India’s information technology services industry could benefit from the next phase of artificial intelligence adoption, according to a research report by Anand Rathi. As global companies move beyond building AI infrastructure and focus on deploying AI across business operations, Indian IT service providers could gain new opportunities in systems integration, application modernization, data management and AI implementation.
The report’s central argument is that Indian IT companies can participate in AI-driven growth without taking on the enormous infrastructure investments required to build data centers, acquire computing capacity and develop foundational AI models. However, the opportunity comes with risks: AI-led productivity improvements could reduce prices for traditional IT services, while rising infrastructure spending may constrain clients’ technology budgets.
Key Takeaways
- AI could create new growth opportunities: Enterprise adoption is increasing demand for AI implementation and integration services.
- Asset-light advantage: Indian IT service providers can help businesses deploy AI without funding large-scale computing infrastructure.
- Systems integrators stand to benefit: Companies that connect AI models with existing applications and business workflows could capture more spending.
- Anthropic illustrates the financial stakes: The report highlights the company’s rapid revenue growth alongside substantial future computing commitments.
- Pricing pressure remains a concern: AI automation could reduce the cost of delivering some traditional IT services.
- Growth is not guaranteed: Enterprise budgets, implementation demand and profitability will determine how much Indian IT companies benefit.
Why Anand Rathi Sees an Opportunity for Indian IT
For several years, global technology spending has been heavily influenced by investments in AI infrastructure. Technology companies have committed substantial capital to computing hardware, data centers and the capacity needed to train and operate increasingly powerful AI models.
Anand Rathi argues that the next phase could shift toward deploying those capabilities inside businesses. Companies that have experimented with AI will need to connect models to their existing software, prepare their data, modernize legacy systems and establish security and governance controls.
This transition could benefit Indian IT services firms because these activities closely match their established business capabilities. They already help enterprises implement software, manage technology systems, modernize applications and operate complex IT environments.
The brokerage’s thesis is that AI spending will not remain concentrated among infrastructure providers and model developers. As businesses seek measurable returns from their AI investments, a greater share of spending could flow toward service providers that make AI useful in everyday operations.
From Building AI Infrastructure to Deploying AI
The distinction between AI infrastructure and AI implementation is central to the report.
| AI investment area | Typical activities | Potential beneficiaries |
|---|---|---|
| AI infrastructure | Data centers, computing hardware and large-scale model infrastructure | Cloud providers and infrastructure operators |
| AI model development | Training and operating foundational models | AI model developers and technology platforms |
| Enterprise AI deployment | Connecting models to business applications and workflows | IT services firms and systems integrators |
| AI operations and governance | Monitoring performance, managing access and controlling risk | IT services, cybersecurity and consulting providers |
| Legacy modernization | Updating older applications and preparing data for AI | Application modernization and technology consulting firms |
The infrastructure layer requires significant capital and involves risks related to financing, capacity utilization and long-term commitments. Enterprise implementation, by contrast, can be delivered through services, software integration and ongoing operational support.
Indian IT companies do not need to own the underlying AI infrastructure to help customers use it. They can work with multiple model providers, integrate AI into existing systems and manage deployments for clients.
That model-agnostic approach could become an advantage if enterprises want flexibility rather than committing their entire technology strategy to a single AI platform.
Anthropic Highlights the Cost of the AI Race
Anand Rathi uses Anthropic to illustrate the financial commitments associated with the AI infrastructure boom.
According to figures cited in the report, Anthropic generated $16.3 billion in revenue during the first half of calendar year 2026, approximately 14 times its revenue in the corresponding period a year earlier. The company also reported adjusted operating profit of $1.6 billion for that period.
However, the report highlighted approximately $321 billion in leases yet to commence, illustrating the scale of future financial commitments associated with expanding computing capacity.
These figures should be interpreted carefully. Future lease obligations are not the same as current debt or cash expenditure, and they do not establish that Anthropic will be unable to meet its commitments. They do, however, demonstrate how much capital and long-term planning can be involved in operating AI infrastructure at scale.
The brokerage’s argument is that this level of capital intensity creates a different risk profile for infrastructure operators than for service providers. Indian IT firms may be able to capture downstream demand for AI deployment without assuming comparable infrastructure commitments.
Why Systems Integrators Could Gain From Enterprise AI
Systems integrators help businesses connect different technologies so they work together. In an AI deployment, that may involve linking a model to company databases, customer-service platforms, internal applications and existing workflows.
Businesses often need more than access to an AI model. They must determine which tasks are suitable for automation, ensure data is reliable, establish permissions and integrate AI outputs into operational processes.
Anand Rathi points to Anthropic’s growing sales, marketing and partnership expenditure as evidence of the importance of distribution and implementation partners in enterprise AI adoption.
The report said Anthropic’s sales, marketing and partnership expenses rose approximately 27 times year over year to $3.2 billion in the first half of 2026. This included a $2.7 billion year-over-year increase in channel-partner fees.
It also cited approximately 11,900 enterprise customers generating more than $100,000 in annual revenue as of September 2026. Around 80% of Anthropic’s revenue in the first half of the year was consumption-based, according to the report.
These figures suggest that enterprise AI is developing into a broader commercial ecosystem. Model developers provide the underlying technology, while partners can help customers select, integrate and deploy it. Indian IT services companies could compete for a portion of that implementation work.
Which IT Services Could See More Demand?
The shift toward enterprise AI could generate opportunities across several established service categories.
| Service category | Why demand could increase |
|---|---|
| Data preparation | AI applications require accessible, reliable and well-organized business data |
| Systems integration | Models must connect with existing software and enterprise workflows |
| Application modernization | Older systems may need upgrades before they can support AI-enabled processes |
| Cybersecurity | Organizations need controls for AI access, data exposure and automated actions |
| AI governance | Businesses must monitor model behavior, permissions and compliance requirements |
| AI operations | Deployed systems need ongoing monitoring, optimization and maintenance |
The commercial opportunity will depend on whether companies move beyond pilot projects and deploy AI in production. A successful demonstration does not necessarily translate into a large services contract.
Indian IT firms will also need to show that they can deliver measurable improvements, such as lower operating costs, faster processes or better customer experiences. Clients are likely to scrutinize the business value of AI projects rather than spending simply because the technology is available.
The Risks: AI Could Also Pressure IT Services Revenue
Anand Rathi’s outlook is not an unconditional prediction of an AI boom for Indian IT. The report also identifies risks that could weigh on growth and profitability.
AI-led pricing deflation: If AI tools allow developers and service teams to complete tasks more quickly, customers may expect to pay less for the same work. Productivity gains can therefore reduce revenue per project unless service providers win additional business or move into higher-value services.
Higher client infrastructure spending: Companies investing heavily in AI computing may reduce spending on other technology initiatives. Even when AI adoption creates new opportunities, the overall technology budget may not expand at the same rate.
Uneven enterprise adoption: Some businesses may delay implementation because of data quality problems, security concerns, unclear returns or integration costs.
Changing skill requirements: IT companies will need employees who can work with AI models, data engineering, cloud systems, cybersecurity and automated workflows. Firms that fail to adapt could struggle to capture the new demand.
The report also points to workforce rationalization at Microsoft as a signal of potential AI-led pricing pressure. Such developments do not prove that all IT services will shrink, but they reinforce the need for companies to adapt their business models.
What This Could Mean for India’s IT Industry
The opportunity is significant because Indian IT services firms already operate across global enterprise technology environments. They have experience managing large software projects, maintaining legacy applications and supporting clients across industries.
Those capabilities could help businesses move from AI experimentation to production deployment. However, the benefits are likely to vary by company, depending on its AI expertise, client relationships, service mix and ability to deliver projects profitably.
For investors, the distinction between AI exposure and AI revenue is important. A company may announce AI partnerships or training programs without generating substantial new business. More useful indicators include AI-related revenue, deal conversions, deployment scale, margins and evidence that clients are renewing or expanding AI engagements.
The report supports a potential growth opportunity for the sector, but it does not guarantee that every Indian IT company will benefit equally.
The Bigger Picture
The AI boom is moving into a phase in which enterprises must demonstrate returns on their technology investments. Building models and computing capacity is only part of the process; companies also need to integrate AI into the systems employees use, protect sensitive information and redesign workflows around the technology.
That creates a potential opening for Indian IT services firms because their core strengths include integration, modernization and ongoing enterprise support. The asset-light model could allow them to benefit from AI adoption without carrying the same infrastructure burden as model developers and data-center operators.
However, the industry faces a balancing act. AI can create demand for new services while reducing the time and labor required to deliver existing ones. The winners will likely be companies that turn productivity improvements into new revenue, build specialized expertise and demonstrate measurable value to customers.
FAQs
1. What does Anand Rathi’s report say about Indian IT companies?
The report argues that Indian IT services firms could benefit as global businesses shift from building AI infrastructure toward deploying AI in enterprise applications and workflows.
2. Why could Indian IT companies benefit without building AI infrastructure?
They can help enterprises integrate AI models into existing systems, prepare data, modernize applications and manage deployments. These services do not require them to fund large data centers or develop foundational models themselves.
3. Why does the report discuss Anthropic?
Anthropic’s rapid revenue growth and large future computing commitments illustrate both the commercial opportunity in AI and the capital-intensive nature of building AI infrastructure. The contrast supports Anand Rathi’s argument for an asset-light services model.
4. Which Indian IT companies could benefit from enterprise AI adoption?
Large and mid-sized Indian IT service providers could compete for AI integration, modernization, data, cybersecurity and AI operations work. Actual benefits will depend on their capabilities, customer demand, deal execution and profitability.
5. Could AI hurt India’s IT services industry?
Yes. AI may automate tasks that previously required more human labor, putting pressure on service prices and traditional revenue models. Rising infrastructure expenditure could also constrain client budgets. Companies will need to capture new AI-related work to offset these pressures.
Looking Ahead
The coming quarters will show whether enterprise AI adoption translates into sustained services revenue for Indian IT companies. Investors and industry observers should monitor actual AI-related revenue, project conversions, client spending, margins and demand for integration and modernization services rather than relying solely on AI announcements.
Anand Rathi’s thesis is that the next phase of AI investment could create a meaningful opportunity for India’s services industry. That opportunity is credible, but its scale will depend on execution: companies must help clients deploy AI successfully, demonstrate measurable returns and adapt their traditional services as automation changes how technology work is delivered.
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