Artificial Intelligence (AI) in M&A

Session 1: Core Prompting Principles

Note that these prompting principles apply to a wide range of disciplines. Not only M&A but also law and corporate finance

• Universal application across AI platforms: Prompting principles apply to all major LLMs (ChatGPT, Claude, Gemini, etc.)

• Techniques now relevant for Microsoft Copilot (which now has access to GPT-5 & Anthropic /Claude)

• A framework ensuring consistency regardless of which AI platform your organisation deploys

• Where is AI used in M&A? Where AI works effectively in M&A

• Where AI has limitations in M&A

• Setting up your LLM for professional use: Profile configuration

• Custom instructions examples

• Understanding data limitations & constraints: Token limitations across platforms

• Various strategic workarounds

• Professional data considerations

• Practical verification & prompting techniques: Hallucination filters

• Temperature filters

• SCOPED framework overview: The SCOPED Framework – a systematic approach to prompting

• Components of the SCOPED framework: S = Self: Your role, professional context and tone

• C = Context: Deal situation & participants

• O = Objective: What decision does this support

• P = Parameters: Format, length, detail level, style, tone

• E = Execute: Precise execution instructions

• D = Due diligence (Verification)

• Enhanced prompting techniques: Chain of thought methodology – use and application

• Tree of thought methodology – use and application

• Combined CoT & ToT methodology – use and application

• Sequential prompting

• Agents in M&A Emerging applications

• Key limitations

Session 2: M&A-Specific Prompting Case Studies & Worked Examples

This session demonstrates advanced artificial intelligence M&A prompting techniques. Here, we explore four comprehensive case studies drawn from live M&A transactions. Rather than theoretical exercises, each example walks through the complete methodology progression from initial prompt development to sophisticated output refinement.

Presentation Format: Each case study follows a structured demonstration sequence. We begin with the commercial challenge and stakeholder dynamics. We then observe the systematic application of the SCOPED framework to develop targeted initial prompts. The core demonstration involves live Chain-of-Thought and Tree-of-Thought methodologies, showing how sequential prompt refinement transforms basic outputs into genuinely useful professional work product.

Practical Focus: These are not simplified academic scenarios but authentic deal situations where AI-assisted analysis directly impacts transaction outcomes. You'll observe how different prompting approaches handle complex commercial realities - from managing multi-party seller dynamics in earn-out negotiations to navigating intercompany transfer pricing disputes within locked-box mechanisms.

Learning Approach: Each walkthrough reveals the decision-making process behind prompt construction, demonstrates common failure modes and recovery strategies, and shows how to iterate towards outputs that meet professional standards for accuracy, nuance, and commercial insight.

Takeaway Value: By the end of the session, you'll have observed proven methodologies for four critical M&A workstreams, complete with reusable prompt frameworks and quality control checkpoints that you can immediately apply to live matters.

The session emphasises practical application over theoretical understanding - showing precisely how sophisticated prompting transforms routine AI tools into powerful analytical engines for complex deal work.

Case Study 1: Valuation (Trading Comparables Analysis)

Case Capsule: Private equity consortium (Permira/CVC) evaluating 100% acquisition of NexGen Electronics Ltd, a UK private electronics manufacturer (£95m revenue, £22m EBITDA, 18% growth). Management seeking £275m for full equity stake. The assignment requires a comprehensive trading comparables analysis using 7 listed UK/EU electronics companies to determine a fair valuation range and negotiation strategy.

AI Methodology Walkthrough: Using SCOPED + COT framework to construct systematic valuation prompts. Participants observe the step-by-step development from basic prompt to investment committee-ready output. Demonstration covers:

• Building comprehensive context (private target using UK GAAP vs listed IFRS comparables)

• Filtering comparable companies and identifying outliers

• Calculating meaningful multiple ranges with statistical analysis

• Sequential prompting to refine adjustments: private company discount, size adjustments, control premium, growth differentials

• Chain-of-Thought methodology to document reasoning and catch errors

• Generating professional IC memos with sensitivity analysis and negotiation tactics

Key Learning Points:

• Systematic prompt construction eliminates ambiguity and improves output quality

• COT methodology creates an audit trail for investment decisions

• Proper context (accounting standards, deal structure, ownership %) prevents errors

• AI can handle complex adjustments but requires precise instructions

• Time reduction from 3-4 hours to 45 minutes while improving consistency

Case study 2. Locked Box – value accrual Case Capsule: Private equity director evaluating the £96m acquisition of ThermoServ Group, a seasonal HVAC maintenance business where 70% of EBITDA is earned in six months. The challenge is to determine how to structure the value accrual during the locked-box period; first, through the traditional interest approach; secondly, using the cash-profits ticker, or thirdly, using the two-stage seasonal ticker.

M&A Artificial Intelligence Methodology Walkthrough: Using the SCOPED + Tree-of-Thought framework, participants see how AI can structure a valuation problem with several competing solutions. The ToT process builds three reasoning branches — for each value accrual method — and compares them on IRR impact, implementation complexity, and negotiation practicality before converging on the most defensible approach.

Demonstration covers:

• Building deal context and parameters for value-accrual analysis

• Generating structured prompts for each accrual option

• Applying ToT reasoning to weigh commercial, financial, and execution trade-offs

• Synthesising a convergent recommendation suitable for an investment committee presentation

Key Learning Points

• Tree-of-Thought reasoning supports balanced evaluation of multiple structuring routes.

• SCOPED prompting ensures clarity, consistency, and verifiable output.

• Demonstrates how AI can model professional judgment on contested valuation mechanisms, improving both analytical transparency and negotiation readiness.

Session 3: Practice Exercises

Participants will use a SCOPED Prompt Template (which they will use on one or more Scenarios)

Scenario 1 – Earn-Out Structuring: Balancing Cash and Upside Case Capsule: You are a corporate finance executive advising a private-equity buyer that is acquiring TechPrecision Ltd, a mid-market industrial-tech business with three equal founders.

• The two older founders want maximum cash at completion and limited post-deal exposure.

• The younger founder prefers a longer earn-out with higher upside potential and is willing to stay on for three to five years.

The buyer wants to retain all three founders for at least two years while protecting against over-optimistic profit forecasts.

Task for participants: Using the SCOPED prompt template, develop an AI prompt that will:

• Generate alternative earn-out structures reflecting these differing priorities.

• Identify performance metrics and durations suitable for each founder profile.

• Suggest how to balance certainty (cash) and incentive (contingent value) while maintaining buyer control.

Objective: Produce a professional, negotiation-ready summary of options the buyer could present to the sellers.

Scenario 2 – Bridging the Value Gap: Vendor Notes and Deferred Consideration

Case Capsule: A corporate buyer values a target at £45 million, but the founders insist on £50 million. The buyer is considering offering vendor loan notes or deferred cash payments to close the gap. The seller wants assurance of payment and limited credit risk; the buyer wants to protect cash flow and avoid overpaying if post-deal performance declines.

Task for participants: Using the SCOPED prompt template, construct an AI prompt that will:

• Compare vendor loan notes versus deferred cash as mechanisms to bridge the valuation gap.

• Identify key financial, tax, and commercial trade-offs for each option.

• Summarise which approach best aligns with the buyer’s financing constraints and negotiation leverage.

Objective: Generate a concise, investor-style recommendation setting out pros, cons, and indicative structuring terms.

Training Objectives

Upon completion of this M&A artificial intelligence workshop, participants will be able to:

• Develop sophisticated prompt engineering techniques tailored for M&A applications. As well as ensuring consistent and reliable AI outputs.

• Test and select the best AI tools for M&A workflows, understanding their capabilities, limitations, and optimal use cases.

• Create robust quality control frameworks for AI-generated outputs in high-stakes transaction environments.

• Install effective risk management protocols of AI for M&A due diligence, contract review, and financial analysis.

• Structure and execute M&A due diligence that maintains accuracy and significantly improves efficiency.

• Develop strategies for managing AI limitations and biases in M&A contexts, ensuring reliable and trustworthy outputs.

Training Course Summary

This comprehensive program gives M&A professionals cutting-edge AI implementation strategies and practical skills for modern deal-making. Beyond significant time savings, the course emphasises how AI enhances the quality of outputs, delivering more precise drafting, better risk identification & tailored strategic insights that drive superior client outcomes. Stand-Alone AI Courses for Adjacent Practice Areas Besides this AI for Mergers and Acquisitions programme, we offer separate half-day and full-day classes that apply the same prompt-engineering methodology to other high-value legal and advisory workflows. Popular courses include:

Course

Primary Audience

Key Skills & Outcomes

AI in Litigation & Dispute Resolution

Litigation teams, arbitration specialists

Draft pleadings, discovery requests and witness outlines; privilege‑preserving document review; precedent search automation.

AI in Restructuring & Insolvency

Restructuring lawyers, turnaround advisors, special‑situations bankers

Rapid covenant‑breach analysis; AI‑driven scenario modelling; stakeholder communications drafting.

AI in Tax Structuring

Transaction‑tax partners, tax analysts

Cross‑border structuring, prompt frameworks; anti‑avoidance diagnostic prompts; drafting ruling requests.

AI for Regulatory & Compliance

In‑house counsel, compliance officers

Horizon scanning of emerging regulations; automated risk‑register drafting; regulatory submission generation.

AI‑Enabled ESG Due Diligence

ESG specialists, deal teams

Sustainability clause review; supply‑chain risk flagging; greenwashing detection prompts.

Frequently Asked Questions

Q: We already use Microsoft Copilot. Do we still need this training? A: Absolutely. Copilot recently acquired access to ChatGPT, making it significantly more powerful than earlier versions, but also making proper prompting technique essential. Most users get mediocre results from Co-Pilot because they treat it like a search engine rather than applying structured prompting frameworks. This course teaches you how to extract genuinely useful M&A analysis from Copilot and other AI tools, rather than generic summaries.

Q: My firm uses DealCloud/PitchBook/Kira Systems. Will this course help with those platforms?   A: Yes. While we don't provide platform-specific training on proprietary databases (which typically comes from the vendors themselves), we teach the fundamental AI interaction skills that improve your effectiveness on any AI-enabled platform.

Q: What's the difference between this course and vendor training?   A: Vendor training teaches you how to use their platform's features. This course teaches you how to get better results from AI components regardless of the platform, including prompt engineering, output verification, and risk management skills that transfer across all AI tools.

Q: Will we be doing hands-on prompting exercises during the course? A: No. The course uses live demonstrations of prompting techniques rather than participant exercises. Developing a sophisticated M&A prompt—particularly using Chain-of-Thought and Tree-of-Thought methodologies—can easily take 30-45 minutes to refine properly. Additionally, the same prompt can generate different results across participants (due to how LLMs work), and some platforms have significant response lag times, which would make synchronised group exercises impractical in a four-hour session.

Instead, you'll observe best-practice prompting in real time through detailed case studies, understanding the complete thought process behind effective AI interaction. You'll also receive prompt templates that you can adapt and use immediately in your own M&A work. This approach gives you both the conceptual framework and practical tools to commission and evaluate AI work effectively—far more valuable than struggling with basic prompts under time pressure.