How India’s Top CTOs Are Using AI
The first CTO-level benchmark of enterprise AI adoption in India reveals a market that has moved past experimentation and is now reorganising itself around what works.
In February, we published India’s first builder-level AI survey, taken by 244 respondents, mostly early-stage founders and developers. The findings: Anthropic leading in intent, Claude Code dominating coding, and agentic workflows as the consensus next bet.
This time we went upstream. We surveyed senior technology leaders at 77 of India’s most consequential tech companies built in the last two decades. Nearly half of respondents are CTOs (49%), alongside Heads of AI (21%) and VPs of Engineering (19%), representing companies such as Meesho, upGrad, ixigo, Pine Labs, CRED, Jar, Dream11, etc, with a median company size of 500-2,000 employees.
This is a benchmark of what India’s foremost technology companies have already deployed, what impact they are measuring, and how they are restructuring their organisations in response.
If anyone is still debating whether India's technology industry has "adopted AI," the answer from the people who actually sign off on production deployments is unambiguous: this is not experimentation, it is an operational reality.
The Model Wars Look Different From the CTO’s Chair
In our builder & developer survey, OpenAI led production usage at 48%, with Anthropic at 25% and Google at 15%. Clear hierarchy with a clean story. The CTO-level picture is fundamentally different.
Three things jump out.
First, the gap has compressed. OpenAI still leads, but by single digits over Gemini and Anthropic. When you are a CTO running multiple production workloads - customer support in one stack, coding tools in another, content generation in a third - you don’t pick one vendor. You pick three. The average CTO in this survey uses 3 model vendors simultaneously. 44% use all three of OpenAI, Anthropic, and Gemini in parallel. This is the multi-model future arriving in production, not as a theoretical best practice, but as an operational necessity.
Second, Google Gemini’s position is far stronger among enterprises than among startups. At 68% penetration, Gemini is effectively tied with OpenAI in the CTO stack. The likely driver is distribution: Google Cloud relationships, Android ecosystem integration, and enterprise procurement pathways that simply don’t exist for early-stage builders paying with credit cards.
Third, and the most interesting one, Chinese open-weight models have achieved 48% penetration among India’s top CTOs. In February, we found only 3% of builders using Chinese open weights. Among CTOs at companies with 2,000+ employees, the number shoots up to 66%! Larger companies naturally end up using a wider mix of models across teams and use cases, so this indicates more about the breadth of experimentation. DeepSeek, Qwen, and their cohort are now in production at the majority of India’s large technology companies.
While Everyone Else is Split, Deeptech Chooses Chinese Models
While most of the ecosystem remains split across OpenAI, Anthropic, and Google, deeptech startups are making a very different and far more opinionated choice. 71% of deeptech companies are using Chinese open-weight models in production, making them the single most adopted category in this segment by a wide margin. This divergence is not accidental since deeptech teams are fundamentally more performance-sensitive, control-oriented and infrastructure-heavy (often building at the model/system layer rather than just consuming APIs).
Chinese open-weight models optimize exactly for these constraints. In contrast, sectors like SaaS and fintech—where speed to market and reliability matter more—continue to favor closed models from OpenAI and Anthropic.
What this signals is important: the deeper you go into the AI stack, the more the center of gravity shifts from closed APIs to open-weight ecosystems. Deeptech isn’t following the market but rather leading a structural shift in how AI systems are actually built. And increasingly, that stack is not Western by default.
Engineering Productivity is Table Stakes. The New Frontier is Revenue.
94% of CTOs report measurable productivity lift in engineering and product functions. 88% have coding AI tools live in production. This is no longer a finding but a settled fact of the Indian technology industry. The more interesting question: where is AI creating value beyond engineering efficiency?
Marketing is emerging as the second major AI beachhead at 57%. Revenue functions such as sales development, outbound, and pipeline are at 42% and climbing. Finance and legal sit at 36%, constrained by accuracy requirements and regulatory stakes.
But the headline finding is on impact measurement. When asked about the primary impact of their most important AI use-case:
Nearly half of India’s top CTOs say AI is already driving measurable revenue lift. Not “we think it might.” Not “our projections suggest.” But rather measured, reported, and in production. Among companies where AI touches >50% of workflows, 69% report revenue lift. Among companies with 5–50% penetration, only 21% do. There appears to be a threshold beyond which AI stops being a cost optimisation play and becomes a revenue driver.
The Great Re-Org Is Underway—But It’s Not What You Think
70% of CTOs say AI is actively changing their organisation structure and hiring plans. But the nature of the change is counterintuitive.
The dominant pattern is not “AI replaces headcount.” It is “AI restructures what headcount does.” 56% are reorganising their teams while continuing to hire aggressively. This is the shape of an industry that has found AI-driven leverage but hasn’t found AI-driven substitution, at least not across the board.
Where substitution is happening, it is concentrated:
Support and operations is the kill zone. 45% of CTOs report headcount reductions there, and the number scales with company size where 59% of companies with 2,000+ employees are cutting support/ops roles. Customer support is live in production at 52% of companies, and AI-powered support at scale is demonstrably cheaper per resolution than human agents.
Engineering headcount is being trimmed at 23% of companies. The framing from CTOs in follow-up conversations is that they are not hiring fewer engineers but rather fewer engineers for the same output, and reallocating the surplus into new product surface area. GTM and sales remain largely untouched at 6%. The sales org is the last fortress. Whether that holds through 2027 depends on how fast agentic outbound matures.
Even in India, Accuracy Beats Cost as the #1 AI Blocker
In a market assumed to be highly price-sensitive, the biggest constraint to scaling AI isn’t cost—it’s getting the models to be accurate and reliable enough in production
This is the signature of a market that has moved past “can we afford AI?” to “can we trust AI in production?” When your customer support bot hallucinates a refund policy that doesn’t exist, the cost per API call is irrelevant. The blocker landscape shifts dramatically by company size:
Large companies have largely solved for organisational readiness (14%) and are now stuck on the harder problems: quality at scale, cost at volume, and integration with legacy systems. Small companies face an inverted profile where security and org readiness are their primary constraints.
The regulated industry gap: 80% of Fintech CTOs and 100% of Healthcare CTOs cite data security as a scaling blocker. Compare that to 7% in E-Commerce. This is not a sentiment but a structural gap that requires purpose-built infrastructure of data residency, audit trails, model governance, and deterministic fallback systems.
The Priority Stack: Customer-Facing AI is the Consensus Bet
Over half of India’s top CTOs say their number one AI priority is shipping more customer-facing AI features. Internal productivity where most of the current deployment is concentrated is already in the rearview mirror as a primary objective. This priority intensifies with scale. 76% of CTOs at companies with 2,000+ employees are focused on customer-facing AI, versus 29% of sub-500 companies. The larger the company, the more confident the bet on AI as a product feature rather than an internal efficiency tool.
This is the shift from AI as back-office automation to AI as product surface area. It is the most consequential strategic reorientation in the dataset, and has implications for every AI startup selling to Indian enterprises. The buyer’s question is no longer “how do I make my team more productive?” but rather “how do I make my product smarter?”
The Inference Layer: Hybrid Wins
The majority of CTOs (52%) run hybrid inference combining API calls, managed platforms, and self-hosted models depending on the workload. This is the mature pattern where you use APIs for low-volume tasks, hyperscaler-managed inference for compliance-sensitive production, and self-hosted open-weights for high-volume cost-sensitive workloads.
Among 2,000+ employee companies, hybrid adoption hits 69%. The pure API-first pattern that dominated our early-stage builder survey is a stage of development, not a steady state. As companies scale, they graduate to a portfolio approach.
What All of This Means
The multi-model enterprise is here. 44% of CTOs run all three major vendors in parallel. Startups building model-agnostic infrastructure, such as routing layers, eval frameworks, and prompt management across providers, are building for the actual architecture of enterprise AI, not the hypothetical single-vendor world.
Chinese open-weights are the stealth story of Indian enterprise AI. 48% penetration overall, 66% among large companies, by far the preferred choice amongst Deeptech. This is not going to show up in API revenue figures from US model providers. But it’s reshaping the inference cost curve and the competitive dynamics of the Indian AI stack from below. The companies that help enterprises manage, fine-tune, and deploy open-weight models at scale have a large and rapidly growing addressable market.
Quality, not cost, is now the primary constraint. The market has flipped from “can we afford it?” to “can we trust it?” This is an opportunity for companies to build evaluation, monitoring, and quality assurance infrastructure. The companies that can guarantee output quality at scale will command pricing power that pure inference providers cannot.
Support/Ops is being restructured. Sales is not. Engineering is in between. The workforce impact of AI is concentrated and specific. If you are building AI tooling, the highest willingness-to-pay is in customer support automation (where ROI is already proven) and engineering productivity (where adoption is universal). Sales AI is still a future bet.
The shift from internal AI to product AI is the defining strategic move of 2026. 52% of all CTOs and 76% of large-company CTOs say shipping customer-facing AI is their top priority. This is the transition from AI as a cost centre to AI as a product differentiator. Startups that help enterprises embed AI into their products, not just their operations, are building for the next phase of demand.
Regulated industries need dedicated infrastructure. 80% of fintech and 100% of healthcare CTOs cite security as a blocker. This is not a sentiment. It is a structural gap. The companies that build data residency, model governance, and audit-trail infrastructure specifically for Indian regulated industries will find a market with high barriers to entry and sticky customers.
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Startup Signals
Spotlighting brand new emerging AI startups from India every month, early and undiscovered.
ProactAI - Vision AI for India’s 234M CCTV Cameras
India has 234M installed CCTV cameras generating 100+ petabytes of video daily, and less than 0.1% gets analyzed. ProactAI converts this passive infrastructure into real-time intelligence using a ViT-based engine they call Bhaskara - targeting retail shrinkage, manufacturing safety, and defence surveillance. Founded by three IIT alumni (IIT BHU, IIT Bombay, NSUT) with combined 50+ years across HDFC ERGO, Octro, HT Media, and Razorpay. Three paying retail clients (including Nike/RJCorp and Davaindia) within six months of founding. Claims 98% accuracy vs. 70% from legacy CNN-based competitors at 1/10th the cost.
https://proactai.ai/
PhotonSilica - Indian Fabless Chip Design for Defence & Space
India’s semiconductor ambitions need fabless chip designers building for indigenous defence and space applications. PhotonSilica is designing mixed-signal ICs and photonic processors targeting the 28nm–130nm range, which represents roughly 80% of defence and industrial semiconductor demand. Founder Tushar has secured TSMC MPW shuttle access through Taiwan’s semiconductor diplomacy channel and is pursuing iDEX and IN-SPACe contracts where government co-funding dramatically improves seed-stage capital efficiency.
https://photonsilica.in/
Zyra - India’s First AI Insurance Expert
India has 550M+ insured lives, but most policyholders don’t understand what they’re paying for - jargon-filled documents, fragmented management, and an agent model built on commissions rather than clarity. Zyra is an AI-native consumer app that reads your policy, scores your coverage (via a proprietary “Z-Score”), identifies gaps, and provides unbiased guidance - no spam, no agent calls. The product is IRDAI-regulated and ISO 27001 certified. 10,000+ policies analyzed, ₹5,000+ crore in coverage reviewed across 50+ cities and is live on both iOS and Android.
https://heyzyra.ai/
Survey Methodology
All responses are voluntary and self-reported. Results reflect the composition of respondents and are weighted toward technology companies with active AI deployment.
These are some interesting charts.