India's big-money investors face a new wild card

Artificial intelligence (AI) is no longer just helping private equity (PE) investors analyse companies. It is changing which companies they consider investable.

For India's PE investors, the question is not whether a target uses AI, but how AI could affect its revenues, margins, competitive position and exit prospects over the next four to five years. AI’s potential business impact is becoming a core part of the investment thesis.

“The biggest shift is that PE diligence has moved from asking ‘Is this a good business?’ to asking ‘How will AI reshape this business, and can management capture that value faster than competitors?’” Shyan Mukerjee, Partner and Technology Sector Leader at EY-Parthenon, India told ET Online.


That calls for a more detailed examination of a company’s underlying data.

“The real shift is from sampling to full coverage,” Naresh Biyani, Founder and CEO of Capwise Financial Services told ET Online.

Traditional diligence often involves reviewing a selection of customer contracts, invoices or other documents. AI allows investors to examine much larger volume of data and identify patterns that may be difficult to identify through manual review.

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Biyani said AI can be used to interrogate data rooms at scale, compare terms across customer and vendor contracts, analyse entire ledgers for revenue-recognition or related-party patterns, and test management claims against customer, dealer or employee sentiment. That can bring issues to light earlier, which may otherwise remain buried in the long tail of a business. Change-of-control or exclusivity clauses, discount leakage, deterioration in working capital by geography or concentration within a seemingly diversified business can be identified sooner.

Mukerjee said AI can identify patterns across datasets that a fully human team may struggle to analyse within a conventional diligence timeline.

It can also connect information that is usually examined separately, such as financial data, customer behaviour, contracts, operational metrics and management commentary, helping investors identify inconsistencies or previously unseen value-creation opportunities.

This is broadly in line with the direction of mergers and acquisitions (M&A) diligence globally. KPMG's 2026 Global M&A Outlook mentions AI’s growing role in target screening, valuation modelling, competitive intelligence, contract review and risk monitoring. The bigger change, it argues, is not simply speed but the scope and depth of analysis.

But AI does not eliminate the need for judgement. The technology can surface a pattern; the deal team still has to determine whether it matters, how much it is worth and what should be done about it.

AI claims and readiness

“Three years ago, AI would rarely have appeared on a diligence checklist unless the target was a technology company. Today, investors are asking a broader set of questions across sectors,” Biyani said.

Investors are also examining whether companies are overstating their AI capabilities.

“Buyers actively test for AI-washing in information memoranda, and overstated claims damage credibility across the rest of the process,” Biyani said.

For Mukerjee, the assessment has expanded into what EY-Parthenon describes as AI-readiness and AI-impact diligence. Investors are examining the potential impact of AI on the market, the maturity of AI within the product and technology stack, AI-enabled R&D, operational adoption and the readiness of the workforce and management team.

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The AI winner and the AI loser

That question becomes more complicated for traditional businesses.

“You don’t need to be an AI company for AI to materially change your investment case anymore,” Ashish Taneja, member of the Indian Venture and Alternate Capital Association (IVCA) and Founder and CEO of growX ventures told ET Online.

For investors, he said, three questions matter: can AI take costs out of the business, can it create new revenue, and can it disrupt the business before the investment horizon ends?

This is especially relevant for businesses built around repeatable information work, including IT services, BPO, financial services, healthcare, software and professional services.

But the answer is not automatically positive.

An IT services company may use AI to deliver the same outcome with fewer people and potentially expand margins. At the same time, customers may demand a lower price for that outcome. The economic benefit could therefore be split between the service provider and its customer.

“Who actually captures the value created by AI, the company or the customer?” Taneja said.

If AI improves productivity but also weakens pricing power, the investor cannot simply model higher margins and declare victory. Conversely, if a company can use proprietary data, workflow integration, customer relationships or switching costs to retain the value created by automation, its competitive position may strengthen.

Taneja said investors should therefore look beyond whether a company has adopted a particular model. The more important questions are whether customers use the product, whether they renew, what the unit economics look like and whether the business becomes stronger if a frontier model becomes significantly better.

“If the frontier model is twice as good tomorrow, does the company get stronger or irrelevant?” he said.

AI readiness has both an upside opportunity and a terminal-value risk, Biyani said. A business with clean data, modern systems and management capable of executing an AI transformation may have more room for operational improvement. A business whose moat can be replicated by AI may face pressure on its eventual exit multiple.

The deal does not end at the closing

The AI assessment is also moving beyond the acquisition process into portfolio value creation.

As Deloitte South Asia's Private Equity Leader, Nishesh Dalal, puts it, AI is still at a nascent stage of adoption in Indian PE, but is already being used across deal sourcing, diligence and portfolio management.

“AI is transforming different facets of PE by automating deal sourcing with data-driven insights, accelerating due diligence through automated document analysis and optimising portfolio management with predictive analytics and continuous monitoring,” Dalal said.

For portfolio companies, the opportunity can extend to finance, procurement, customer service and other back-office functions. Mukerjee said leading funds are also exploring agentic operations and portfolio-wide AI operating models, including dedicated AI or transformation centres of excellence.

Bain & Company's India PE Report 2026 similarly identifies AI as a core investment lens and a value-creation lever. PE firms are looking at automation, productivity, pricing optimisation and intelligent operations alongside opportunities in AI infrastructure, such as data centres and compute.

That also changes what investors may expect from management after an acquisition. Buying an AI tool is no longer enough. The question is whether management can redesign workflows, integrate systems, make data usable and assign accountability for delivering measurable gains.

AI has to show up in the numbers

There is, however, a risk of the enthusiasm around AI getting ahead of the economics.

Grant Thornton's 2026 AI Impact Survey, conducted globally, highlights an “AI proof gap”, explaining that organisations may be deploying AI without sufficiently strong strategy, governance and measurement frameworks to demonstrate returns. That makes the distinction between experimentation and durable value particularly relevant to investors.

For PE investors, Taneja said, the test ultimately has to be financial.

That means looking at revenue per employee, cost to serve, conversion rates, churn, product-development economics and gross margins. If AI creates a new product, investors need to know whether customers are actually paying for it. If it reduces costs, they need to establish whether the savings persist through the investment period.

“The minute AI changes durable cash flows, it becomes valuation. Until then, it is just a narrative,” Taneja said.

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AI changes the negotiation, too

Citing a recent mid-market sell-side mandate involving an Indian IT and data-services company serving US clients, Biyani said the buyer modelled AI-led compression in billable effort and argued that a meaningful portion of the company's revenue could face pricing pressure within three years. The buyer then proposed shifting a significant part of the consideration into an earn-out.

The seller anticipated the issue, analysed its contract base and delivery data, and demonstrated that a substantial portion of its work had already moved towards outcome-based pricing. The company was also using AI internally to improve margins.

Biyani said this reframed the AI discussion from a threat to revenue into evidence that management was already capturing an “AI dividend”. The earn-out was subsequently reduced materially, while the headline valuation was maintained.

The example illustrates how AI is reshaping negotiations: buyers can use it to find weaknesses faster, while sellers can deploy the same analytical capabilities to identify and address those weaknesses before they become price chips.

“AI is not just accelerating deals, it is reshaping how value is argued,” Biyani said.

The investment case, ultimately, is not whether a company has AI. It is whether AI changes the business enough and for long enough to change the cash flows an investor is buying.