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 for every stakeholder in the deal cycle. Our Cognitive Hive AI (CHAI) framework goes 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.
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.
AI delivers value in M&A through efficiency improvements and enhanced human capabilities throughout the deal lifecycle. We'll explore some use cases, though this article is certainly not exhaustive of all the applications of AI to M&A.
Business owners approaching exit face two imperatives: increasing EBITDA and justifying a premium multiple. Strategic AI implementation often delivers both.
By applying AI to eliminate operational inefficiencies (or to create new capabilities or offerings), sellers boost 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 increase revenue, such as relationship management, product development, and market expansion.
Let's look at a few common patterns for how AI drives higher valuation for sellers.
Companies possess valuable knowledge trapped in scattered documents and employee minds. AI knowledge systems leverage institutional and tribal knowledge into proactive agents that initiate action and drive results.
It starts with standardizing documentation and building a repository that serves as a single source of truth. From there, organizations can train an AI system on that institutional knowledge. Finally, the virtual subject matter expert (AI) can be extended into all sorts of internal and external specializations, including the following:
These agents can analyze patterns, spot opportunities, and initiate workflows. This creates operational advantages that continue delivering value long after key employees depart. All of which directly increases the business's worth to acquirers concerned about post-acquisition continuity.
Most companies operate with disconnected systems that demand manual data transfer between platforms. Intelligent middleware connects these systems without expensive replacements.
When systems communicate and data flows across departments, companies eliminate data entry, reduce errors, and gain unified operational visibility. This improves efficiency and decision quality while solving a common acquirer concern: post-acquisition integration complexity.
Companies accumulate unique operational data with untapped insights. Advanced analytics solutions convert this data into actionable intelligence that drives real value.
Data can reveal revenue opportunities, high-potential customer segments, early churn indicators, and operational inefficiencies invisible in standard reports. Insights can also fuel new product or service offerings, or uncover ways in which offerings can be delivered faster, cheaper, or better. These initiatives directly improve EBITDA while demonstrating analytical sophistication that acquirers value.
Adding AI capabilities to existing offerings creates immediate value. Embedding intelligence increases market appeal and profitability.
This includes adding predictive capabilities to equipment, incorporating personalization into software, or enabling self-service through intelligent interfaces. These enhancements justify premium pricing and create competitive advantages that acquirers recognize.
Any AI capability that extends an existing product or service to make it better, faster, cheaper, or to differentiate it from competitors.
Most companies are full of inefficient processes ripe for automation. Some are "lower-hanging fruit" than others, and some deliver knockout value when improved.
Start with through audit/analysis of existing processes and systems to identify inefficiencies, then grade the findings according to potential automation ROI.
These and other practical solutions cut costs and scale operations. Buyers recognize these improvements as opportunities to extract additional value when integrating acquisitions, which can positively impact valuation multiples.
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.
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.
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.
AI systems can enhance the efficiency and effectiveness of due diligence. Here are some of the obvious use cases:
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 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 comprehensive, as we could spend pages on the applications for AI and machine learning technologies within a single discipline.
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.
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.
Looking beyond CIMs, investment banks have a lot of other processes which could be at least partially automated.
Bankers spend 15-20 hours weekly searching for acquisition targets. Machine learning systems could analyze company databases, news events, regulatory filings, and market signals to identify promising opportunities. These systems could score prospects against specific criteria and deliver pre-qualified leads.
BizForesight will also reduce the time spent on deal sourcing for participating investment banks.
Comp analysis requires extensive data collection and standardization. Intelligent data extraction could pull financial metrics from public filings, adjust for accounting differences, and identify relevant peers. Real-time market data integration could update valuations automatically as conditions change. This approach could cut research time by 60-70%.
Additionally, BizForesight will provide comps for participating partner banks.
Analysts spend hours every week updating models when inputs change. Automated data pipelines could connect models directly to source data and refresh calculations. When a client adjusts forecasts, all linked analyses could update. This would eliminate version control problems, free up analysts for other tasks, and reduce errors.
Document review consumes a lot of junior bankers' time. Advanced document processing could analyze data room contents, identify potential issues, extract key terms, and generate summary findings. Pattern recognition could flag inconsistencies across documents and detect missing information. This would let bankers focus on implications rather than information gathering.
Matching deals with investors requires thorough research. Predictive algorithms could analyze investor portfolios, stated preferences, historical behavior, and current capacity to identify ideal matches. These systems could rank investors by interest likelihood and suggest outreach strategies. This approach could improve conversion rates and accelerate fundraising.
Intelligent workflow systems could extract key information, verify completeness, flag non-standard terms, and manage material distribution. Automated tracking could monitor information access and handle follow-up. This would improve compliance and responsiveness.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
AI can assist with the creation of all the different document types that are generated across in the course of a business transaction. These include, but are not limited to, the following:
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 and machine learning solutions can often flag 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 solutions can track filing deadlines, flag potential regulatory concerns based on market concentration analysis, and monitor 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 and technical debt. Our system-of-systems approach seeks to connect 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.
Yes, AI systems can be trained to match potential acquisitions against predefined targets and score them. If a PE firm (or other acquirer) has access to detailed information about a broad range of companies for sale, an AI solution can greatly reduce human analysis needed.
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.
Talbot West brings Fortune-500-level consulting and business process discovery to the mid-market. We then implement cutting-edge AI solutions for our clients.