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AI in M&A

AI across the M&A lifecycle

By Jacob Andra / Published May 7, 2025 
Last Updated: May 8, 2025

Executive summary:

The mergers and acquisitions lifecycle is full of opportunities for AI to make a difference. Sellers can leverage automation to increase EBITDA and expand their multiple. Buyers can use AI to make the post-acquisition process smoother. 

And M&A professionals, from investment banks to accounting firms, can improve processes from quality of earnings report generation to confidential investment memorandum (CIM) creation and much more.

Talbot West creates efficiencies for M&A with AI solutions that make professional services faster, more accurate, and better all around. Our Cognitive Hive AI (CHAI) framework goes far beyond large language models to assemble the precise set of capabilities needed for each task or use case. 

Talbot West is building BizForesight in collaboration with Capitalize Network to provide AI-powered exit advisory services and dealflow for M&A professionals. 

To learn more, schedule a free consultation. 

Schedule a free consultation

According to KPMG's 2025 M&A Deal Market Study, 77% of dealmakers use AI in their M&A processes, though with variation in sophistication and integration.

Most current AI usage by M&A dealmakers involves the incorporation of commercial chatbots such as ChatGPT. While these prove valuable for specific types of tasks, they introduce data privacy concerns. They're also generalists that fail to deliver the level of impact that a task-specific solution would. Furthermore, while strong on language-based tasks, large language models are weak at data-heavy roles, which limits their efficacy for many M&A duties.

Talbot West guides M&A stakeholders to look beyond disconnected, generalist AI tools and instead implement ensemble architectures designed for specific M&A roles and responsibilities. We also encourage a comprehensive approach that links each phase of the acquisition lifecycle. This system-wide perspective changes deal dynamics across sourcing, due diligence, closing, and integration, to deliver measurable advantages to all participants.

Takeaways
M&A is full of time-consuming processes.
AI makes many of these processes more efficient.
Large language models are over-represented in M&A.
Ensemble architectures (including LLMs along with other types of AI) are the answer.
It’s important to keep a human in the loop for quality control.

Where AI creates value in M&A

AI delivers value in M&A through efficiency improvements and enhanced human capabilities throughout the deal lifecycle.

For sellers: maximizing exit value

Business owners approaching exit face two imperatives: increasing EBITDA and justifying a premium multiple. Strategic AI implementation addresses both.

By applying AI to eliminate operational inefficiencies, sellers boost current profits and demonstrate operational sophistication that warrants premium multiples.

Mid-market companies typically find that documentation processes, compliance requirements, data entry, and resource scheduling consume disproportionate staff time. AI solutions that automate these areas free up skilled personnel for high-value activities that directly impact EBITDA, such as customer engagement, product development, and market expansion.

For buyers: enhancing post-acquisition integration

Post-acquisition integration presents the greatest risk to value capture in M&A. McKinsey research confirms that 70% of integrations miss their synergy targets, largely due to information silos and system incompatibilities.

A modular approach to integration addresses these challenges. Instead of imposing immediate system consolidation (which disrupts operations and risks data loss), forward-thinking acquirers implement intelligent middleware solutions that connect disparate systems while maintaining business continuity.

  • Preserves institutional knowledge stored in legacy systems
  • Creates unified reporting across previously disconnected departments
  • Enables data flow between acquirer and target systems
  • Accelerates synergy capture compared to traditional methods

Talbot West’s Cognitive Hive AI (CHAI) architecture allows acquirers to prioritize high-value integration points first, generating early wins while building toward comprehensive system unification. For companies pursuing roll-up strategies, this capability becomes valuable, as each acquisition connects to the existing ecosystem without requiring disruptive standardization.

For professional services

All of the professional services that fuel the deal lifecycle—investment banks, corporate accounting firms, law firms, etc.—process and package knowledge in various ways, all of which are ripe for automation. We'll get more into some of these processes below.

Due diligence with AI-enhanced analysis

AI systems can enhance the efficiency and effectiveness of due diligence. Here are some of the obvious use cases:

  • Processing contract portfolios to flag unusual clauses, inconsistencies, and potential liabilities
  • Analyzing financial data to surface anomalies and identify patterns
  • Cross-referencing operational metrics with market conditions to validate revenue projections
  • Generating summaries so senior staff can focus on strategic implications

The KPMG study found that deal teams using AI for due diligence completed the process 60% faster than their non-AI counterparts. This speed advantage creates a competitive edge in auction scenarios where timeline pressures intensify.

Well-designed systems maintain clear decision paths and human oversight at critical junctures. Built-in explainability provides supporting evidence for recommendations.

AI impact across the M&A ecosystem

AI solutions find a role in the process of every major M&A service, from wealth managers to exit planners to valuation companies. Here are a few of the major players, and a few of the ways AI can move the needle for them. This is by no means exhaustive, as we could spend pages on the applications for AI and machine learning technologies within a single discipline.

Investment banks

CIM creation consumes weeks compiling financial data, company descriptions, and market analysis. AI systems could automate portions of this process while preserving quality and customization.

  • Extract financial information from data room documents
  • Draft initial company and market descriptions
  • Incorporate customer data for growth narratives
  • Update models when underlying data changes

Human bankers would focus on strategic positioning rather than document formatting and data compilation. This reallocation of effort would allow banks to handle more deals with existing teams while improving quality.

Transaction attorneys

M&A attorneys manage increasing transaction complexity while clients expect fee stability. This squeezes margins for firms handling middle-market transactions on fixed-fee arrangements.

AI efficiencies can improve margins, deliver solutions faster, and improve overall quality.

Document generation

Rather than drafting from static templates, AI systems could generate initial drafts based on deal parameters. These systems might incorporate firm-specific language, recent judicial interpretations, and deal-specific risk factors. They would eliminate repetitive drafting work so attorneys can dedicate time to strategic guidance and document polishing.

Material difference identification

When reviewing counterparty markups, AI could highlight substantive changes while filtering stylistic edits. This capability would prove valuable during compressed timeframes, enabling attorneys to address modifications that could affect transaction outcomes immediately.

Disclosure schedule automation

Disclosure schedule preparation demands time in sell-side representation. AI systems could extract information from data room documents, populate disclosure schedules, and flag inconsistencies for attorney review. For fixed-fee engagements, this automation would directly improve profit margins while maintaining work quality.

Accounting firms

AI will cut QoE time by automating data-heavy tasks. It can extract financial data across systems and standardise data formats. Pattern recognition will flag potential EBITDA adjustments and working capital anomalies. 

AI will detect customer concentration risks and covenant issues before humans spot them. It will handle repetitive calculations so analysts can apply judgment on complex issues that AI can't resolve. Statistical analysis will identify trends human eyes might miss. We estimate that AI will make QoE 40% more efficient while making it dynamic instead of static.

Beyond QoE, accountants perform many other tasks that can partially be automated. Rather than replacing accountants, these tools redirect their expertise toward higher-value advisory roles where human judgment creates the greatest client impact.

Due diligence support

AI can analyze transaction histories, identify unusual patterns, and flag inconsistencies across financial statements. Systems can validate revenue recognition practices, analyze customer and vendor concentration, and verify expense classifications. This reduces manual review time by 50-70% while improving accuracy.

Tax structure optimization

AI can model multiple transaction structures to identify tax-efficient approaches. It analyzes historical rulings, current code provisions, and jurisdiction-specific requirements to identify optimization opportunities. The system can calculate expected tax impacts across different scenarios, enabling accountants to focus on strategy rather than computation.

Purchase price allocation

AI streamlines the complex process of allocating purchase price across tangible and intangible assets. It can analyze similar transactions, apply appropriate valuation methods, and create detailed supporting documentation. This accelerates PPA preparation while maintaining defensible valuations that satisfy auditor scrutiny.

Financial projections validation

AI systems evaluate projection assumptions against historical performance, industry benchmarks, and economic forecasts. They identify overly optimistic assumptions, calculation errors, and internal inconsistencies. This enhances the credibility of financial models while reducing review time.

Transaction integration planning

AI creates detailed roadmaps for financial system integration, accounting policy alignment, and reporting consolidation. It identifies potential conflicts in the chart of accounts, financial controls, and compliance approaches. This accelerates post-close integration while reducing the risk of financial reporting disruptions.

The system-of-systems approach

Organizations should move beyond isolated AI applications toward a system-of-systems approach that connects separate capabilities. This architectural shift acknowledges that M&A involves interlocking workflows that benefit from shared intelligence.

  • Channel insights from due diligence into integration planning
  • Present unified data views across financial, operational, and legal workstreams
  • Maintain explainability and human oversight throughout critical processes
  • Begin with high-ROI implementations before expanding

Organizations using this comprehensive approach would see compounding benefits. As data flows between modules, each component gains additional value, creating an intelligence ecosystem that strengthens with use.

The future of M&A is AI-enhanced, not AI-replaced

Human expertise is needed in the M&A process, but it can be delivered more efficiently with AI augmentation. As dealmakers increasingly adopt AI across the transaction lifecycle, the competitive advantage will shift to those who implement comprehensive, connected systems rather than disconnected point solutions.

Forward-thinking M&A professionals are already discovering how modular, explainable AI can turn weeks of analysis into days of insight. The most successful implementations maintain human judgment at critical decision points while automating the time-consuming tasks that drain resources and extend deal timelines.

The most significant value comes from implementing solutions that grow more powerful over time, creating an organizational intelligence system that preserves institutional knowledge, accelerates deals, and enhances quality across every phase of the M&A lifecycle.

At Talbot West, we design and implement AI solutions tailored to your specific M&A processes. Whether you're preparing a business for sale, streamlining due diligence, or enhancing post-acquisition integration, our modular approach delivers immediate ROI while building toward comprehensive capabilities.

Introducing BizForesight

BizForesight is an AI-powered business assessment tool that provides business owners with clear valuation insights and strategic guidance about their companies. Launching summer 2025, the platform will deliver sophisticated analysis of a company's worth, benchmark against successfully sold businesses, and align strategic pathways with the owner's objectives.

BizForesight will match business owners with professional service providers who can help them achieve their goals. This creates a powerful engine driving qualified deal flow for M&A attorneys, wealth managers, accountants, and other professionals.

BizForesight is a partnership between Talbot West and Bill McCalpin. Talbot West brings mid-market AI expertise and specializes in developing advanced solutions for complex business challenges. Bill McCalpin contributes unparalleled M&A knowledge as the current Chair of the Alliance of Mergers & Acquisitions Advisors and founder of Capitalize Network, an investment bank with a 100% success rate in helping clients exit. 

Together, we've created a platform that makes elite M&A guidance accessible at scale while generating valuable opportunities for professional service providers across the industry.

Bizforesight’s AI is trained on thousands of real business deals, which makes it the most reliable valuation tool on the market. It features an ensemble architecture; Instead of a single large language model, Talbot West's Cognitive Hive AI (CHAI) framework delivers a much more robust AI assessment and advisory experience. 

Take the next step in M&A evolution

Schedule a free consultation to discuss how AI can enhance your specific M&A processes and objectives. Our team will identify exactly where implementation will create the greatest value for your organization.

Schedule your free consultation

FAQ: AI in M&A

AI can assist with:

  • Confidential Information Memorandums (CIMs)
  • Disclosure schedules
  • Due diligence reports
  • Quality of Earnings analyses
  • Purchase agreements
  • Integration plans and timelines
  • Post-close reporting

Each implementation matches your firm's templates, terminology, and quality standards.

Our implementations include comprehensive coverage frameworks that analyze all relevant documents and data sources. Risk-detector modules identify common deal-killers and material issues. Clear traceability shows what was analyzed and how conclusions were reached. And finally, expert human review is necessary to validate everything. 

AI identifies non-obvious patterns across disparate data sets. During due diligence, our implementations often find unexpected synergy opportunities by correlating operational data, customer information, and market dynamics. 

AI tracks filing deadlines, flags potential regulatory concerns based on market concentration analysis, and monitors compliance with existing consent decrees. For cross-border transactions, these systems identify country-specific regulatory requirements and track compliance across multiple jurisdictions.

Standalone tools solve isolated problems but create information silos. Our system-of-systems approach connects all aspects of the M&A process. Findings from due diligence flow into integration planning, and valuation analyses inform negotiation strategies. This connected intelligence creates compounding benefits throughout the transaction lifecycle.

We implement multiple security layers. Access controls restrict information flows based on roles. Data minimization limits processing to essential information. Our systems can anonymize sensitive content. For maximum security, our on-premises deployments keep all data within your environment rather than processing in external systems.

Our systems process documents in multiple languages, identify jurisdiction-specific requirements, and account for different accounting standards and legal frameworks. This normalizes information across countries while preserving important jurisdictional distinctions, reducing the complexity of managing multi-country transactions.

AI can scan markets for acquisition targets that complement existing portfolio companies based on specific criteria. It analyzes financial data, market positioning, customer overlap, and operational synergies to identify candidates. For PE firms pursuing roll-up strategies, this creates a pipeline of qualified targets that might be missed by traditional sourcing methods.

AI checks formula consistency, identifies circular references, verifies data inputs against source documents, and tests assumption reasonableness against industry benchmarks. It can run sensitivity analyses across thousands of scenarios to identify model vulnerabilities. This improves model reliability while reducing review time.

AI extracts relevant data from financial statements, calculates working capital requirements based on historical patterns, identifies seasonal anomalies affecting closing date calculations, and flags potential dispute areas based on accounting policy differences. This reduces preparation time by 50-70% while improving accuracy.

AI generates checklists tailored to specific transaction types, tracks document status across parties, prioritizes critical path items, predicts potential delays, and generates status reports. This helps transaction attorneys ensure nothing is missed during closing, reducing the risk of delays.

AI generates draft representations based on transaction specifics, identifies necessary qualifications based on due diligence findings, tracks disclosure schedule references, and flags compliance issues. This reduces drafting time while aligning with due diligence findings, minimizing post-close disputes.

About the author

Jacob Andra
Jacob Andra is the CEO of Talbot West and serves on the board of 47G, a Utah-based public-private aerospace and defense consortium. He spends his time pushing the limits of what AI can accomplish, especially in high-stakes use cases. Jacob also writes and publishes extensively on the intersection of AI, enterprise, economics, and policy, covering topics such as explainability, responsible AI, gray zone warfare, and more.

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