From deal sourcing to deal strategy: How AI is reshaping M&A in India

For decades, M&A deal sourcing has been a reactive discipline, driven by banker relationships, personal networks, and static databases. By the time an opportunity surfaces, multiple bidders are involved, valuations reflect competitive pressure, and the ability to shape terms has passed. Outcomes are determined less by strategic intent and more by timing and access.

At the heart of this is a single bottleneck, the inability to identify targets early enough to engage before a formal process begins. This gap more than any other factor determines whether a buyer leads a deal or competes for one. AI is beginning to close it. Having already transformed analytics and financial modelling, it is now moving into functions historically built on relationships, judgment, and trust. M&A is the latest frontier.

The broader shift is clear. According to Bain & Company's Global M&A Report 2026, AI adoption across the deal lifecycle has more than doubled, with 45% of practitioners now deploying it at various stages, a shift in scale and scope that points to structural change.

Deal Sourcing: The structural roadblock in deal discovery

Deal sourcing consumes the majority of M&A effort, particularly in the mid-market segment of $5–100 million transactions. This is also the segment most underserved by traditional intermediaries, as investment banks typically prioritize larger deals where fees justify the effort. As a result, corporate development teams spend months identifying targets, conducting research, and initiating outreach through fragmented networks.

This creates a paradox where organizations best positioned for acquisitions are often last to know about high-fit targets. Promising companies remain invisible not because they lack potential, but because discovery channels are fundamentally broken. By the time an opportunity arrives, valuations reflect competitive interest rather than strategic fit, and the window for differentiated engagement has already closed.

This inefficiency is becoming more significant as M&A activity itself evolves.  According to EY's US M&A Activity Insights for December 2025, deal volume rose even as deal value declined, a clear signal that the market is moving toward mid-market and smaller transactions.

Private equity firms are entering 2026 with record dry powder exceeding $3.2 trillion globally, over $1.1 trillion of it allocated specifically for buyouts. That capital needs to find the right targets before they are widely shopped and before valuations reflect a competitive process. 

India’s technology sector reflects this structural shift. Technology M&A in India rebounded sharply in 2025 after a multi-year slowdown. Tech M&A activity exceeded 140 deals in 2025, doubling compared to 2024, according to the Zinnov-Nasscom Indian Tech Startup Report 2025. This recovery was driven not by a return to scale-led acquisitions, but by a shift toward capability-driven deals, with buyers prioritizing differentiated IP, engineering depth, and strategic technologies.

Indian corporates are playing an increasingly prominent role in this transformation. Their share of tech M&A has steadily grown over the past five years, rising to approximately 36% of deals in 2025. This reflects a structural shift in how companies approach growth. Rather than relying solely on internal development cycles, companies are using acquisitions to secure technology capabilities, talent, and digital infrastructure more quickly and with lower execution risk. This makes precision in discovery critical.

From static search to continuous intelligence

AI fundamentally shifts deal sourcing from reactive discovery to proactive identification. Agentic AI systems can translate acquisition theses into structured search models. Using natural language processing and multi-factor analysis, AI copilots scan millions of companies globally, evaluating strategic fit, growth signals, leadership changes, funding activity, and synergy potential.

Instead of relying on static databases, AI systems improve through feedback loops. They learn which targets progress to engagement, which criteria correlate with successful outcomes, and which signals indicate seller readiness. Over time, this creates institutional intelligence aligned with the buyer’s strategy.

The result is a compounding advantage: discovery becomes faster, more precise, and increasingly differentiated. Acquisition teams move from episodic searches to continuous discovery capabilities that improve with every interaction.

With a contextual AI platform, a corporate development team can input a mandate such as: “Acquire cloud-native data engineering firms serving healthcare clients.” Within hours, the system generates ranked targets, complete with automated company briefs, strategic fit analysis, funding history, and leadership insights, allowing outreach to begin before competitors are even aware of the opportunity.

Consider a SaaS company seeking acquisitions in adjacent product categories. Traditionally, this required months of research and banker outreach. Now, a team can input its mandate in natural language and receive ranked targets within hours, complete with strategic fit analysis and automated company briefs, allowing engagement to begin before competitors are aware of the opportunity.

A shift already underway

AI does not replace the human foundations of M&A, judgment, relationships, negotiation, and trust. Instead, it enhances where human effort is applied. By reducing time spent searching for acquisition targets, AI allows corporate development teams to focus on evaluating strategic fit, building early relationships, and shaping transaction outcomes. Human insight moves upstream, where it creates the most value.

As market complexity increases, competitive advantage in M&A is shifting. Deal sourcing is evolving from a manual, relationship-driven activity into a continuous, intelligence-driven capability. Instead of spending months hunting for opportunities, corporate development teams with AI copilots can engage earlier and shape outcomes sooner. In this environment, advantage will not belong to those with the largest networks. It will belong to those with the earliest, most actionable intelligence.

(Maneesh Bhandari is the Co-founder and CEO of Growthpal, an AI-led M&A deal sourcing platform)