Executive Summary

The emerging markets’ investment landscape is at a turning point. Artificial intelligence (AI) and generative AI are no longer buzzwords but practical tools that can help Private Equity (PE) firms work faster, smarter, and deliver more value to investors and portfolio companies, especially in complex and resource-constrained environments.

With technical assistance support from the Swiss Investment Fund for Emerging Markets (SIFEM), and in partnership with Ernst & Young Advisory, SPE Capital tested 24 different AI use cases across eight business areas. The results presented in this working brief show that AI enables better decisions, improves risk management, and generates greater value and impact, all while improving time efficiency.

Given the rapid pace of AI-technology development and the rising trend of AI-integration in the private equity space, this brief should be read in the context of when the tests that informed this brief were conducted (June 2025). Some of the key learnings and way forward could be widely applicable across asset classes beyond PE.

The Opportunity: Why AI and Why Now?

• AI is ready for integration into investment operations: Modern AI tools can now handle complex tasks like extracting and analysing data from financial statements, reviewing legal contracts, and generating investor reports with high levels of accuracy.
• Competitive pressures are driving greater AI integration: Firms using AI can process deals faster, conduct deeper analyses, and communicate more effectively with investors. While first-mover costs (and risks) may be high, the returns seem to not only justify but exceed them.
• AI can enable more efficient and effective talent allocation: While AI tools can empower teams and help them manage larger volumes of deals without expending additional hours, they can also unlock insights from the vast data that PE firms already have, allowing staff to focus on leveraging such insights rather than generating them.

What SPE Capital Did and How?

SPE Capital systematically evaluated 24 AI use cases across eight functional domains, as shown in table below, using a rigorous testing methodology that included real-world document processing, report generation, and legal analysis tasks.

What Did We Learn?

Testing analysis revealed significant quantifiable benefits across all assessed domains, with productivity gains representing a key dimension, alongside enhanced decision-making quality, consistency and standardization, and scalability without proportional cost increases.

Using a value-versus-feasibility framework, three top priorities were identified, out of the eight tested above, for a deeper-dive analysis. These included:

• Smart Reporting and Analysis: AI can cut report preparation from days to hours while ensuring accuracy and consistency.
• Contract and Legal Documentation Management: AI tools can spot gaps and errors in contracts and aid the automation of reviews. While human oversight remains critical, AI tools help reduce risks.
• Market Screening: Some AI tools can help track market trends and reputational risks, making due diligence not only faster but also more thorough.

The most important takeaways from these results can be summarized as follows:

• Remarkable potential for unlocking productivity gains in investment operations: Some AI applications tested by SPE Capital show that for certain teams, this could save as much as two months of work per year compared to business-as-usual.
• Enhanced quality, not just quantity: AI can reliably extract and analyse information from many sources, consistently providing a fuller picture for better decision-making.
• Wide variety of AI tools available with notable variation in performance: SPE Capital’s tests showed that AI tools can vary widely in terms of their quality and reliability. Different tools might be suitable for different sets of tasks and while some are generic and offer wider applications, some are highly specialized in a particular domain. Given the rapidly changing landscape of AI-technology and tools, real-world testing of these tools is critical before adoption and/or integration into existing systems.

Practical Tips for Fund Managers

Based on the findings of SPE Capital’s assessment, below are some practical tips for fund managers that are actively looking to adopt/integrate AI into their business operations:

• Start with what works: Consider beginning with high-impact use cases like smart reporting and contract management where AI tools have demonstrated strong performance and reliability. Focus on a few key areas rather than trying to integrate AI across all domains at once.
• Diversify your AI solutions basket, but test before buying: Consider using one or two general AI platforms that can support you across most tasks, and one or two specialized tools for areas like legal or ESG analysis. Given differences across tools and platforms, especially in a fast-evolving technology landscape, make sure to conduct real-world tests before purchasing specific products.
• Invest in people and processes to make the most of AI integration: AI tools and platforms work best when staff members and teams are trained and when processes are adapted to new ways of working. Build the rails for better AI integration by investing in capacity building and improving internal processes (e.g., data classification and storage).
• Review and strengthen cybersecurity and data protection: While integration of AI into business operations can unlock significant benefits, it can also increase exposure to new cyber and data threats. Ensure AI processes and tools meet regulatory requirements and data protection standards and invest in strengthening third-party risk management practices.
• Foster knowledge-sharing: Consider collaborating with industry peers to share lessons and to avoid common pitfalls. This will help accelerate industry-wide progress which benefits everyone.

How Can Development Finance Institutions Help?

Beyond generating more value for investors, greater integration of AI in investment operations, especially in emerging markets, can unlock higher levels of impact. Development finance institutions (DFIs), as key emerging markets investors, can play a unique and valuable role in supporting responsible AI adoption:

• Catalysts for best practice and risk mitigation: While meeting regulatory requirements in relevant jurisdictions (where available) on AI adoption is a bottom-line, DFIs can jointly act as catalysts for best practice by setting standards for data governance, transparency, ethical AI use to encourage responsible AI adoption, and by providing guidance on risk management practices related to cybersecurity and data privacy, among others.
• Support for capacity building: Integration of AI can unlock significant value from both financial returns and impact point of views. DFIs can help emerging market investors and their portfolio companies to build skills for effective AI use through training and technical assistance programs.
• Help foster collaboration and peer-learning: DFIs, with their globally distributed portfolio, can help bridge knowledge gaps across industries by encouraging knowledge sharing and collaboration, which ultimately helps build a more robust ecosystem for integration of AI in private equity as well as other asset classes.

Conclusion

The experiences and lessons from SPE Capital’s rigorous testing demonstrate that targeted, real-world adoption of AI can yield substantial gains, provided organizations invest in the right tools, people, and processes.

While the scope of the current project was limited to identifying opportunities to leverage AI in PE operations, it is equally important, if not more, to evaluate the risks that integration of AI can expose fund managers to, particularly when done without having adequate safeguards in place to avoid or mitigate them.

As the landscape continues to evolve, it is essential that fund managers, investors, and development finance institutions collaborate, share knowledge, and champion responsible AI adoption.