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      Artificial intelligence, and especially generative AI, is changing how transactions are sourced, evaluated through due diligence and delivered.

      As timelines compress and competition intensifies, deal teams in Switzerland need AI-powered tools that turn complex data into decision-ready insight.

      The following content explains what GenAI and machine learning mean for dealmakers, where AI in M&A delivers value across the lifecycle, practical use cases already in the market, and the actions that set successful programs apart in corporate finance and transaction advisory services.

      Benedikt Lindheim
      Benedikt Lindheim

      Director, AI Lead Deal Advisory

      KPMG Switzerland

      What is GenAI? A simple guide for dealmakers

      Generative AI is an advanced form of AI that creates new content, including text, code and structured summaries, based on patterns learned from large datasets. Unlike narrowly scoped models that address a single task, GenAI understands context and can answer questions, explain drivers and propose options in plain language.

      For M&A teams, that means quicker screening of target companies, clearer investment theses and rapid hypothesis testing leading to decision-ready outputs without adding headcount. When embedded into the deal lifecycle with the right guardrails, GenAI augments judgment rather than replacing it and complements existing AI technologies used in corporate finance.

      The value of AI across the deal lifecycle

      AI creates value when it is mapped to the natural rhythm of a transaction. From early market sensing to post-close value tracking, models can compress time-to-insight, reveal potential risks and quantify synergy potential with a level of granularity that manual methods struggle to match.

      The areas below illustrate where deal teams are realizing the most tangible gains today across mergers and acquisitions activity.

        Smart target screening


        By blending internal performance data with external signals such as news, hiring trends, web traffic and product reviews, AI highlights targets aligned with strategic themes and M&A opportunities.

        Teams can score adjacency, momentum and risk in real time, prioritizing outreach where the chances for a successful deal are the highest.

        This shifts sourcing from opportunistic to systematic while maintaining human oversight for strategic fit and sector theses.

        AI in due diligence


        In financial, commercial, IT and technology diligence, AI accelerates the review of data rooms, management materials and unstructured documents. Models support the M&A due diligence process by surfacing anomalies, trends and red flags earlier, allowing specialists to focus on material issues and judgment calls.

        Approaches include due diligence analysis on financial statements, AI-enabled contract review in M&A and risk assessment in due diligence checklists to drive efficiency and reduce the time-to-review across the due diligence process.

        Value creation planning


        AI quantifies value beyond top-down benchmarks. Cost-to-serve analytics, elasticity modeling and network design reveal where price, mix and footprint changes drive the greatest impact.

        Functional teams will translate these insights into specific initiatives, owners and milestones, creating a data-anchored bridge from diligence to delivery. These capabilities enable AI for value creation in M&A and inform AI in business valuation for strategic transactions.

        Ongoing value tracking


        Post-close, AI maintains a live line of sight to KPIs, exceptions and follow-ups.

        Instead of static reports, teams receive timely prompts that keep the original deal thesis in focus and enable course corrections before value leaks.

        This represents the next evolution of BI reporting in your finance function, supporting long-term value delivery across post-merger integration.

        Predictive risk management


        Predictive techniques model downside scenarios before term sheets are signed.

        Deal teams can stress-test volatility in working capital, revenue concentration and customer churn to understand how resilient the investment thesis is under different market conditions. The result is a more informed negotiation stance and a clearer view of risk-adjusted returns.

        Practical use cases for AI in Deal Advisory

        While the potential is broad, value concentrates in use cases that sit close to core deal decisions and have rich, available data.

        The examples below are already in production with deal and value creation teams, combining secure platforms with governed access to sensitive information.

              • AI-enabled Due Diligence (AiDa)

                AiDa deploys a set of purpose-built AI agents to scan virtual data rooms and financial datasets, identifying trends, risks and areas that require further transparency.

                A secure chatbot interface enables deep-dive Q&A on management reports and diligence packs, helping teams test hypotheses quickly and trace evidence back to source documents.

                Built on enterprise-grade foundations, AiDa compresses the initial exploration phase and improves the signal-to-noise ratio for experts, enabling earlier insight, more focused requests and tighter alignment between findings and negotiation levers.

                AiDa is an AI M&A tool that supports financial, legal and IT due diligence in the M&A process.

              AI-enabled Due Diligence (AiDa)

              AiDa applies AI-driven analytics based on past project experience.

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              • AI Network Optimization

                Network models optimize distribution center footprints, inventory levels and transport flows.

                What-if simulations explore resilience under disruption, from a distribution center outage to supplier delays, allowing teams to prepare pragmatic playbooks rather than generic contingency plans.

                In diligence, this strengthens views on capital requirements and service-level risk. After close, it provides a route to lower inventory, fewer stockouts and a footprint aligned to growth, with decisions justified by transparent trade-offs through predictive analytics.

              AI Network Optimization

              AI network optimization helps navigate complexities, unlocking efficiencies, and reducing inventory by up to 40%.

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              • True Profitability Model (TPM)

                TPM brings SKU, customer and order-level profitability into a single view.

                By allocating cost-to-serve with precision and using GenAI to generate plain-language insights, teams move beyond averages to understand which products and relationships truly create value in private equity deals and strategic transactions.

                For investors and CFOs, TPM informs portfolio choices, pricing moves and service-level commitments pre- and post-close, often translating into measurable margin uplift and stronger cash flow. Clear, defensible evidence supports change management across sales, operations, finance and human resources.

              True Profitability Model (TPM)

              TPM delivers clear visibility into the profitability of products, customers and orders, enabling data-driven decisions and helping companies improve margins.

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              • AI Due Diligence

                AI is now a critical value driver in M&A transactions, making it essential for buyers and investors to accurately assess a target company’s AI readiness and potential impact.

                Our AI Due Diligence service offers a comprehensive, structured evaluation of ten key dimensions: strategy, data, technology, talent, governance, cybersecurity, adoption, value creation, investment and scalability. This service is delivered through a proven three-phase process: discovery, analysis and reporting.

                This approach provides decision-ready insights and quantifies risks and opportunities that lead to enterprise valuation impacts. It also empowers clients to make informed investment decisions, negotiate better terms and accelerate post-deal value creation, all while minimizing surprises and mitigating key risks.

              AI Due Diligence

              AI Due Diligence allows buyers and investors to discover hidden value, verify AI capabilities and make smarter investment decisions with less risk.

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              Strategic benefits for your M&A transactions and deals

              Adopting AI across the deal lifecycle reshapes both the pace and quality of decision-making.

              Teams move from manual review to targeted analysis, compressing the time it takes to reach a robust and validated investment thesis while strengthening the risk lens applied to each assumption. Synergy cases are validated with detailed cost-to-serve analysis, price elasticity and network modeling.

              After close, continuous monitoring keeps leaders close to the value drivers that matter, enabling early course corrections and protecting returns. Together, these shifts help organizations compete with confidence in faster M&A processes, negotiate with greater clarity and realize value more reliably after signing, using AI in mergers and acquisitions across deal and transaction advisory services.

                Five key actions for AI success in M&A

                Successful programs start small, connect directly to deal outcomes and scale on secure foundations.

                The actions below reflect what works in practice, balancing ambition with governance so that AI augments expert judgment rather than adding noise.

                      1. Set a clear vision tied to value

                       

                      Anchor AI initiatives to explicit deal objectives such as speed to thesis, risk reduction or value capture. Define the metrics that matter, for example days to first insight, variance in working capital forecasts or realized pricing uplift, and track them from pilot to scale.

                      A crisp vision prevents tool sprawl and helps stakeholders understand why AI belongs in the transaction process.

                      Communicate the reason and the expected behaviors, not only the technology, so teams know how decisions will change across mergers and divestitures.

                      2. Prioritize high impact, data ready use cases

                       

                      Focus on where data is available and decision cycles are short, such as a diligence sprint, network scenario planning and profitability analysis. Select two or three use cases, deliver them end-to-end and document the operating model that supports them.

                      Early wins build confidence and create a blueprint for subsequent waves. Establish intake criteria and a lightweight governance forum so you can scale without reinventing controls for every project, including steps in the M&A due diligence process and supporting due diligence checklists.

                      3. Build secure, scalable foundations

                       

                      Choose platforms that integrate with finance systems and data-room tooling, with strong identity, privacy and audit controls. Define data classification rules and retention policies upfront, including Swiss and EU residency options where required.

                      Scalable foundations reduce rework, accelerate onboarding of the next use case and reassure deal counterparties that sensitive data is handled responsibly. Standardize environments, model registries and monitoring so compliance and performance are consistent across transactions and across M&A tech stacks.

                      4. Design human in the loop workflows

                       

                      Map where human judgment is essential, including materiality thresholds, exceptions and sign offs, and embed these checkpoints into the workflow. Provide users with model explanations and traceability to source documents so that insights can be defended in negotiations.

                      Human in the loop design improves quality and adoption, and it is central to responsible AI. Equip reviewers with playbooks for escalation and feedback so models continue to learn from expert interventions during financial due diligence in M&A and related reviews.
                       

                      5. Upskill deal teams and codify ways of working

                       

                      Train teams in prompt design, validation techniques and the limits of automated outputs. Create reusable checklists and playbooks that capture what good looks like for each use case, from diligence Q&A patterns to opportunity backlogs for value creation plans.

                      Codifying practice turns individual wins into repeatable capabilities across transactions. Reinforce skills with communities of practice and short, scenario-based exercises so habits form around quality, speed and accountability in AI-enabled deal advisory and post-merger integration.

                      Accelerate your M&A and deals with AI – start your journey with KPMG

                      Discover how KPMG Switzerland helps deal leaders unlock the full potential of AI in M&A and Deal Advisory.

                      We combine sector knowledge with proven delivery to bring AI into the moments that matter across the deal lifecycle, from target screening and due diligence to integration and value realization.

                      Meet our experts on the following topics:

                      AI-enabled Due
                      Diligence (AiDa)

                      True Profitability
                      Model (TPM)

                      AI Due
                      Diligence

                      AI Network
                      Optimization

                      Benedikt Lindheim
                      Benedikt Lindheim

                      Director, AI Lead Deal Advisory

                      KPMG Switzerland

                      Martin Lenke
                      Martin Lenke

                      Director, Deal Advisory

                      KPMG Switzerland

                      Ines Michel-Leitao
                      Ines Michel-Leitao

                      Director, Tech M&A

                      KPMG Switzerland

                      Ravi Chawla
                      Ravi Chawla

                      Senior Manager, Deal Advisory

                      KPMG Switzerland