Digital M&A using Claude skills– The Singularity is Nearer
By Dr. Karl Michael Popp, published on manda-automation.com
The convergence of agentic AI, structured process models, and purpose-built tooling is fundamentally reshaping how M&A transactions are sourced, executed, and integrated. What was once a largely relationship-driven, document-heavy profession is rapidly becoming a data-driven, partially automated discipline — and the pace of change is accelerating.
The M&A Reference Model: A Structured Foundation for Automation
Systematic automation of M&A requires a structured, comprehensive model of all M&A tasks and data objects. The M&A Reference Model provides exactly this foundation.
Methodologically, the model is grounded in the Semantic Object Model (SOM), a formal framework for describing business tasks. Each task in the model is defined by:
• Goals — what the task should achieve
• Objectives — measurable outcomes (e.g., information asymmetry minimized, risk minimized, synergy maximized)
• Procedures — the step-by-step actions that make up the task
• Task Object Schema — the data objects the task reads and writes
A concrete example is the M&A Strategy Creation task. Its goal is to produce a defined M&A strategy. Its task object schema covers the buyer's ecosystem, corporate strategy, strategic goals, strategic risks, supply chain coverage, and M&A strategy — all the data structures an AI agent needs to reason about and produce outputs.
The five phases covered are: M&A Strategy, Business Case Creation, Due Diligence, Signing & Closing, and Merger Integration. The model also reflects automation offered by 128 mapped tools reflects rapid market development in M&A-specific software.
Here are examples of AI Skills based on the reference model you can buy:
Go-to-market due diligence assesses whether a target can acquire customers, scale revenue, and meet buyer growth goals. Core areas: market sizing/segmentation; competitive positioning, differentiation, and pricing; customer/channel analysis (CAC, retention, LTV, sales motion); product-market fit, roadmap, and monetization; GTM economics modeling (CAC, payback, margins, unit economics); sales/marketing capabilities (org, processes, pipeline, KPIs); and integration readiness (culture, systems, operations). Effective GTM diligence blends quantitative models and operational diagnostics to reveal realistic revenue scenarios, key risks, and value-creation levers.
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Automation in M&A Phase 1: Target Search
The first phase of M&A — identifying and qualifying targets — is where tool-based automation has advanced furthest. A tool-powered target search now maps across four core tasks:
• Define selection criteria and market — the most tool-rich task, with up to 40 tools available
• Scan sources for potential targets — partially fully-automated (green in the reference model) - 40 tools
• Review companies to join the longlist — 27 tools available
• Define the longlist of targets — 15 tools available
Beyond conventional database searches, AI is now enabling non-obvious target identification through three distinct exemplary approaches:
• Likelihood-to-sell signals — tools like Growthpal analyze behavioral and financial signals to predict which companies may be open to a transaction
• Patent-based technology mapping — tools like PATEV map patent portfolios to surface technology-rich targets that may not appear in traditional financial screens
• Multi-layer supply chain intelligence — tools like ABRAMS World Trade Wiki use trade data to identify companies embedded in strategically relevant supply chains
These approaches expand the effective target universe well beyond what human researchers can identify manually.
Automation in M&A Phase 2: AI-Accelerated Due Diligence
Due diligence is where agentic AI is creating the most dramatic efficiency gains. AI-augmented DD can now be completed in 2–4 weeks when the data room is fully populated before kick-off — and agentic AI can process entire data rooms in hours, flag critical risks, and generate preliminary findings reports.
The acceleration spans three DD workstreams:
Commercial DD
• Revenue forecasting (e.g., Modelyzr)
• Real-time KPI benchmarking against sector comparables
• Sentiment analysis of customer reviews and market perception
Financial & Legal DD
• AI contract review, identifying risk clauses and reps & warranties exposure in minutes
• Automated flagging of revenue recognition anomalies and accounting irregularities
• Regulatory compliance screening across multiple jurisdictions
Technology & IP DD
• Code quality analysis and technical debt assessment (e.g., Sema Software)
• AI patent landscape mapping and IP freedom-to-operate analysis (e.g., PATEV)
• Security vulnerability scanning and data architecture review
Skill Files: Making M&A Tasks Executable by AI
A key concept enabling AI-native M&A workflows is the skill file — a structured markup document that describes an M&A task in enough detail that an AI agent can execute it reliably.
Skill files encode the step-by-step process, the questions to ask, the data objects to produce, and the decision criteria to apply.
A sample Financial Due Diligence skill file, for example, runs to ten pages and starts with:
• “Scoping: Confirming transaction perimeter, diligence period (typically 3–5 fiscal years plus LTM), data room setup, and SPA mechanics (locked-box vs. completion accounts, cash-free/debt-free definition, working capital true-up)
• Historical financial statement review: Basis of preparation (IFRS/GAAP), auditor identity, reconciliation to management accounts, revenue recognition policy, key estimates
• Working capital analysis: Monthly NWC series, trailing-12-month averages vs. 24/36-month baselines, DSO/DPO/DIO trend analysis, AR and inventory aging, normalization for one-time items”
Snippet of skills file
The output of a skill-driven DD process is a structured findings report. A sample buy-side red flag report based on just three documents — an Information Memorandum, preliminary accounts, and management accounts from one month— was able to identify three critical risks for a German services SME:
Snippet of results report automatically generated
1. Single-customer dependency (Key Customer A ≈ 88% of revenue, no contracts) — Critical
2. Strategic-owner risk (Key Customer A acquired by a NYSE-listed multinational, procurement rationalization underway) — Critical
3. End-of-industry structural decline (German commercial print volumes in secular decline; product mix maximally exposed to digital substitution) — Major
Additional flags included unstable historical earnings, owner-dependent management, no strategic sales function, and a legal form requiring specific tax structuring. This level of preliminary analysis, generated from three documents, illustrates the practical value of structured skill files in M&A workflows.
Skill files work for most tasks in the M&A process. An example for a skill file for IT Due Diligence is here. The skills file for financial due diligence will be published soon.
Building an AI-Native M&A Process: A Roadmap
The path to a fully AI-native M&A advisory capability is sequential, not instantaneous. A practical roadmap breaks into two horizons:
Now (2026)
1. Deploy AI target screening tools to continuously monitor a defined universe of companies
2. Build a proprietary deal outcome database for AI model training
3. Integrate AI-powered data room analysis into the DD workflow
4. Create AI skill files for each task in the M&A process
5. Upskill senior advisors in AI literacy and agentic tooling
Near (2026–27)
1. Agentic DD agent that autonomously reads and summarizes entire data rooms
2. Proprietary AI valuation engine trained on closed M&A transactions
3. Client-facing AI deal intelligence portal with live market signals
4. AI-generated LOI and term sheet drafting with precedent analysis
5. Synergy quantification agents integrating operational data
Source: McKinsey AI Transformation Manifesto (Apr 2026), Bain Technology Report 2025
One conclusion stands out clearly: the competitive moat is not the AI tools themselves, since those are broadly available. The advantage comes from how fast and how deeply firms embed them into core advisory workflows — and from the proprietary deal data they accumulate over time.
Key Takeaways
• A sound corporate strategy considers both organic and inorganic growth vectors; the ability to execute inorganic moves must be built, not improvised.
• A holistic, agentic, and data-driven M&A strategy is achievable today with available tools and structured process models.
• The M&A Reference Model — now comprising 70 tasks, 2,491 questions, 1,134 data objects, and 128 mapped tools — provides the foundation for systematic automation across all five M&A phases.
• AI skill files translate structured process knowledge into executable agent instructions, enabling consistent, high-quality output from AI systems working on M&A tasks.
• The firms that will lead in AI-native M&A are not waiting for better tools — they are embedding the current generation of tools into their workflows and accumulating proprietary data now.
More tools, frameworks, and automation resources at manda-automation.com
Sources
Industry Research & Reports
WEF — AI Agents in Action: Evaluation & Governance (November 2025)
PwC — 2026 AI Predictions
McKinsey — AI Transformation Manifesto (April 2026)
Bain — Technology Report 2025
A Modern Post-Merger Integration Playbook: From M&A Models to AI Solutions
By Dr. Karl Michael Popp
Master integration due diligence to transform your M&A success. Learn more at manda-automation.com