Can Infosys’ Topaz platform help close its GenAI revenue gap with Accenture and TCS in 2025?

Infosys Limited (NSE: INFY), the Indian technology consulting powerhouse, launched its generative AI-first platform, Topaz, in 2024 to accelerate enterprise transformation and monetize GenAI capabilities. In an environment where Accenture plc has already reported over $5.6 billion in GenAI bookings since 2023 and Tata Consultancy Services Limited has disclosed a $900 million pipeline through its Ai.Cloud unit, Infosys is under pressure to convert Topaz engagements into tangible revenue. The company’s success in fiscal year 2026 will depend on how effectively it scales Topaz, achieves client adoption beyond proof-of-concept stages, and discloses revenue impact with greater clarity.

Founded in 1981, Infosys transitioned from application development and outsourcing into high-end consulting, engineering, and digital services. The Topaz platform reflects a broader strategic realignment, marking a shift from resource-led delivery to AI-enabled solutions integrated across industry verticals.

How is Infosys’ Topaz platform structured to accelerate GenAI adoption and drive enterprise transformation in 2025?

Topaz is a proprietary generative AI platform developed by Infosys Limited that sits at the core of the Indian consulting major’s enterprise transformation strategy. Launched in 2024, the Topaz suite combines over 12,000 reusable AI artifacts, more than 150 domain-specific pre-trained models, and a modular architecture designed to be rapidly deployable across enterprise functions. It aims to abstract complexity and accelerate time-to-value in high-friction areas such as marketing automation, legal contract parsing, financial reconciliations, predictive supply chain logistics, and workforce performance enhancement. By mid-2025, Topaz has emerged as one of the most aggressively positioned enterprise GenAI platforms among global IT services players.

Infosys has embedded a “responsible by design” framework into Topaz from inception. This includes integrated AI observability, privacy-preserving inference layers, traceable model lineage, and alignment with emerging regulations such as the EU AI Act and India’s Digital Personal Data Protection Act. These controls are especially important as Infosys targets industries like banking, life sciences, energy, and government—sectors where AI adoption is often constrained by compliance and audit requirements. Unlike generic LLM wrappers or chatbot frameworks, Topaz is being developed as a full-stack system that can serve both predictive and generative workloads in production-grade environments.

At the center of this platform lies the Agentic AI Foundry, a development and runtime environment designed to enable customers to configure, deploy, and govern large fleets of AI agents. These agents are built for role-specific tasks—such as invoice validation, product recommendation, policy comparison, and code refactoring—and can interact with core IT systems through APIs, event streams, and secure data lakes. In early 2025, Infosys announced its ambition to scale the deployment of over 200 such agents for clients in sectors like financial services, telecom, healthcare, and manufacturing. This scale places Topaz at the frontier of GenAI enterprise agent deployment, comparable only to a few AI-first consulting hybrids and platform-native competitors.

One of Topaz’s most critical differentiators is its tight integration with cloud hyperscalers and silicon vendors. Infosys has structured its platform to run on Google Cloud Vertex AI, Amazon Bedrock, and Azure OpenAI Service, allowing flexibility across customer cloud estates. The platform also leverages NVIDIA’s accelerated computing stack for model training and inference in edge-to-core topologies. This multi-cloud and multi-architecture compatibility gives Topaz a strategic advantage in large transformation deals where clients demand composable AI services aligned with their preferred cloud ecosystem.

In terms of business use cases, Infosys has positioned Topaz not only as a technical framework but also as a business outcome accelerator. For instance, in retail and e-commerce, Topaz is being used to personalize pricing and promotions using real-time inventory signals and shopper behavior patterns. In insurance and financial services, the platform supports rapid document processing, fraud detection, and advisor support through LLM-powered underwriting assistants. In the context of SAP S/4HANA Cloud transformations, Topaz assists with legacy code remediation, automated testing, and post-migration analytics, reducing effort and cycle time by 15 to 20 percent according to Infosys internal benchmarks.

The extensibility of Topaz is further supported by Infosys’ open collaboration ecosystem. As of June 2025, Topaz APIs and toolkits are interoperable with open-source libraries, third-party vertical models, and enterprise data management platforms. Clients can customize their AI agents, embed them in existing applications, or create composite agents that combine rule-based and generative inference mechanisms. This openness enhances Infosys’ appeal in global tenders where AI sovereignty and long-term support are key procurement criteria.

Lastly, Topaz is deeply integrated with Infosys’ own delivery fabric. Project managers and software architects across Infosys’ global delivery centers now leverage Topaz to drive intelligent automation, forecast risk, and optimize resource allocation. This dogfooding approach not only validates the platform’s capabilities in real-time but also supports a feedback loop that accelerates product refinement.

Overall, Topaz is Infosys Limited’s boldest bet in its four-decade history, designed to move beyond traditional IT outsourcing toward AI-native transformation partnerships. If Infosys succeeds in scaling Topaz engagements into enterprise-wide deployments that deliver measurable ROI, it may well emerge as a credible GenAI platform contender against larger, more vocal peers such as Accenture’s reinvention services and Tata Consultancy Services’ Ai.Cloud unit. The year 2025 marks a critical inflection point—both for Infosys as a GenAI services integrator, and for Topaz as an AI operating layer for digital enterprises worldwide.

How does Infosys Topaz stack up against Accenture’s transparent GenAI bookings and TCS’s Ai.Cloud pipeline momentum?

Accenture has reported over $5.6 billion in GenAI bookings since 2023 and continues to issue quarterly revenue breakdowns related to AI-led workstreams. Tata Consultancy Services Limited has combined its AI and cloud initiatives under the Ai.Cloud unit, which currently maintains a GenAI pipeline of $900 million and includes more than 250 active engagements across financial services, manufacturing, and retail. In contrast, Infosys Limited has confirmed more than 225 Topaz engagements as of Q1 fiscal 2026, including deployments at Booking Holdings, Siemens, and other enterprise accounts. However, the company has yet to report Topaz revenue as a standalone line item. Institutional investors and analysts suggest that this lack of financial disclosure is limiting valuation rerating, even as Topaz use cases expand in depth and complexity.

What measurable business value have enterprises achieved through Infosys Topaz implementations as of mid-2025?

According to Infosys Limited, Topaz implementations have delivered measurable productivity and efficiency gains across multiple industries. One banking client reduced over 2,000 operational processes from multi-day workflows to near-real-time execution. A national logistics provider implemented Topaz-powered orchestration to integrate vendor operations and partner networks. In SAP modernization engagements, clients have achieved up to 20 percent cost savings and 15 percent faster deployment timelines. Infosys has also highlighted improvements in areas such as predictive billing, customer support automation, and software defect detection. While these metrics validate Topaz’s potential, institutional stakeholders continue to call for clearer reporting on recurring revenue derived from such engagements.

How are talent and operational models evolving at Infosys to support scaling of GenAI use cases and responsible deployment across industries?

To support Topaz at scale, Infosys Limited has invested heavily in workforce transformation. Thousands of engineers have received GenAI certifications, including advanced agentic AI training programs offered through Infosys’ Bengaluru innovation campus. The company has also launched customized small language models tailored to sectors such as banking, telecom, and logistics, along with ecosystem collaborations focused on energy-efficient AI compute. The Responsible AI framework built into Topaz is designed to address client concerns about fairness, bias, data protection, and model explainability. However, Infosys and its peers continue to face talent retention challenges, particularly in AI product engineering and enterprise architecture. Analysts suggest that further improvements in onshore delivery capacity and agile squad deployment could enhance execution speed and client intimacy in North America and Europe.

What are analysts and institutional investors expecting from Infosys’ Topaz platform and GenAI revenue execution by FY26?

Investor expectations for Infosys Limited are centered on three milestones: explicit Topaz revenue disclosure, margin expansion from GenAI-driven productivity, and multi-year deal conversions in regulated sectors. Analysts note that Infosys trades at a discount to Accenture and Tata Consultancy Services, primarily due to revenue growth differentials and a more conservative financial disclosure posture. If Topaz engagements begin contributing visible revenue in the second half of FY26, investors believe a re-rating could occur. This would likely depend on Infosys demonstrating its ability to move from pilot to production in a scalable, replicable manner, particularly in banking, insurance, life sciences, and cloud-native software services.

What execution risks could hinder Infosys from converting Topaz platform momentum into revenue and investor confidence?

Infosys Limited faces several risks that could stall Topaz monetization. The transition from proof-of-concept to full deployment remains a multi-quarter challenge, especially with clients in regulated industries. Enterprise adoption of GenAI still requires assurances around compliance, data sovereignty, and third-party model integration—factors that add friction to large-scale rollouts. Additionally, attrition among GenAI-skilled professionals remains elevated, with hyperscalers and AI startups aggressively recruiting from mid-tier management and engineering ranks. Another constraint may be Infosys’ current dependency on SAP ecosystems and partner-based deployments, which could limit GenAI use case diversification unless broader platform independence is established.