Artificial intelligence is rapidly transforming how private equity (PE) investors in India evaluate potential acquisitions. The focus of investment diligence is shifting from a standard assessment of current business strength to a more granular investigation of how AI might reshape a company's competitive standing, margins, and revenue potential over the next four to five years. For institutional investors, the primary objective is no longer merely determining if a target utilizes AI, but establishing whether that technology serves as a catalyst for sustainable, long-term cash flows.
Shyan Mukerjee, Partner and Technology Sector Leader at EY-Parthenon India, noted that the core of PE diligence has fundamentally evolved. According to Mukerjee, investors are moving beyond the question of whether a business is sound and instead asking how AI will disrupt the industry and whether the current management team has the agility to capture value more effectively than its competitors. This transition necessitates a significantly more rigorous examination of the underlying data structures within target companies.
Traditional due diligence methods have historically relied on sampling specific customer contracts, invoices, and select financial documents to form a judgment. However, the integration of AI allows investors to transition toward comprehensive, full-coverage analysis. Naresh Biyani, Founder and CEO of Capwise Financial Services, explained that AI enables the interrogation of data rooms at scale. This technology can compare terms across entire sets of vendor contracts, analyze complete ledgers for revenue recognition patterns, and test management claims against employee or customer sentiment, potentially bringing hidden issues to light before they become liabilities.
The scope of this analysis extends to identifying irregularities that might otherwise remain buried in the operational details of a business. These include change-of-control clauses, discount leakages, or concentrations of risk within seemingly diversified portfolios. By connecting disparate data points—such as financial metrics, operational logs, and customer behavior—investors can uncover inconsistencies that a human team might struggle to map during a standard M&A timeline. Global trends, as noted in recent industry outlooks from firms like KPMG, suggest that this increased depth and scope is defining the current era of mergers and acquisitions.
Despite these technological advancements, experts emphasize that AI does not replace the requirement for human judgment. While the technology is adept at surfacing complex patterns, the investment team remains responsible for determining the significance of those findings, their valuation, and the subsequent strategic actions. A critical component of this new diligence process is the verification of AI capabilities. Investors are increasingly wary of 'AI-washing,' where companies may exaggerate their technological prowess to appear more attractive or to secure funding.
The assessment now includes an evaluation of 'AI-readiness,' which covers everything from the maturity of a company’s technology stack and its R&D processes to the digital literacy of the workforce. Ashish Taneja, a member of the Indian Venture and Alternate Capital Association and CEO of growX ventures, stated that for traditional businesses, the presence of an AI strategy is becoming a material factor in investment cases. Investors are evaluating whether AI can effectively reduce costs, generate new revenue streams, or act as a defensive moat against future disruption.
However, the economic outcome of AI adoption remains nuanced, particularly for service-oriented sectors like BPO, healthcare, and IT services. Taneja pointed out that while AI can drive productivity and expand margins, it may also lead to pricing pressure from customers demanding lower costs for the same outcomes. Consequently, the central question for investors is identifying who captures the value created by automation—the service provider or the client. Moving forward, the strength of a business will depend on its ability to retain value through proprietary data, deep workflow integration, and established customer relationships, rather than simply adopting existing tools.
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