AI Trends in M&A

🚀 Can AI Finally Fix Insurance M&A? Insurance M&A has promised scale, efficiency, and strategic advantage for decades—but too often, it delivers fragmented integration, lost talent, and valuation misses. Having been deep in the trenches of insurance M&A, I’ve seen firsthand that deals don’t struggle due to a lack of intent. They struggle because scale, integration, and valuation are fundamentally misunderstood. 🔥 Scale ≠ Strength More premiums ≠ more profitability. Most M&A playbooks assume cost synergies through size—yet they overlook the decentralized realities of policy admin, commission structures, and actuarial models. If your infrastructure isn’t built to flex and adapt, scale becomes a burden, not a benefit. 🧩 Integration: The Blind Spot Financial consolidation is easy. But workflow intelligence? That’s where deals break down. Legacy systems weren’t designed to talk to each other, so post-merger "integration" often creates parallel processes instead of true unification. The result? Inefficiency, workarounds, and operational drag. 💰 Valuation: The Mismatch Problem Traditional M&A valuation models assume static conditions. But what if AI could dynamically assess integration complexity, execution risk, and real operational lift? Instead of relying on outdated projections, AI-driven M&A could continuously refine strategies in real time. 🔮 AI: The M&A Game-Changer? We’re at a tipping point. AI-driven analytics, decentralized processing, and embedded decisioning could finally shift M&A from guesswork to precision. Imagine an M&A process where: ✅ AI optimizes workflows from Day 1—no more five-year reconciliation roadmaps. ✅ AI dynamically refines valuation models as data evolves—eliminating bad assumptions. ✅ AI predicts execution risks before they derail post-merger integration. The old M&A playbook is broken. The question is—will insurance leaders embrace AI-driven, dynamic execution, or keep pushing the same strategies expecting different results? What do you think? Where will AI make the biggest impact in insurance M&A? Let’s discuss. 👇 #InsuranceM&A #AI #DigitalScale #OperationalExcellence

Continuing this week's discussion of AI in M&A, I'd like to share some thought leadership from Bain & Company. According to their research, GenAI is being used in about 16% of deals, and that number will skyrocket to 80% over the next three years. Other key insights from their survey report include: - Early adopters, mainly in tech, healthcare, and finance, are using GenAI for target identification and document review. - 85% of users report that GenAI meets or exceeds expectations, noting benefits such as increased productivity, faster timelines, and reduced costs. Current applications are in line with what other thought leaders have said. AI is currently being used for: - **Target Identification**: AI helps identify acquisition targets that traditional tools might miss, enhancing deal sourcing. - **Document Review**: AI parses large volumes of data quickly, identifying critical information and deviations, which saves time and focuses attention on problematic areas. - **Data Room Management**: AI automates filing, document search, and Q&A, improving efficiency. AI includes a number of challenges: - Survey respondents note that while AI can streamline tasks, it still requires human oversight to ensure accuracy and relevance. - Common concerns include data inaccuracy, privacy, and cybersecurity risks. Incorporating AI needs to be done strategically. Here are their suggestions: - Value Differentiation: Companies need to identify where GenAI can provide the most value, focusing on high-impact areas. - Building Differentiation: Preparing and leveraging proprietary data can build a sustainable competitive edge. - Risk Mitigation: Ensuring data accuracy and implementing strong security measures are crucial for effective AI integration. Conclusion: GenAI presents significant opportunities to enhance M&A processes, but its true potential lies in combining AI capabilities with human expertise. Early investment and targeted application will be key to realizing its full benefits. Does this line up with what you're seeing? https://lnkd.in/gGKFpttJ

📊 EY’s June 2025 M&A snapshot reveals a clear trend 🔹 +68.3% YoY in #deal value (for $100M+ deals) 🔹 Fewer deals, but larger and more strategic—$82B across five megadeals 🔹 60%+ of total value from #corporate #buyers focused on #TMT and infrastructure 🧠 The most telling move? Salesforce’s $8B acquisition of Informatica announced last month. This deal marks a shift: buyers are no longer just acquiring intelligence—they’re acquiring the #datagovernance backbone to support it. What’s driving buyer behavior? 1️⃣ #Governance as Core #Infrastructure Informatica’s integration into Salesforce underscores how #MDM, lineage, and cataloging tools are now foundational to deploying responsible, enterprise-grade AI. 2️⃣ #Security & #Compliance by Design With 80% of AI pilots failing to scale, success increasingly hinges on LLM-safe zones. Platforms like Snowflake Cortex, Databricks Mosaic, Informatica CLAIRE, and Amazon Web Services (AWS) DataZone are embedding governance directly into orchestration flows. 3️⃣ Time-to-Insight #Acceleration #Data-native platforms like Sigma, Hex, Coda and even legacy-modern hybrids like Informatica - are reducing insight latency by 30–98%, driving deal interest. #Strategic M&A isn’t chasing volume—it’s targeting resilient, secure, and AI-scalable infrastructure at the data layer. #MergersAndAcquisitions #DataEcosystem #Informatica #Cybersecurity #DataGovernance #GenAI #CloudSecurity #TMT #PrivateMarkets #StrategicBuyers

AI Won’t Replace Dealmakers, But It Is Becoming An Essential Tool AI is fundamentally reshaping the M&A landscape—not by replacing human judgment, but by enhancing efficiency, accuracy, and strategic insight at every stage of the deal process. From my experience, here’s where AI is making the biggest impact: Target Identification AI rapidly scans vast datasets to surface high-potential acquisition targets that align with strategic, financial, and cultural goals—often revealing opportunities traditional methods would miss. Due Diligence AI automates document review, contract analysis, and risk assessment. Natural Language Processing (NLP) tools quickly flag red flags, hidden liabilities, and key contractual terms—saving time and improving precision. Valuation and Forecasting Predictive analytics models assess historical performance and simulate growth scenarios, helping dealmakers better understand value, risks, and synergies. Deal Execution AI supports negotiation and execution by summarizing diligence findings, drafting memoranda, and even sourcing relevant case law—freeing up professionals to focus on higher-order thinking. Post-Merger Integration AI-powered tools streamline integration with task automation, milestone tracking, and synergy identification—critical for delivering long-term deal value. Continuous Market Monitoring AI keeps a constant pulse on the market, identifying new risks and targets to keep the pipeline fresh and relevant. The Bottom Line Speed. Accuracy. Insight. Efficiency. AI is making M&A faster, smarter, and less risky—ultimately enabling companies to extract more value from their transactions. When using AI always be sure to verify the data being provided. #MergersAndAcquisitions #AIinM&A #Dealmaking #CorporateStrategy #PrivateEquity #LegalTech #Innovation #DueDiligence #PostMergerIntegration #FutureOfWork