AI in Dealmaking: From Target Sourcing to Close

AI in Dealmaking: From Target Sourcing to Close

The budding artificial intelligence sector didn’t just have a good year in 2025. AI startups had a blowout. Figures from 2024 show that deal volume jumped 53%, but that’s almost boring compared to what happened to valuations. Valuations exploded 288% higher than the year before. By Q3 2025, AI and machine learning companies had pulled in $192 billion in funding, claiming half of all global venture capital flowing into the market. These numbers aren’t just hype around AI, but indicate a complete rewiring of how dealmaking actually works.

The traditional mergers and acquisitions (M&A) playbook of networking, broker dependencies, and manual target tracking has become outdated and a liability. Firms still relying on those methods are discovering what it feels like to consistently arrive second to opportunities they never even saw coming. They are outmaneuvered by competitors using AI in M&As to find deals, analyze them, and move with precision that human-only processes simply cannot match.

Welcome to the new reality of artificial intelligence in mergers and acquisitions. The traditional playbook of cold outreach, networking events, and manual spreadsheet tracking is giving way to something faster, smarter, and more effective.

How Can AI Improve Deal Sourcing?

The old way of finding deals had dealmakers relying on the same tired methods for decades. They’d tap their networks, wait for brokers to bring opportunities, and hope they weren’t missing anything good. The problem is they were missing plenty. According to SPS’s 2024 Deal Origination Benchmark Report, dealmakers typically see just 16.5% of relevant deals in their target markets. That means more than 80% of potential opportunities slip through the cracks, a blind spot that’s become impossible to tolerate in today’s competitive environment.

AI for dealmakers flips this dynamic entirely. Machine learning algorithms now scans millions of data points from financial statements, news feeds, regulatory filings, patent databases, and even social media activity to surface companies that match specific investment criteria. These systems don’t just find targets, they predict which companies are entering growth phases or facing operational challenges that might make them receptive to conversations about capital or acquisitions. Natural language processing tools monitor sentiment shifts and emerging business challenges in real time, giving dealmakers an early warning system that human analysts simply can’t match.

Firms using AI-driven deal sourcing are already seeing results that separate them from the pack. Bain’s 2025 M&A Report found that 36% of the most active acquirers are using generative AI for M&A. These aren’t random players but firms that consistently outperform their less active counterparts in total shareholder returns. More than 60% of firms are now using at least one AI tool to improve sourcing, screening, or diligence, according to Bain’s research.

Predictive Analytics for M&As

Predictive analytics platforms for acquisitions forecast which companies are most likely to seek funding or deals in the near term, giving firms a timing advantage before public news drops.

This kind of AI-powered deal sourcing transforms how firms allocate resources. Instead of spending weeks building preliminary analyses only to find a target doesn’t fit, teams can use predictive scoring models to rank opportunities based on strategic alignment, growth potential, and likelihood of conversion. Some firms report up to 70% reduction in manual diligence hours through AI-assisted document parsing, anomaly detection, and comparative analytics.

The technology also helps firms spot patterns they’d otherwise miss. AI systems can analyze alternative data sources like web traffic, hiring trends, and transaction volumes to verify growth claims that might not be visible in traditional financial statements. Blackstone, one of the largest private equity firms, uses AI-driven tools to analyze market data and identify high-potential investment opportunities faster, leading to increased deal flow and improved investment outcomes.

AI-Driven Acquisitions and Corporate Dealmaking

Strategic acquirers are also embracing AI-powered M&A analytics to boost their AI capabilities through targeted consolidations. Three of the largest tech deals of 2024 centered on AI:

Using AI to find acquisition targets helps corporate development teams move beyond static industry classifications. AI tools for investment banking deal flow can dynamically map companies by what they actually do and how they describe themselves, making it easy to identify players in niche, emerging, or intersecting verticals that traditional databases might miss entirely.

The impact stretches across the entire deal lifecycle. AI for cross-border M&A opportunities helps navigate different regulatory environments and reduces communication barriers between global teams. For AI-enhanced due diligence processes, natural language processing can identify red flags in agreements while predictive analytics verify claims using data sources human analysts would struggle to access at scale.

What Are the Best AI Tools for M&A?

Purpose-built AI tools for finance are in a class of their own. Generic AI models trying to be useful for finance miss the mark because they’re optimized for broad applicability rather than expertise in dealmaking. Cyndx has built a complete AI platform where every tool shares the same underlying data and infrastructure across 32 million global companies.

  • Finder and Acquirer help identify companies that are likely to raise private capital or become acquisition targets, matching them with the most relevant sponsors and buyers. Together, they significantly reduce wasted outreach by focusing deal teams on the most actionable opportunities.
  • Projected to Raise (P2R) uses proprietary AI models to forecast which companies are likely to seek funding in the next six months.. This gives investors and advisors a critical timing advantage, allowing them to engage before a process becomes competitive.
  • Raiser enables firms to identify the best financial and strategic investors for specific sectors, deal sizes, and funding stages by analyzing billions of data points across the global private markets ecosystem.
  • Scholar, Cyndx’s generative AI research tool, produces comprehensive 50+ page deep-dive research reports in minutes, supported by high-quality citations. It draws from both Cyndx’s proprietary database and vetted external sources, accelerating diligence and thematic research without sacrificing rigor.
  • Valer, our business valuation software, conducts sophisticated and customizable business valuations with just a few inputs.

The critical distinction is integration. When your deal sourcing platform, valuation tool, investor identification system, and research platform all share data, insights from one tool inform the others. This eliminates the biggest time sink in traditional M&A workflows: reconciling information from different sources.

Deal sourcing with artificial intelligence isn’t a coming trend. It’s already here. Companies that master AI in M&A over the next few years will identify targets faster, execute diligence more efficiently, and deliver superior returns.

The question isn’t whether AI will transform dealmaking, but whether your firm is part of the transformation. Get started now.