption comes with implications and responsibilities, which Infosys is trying to address one use case at a time. For example, earlier in 2024, Infosys launched the Responsible AI Suite, which includes accelerators across three main areas: Scan (identifying AI risk), Shield (building technical guardrails) and Steer (providing AI governance consulting). These capabilities will help Infosys strengthen ecosystem trust via the Responsible AI Coalition. Infosys also claimed it was the first IT services company globally to achieve the ISO 42001:2023 certification for ethical and responsible use of AI.
 
Regardless of the client’s cloud and AI adoption maturity, everyone TBR spoke with and those who presented at 2024 Infosys Americas Confluence agreed that the need for data strategy and architecture comes first. Two separate customers perfectly summarized the state of AI adoption: “You can’t get to AI without reliable data across the supply chain,” and “GenAI is not a magical talisman. Companies need to build true AI policy and handle GenAI primitives before scaling adoption, with the shift in mindset among developers and users a key component.”

Infosys recognizes that AI adoption will come in waves. The first wave, which started in November 2022 and continued over the last 18 to 24 months, was dominated by pilot projects focused on productivity and software development. In the current second wave, clients are starting to pivot conversations toward improving IT operations, business processes, marketing and sales. The real business value will come from the third wave, which will focus on improving processes and experiences and capitalizing on opportunities around design and implementation. Infosys believes the third wave will start in the next six to 12 months. While this might work for cloud- and data-mature clients, only a small percentage of the enterprise is AI ready across all components including data, governance, strategy, technology and talent. Thus, it might take a bit longer scale for AI adoption to scale.

But as Infosys continues to execute its pragmatic strategy, the company relies on customer success stories that will help it build momentum. As another customer positioned it, “Infosys knows the data and processes. They know what they are talking about. In [the] 11 years since we have worked with them, they have not missed a single release with their … team delivering the outcomes.”

We believe Infosys’ position within the ecosystem will also play a role in how fast and successful the company is when it comes to scaling AI with clients. Infosys’ AI-related messaging includes 23 AI playbooks, which focus on value realization spanning technical and business components, such as Foundry and Factory models, as well as change management.

Of course, AI and GenAI will also disrupt Infosys’ business model and service delivery. And while many of its peers are still debating internally how to best position themselves with clients and pitch the value of GenAI without exposing their business to too much risk in the long run, Infosys’ thoughtful, analytics-enabled approach to commercial and pricing model management has positioned the company favorably with price-conscious clients that have predominantly been focused on digital stack optimization over the past 18 months.
 
Infosys’ success with large deals is a testament to the effectiveness of the company’s strategy. In FY4Q24 Infosys had $4.5 billion in large deals, which is the highest quarterly large deal value for the company. In addition, investing in and transforming right-skilled talent who can support this model are critical components to the company’s success. While Infosys has trained 270,000 of its employees on AI, we believe it is the composition and depth of these skills that vary across service lines and clients, especially as outcome-based pricing models now represent half of the contracts in some service lines.

Infosys’ Investments in Engineering and Marketing Strengthen Company’s Position as a Solutions Broker

Navigating the hype of GenAI requires Infosys to also recognize and place bets on other areas that are tangential and have a more immediate impact on its value proposition and overall financial performance.

Infosys Tries to Bring CIOs and CMOs Together Through Infosys Aster

Building off the success of Infosys Cobalt and Infosys Topaz, the company launched Infosys Aster, a set of AI-amplified marketing services, solutions and platforms. While Infosys Cobalt and Infosys Topaz have horizontal applications, the domain-specific nature of Infosys Aster provides a glimpse into what we might expect to see from Infosys in the near future, given the permeation of GenAI across organizational processes. Additionally, the marketing orientation of Infosys Aster is not surprising since most GenAI use cases are geared toward improving customer experience.

Built around three pillars — experience, efficiency and effectiveness — Infosys Aster will test Infosys’ ability to capitalize on a new wave of application services opportunities and create first-party data-unique solutions rather than providing off-the-shelf solutions just to ramp up implementation sales.
 
With DMS continuing to act as a conduit for broader digital transformation opportunities for Infosys, we expect the company to use Infosys Aster to position its marketing services portfolio in a more holistic manner, creating a bridge between CMOs and CIOs and also bringing parts of Infosys’ Business Process Management subsidiary into the mix to position the company to capture marketing operations opportunities. Infosys Aster provides a comprehensive set of marketing across the value chain of strategy, brand and creative services, digital experience, digital commerce, marketing technology (martech), performance marketing and marketing operations.
 
Although this is an area of opportunity for Infosys, rivals such as Accenture have an advantage in the marketing operations domain. We do believe the greater opening for Infosys comes from focusing more on driving conversations around the custom application layer and steering client discussions toward achieving profitable growth through the use of Infosys Aster. Client wins such as with Formula E and ongoing work with the Grand Slam tennis tournaments also allow Infosys to demonstrate its innovation capabilities beyond traditional IT services. Part marketing and part branding, wins such as these elevate Infosys’ capabilities. Executing against its messaging is key for Infosys.

Infosys Engineering Services Will Close Portfolio and Skills Gaps Between IT and OT Departments

Infosys Engineering Services remains among the fastest-growing units within the company as Infosys strives to get closer to product development and minimize GenAI disruption on its content distribution and support position. Since the 2020 purchase of Kaleidoscope, which provided a much-needed boost for the company to infuse new skills and the IP needed to appeal to the OT buyer, Infosys has further enhanced its value proposition to also meet GenAI-infused demand.

Infosys recently announced the acquisition of the India-based, 900-person semiconductor design services vendor InSemi, which presents a use case where the company applied a measured risk approach to enhance its chip-to-cloud strategy as it tries to balance its portfolio of partner-ready solutions, such as through NVIDIA, with a sound GenAI-first cloud-supported story. Shortly after, Infosys also acquired Germany-headquartered engineering R&D services firm in-tech. The purchase will bolster Infosys’ Engineering Services R&D capabilities and add over 2,200 trained resources to regional operations across Germany, Austria, China, the U.K., and nearshore locations in the Czech Republic, Romania, Spain and India, supporting Infosys’ opportunities within the automotive industry. The purchase of in-tech certainly accelerates these opportunities, bringing in strong relationships with OEM providers, which is a necessary steppingstone as Infosys tries to bridge IT and OT relationships.

We do not expect Infosys’ cloud business Infosys Cobalt to slow down anytime soon given the company’s market position for infrastructure migration and managed services as well as its well-run partner strategy with hyperscalers. Adding semiconductor design services bolsters that value proposition as buyers consider whether to use price-attractive CPUs or premium-priced GPU data centers. The latter currently dominates the marketplace, and we expect that trend will not change for at least the next 18 to 24 months. But having semiconductor engineers on its bench can help Infosys start supporting CPU-run models, further appealing to more price-sensitive clients. Meanwhile, Infosys is planning to train 50,000 of its employees on NVIDIA technologies. Lastly, the close collaboration between Infosys Engineering Services and Infosys Living Labs further extends the company’s opportunities to drive conversations with new buyers and demonstrates its ability to build, integrate and manage tangible products.

Infosys’ Reliance on Partners Provides a Strong Use Case of Trust and the Future of Ecosystems

The mutual appreciation between Infosys and partners was amplified throughout 2024 Infosys Americas Confluence. From a dedicated Partner Day to partner-run demos and various sponsorship levels to main-stage presentations, the experience reminded TBR of an event that a technology vendor would typically set up (think: Adobe Summit, AWS re:Invent, Dreamforce, Oracle OpenWorld, to name a few).
 
Infosys’ decision to feature some of its key alliance partners in a similar way that the tech companies do suggests a strong alignment between parties starting with the top-down executive support, through mutual investments in both portfolio and training resources, and most importantly, knowledge management between the parties. In conversations throughout the event with partners, it was evident that Infosys’ strategy is consistent regardless of the length of relationship, from decades-long relationships such as with SAP or an emerging but fast-growing alliance such as with Snowflake. All partners agreed Infosys’ humble approach to managing relationships has put them at ease in working with Infosys and delivering value to joint clients.

After attending Infosys’ U.S. Analyst and Advisor Meeting in Texas in March, TBR wrote about Infosys’ relationship with Oracle, highlighting the level of trust and transparency Infosys typically deploys with partners. In TBR’s Summer 2024 Voice of the Partner Ecosystem Report we wrote: “Services vendors most frequently rely on their direct sales efforts and permission to demonstrate value with customers to drive revenue. Using demos and proof-of-concept discussions as a frequent tactic to engage with clients also highlights many of the profiled vendors’ consulting heritage.

The technical expertise came through very vividly and aligned with Infosys’ strengths in playing within its own swim lane. In a main-stage discussion, Infosys and Hewlett Packard Enterprise (HPE) discussed at length the role each plays in pursuing opportunities in areas such as GenAI and the need for greater interactions through multiparty model including the value NVIDIA brings to the table, for example. While one could argue that Infosys’ alliance partner strategy mirrors that of many of its competitors as it seeks to secure foundational revenue opportunities while pursuing innovation through a measured risk approach, the company strives to differentiate by acknowledging its strengths and sticking to them rather than branching too far into partners’ territory, which enterprise buyers strongly appreciate.

Land-and-execute Approach and Expansion Will Follow Naturally

Close to a decade ago, TBR analyzed what Infosys’ five-year strategy should look like. While the company went through leadership and strategy changes during this period to such an extent that one could cite concerns about consistency, those days are over. Infosys now has a well-grounded strategy with executives executing on a clear vision rooted in a land-and-execute approach rather than the typical land-and-expand framework many of its peers aspire to. This puts greater pressure on the company’s quality and talent-retention strategies. While no one is immune to macroeconomic headwinds, the internal growth and training opportunities the company provides for its employees across all levels provides a strong backbone to a culture of learning and trust.

TBR will continue to cover Infosys within the IT services, ecosystems, cloud and digital transformation spaces, including publishing quarterly reports with assessments of Infosys’ financial model, go-to-market, and alliances and acquisitions strategies. Access reports as soon as they’re available with TBR Insight Center™ access.

Oracle Is Charting a Path for Unprecedented Growth with Its ‘Infrastructure Anywhere’ Vision

Oracle has among the most complete, full-stack cloud portfolios, from infrastructure to database to applications. While Oracle Cloud World 2024 covered a sizable landscape, one theme stuck out during the four-day event: deployment flexibility. This theme reflects how much Oracle has changed compared to 2016, when Gen2 OCI (Oracle Cloud Infrastructure) launched.

With multitenant OCI, Dedicated Regions, Cloud@Customer and Oracle Alloy, a specialized service where customers white label OCI services inside their own data centers, Oracle has quickly emerged as one of the most flexible, delivery-agnostic IaaS vendors on the market. Of course, the other big component of Oracle’s “infrastructure anywhere” vision is multicloud, in which customers can run Oracle databases as native services hosted in the data centers of Oracle’s biggest hyperscaler competitors.

Not only does this move reflect a major maturity leap for Oracle, in which Oracle cozies up to its rivals to better address the needs of the customer, but it is also critical to the company’s financial strategy. In addition to giving Oracle the flexibility to allocate more capex dollars toward strategic compute and storage resources as opposed to land and buildings, this strategy will help Oracle get its on-premises database support base to the cloud faster. In doing so, Oracle may forfeit lucrative support and license contracts, but the company reports that for every $1 in lost license and support gross profit it could realize as much as $5 in gross profit in the cloud, which is a testament to how quickly the cloud business is growing.

The multicloud strategy is also one of the reasons Oracle awed financial analysts not only by raising its FY26 revenue targets by $1 billion, to $66 billion, but also by setting a FY29 goal of $104 billion. This target, backed by Oracle’s $99 billion RPO (remaining performance obligation) balance, implies an average corporate revenue growth rate of roughly 16% over the next five years. This kind of growth was once unheard of for Oracle, but with cloud now overtaking support as the biggest business, Oracle is a different company, and the OCI growth trajectory instills a degree of optimism in Oracle’s ability to disrupt a highly saturated market in the years to come.

Announcing Oracle Database@AWS

Based on interactions at Cloud World, it is clear the Oracle Database@AWS announcement was the most noteworthy. In our view, given Oracle already launched Oracle Database@Azure, and more recently Oracle Database@Google Cloud, which is now live in four regions, it was only a question of when, not if, Amazon Web Services (AWS) would partner with Oracle.

With this announcement, Oracle officially saved the biggest hyperscaler for last, onboarding all the critical partners it needs to migrate legacy database customers and accelerate cloud revenue growth. In terms of how this alliance will work, it is no different than the approach Oracle takes with Microsoft Azure and Google Cloud; Oracle will deliver the hardware and networking inside AWS data centers so customers can provision Oracle database services natively from the AWS console and have the system run in AWS, just as it would if it was hosted in OCI.

The approach of physically embedding OCI within other clouds as opposed to just bolting Oracle Database on to other infrastructure through a standard interconnection is important as it will not only give customers the native AWS, Azure and Google Cloud Platform (GCP) experiences they are used to, but also limit latency as the Oracle Exadata hardware is physically located with the appropriate hyperscaler.

One could argue Oracle is taking a lot of risk with this strategy, as it is essentially bringing customers and their data closer to AWS, Azure and GCP. But in the age of mounting competition, not to mention generative AI (GenAI), it is a risk worth taking. As one customer at a major financial services firm recently told us, “The GenAI decision makers will not be the old world relational database experts,” and these alliances could help ensure Oracle stays relevant in cloud GenAI discussions by making it easier for customers to use the data within Oracle Database for RAG (retrieval augmented generation), to fine-tune foundation models and build new applications using tools many customers are likely already using, like Amazon SageMaker.

We should also point out the concept of data gravity. Customers leveraging these multicloud services will still be established Oracle Database customers with some Oracle SaaS presence, and therefore the bulk of their business data gravity will naturally reside within OCI. Those customers may still be inclined to keep their databases within OCI and not extend to other clouds, but with this strategy, Oracle is at least giving them the option to do so. We expect that these multicloud offerings will gain a lot of traction among Oracle Database customers that have big application footprints on other clouds.

Oracle Analytics Is the Glue Between IaaS and SaaS

Analytics, and the ability to turn data into business insight, is the ultimate objective for nearly every organization. With popular tools like Power BI and Tableau as well as neutral data platforms like Snowflake on the market, customers have a lot of choices when crafting the analytics stack.

But customers have also made it clear they want to limit the integration burden, and one of the compelling things about Oracle’s approach to analytics is how it can store customers’ operational data from Fusion applications in the Autonomous Data Warehouse (ADW) for analytics as part of a single SKU. This approach, productized as Fusion Data Intelligence (FDI), reinforces the value of Oracle playing in both the SaaS and IaaS markets and its ability to deliver a unified solution.

Evolving the Data Lake Strategy and Competing as a Unified Solution

Access to operational data in the Fusion suite will remain the hallmark differentiator for FDI, but it is on the infrastructure side where Oracle took a big leap forward with the launch of Intelligent Data Lake. Oracle has been elevating its data lake strategy and positioning for some time, but this announcement puts Oracle more squarely into the space.

At its core, Intelligent Data Lake is a reworking of existing OCI capabilities, such as cataloging and integration, to create a single abstraction layer that in true data lake fashion, allows customers to query data on object storage, such as Amazon S3 or Microsoft OneLake, with support for the popular Apache Iceberg and Delta Lake frameworks.

To be fair, with Fabric and BigLake, Microsoft and Google Cloud, respectively, have been similarly making advancements with the data lake architecture to better address analytics workloads. However, Oracle is not only adding the simplicity and performance benefits of the data lake but also delivering the architecture in a way in which customers can run the entire data pipeline and still have all the analytics components in a single SKU.

With Oracle’s launch of a native Salesforce integration with FDI, which allows customers to combine their CRM and Fusion data within the lakehouse architecture, Oracle’s vision of embedded clouds at the database layer is extending to analytics.

Though FDI’s draw will still be primarily with existing Oracle customers, the company is clearly taking steps to help combine Fusion with non-Fusion data and make its platform more relevant within the cloud ecosystem. While FDI may not rip and replace the analytics footprint within any particular account, we could see scenarios where FDI displaces some components of the stack, such as Snowflake at the infrastructure layer, or on the analytics side, PowerBI in Microsoft Fabric.

New Applications Are Being Built on the Analytics Stack

In general, scaling the existing platform components of Oracle Analytics is a top priority for the company, but there is another emerging piece of the analytics vision: Intelligent Applications. Coming soon, Oracle will offer applications — People Leader Workbench for HCM and Supply Chain Command Center for SCM — that sit on top of the Fusion system of record, within the FDI platform.

This approach should allow Oracle to target a broader set of personas. For example, in People Leader Workbench, it is not necessarily about reaching only the C-Suite but rather anyone who manages people and can benefit from data-driven insights on their people, and most notably, take action on that insight by connecting back to the Fusion HCM system of record.

What About GenAI?

GenAI has officially exited the hype cycle and is being widely deployed within the enterprise, but when it comes to analytics, capabilities like dashboarding, semantic models and visualization are still taking precedence It is still early, but customer feedback suggests that if data is properly configured and there are guardrails in place, GenAI in analytics has a lot of potential.
 

Dive into the complexities of vendor partnerships in this recent TBR Insights Live session — Click the image below to watch on demand today!

One of the key announcements at the event was the general availability of Analytics Cloud AI Assistant in Oracle Analytics Cloud (OAC), which is based on a large language model (LLM) so customers can ask questions about their data. Staying in line with the rest of the Oracle strategy, where GenAI is fully embedded into the portfolio and available to customers at no added cost, the analytics assistant will be available to OAC customers for free as part of their existing instances.

Speaking of SaaS and IaaS

From database alliances to the data lake architecture, Oracle has made many calculated moves at the PaaS layer to better compete for strategic workloads. But there are other innovations and key developments in the upper and lower rungs of Oracle’s cloud portfolio.

Oracle Targets Complete End-to-end Process Automation with AI Agents in Fusion Suite

Since it first entered the GenAI game in late 2023, Oracle stood out in the SaaS market for not upcharging customers for GenAI in their SaaS applications. This speaks to Oracle’s play at the IaaS layer with the OCI GenAI Service, which is native to the same infrastructure where all Oracle’s SaaS applications live.

Logically, this approach means that as Oracle’s LLM partners, which host in OCI, push the boundaries of their models, Fusion customers stand to benefit in not just using GenAI for basic assisted authoring and summarization use cases (e.g., writing a job description in Fusion HCM or summarizing customer calls in CX), but actually contextualizing data. In the long term, this could mean providing reasoning on that data to manage more complex workflows and deliver business recommendations.

At this time last year, Oracle announced 50 GenAI use cases in the SaaS suite. This year, the applications team announced the number of use cases has grown to over 100, while there are now more than 50 AI agents within the Fusion suite. This announcement marks a progression in how Oracle is moving from more generic prompt-and-response use cases in Fusion to actual contextualization use cases, by applying LLM-based RAG agents to address specific goals and roles within a particular business function. In Fusion HCM, this could include a benefits analyst agent, offering users the ability to ask questions, such as which health plan features are available, based on the enrollment data contained in Fusion HCM and the health plan document specific to the company.

But the most commonly cited example throughout the event was the Document IO agent in Fusion ERP, which can convert a picture of a quote in a particular currency into U.S. dollars and automatically create and load a purchase order (PO) within the system. With these AI agents, we see Oracle taking the next big step in addressing more complete process automation and productivity enhancements within its SaaS portfolio, and ultimately a shift in mindset where it is less about delivering an ERP system or an HCM system but more about completing end-to-end business process and experience.

OCI Strategy Centers on Growing Within the Large Enterprise and Attracting Cloud-natives

Oracle’s ability to offer among the most flexible cloud delivery methods is the focus of the OCI strategy and strategic road map, led by high-profile partnerships with AWS and others. But Oracle’s strategy is about being agnostic to not only where customers run OCI but also how they run OCI.

For example, at Cloud World Oracle announced Dedicated Region 25, a longtime investment and feat of engineering that essentially consolidates a standard Oracle Cloud region into just three racks, which we physically saw on the keynote stage. This configuration extends the value proposition of Dedicated Region, where customers can get the scale and economics of the public cloud inside their own data centers.

Dedicated Region 25 could also play a big role in helping Oracle reach new customers. Oracle’s multicloud alliances will undoubtedly be appealing to the large enterprise customer base, but offerings like Dedicated Region 25 could help Oracle attract cloud-native and AI companies looking for a more compact footprint that can still scale to support critical workloads.

Conclusion

Led by its partnership with AWS, Oracle Cloud World 2024 told a story of a maturing business that is turning competitors into partners to better address the needs of the customer. By keeping the lifeblood of the cloud stack, the database, relevant in customers’ cloud transformations, Oracle also ensures it remains competitive in GenAI scenarios, which aligns with the GenAI investments the company is making in other areas of the stack, from analytics to Fusion applications.

As the company continues to navigate as a full-stack vendor catering to the existing Oracle base, while simultaneously gaining relevance in the broader cloud ecosystem, there is a lot of potential ahead, and Oracle is well on its way to becoming a $100-plus billion company.