Zensar’s Q2FY27 Results Reveal How AI Is Reshaping IT Services Growth

Zensar Technologies delivered 4.1% sequential constant-currency revenue growth in Q2FY27, taking quarterly revenue to $166.2 million, while its EBITDA margin expanded 120 basis points to 15.8%. More significantly, AI-influenced deals accounted for 36% of its quarterly order book, signalling a shift in enterprise technology spending. But AI-related deal momentum does not automatically translate into recognized AI revenue, higher margins, or measurable customer returns. Zensar’s latest performance offers a useful test of a larger industry question: can technology services companies turn AI adoption into a repeatable and profitable commercial model?

BOARDROOM BRIEFING: KEY TAKEAWAYS

• Zensar Technologies reported $166.2 million in Q2FY27 revenue, representing 4.1% sequential constant-currency growth and 11.5% year-on-year growth in INR terms.
• AI-influenced Deals represented 36% of the quarterly order book, indicating commercial demand without establishing AI’s contribution to recognized revenue.
• EBITDA Margin improved to 15.8%, expanding 120 basis points sequentially as operational efficiency and utilization strengthened.
• Banking and Financial Services delivered 7.1% sequential constant-currency growth, while Telecommunications, Media and Technology declined 2.2%.
• Enterprise AI Applications are moving beyond experimentation into procurement, inventory optimization, application modernization, and agentic workflows.
• IT Services Profitability will increasingly depend on contract economics, workforce productivity, pricing discipline, and the ability to convert AI investments into measurable client outcomes.

1. Zensar’s Q2FY27 Performance Signals a Changing IT Services Market

Zensar’s Q2FY27 results demonstrate sequential revenue acceleration alongside improving operating profitability, although demand remains uneven across its business verticals.

The Pune-headquartered technology services company reported quarterly revenue of $166.2 million, equivalent to approximately ₹1,585.2 crore. Revenue increased 4.1% sequentially in constant-currency terms, compared with 5.1% sequential growth in INR and 11.5% year-on-year growth in INR.

These figures measure different things. Constant-currency growth removes the impact of exchange-rate movements, making it more useful for evaluating underlying operating momentum. INR growth reflects the company’s reported domestic-currency financial performance.

EBITDA margin reached 15.8%, improving 120 basis points sequentially. The company also reported a quarterly order book of $151.5 million, with AI-influenced deals representing 36%.

The distinction between revenue and order-book composition matters. Revenue measures services recognized during the reporting period, whereas the order book provides an indication of contracted business and future commercial activity.

The earnings call also disclosed utilization of 87.6%, an improvement of 250 basis points sequentially, and a workforce exceeding 12,000 employees.

These figures suggest that Zensar is combining revenue expansion with stronger resource utilization. However, a single quarter cannot establish whether those improvements will persist.

The more revealing question is whether AI-related demand can sustain this performance as enterprise clients become increasingly selective about technology spending.

Source: Zensar Technologies’ Q2FY27 financial announcement and October 29 earnings call.

2. The Economics of AI-Led IT Services: Revenue Is Only Half the Story

AI-led IT services growth creates sustainable financial value only when productivity improvements translate into additional revenue, lower delivery costs, or stronger contract profitability.

Traditional IT outsourcing economics have relied heavily on engineering hours, workforce deployment, project duration, and resource utilization. AI-assisted development, automated testing, and intelligent application maintenance challenge that structure by allowing smaller teams to complete certain tasks more efficiently.

For technology service providers, the opportunity is substantial. The risk is equally significant.

Consider a software modernization project that previously required 30 engineers working over 12 months. AI-assisted coding, testing, and documentation may reduce the delivery effort. The client receives a faster implementation, but the provider must reconsider how the project is priced.

Under a time-and-materials arrangement, fewer billable hours can reduce revenue. Under a fixed-price contract, improved productivity can strengthen margins if the delivery cost falls without compromising quality.

Outcome-based pricing introduces another possibility: compensation becomes more closely associated with measurable business results.

The financial equation is changing:

Revenue growth + Utilization improvement + Delivery productivity + Pricing realization − AI investment costs − Wage inflation = Sustainable operating profitability.

This is an analytical framework, not a reported Zensar calculation.

The company’s Q2 performance offers some evidence of operating improvement. Utilization reached 87.6%, up 250 basis points sequentially, while EBITDA margin increased to 15.8%.

But the earnings call also identified upcoming wage increases and furlough-related effects as factors management must manage.

For executives evaluating AI automation and the future of software engineering, the important distinction is between technical productivity and financial productivity. Faster development is useful; better contribution margins and improved customer economics are what make it commercially sustainable.

3. AI-Influenced Deals: Turning Enterprise Demand Into Commercial Results

Zensar reported that AI-influenced deals accounted for 36% of its Q2FY27 order book, indicating growing demand for AI-related services without establishing how much AI independently contributes to recognized revenue.

The distinction is particularly important for investors.

During the October 29 earnings call, CEO Manish Tandon explained that Zensar had moved away from conventional proof-of-concept exercises toward proof-of-value engagements. Management said this change had improved the conversion of experimental AI projects into production programs.

Tandon described the company’s proof-of-value-to-production conversion success as exceeding 80%.

That is a management-reported figure, not an independently audited industry benchmark. It should be treated as an indication of the company’s stated delivery experience rather than proof of AI profitability.

The CEO also acknowledged that the company does not separately report AI revenue because defining what constitutes AI revenue has become difficult as AI capabilities increasingly appear across conventional technology services.

This admission is commercially significant.

AI-influenced order-book share is a demand indicator, not a revenue-recognition metric. Investors need additional information about contract values, delivery milestones, realized revenue, customer retention, and profitability before determining whether AI is creating incremental economic value.

The New Contract Economics of Enterprise AI

Enterprise buyers are increasingly interested in commercial arrangements that reward measurable outcomes rather than simply the deployment of additional technology resources.

The principal models include:

• Time-and-materials: Clients pay for deployed resources and hours worked.
• Fixed-price: Providers receive an agreed fee for defined deliverables.
• Outcome-based: Payments are linked to agreed operational or business results.
• Productivity-linked: Commercial terms reflect measurable efficiency improvements.

Management’s earnings-call commentary pointed toward greater adoption of fixed-price and outcome-based arrangements over the following three to four quarters.

That transition could reshape how technology services companies recognize productivity gains and distribute the economic benefits of AI.

For CFOs, the central question is whether the provider retains sufficient value from improved delivery efficiency. For CIOs, the challenge is securing measurable savings without introducing quality, security, or operational risks.

4. The Uneven Demand Picture: Why Some IT Services Verticals Are Growing Faster

Zensar’s Q2FY27 business vertical results reveal that enterprise technology spending remains uneven, with banking and financial services outperforming several other sectors.

The company’s constant-currency performance shows substantial differences between industries.

Source: Zensar Technologies Q2FY27 financial announcement. All figures represent constant-currency growth.

Banking and Financial Services was the strongest performer, supported by demand for technology modernization and enterprise transformation. Financial institutions continue to face pressure to improve digital customer experiences, strengthen risk management, modernize legacy infrastructure, and improve operational efficiency.

Healthcare and Life Sciences recorded sequential growth, but its year-on-year performance remained negative. Manufacturing and Consumer Services also improved sequentially while remaining broadly flat year-on-year.

The most pronounced weakness appeared in Telecommunications, Media and Technology, which declined 21.6% year-on-year in constant-currency terms.

This divergence matters because AI adoption does not affect every enterprise technology budget equally. Spending priorities, client demand, project timing, and industry-specific economic pressures influence the revenue performance of individual service providers.

Geographically, Zensar reported sequential constant-currency growth of 4.5% in the United States, 1.3% in Europe, and 6.7% in Africa.

The numbers point to broad geographic improvement, but the vertical-level divergence makes it difficult to characterize the quarter as a uniform recovery.

Investors should examine whether growth becomes more balanced across business segments rather than relying exclusively on the strongest-performing vertical.

5. Enterprise Applications: Where AI Is Moving From Pilots Into Operations

Zensar’s disclosed Q2FY27 client engagements illustrate how enterprise AI is being applied to operational processes, technology modernization, and business decision-making.

The company’s reported wins provide practical examples of AI moving beyond demonstrations.

Predictive inventory optimization: For a book distribution business, Zensar described AI-driven predictive recommendations intended to reduce waste and improve purchasing decisions. The commercial measures to monitor include inventory turnover, stockout frequency, excess inventory, and working-capital requirements.

Procurement and supply chain transformation: A global mining company engaged Zensar for AI-led procurement and supply chain transformation using intelligent automation. Relevant performance indicators include procurement cycle time, supplier performance, purchasing compliance, and process efficiency.

Agentic AI workflows: A global wealth management firm engaged the company for change and release management transformation involving agentic workflows and operational intelligence. Deployment success should be evaluated through approval time, release frequency, incident rates, and exception handling.

Application modernization: An enterprise software provider pursued a unified cloud-native customer portal to consolidate legacy systems. The strategic measures include application maintenance costs, integration complexity, system availability, and customer experience.

AI-enabled software delivery: Zensar also disclosed deployment of its proprietary ZenseAI.Guidewire capabilities to automate release impact analysis and CI/CD workflows for an insurance provider.

These engagements show the range of operational problems technology providers are addressing.

But there is an important evidence limitation: Zensar has not disclosed independently verified financial ROI for each of these engagements. The examples establish the nature of the work, not the magnitude of the resulting savings.

For enterprise leaders, the lesson is straightforward. AI procurement decisions should begin with an operational problem, a measurable baseline, and an agreed financial outcome.

6. The Contrarian Reality: AI Adoption Does Not Guarantee IT Services Growth

AI adoption can improve delivery productivity while simultaneously weakening traditional IT services revenue models through reduced billable hours and client-driven pricing pressure.

This is the central contradiction facing the industry.

A provider that automates software testing may complete more projects with fewer engineers. That creates additional delivery capacity, but it also changes the economics of resource-based contracts.

Five risks deserve attention.

• Revenue cannibalization: Automated work can reduce billable engineering hours under traditional contracts.
• Pricing compression: Clients may demand lower fees when providers use AI to reduce delivery effort.
• Workforce disruption: Conventional coding and testing skills must evolve toward architecture, AI orchestration, systems integration, and domain expertise.
• Implementation complexity: Enterprise deployments require data governance, cybersecurity, model oversight, and integration with existing infrastructure.
• Profitability volatility: Wage increases, software costs, AI infrastructure expenses, and delivery-mix changes can offset efficiency gains.

Zensar’s own earnings call reinforces this concern. Management described a measured client-spending environment and acknowledged that increasing AI penetration could contribute to muted order-book growth across the wider industry.

The company also reported that annual wage increases would be implemented in two batches during Q3 and Q4 of FY27.

This makes margin sustainability a more important indicator than a single quarter’s expansion.

The strongest AI services providers will not necessarily be those deploying the most tools. They will be those that redesign contracts, delivery operations, and workforce structures to retain value from productivity improvements.

7. The Strategic Playbook: What CIOs, CEOs, and Investors Should Monitor

Evaluating AI-led IT services growth requires a combined assessment of commercial demand, delivery efficiency, financial performance, and measurable client outcomes.

Executives should resist treating AI adoption announcements as evidence of commercial success.

For CEOs and CFOs

• Separate AI-influenced order-book figures from recognized AI revenue.
• Track revenue growth alongside EBITDA margin and operating cash flow.
• Examine contract profitability and pricing realization.
• Evaluate whether productivity improvements offset wage and technology costs.
• Monitor working-capital efficiency and receivables.

For CIOs and CTOs

• Evaluate AI providers on production deployments rather than demonstrations.
• Establish baseline measurements before implementation.
• Track software delivery cycle time, defect rates, and release frequency.
• Assess data readiness, cybersecurity, and AI governance.
• Require clearly defined business outcomes in vendor agreements.

For Investors

The following scorecard provides a practical framework for evaluating technology services companies.

Zensar’s earnings call also disclosed DSO of 77 days, making receivables management another important metric to monitor alongside revenue growth and margins.

The company reported cash and investments of $297.3 million after a dividend payout of $29.9 million. These figures provide additional context for evaluating its financial flexibility.

The strategic priority is to connect AI investment with operating results rather than measuring success through adoption announcements alone.

8. Executive Outlook: The Next Competitive Advantage in IT Services

The next phase of IT services competition will depend on whether providers can convert AI-driven productivity into durable revenue, stronger margins, and measurable enterprise value.

Zensar’s Q2FY27 results provide evidence of commercial momentum: sequential revenue growth of 4.1%, EBITDA margin expansion to 15.8%, and AI-influenced deals representing 36% of its order book.

But the figures also expose the questions that investors should continue asking.

How much of the AI-influenced order book will become recognized revenue? Can productivity gains withstand client pricing pressure? Will stronger utilization and operating margins persist after wage increases? Can growth expand beyond the strongest-performing business verticals?

Those questions matter more than the headline percentage attached to AI-related deals.

For enterprise technology buyers, the priority should be selecting providers that combine technical capability with industry expertise, secure execution, and measurable business outcomes. For investors, the focus should remain on conversion, cash generation, and profitability.

AI adoption may create the demand. Commercial execution will determine who captures the value.