How Artificial Intelligence Is Reshaping Technology Company Valuations?
Technology valuation in India has traditionally followed a fairly predictable approach: assess revenue growth, apply a sector-based multiple, and adjust for profitability. In 2026, that approach is being substantially revised. Artificial intelligence has not created a new kind of fast‑growing startups. AI is also splitting the domestic technology sector. The split depends on one question: is a company really built around Artificial intelligence or has Artificial intelligence just been added to an old product?
The scale of this shift is considerable. In the quarter of 2026 Indian AI startups collected about $1.48 billion. That is around 38% of all the money given to startups in India during that time. India now has than 4,500 startups that focus on artificial intelligence. More than 480 of these companies have received money from investors. These companies together have gotten about $3.4 billion, in investments. The domestic AI market is projected to reach $126 billion by 2030, supported by government initiatives such as the IndiaAI Mission.
India's AI funding, however, remains modest relative to global markets. Global AI startups attracted close to $202 billion in venture funding in 2025, with US companies absorbing the majority. India's opportunity is genuine, but it is being financed at a markedly different pace and risk tolerance than in markets such as Silicon Valley.
A Tiered Valuation Market
Indian investors are increasingly classifying technology companies into distinct tiers, each commanding materially different multiples.
AI-native companies, built from inception around AI, are achieving premium valuations well beyond those historically available to traditional SaaS or IT services businesses. Krutrim, India's first AI unicorn, reached a $1 billion valuation on a $50 million raise, reflecting the premium the market assigns to a large language model built for Indian languages, even ahead of meaningful commercial revenue.
AI infrastructure companies are commanding some of the multiples in the sector. Neysas $1.2 billion Blackstone‑led investment in February 2026. Structured as $600 million in equity alongside $600 million in debt. Became Indias AI infrastructure transaction illustrating the premium assigned to foundational AI infrastructure rather, than applications alone.
Legacy SaaS and IT services companies, by contrast, face a considerably more conservative valuation environment. Across the world AI‑native software companies are valued at about twenty‑five to thirty times their revenue. By contrast public SaaS companies that do not use AI in a way are valued at only about six to seven times revenue. This gap is also showing up in India in the same proportion.
Pressure on Legacy Technology Businesses
This divide carries significant implications for India's traditional technology sector, including its large and established IT services and SaaS businesses.
AI tools now do jobs that used to need special software or outside help. They handle tasks like processing documents doing analysis and basic coding. Because of that the market is more careful, about companies that cannot show clearly that AI makes their business better of harming it. This is particularly relevant to India's IT services sector, historically built on scaled human effort of exactly the kind now subject to AI-driven automation.
The prevailing analyst view is not that Indian SaaS or IT services businesses are losing their value altogether, but that AI is exposing the distinction between companies with durable, defensible workflows and proprietary data, and those offering replaceable services increasingly performed by AI tools at a fraction of the cost.
Implications for Valuation Practice
For any valuation of a technology company. Whether it is for a funding round an M&A transaction or a Rule 11UA compliance requirement. There are several practical considerations that must be taken into account.
Comparable company selection must now explicitly account for AI positioning, not merely sector and revenue size. Treating a genuinely AI-native company and an AI-adjacent one as comparable, on the basis that both fall under "technology," can produce a materially inaccurate valuation.
Discounted cash flow assumptions require considerably more scrutiny. Growth projections once considered reasonable for a SaaS or IT services business may no longer be defensible where the company's core service is genuinely exposed to automation. Conversely, a company with a real, defensible AI moat may justify growth assumptions that would have appeared implausible only a few years ago.
Data moats and defensibility must be assessed explicitly, rather than inferred from revenue growth and margins alone. Whether an AI capability rests on genuinely proprietary data, or is readily replicable using widely available models, has become a material factor in valuation one that Indian valuation practice has not historically needed to weigh so heavily.
Artificial intelligence has not simply added a new category to India's technology sector; it is actively restructuring the hierarchy by which Indian technology companies are valued. Once a company was simply called a "technology" firm. That label set its valuation multiple. Today that label no longer matters. Instead whether AI plays a core role, in the company now shapes the multiple. Founders, investors and valuers all face a question in 2026: where does the business fall on this spectrum? Generic sector‑wide comparisons cannot answer that question alone.
