Generative AI (GenAI) can create images, text, videos, and other forms of media in response to prompts. It has become a serious topic across industries, including M&A.
At the moment, GenAI's involvement in M&A is still in its early stages. Professionals involved in M&A, including consultants, lawyers, and investment experts, are currently assessing the available tools, how to use them, their effectiveness, and the potential risks they pose in the short, medium, and long term. The impact of GenAI on the M&A lifecycle could change the future needs and services of professional providers like consulting firms and law firms.
This article examines the impact of GenAI across the major phases of the deal lifecycle.By providing real-world use cases, it aims to help M&A professionals think through where AI can increase efficiency and where it cannot.
AI can help M&A teams automate or accelerate parts of brainstorming, secondary research, document analysis, data review, diligence workflows, communications, and PMO tracking.
It should not replace human judgment, proprietary deal context, primary research, legal review, tax structuring, or integration strategy.
For now, GenAI can assist with low- and medium-complexity tasks. In some cases it can take on more of the work as capabilities develop. As a starting framework, that breakdown applies across the following lifecycle phases:
How AI Can Support M&A Strategy, Deal Sourcing, and Pipeline Management
Currently, GenAI can assist by providing suggestions or ideas. However, due to the lack of access to private data and non-recorded data, GenAI cannot develop a complete M&A vision and corresponding strategy without human involvement.
Two examples of where this falls short:
Private data: An acquirer has a proprietary product about to launch and is determining its M&A vision to best complement that new product. GenAI doesn't have access to that product roadmap.
Non-recorded data: Key meetings regarding product vision and strategy that were never recorded. Without access to that context, GenAI will recommend a vision that isn't fully relevant or customized.
How AI Can Accelerate M&A Due Diligence
GenAI can draw on its training data and pattern recognition to surface insights and flag issues. It has the potential to deliver analytical depth and speed that reduces reliance on functional SMEs across large transactions.
This area is broad and covers many domains: finance, commercial, operational, insurance, real estate, environmental, organizational, and more. Below are four concrete examples. For a deeper practitioner take on the process, this episode on strategic due diligence in M&A is worth the listen.
AI Use Case: Operational Due Diligence and Vendor Spend Analysis
Companies often compare two sets of cost spending categories and vendors to identify cost savings. GenAI can enhance existing data science and analytics tools like Alteryx to provide more detailed analysis of variances between different datasets, covering both numerical and textual data.
Situation
A global market leader in manufacturing chemicals is merging with a similar-sized competitor. Each company has approximately 1,000 suppliers.
Task
An M&A practitioner is tasked with identifying non-labor cost savings.
Action
The practitioner needs to compile all vendor data, clean it, sort spending in descending order by vendor, identify overlapping vendors (with the help of fuzzy match), review contracts for consolidation opportunities, and calculate savings based on the deal thesis.
Opportunity
GenAI can integrate data science with document analysis, perform calculations on cost savings per SKU, and flag potential risks like termination clauses and fees.
AI Use Case: Commercial Due Diligence and Market Research
The strongest near-term application for GenAI in commercial due diligence is desktop research: competitive and market landscape analysis. M&A practitioners have already used it to generate ideas, conduct research, and synthesize findings such as market headwinds and tailwinds. Primary research, including voice of customer studies and market sizing in niche industries, still requires practitioners to design tailored questionnaires.
Situation
A private equity firm is evaluating a potential acquisition of a medical claims editing software from a market-leading healthcare provider.
Task
Assess the commercial attractiveness of the acquisition target.
Opportunity
GenAI can assist with secondary research. Calculating an estimated range for the target addressable market in niche industries remains a top opportunity. This process relies on primary and secondary research and a methodology built by an M&A practitioner. Future GenAI capabilities could help determine how best to calculate an estimated range and surface one.
AI Use Case: Legal Due Diligence and Regulatory Research
In M&A-intensive industries with heavy regulatory oversight: banking, healthcare, airlines. GenAI can help with research and compliance by analyzing large volumes of legal documents and identifying potential compliance risks. Antitrust is a common reason deals face regulatory scrutiny; M&A practitioners and attorneys can use GenAI to assist with research and litigation strategies.
AI Use Case: Tax Due Diligence and Legal Structure Review
A tax attorney advises on the post-close legal structure to minimize tax liabilities. Future GenAI capabilities may offer the ability to analyze all legal, tax, and financial documents and present optimal legal structures with the least estimated tax liability while remaining compliant with applicable laws.
How AI Can Support M&A PMO and Integration Planning
In the future, GenAI will be able to create customized templates and processes based on factors such as deal type, size, industry, and function. It will also provide estimated ranges for integration budget and resourcing, track value realization through various systems, recommend adjustments based on data, identify potential PMO risks, and develop mitigation plans. Two near-term opportunities:
Functional current state assessment: A functional SME should conduct a comprehensive review of relevant documentation to evaluate the current state and recommend an integrated future operating model. GenAI can assist by enabling a generalist M&A practitioner to cover more ground, reducing advisory costs and speeding up the evaluation process.
Communications: GenAI can assist with designing the communications strategy, including platforms and frequency, and potentially communicating with stakeholders (investors, customers, employees, and regulators) during integration planning and post-close. AI tools can assist by generating personalized communication materials, onboarding and migration guides, and FAQs.
Where This Leaves M&A Practitioners
GenAI has real potential to change how M&A teams work, especially in due diligence and PMO. As AI tools mature and begin absorbing private datasets, that impact will grow. Professional service providers will need to reassess where their expertise adds value as clients build more in-house capability.
The adjustment will take time. But the teams that adapt early, and adapt well, will run more efficient processes and make better decisions as a result.
The takeaway is not that AI replaces M&A judgment. It raises the standard for it. The teams that benefit most will be the ones that know which parts of the process to automate, which parts to review carefully, and which decisions still require experienced operators in the room.
Frequently Asked Questions
How can AI be used in M&A?
AI can assist with brainstorming, secondary research, document review, data analysis, variance analysis, risk flagging, communications drafting, and PMO tracking. The strongest near-term applications are in due diligence and integration planning.
What parts of the M&A process can AI automate?
AI can handle or accelerate lower-complexity tasks like document summarization, data cleaning, competitive research, and template generation. It should not be expected to replace judgment-heavy work like deal thesis development, primary research design, legal structuring, or stakeholder alignment.
Can AI replace M&A advisors or deal teams?
Not in the near term. AI lacks access to private company data, unrecorded context, and the judgment required to weigh competing considerations in a live deal. Professional service providers will need to adapt their value proposition over time, but the replacement scenario assumes capabilities that do not yet exist.
How can AI help with due diligence?
AI can accelerate document review, flag risk areas, perform variance analysis on vendor or financial data, support competitive and market research, and surface potential compliance issues. It is most useful as an analysis accelerant paired with a practitioner who knows what questions to ask.
What are the risks of using AI in M&A?
Key risks include overreliance on AI outputs without human validation, model hallucination in factual or legal contexts, inability to account for proprietary or unrecorded deal context, and misapplication to tasks that require primary research or nuanced judgment. Legal and tax recommendations require human review regardless of AI assistance.
