AI Becomes the Backbone of Dealmaking in 2026

In 2025, artificial intelligence moved from promise to proof in mergers and acquisitions (M&A). Deal teams used it to move faster, test more ideas, and reduce friction across the deal life cycle. In 2026, that shift becomes permanent. AI stops being a pilot or side tool. It becomes core deal infrastructure. 

This is the tipping point. Not because the technology suddenly appears, but because behavior changes. Executives now expect AI to sit inside daily workflows. Investors assume it will shape valuation and risk. Regulators expect firms to control it with care. And dealmakers who lag feel the cost in speed, insight, and confidence. 

AI already shaped market direction in 2025. It improved diligence. It sharpened valuation models and required deal teams to ask robust questions about valuation impact. It helped teams plan integration earlier and with more clarity. Buyers also began to compete for companies with real AI assets: strong data, trained models, and scarce talent. That demand lifted valuations and shortened timelines. It also raised the bar for execution post close. 

In 2026, this momentum carries forward. AI no longer supports the deal. It runs through it. 

From pilots to core infrastructure 

The key change is intent. In earlier years, firms tested AI in narrow use cases. They ran pilots. They explored tools. In 2026, leaders design deals with AI in mind from day one. 

This mirrors earlier shifts in M&A. Virtual data rooms once felt risky and unfamiliar. Many teams resisted them. Today, they are essential. No serious deal happens without them. AI follows the same path. It solves real pain points. It saves time. It reduces error. Over time, resistance fades.  

The AI-enabled deal lifecycle 

AI now touches every phase of M&A execution. 

Start with sourcing. Predictive models scan markets for momentum, financial health, and strategic fit. They surface targets earlier. They track them over time. Deal teams spend less effort hunting and more time assessing. 

Move to diligence. This remains one of AI’s strongest use cases, but it has matured. AI now prepares files, organizes data, and automates redaction. It flags anomalies. It speeds compliance checks.  

Next comes valuation and forecasting. Predictive analytics allow teams to test more scenarios and stress assumptions faster. Models update as new data arrives. Teams gain clarity without losing control. 

After close, AI continues to work. Agentic systems track synergy delivery, human capital integration, and long-term returns. They monitor progress against the investment thesis. They surface risks early. Integration becomes more measured and more transparent. 

This end-to-end view defines modern dealmaking. AI does not replace judgment. It amplifies it. 

Speed with safeguards 

Faster deals bring new risks. Dealmakers know this. They handle highly sensitive data. A single breach can cause real harm. Security and privacy sit at the top of the concern list. In fact, 36% of global dealmakers say these issues are critical obstacles to AI adoption. 

There is also uncertainty around rules. More than 70% of dealmakers want government oversight of generative AI. Clear frameworks matter. They set expectations. They build trust. Without them, hesitation grows. 

Leading firms respond by pairing acceleration with governance. They invest in strong cybersecurity. They demand transparency in AI outputs. They avoid black-box decisions. They create audit trails that show how data flows through models. 

Many also align with emerging standards. ISO 42001, the international standard for responsible AI management, is one example. Companies that adopt these frameworks earn credibility with regulators and investors. They turn governance into a competitive advantage. 

A likely path for 2026. 

Looking ahead, two scenarios shape 2026. One is broad acceleration. Deal volumes and valuations rise as AI boosts confidence and efficiency. The other is steadier growth, shaped by cost of capital and regulatory complexity.  

The first scenario appears more likely. Volatility remains, but it now feels normal. As AI proves its value in real workflows, teams lean in. Speed, precision, and transparency become differentiators. 

Success will hinge on intent. Identify where AI creates real gains. Invest in clean, reliable data. Match deployment to governance maturity. Engage local experts early to navigate regional rules. Build flexibility into timelines. 

These steps do not slow deals. They make them durable. 

The rise of AI-native dealmakers. 

The next phase goes further. Agentic AI changes how work gets done. These systems do more than respond to prompts. They act with autonomy. They adapt to context. They retain institutional knowledge.  

In practice, this means AI that monitors targets for months or years. It understands an investment thesis. It watches markets. It brings ideas forward without being asked. It behaves like a digital team member. 

As always-on data rooms and intelligent agents become standard, roles will shift. Junior work changes first. Senior judgment becomes more valuable, not less. Teams that learn to direct, question, and govern AI will lead. 

The real question is no longer whether AI will redefine M&A. That answer is clear. The question is how quickly firms adapt, and how well they manage the risks along the way.  

2025 showed what is possible. 2026 makes it expected. Now is the time to act with purpose, build trust into the stack, and shape a smarter future for dealmaking.