Use cases across the M&A lifecycle
1. Deal sourcing
AI can revolutionize deal sourcing by indexing data sets, including public and non-public financial reports and market trends, by scanning and ranking potential targets according to a specific criteria and valuation to shortlist potential targets. By adhering to consistent processes and leveraging large data sets, AI can improve the efficiency of associates at private equity firms, enabling them to focus on more intangible issues like culture and leadership quality.
- Data-driven discovery AI can scour data to pinpoint investment opportunities, uncover hidden trends and find connections that might not initially be clear to private equity firms’ investment teams. Advanced analytics, including regression and Monte Carlo simulations, enhance this process by offering better insights into market dynamics and company performance, creating a comprehensive approach that enables better target filtering and a more efficient deal process.
- Automating low-value work Deal sourcing can involve repetitive tasks that AI can support, with automation opportunities in prospect identification, data entry and preliminary due diligence checks. Firms such as Pilot Growth use these tools to automate nearly 90% of low-value-added tasks to free up valuable time for both the private equity fund and the portfolio company’s leadership team, allowing them to focus on higher-value activities like building relationships with potential targets and conducting in-depth analyses of the most promising opportunities.
- Agile risk assessment AI aids in identifying and quantifying risks for firms such as KKR for potential investments by analyzing historical data and market trends. This predictive capability allows private equity firms to proactively manage and mitigate risks, ensuring more secure and informed investment decisions that balance risk appetite with anticipated rewards.
HIGHSPRING PREDICTION
Within the next two years, AI will revolutionize deal sourcing for private equity firms, eventually becoming the primary tool for identifying and evaluating investment opportunities. AI-driven platforms will streamline processes by analyzing vast datasets, uncovering patterns not evident to human analysts and providing real-time, data-driven insights. This will significantly reduce the resources required of the investment team and provide opportunities for higher internal rate of return performance. Human oversight will still be required to eliminate biases and drive clarity and transparency into the deal sourcing process.
2. Target company evaluation and due diligence
Comprehensive due diligence is a critical part of any successful merger and acquisition, enabling private equity firms to thoroughly evaluate a target company’s operations, financials and potential synergies. Given the significant amount of data that needs to be ingested and reviewed, AI has emerged as a powerful tool to streamline and enhance this process. By leveraging AI’s financial analysis and synergy identification capabilities, private equity firms can accelerate deal execution timelines, reduce costs and risks and maximize value creation.
- Intelligent document processing AI tools such as Kira.ai examines data, synthesizes critical insights, and highlights potential gaps to streamline workflows during due diligence processes. Stakeholders can also swiftly summarize pertinent information from documents with GenAI to enhance accessibility. This eliminates manual document review, enabling stakeholders to concentrate on more intricate tasks.
- Streamlined financial due diligence, operational due diligence, and synergy benchmarking AI can automate key financial tasks such as quality of earnings and operational due diligence analysis, credit risk evaluation, ratio analysis and synergy benchmarking. Advanced AI can adopt a firm’s priorities and apply those priorities across its deals. Several private equity firms are leveraging tools such as Wokelo that produces detailed, multi-page due-diligence reports in several minutes, supplementing the work provided by other third-party advisors.
HIGHSPRING PREDICTION
Private equity firms will use AI for lower-risk tasks but remain wary of fully automating financial due diligence processes due to current limitations in technology. But by 2026, AI is expected to become the standard initial approach for synergy and financial diligence—both for private equity firms and their service providers. This shift will expose inaccuracies, triggering cycles of system upgrades and investments to enhance AI models through robust human oversight.
3. Integration
AI has the potential to revolutionize the integration of new portfolio companies by transforming their integrations into a data-centric process. It can quickly analyze data for optimization opportunities, map optimal structures, forecast risks, and recommend improvements. It can aid in workforce planning, accelerating timelines, reducing costs and enhancing value creation from acquisitions.
- Effective data management AI excels at data management by ensuring all information is organized, accurate and accessible. Clean, high-quality data empowers further AI functionalities like automation and advanced analytics. Blackstone utilizes AI to enhance operational efficiency across its portfolio companies. The firm employs a robust team of expert data scientists who use AI to gather and analyze insights from its network of portfolio companies. These insights are then shared with the leadership teams of their portfolio companies to improve decision-making in pricing, prioritizing opportunities, staffing and creating AI-generated content for customer engagement.
- AI-powered HR and communications Mergers often involve integrating workforces with diverse skill sets and cultures. AI can assess employee skill sets and pinpoint potential talent gaps, optimizing the integration process by effectively assessing potential turnover and identifying skill gaps between organizations. GenAI such Thoma Bravo’s tool built by its portfolio company Paradox can be used to develop communications materials related to integration that can be tailored to each audience, like internal teams or press releases, to streamline integration execution and proactively mitigate risks.
- Predictive analytics for a smoother path Looking beyond the immediate integration phase, AI’s predictive analytics capabilities, used by firms such as Blackrock, can be a game-changer for areas such as customer demand planning. AI can identify potential roadblocks and predict integration challenges by analyzing historical data from similar mergers. This allows for proactive mitigation strategies that minimize disruptions, leading to a smoother and more successful post-merger outcome.
HIGHSPRING PREDICTION
AI will massify real-time data harmonization and predictive insights, facilitating the seamless integration and operational optimization of new portfolio company acquisitions. Early adopters are already demonstrating significant efficiency gains and enhanced decision-making, which will drive broader implementation of AI tools, though integration and data quality issues will require iterative improvements and robust governance to eliminate maturity issues in technology platforms. These refinement cycles will contribute to establishing AI as an indispensable tool for portfolio integration, ensuring data-driven strategies that create sustained competitive advantages for private equity firms.
4. Value creation
For companies within the investment portfolio, AI will unlock opportunities to enhance value creation through expedited business processes and innovation, generating substantial value across various operational aspects—from process efficiencies to product creation. Ultimately, private equity firms can leverage these technologies to streamline workflows, uncover actionable insights, and drive innovation.
- Financial process efficiency Implementing automation can expedite financial processes, including AI-driven data extraction and validation within procure-to-pay and order-to-cash cycles. By automating data entry and utilizing machine learning algorithms for accuracy checks, portfolio companies can significantly improve their speed and accuracy while reducing manual effort. This creates faster financial reporting cycles, improved data integrity for informed decision-making and the ability to reallocate resources towards higher-value activities.
- Customer-centric insights Unlocking actionable insights on customer behavior, market demands and marketing strategies is crucial for portfolio company growth. AI empowers portfolio companies to analyze vast datasets and uncover previously hidden customer segments. Additionally, GenAI can identify innovative approaches to targeted marketing campaigns, optimized product offerings, and personalized customer experiences. By leveraging these AI-driven insights, portfolio companies can drive increased customer satisfaction, revenue growth and enhanced customer retention, leading to improved ROI.
- Improved reporting Enhancing finance and board reporting is critical to delivering timely and accurate insights towards strategic decision-making. AI-powered automation accelerates data entry, transaction processing and variance analysis, while enhanced analytics ensures accuracy, streamlines reporting cycles, and provides actionable insights for management. This helps improve the speed and accuracy of reporting, allowing stakeholders to focus on making informed decisions from this information.
- Innovative product solutions GenAI is a powerful tool for accelerating product innovation within portfolio companies. Through data analysis, GenAI can uncover business synergies, identify collaboration opportunities and conduct product hierarchy analyses. Furthermore, GenAI can assist in code development, streamlining the product development process and quickly bringing innovative solutions to market. This added value can enhance the overall performance of the portfolio company.
- Operational process optimization Optimizing operational processes is essential for portfolio company success. AI used by firms such as Silver Lake’s portfolio company, Klarna, can streamline and standardize workflows by automating repetitive tasks, reducing manual effort and minimizing human error. Additionally, machine learning models can analyze operational data to identify bottlenecks and drive continuous improvement initiatives. These efficiency gains translate to cost savings and enhance overall performance, creating more attractive exit opportunities for private equity firms.
HIGHSPRING PREDICTION
We predict that portfolio companies will become increasingly comfortable with integrating GenAI capabilities into their practices to develop content and brainstorm with relative ease. We also believe that automation use will continue to grow, minimizing time spent on manual processing.
5. Exit stage
As private equity firms approach the exit stage of an investment, ensuring robust financial reporting, regulatory compliance and maximized valuation becomes paramount. AI is key in streamlining these processes, enhancing accuracy and driving informed decision-making. Private equity firms will continue to leverage AI’s ability to identify optimal timing opportunities to realize maximum potential value.
- Automated system and organization controls assessments Machine learning models can analyze historical data, financial statements and control documentation to identify compliance gaps and potential risks while conducting Sarbanes-Oxley Act and control assessments. This increases efficiency by reducing the time and resources required for the evaluations and improves accuracy by detecting nuanced patterns and abnormalities. For example, AI can quickly sift through vast amounts of financial data to flag transactions or entries that deviate from expected patterns, which human auditors may miss. Additionally, it can cross-reference control documentation against actual data flows to pinpoint control deficiencies.
- Predictive benchmarking AI models can analyze an asset’s financials, market data and industry benchmarks to predict its performance relative to comparable public companies’ post-exit. For example, AI can be leveraged to track revenue growth, profitability and valuation to assist private equity firms in identifying potential risk areas that will hamper its exit valuations. Furthermore, this data can be utilized to pinpoint both the company’s five-year forecast model and the optimal exit timing to maximize returns based on the comparative analysis.
- Proactive quarterly reporting Ahead of exit, it is critical to prioritize corporate performance management tasks like data aggregation, financial forecasting and variance analysis for timely reporting at the portfolio level. AI can automate these corporate performance management processes, ensuring the efficient delivery of financial insights and enabling informed decision-making as the exit approaches. Moreover, AI-driven reporting can flag anomalies, pinpoint areas requiring human review and provide dynamic visualizations to communicate performance trajectories to potential buyers.
HIGHSPRING PREDICTION
By 2026, AI will become the standard initial approach for reporting and benchmarking in private equity. While there is still hesitancy today, early adopters demonstrating AI’s efficacy will prompt widespread normalization. But the shift to AI-driven reporting will expose critical inaccuracies, triggering system upgrades and reinvestments over the next 3-5 years. These adoptions, failure identifications and improvement cycles will propel the space forward as firms harness AI’s potential through enhanced models and human oversight.
