AI Across the Deal Lifecycle: What's Working, What's Changing, and Where Caution Matters

At our recent Corporate Development event, Update on AI Across the Deal Lifecycle, panelists shared how organizations are putting artificial intelligence to work throughout the M&A process, from identifying acquisition targets and evaluating opportunities to conducting due diligence and managing post-closing activities.

The conversation highlighted how quickly AI is becoming part of everyday dealmaking. Today, teams are using it to analyze information faster, uncover potential issues earlier, and take on work that previously required significant time and resources. That said, adoption remains uneven, and important questions around accuracy, confidentiality, and professional judgment continue to shape how the technology is being used.

AI Is Making Deal Sourcing More Targeted

Identifying potential acquisition targets has traditionally involved extensive research, manually developed lists, and broad outreach. AI is changing that, allowing deal teams to take a more focused approach by finding businesses that meet highly specific criteria. One panelist shared an example involving commercial towing operators with municipal, county, and state contracts across Minnesota, Wisconsin, and Iowa. Using AI, the team identified relevant businesses in approximately 10 minutes, allowing them to focus their outreach on a much smaller group of more highly-qualified targets.

For private equity and corporate development teams, this creates an opportunity to spend less time building lists and more time evaluating potential opportunities. Even organizations in the early stages of AI adoption are finding that relatively simple applications can significantly expand what smaller teams can accomplish.

Faster Screening Means More Opportunities Get Considered

AI is also changing how buyers evaluate incoming opportunities. Rather than quickly passing on a potential acquisition because of limited time or resources, teams are now using AI to review confidential information memorandums and answer standard screening questions with references to the underlying documents.

One organization described using AI to evaluate how closely a potential acquisition aligns with its existing platforms, drawing on information from transactions it has completed and opportunities it previously declined. This approach allows deal teams to apply more consistent screening criteria while giving more opportunities an initial review. It also creates efficiencies in related activities like negotiating nondisclosure agreements, provided the organization has clearly established its preferred terms, acceptable alternatives, and escalation requirements.

Due Diligence Is Getting an Earlier Start

Due diligence has always involved bringing together large volumes of information from financial, legal, tax, operational, and technical advisors. AI is making it easier to organize all that information, identify potential concerns, and determine where additional attention may be needed.

One corporate development team described combining quality of earnings, tax, technical, and legal reports into a consolidated summary that prioritizes significant findings. The team uses a secure AI-connected environment to make data room materials more accessible to executives while allowing subject matter experts to conduct their own independent reviews.

The potential value extends beyond efficiency. For example, reviewing customer contracts in bulk can reveal change-of-control provisions involving major customers early in the process, allowing buyers and sellers both to address potential obstacles before they become more significant. For deal teams, earlier visibility into important issues can improve coordination and bring the right specialists into the process sooner, reducing the likelihood of unexpected complications later.

AI Can Organize Complex Data, but Financial Validation Remains a Challenge

Not every acquisition comes with a well-organized data room. In transactions involving smaller or privately held businesses, buyers may receive access to an ERP system or large amounts of raw operating data that require significant work to interpret.

Panelists discussed how AI can bring structure to this information. In one example, a team analyzed approximately 200,000 to 250,000 jobs annually, processing the data month by month to develop a directional understanding of the company's revenue mix. Similar analysis can provide insight into how a potential add-on acquisition might change the composition of an existing platform.

While these applications show promise, panelists also emphasized an important limitation. AI can be effective at organizing information and producing reports, but its ability to validate underlying financial data and support complex financial conclusions still remains less reliable. That distinction matters when transaction decisions depend on the accuracy of financial information. AI-generated analysis can provide a useful starting point, but financial diligence still requires experienced professionals who understand the business, can challenge assumptions, and know how to verify the results.

Data Security and Confidentiality Need to Come First

M&A transactions involve some of an organization's most sensitive information, making data security a critical consideration when introducing AI into the process. Panelists emphasized the importance of using approved tools that meet organizational security standards, protect confidential information, and do not use submitted data to train external AI models. Some organizations are taking additional precautions by keeping highly sensitive information, including healthcare and payroll data, within internally controlled environments.

At the same time, there are also legal considerations. AI conversations may be retained, discoverable, and outside the protections of attorney-client privilege. Organizations need to understand how their tools store information, who can access it, and what safeguards are in place before introducing confidential transaction materials. For businesses exploring AI adoption, establishing clear policies and approved workflows early can reduce risk while allowing teams to take advantage of these emerging tools and technology.

Specific, Well-Defined Tasks Are Delivering the Strongest Results

One recurring theme from the discussion was that AI tends to perform best when given a clearly defined assignment and relevant supporting information. For example, asking AI to compare a specific contract provision against established precedent agreements can produce more useful results than asking it to review hundreds of pages without clear direction. Similarly, AI can extract important deadlines, indemnification periods, and outstanding obligations from transaction documents to support closing checklists and post-closing integration activities.

Panelists also stressed the value of iteration. Asking AI to cite its sources, explain its findings, and check its own work can improve the usefulness of its output. The broader lesson here was that successful AI adoption often begins with identifying repetitive, time-consuming activities where the desired outcome is clear and the results can be reviewed.

Professional Judgment Remains Essential

As shared earlier, the need for experienced professional oversight remains an important part of these conversations. Panelists acknowledged that AI can produce convincing answers that are incomplete or incorrect. In financial analysis, for example, inconsistent or poorly structured data may cause the technology to identify issues that do not actually exist. Experienced professionals are often able to recognize these problems by applying business knowledge, performing basic trend analysis, and questioning conclusions that do not make sense.

This also creates an important consideration to pay attention to when developing talent. As AI takes on more preliminary research and analysis, newer professionals still need opportunities to build the foundational knowledge required to evaluate its work. For advisory firms and corporate development teams, AI can accelerate the initial stages of an assignment, but the quality of the final work still depends on the expertise and judgment of the people reviewing it.

Building AI Capabilities Requires a Long-Term View

With new AI platforms and vendors entering the market, organizations face difficult decisions about which tools to adopt, whether to build their own solutions, and how much to invest. Some panelists shared positive experiences working with interns and recent graduates to develop internal tools tailored to specific business needs. While these solutions can be effective, they also require ongoing maintenance and people who understand both the technology and the business processes it supports.

An additional consideration to watch is how quickly the technology continues to evolve. Today’s leading platforms may look very different in a few years, making flexibility an important part of any long-term AI strategy. Building processes, knowledge bases, and AI workflows that can adapt as tools change is essential. So is encouraging regular experimentation and creating opportunities for employees to share successful applications which can support broader adoption.

Looking Ahead

AI is already influencing how M&A professionals identify targets, evaluate opportunities, manage diligence, and prepare for integration. As the technology evolves, its role across the deal lifecycle will likely continue to expand. At the same time, this panel’s discussion reinforced that adoption requires thoughtful planning. Data security, financial accuracy, professional oversight, and the ability to evaluate new technologies all remain important considerations.

For organizations exploring where to begin, the most practical opportunities may be found in the work teams are already doing. Identifying specific processes that consume significant time, testing AI against those tasks, and building on what proves effective can create meaningful progress without introducing unnecessary complexity. As AI becomes more integrated into the transaction process, the organizations that combine its capabilities with sound judgment, strong processes, and experienced professionals will be positioned to make the most of what comes next.