AI Across the M&A Lifecycle: Emerging Capabilities and Future Possibilities

The mergers and acquisitions space is notoriously time-intensive. From initial deal sourcing through post-merger integration, the process requires thousands of hours spent on analytical and administrative tasks. While human experts will (for the foreseeable future at least) need to be heavily involved at every stage, current AI technologies can reduce human involvement substantially while making deals happen faster and with fewer errors. 

According to KPMG's 2025 M&A Deal Market Study, 77% of dealmakers now use some form of AI in their processes—though with significant variation in sophistication and integration. Most organizations currently implement isolated tools rather than comprehensive approaches that connect each phase of the acquisition lifecycle.

Where AI Makes a Difference

For Sellers: Maximizing Exit Value

Business owners approaching exit face two key challenges: increasing EBITDA and justifying premium multiples. Strategic AI implementation can address both.

Mid-market companies often find that documentation processes, compliance requirements, and routine administrative tasks consume disproportionate staff time. AI systems that automate these areas free up skilled personnel for activities that directly impact EBITDA: customer engagement, product development, and market expansion.

The operational efficiencies gained through well-implemented AI not only boost current profits but also demonstrate operational sophistication that can justify higher valuation multiples during negotiations.

For Buyers: Enhancing Due Diligence and Integration

Due diligence remains one of the most labor-intensive phases of M&A. Early adopters are implementing AI systems that speed up due diligence. Here are some of the subprocesses that are ripe for AI:

• Process contract portfolios to identify unusual clauses, inconsistencies, and potential liabilities
• Analyze financial data to surface anomalies and validate revenue projections
• Cross-reference operational metrics with market conditions to verify growth assumptions
• Generate summaries that allow senior staff to focus on strategic implications

These capabilities help deal teams work faster while identifying risks that might be missed in traditional reviews. 

Post-acquisition integration presents perhaps the greatest opportunity for AI impact. Studies consistently show that 70% of integrations miss their synergy targets, largely due to information silos and system incompatibilities. AI-powered middleware solutions can connect disparate systems while maintaining business continuity. AI capabilities can assist with preserving institutional knowledge stored in legacy systems while enabling data flow between acquirer and target operations.

Impact on Professional Services

The M&A professional services ecosystem—attorneys, accountants, wealth managers, etc.—are ready for AI disruption, and the future will belong to those firms that lean in early to gain the competitive edge that AI automation can bring to their processes. 

Let’s look at a few examples of specific tasks that AI can assist with. 

For investment banks, AI can speed up CIM creation by extracting financial information from data room documents and generating initial content for review. Bankers remain central to the process but can redirect their expertise toward strategic positioning and client relationships rather than spending hours on document formatting and data compilation.

Transaction attorneys, particularly those handling middle-market deals on fixed fees, can leverage AI solutions to enhance their expertise in several ways:

• AI can prepare initial document drafts that attorneys then refine and customize
• AI can identify substantive changes in counterparty markups while filtering out stylistic edits
• AI can extract relevant information from data room documents to help attorneys populate disclosure schedules more efficiently

For accounting firms, AI will reduce the time needed to perform quality of earnings analyses. QoE will be done faster, more accurately, and with dynamic, rolling data instead of static snapshots that may be obsolete by the time the report is compiled. Accountants will maintain control of the analytical process while AI handles routine data extraction, format standardization, and flags potential EBITDA adjustments for human review.

Beyond Language Models: Diverse AI for Diverse Needs

While large language models dominate AI discussions, effective M&A applications typically involve multiple specialized AI types that address complex analytical challenges beyond text processing:

• Time series models that identify seasonal patterns in financial performance
• Quantum machine learning algorithms for complex portfolio optimization
• Agent-based simulation systems that model post-merger organizational dynamics
• Reinforcement learning algorithms that optimize integration sequencing

Each AI type brings specialized capabilities, and the most effective implementations combine these in modular ensembles that decompose complex workflows into discrete subtasks with the correct type of AI assigned to each.

Looking Forward: From Point Solutions to Connected Systems

The future of AI in M&A lies not in disconnected tools but in system-wide approaches that link phases of the acquisition lifecycle. 

• Automatically channeling insights from due diligence into integration planning
• Creating unified data views across financial, operational, and legal workstreams
• Ensuring explainability and human oversight throughout critical decision points

Organizations adopting this comprehensive approach will see compounding benefits as data flows between connected systems, creating an intelligence ecosystem that strengthens with use.

The competitive advantage in M&A will increasingly shift to firms that implement connected, modular systems rather than isolated point solutions—while keeping human judgment at the center of critical decisions.

Jacob Andra is CEO of Talbot West, an AI advisory and implementation firm specializing in modular, system-of-systems approaches to artificial intelligence. Talbot West's Cognitive Hive AI (CHAI) ensemble framework helps organizations implement connected AI capabilities across the M&A lifecycle. Learn more at talbotwest.com.