Artificial intelligence and machine learning capabilities are becoming a standard part of exit process evaluation, for both investors and portfolio companies. Investors are increasingly assessing AI and ML capabilities in their portfolio company evaluations, making the inclusion of AI and ML in exit readiness activities even more critical for businesses. Given this, sell-side IT due diligence processes during exit preparation should now include identifying new strategies for risk mitigation and increasing business value through AI and ML capabilities, or a portfolio company will risk limiting their value during the exit process. 

To ease this shift in the marketplace, there are several opportunities for businesses to deploy AI and ML capabilities to accelerate the exit process, while simultaneously improving operating margins and better leveraging data insights to drive business growth. Exit readiness use cases that have seen an increased emphasis in 2024 include: 

Regulations and Controls  

With the SEC and other agencies increasing pressure on private equity firms considering potential initial public offerings, the compliance status and security expertise of a portfolio company has become an increased area of focus. Though it has always remained a vital part of an evaluation, the impact of AI and ML capabilities on the exit readiness process has become a hot topic for many businesses, with two specific use cases where AI and ML can alleviate the time-intensive nature of these activities: 

  • SOX/Controls: During the exit and IPO readiness process, key criteria for a portfolio company’s public company readiness are their controls and SOX compliance. Combining Generative AI tools like ChatGPT with existing data and analytics preparation tools like Alteryx, a leading tool in automated control assessment, will reduce the time and resources needed to address these issues.   
  • Cyber: With investors and the SEC emphasizing a portfolio company’s cyber posture, there is a greater need to assess code quality to minimize the risk of potential ransomware or loss of proprietary code. To address this, private equity firms are increasingly conducting code reviews with an AI and ML-enabled static application security testing tool to speed up internal evaluations and catch potential threats well in advance.  

It is expected that private equity firms and portfolio companies targeting IPO readiness in late 2024 and 2025 will invest in AI and ML-enabled SOX/controls and cyber processes to improve operating margins and increase business value. Given the uncertain exit environment, companies that implement automated AI and ML controls and cyber infrastructure will be in a more favorable position when IPO momentum returns in late 2024 and 2025. 

Financial and Commercial 

Many private equity firms have had to slow down their exit processes, given the need to justify exit multiples in a high-interest rate environment. Improved quarterly reporting and benchmarking capabilities are critical processes for portfolio company CFOs to have in place, better preparing their companies for the pressure of the exit process.   

  • Quarterly Reporting: With sales to corporates hitting record highs, private equity firms and their portfolio companies must enhance their quarterly reporting capabilities for potential corporate and IPO exits. A leading use case to automate quarterly reporting includes implementing corporate performance management and AI and ML tools to expedite data aggregation and improve financial forecasting, enabling a timelier delivery to corporate stakeholders and shareholders.
  • Predictive Benchmarking: During the IPO and exit process, many portfolio companies  perform comparative analysis between publicly traded assets and their own. To simplify this process, CFOs are leveraging data preparation tools such as Alteryx, Microsoft Power Platform and Tableau and combining them with industry-specific proprietary AI and ML models to benchmark assets quickly. With these tools, businesses can utilize a wide array of data, including competitor trends, to generate a tailored set of key metrics to address and identify gaps ahead of exit evaluations.   

Given the significant pressure on reporting readiness and competitive insights to justify exit valuations, these two use cases will be the most common AI and ML investment areas for private equity firms throughout 2024. Adopting these use cases will also reduce the manual lift from financial planning and analysis personnel, enabling better selling, general and administrative costs ahead of an exit process. 


AI and ML will continue to play an important role in exit readiness preparation. Private equity firms and their portfolio companies should assess this prior to sell-side IT due diligence, as data security, privacy and ethical use cases remain critical challenges to address. Additionally, optimizing and curating data to maximize value from tech-enabled solutions will allow private equity firms to accelerate the exits of their portfolio companies, which is crucial given the current market uncertainty around interest rates.  

While there may be upfront costs and some adoption learnings around AI and ML for private equity firms, the efficiencies gained during deployment will ultimately benefit their portfolio. 

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