No matter who is sourcing or how they do it, this is fundamentally a sales activity. Private capital funds sell a single product, and that product is investment capital. AI tools are rapidly being adopted within leading sales organizations to become more efficient at building sales funnels. Since an important driver of alpha for any private capital fund is maintaining a robust deal flow pipeline (i.e., a sales funnel), it stands to reason that experimenting with how AI tools can streamline and augment traditional sourcing tactics is critical to achieve investment objectives. For example, mundane and time-consuming tasks can be streamlined with AI–such as logging meeting notes into a CRM, scheduling meetings, and suggesting standard responses to emails–which reclaims more time for speaking with intermediaries and potential portfolio companies. AI tools can also augment content marketing and business development research, which are essential to maintaining deal flow. 

Second, let’s discuss how funds screen, work, and close deals. An efficient deal screening process is essential to a robust deal flow pipeline, so as to not bog down the investment team. AI can streamline how deal prospects are categorized and qualified to improve the efficiency of the screening process. For a large private equity fund, this process involves both business development and investment teams with different perspectives and incentives. The decision to work a deal commits significant fund resources of staff time. For early stage venture capital funds, the resource challenge is different. Typically the volume of deal flow is much higher, so partners must quickly assess what deals will get a pitch meeting and how much time can be committed to initial market research.

Next, working and closing deals is at the heart of any fund’s operational success. Speed and accuracy of financial analysis are paramount, and access to quality data about the investment target’s market dynamics are essential. Effective communications–both within the investment team and amongst intermediaries, management teams, corporate counsel, lenders, and consultants–are critical throughout the deal process.

Leading financial institutions engaged in banking, trading, brokerage, fund management,  and insurance are building AI into their research, underwriting, and investment allocation processes. AI can reduce low-value, time-consuming tasks to free up staff time for more value-added activities. Moreover, enhanced access to query more data, explore more scenarios, conduct more analyses, and present clear insights is empowering faster and better decision making. Corporate experiments testing the performance of underwriting loans using AI tools and even trading public securities using AI models trained on large data sets are already proving worthwhile. With venture capitalists and private equity investors touting their own proprietary methods of “moneyballing” their investment methodology, the potential to employ AI tools to generate alpha is real.  

Third, let’s talk about post-investment value creation. We have been living–and making investment decisions–in a lower interest rate environment for 23 years. But times have finally changed as the Fed battles inflation. As the cost of capital has risen and public market exits remain uncertain, the need for post-investment value creation has increased. Rising leverage costs in private equity acquisitions means that small operational improvements, such as pricing increases and trimming G&A expenses, now fund the increased cost of debt but do not drive investment returns. New value creation strategies are required to achieve alpha. For venture capitalists and growth equity investors, higher interest rates and increased competition for customers has led to difficult conversations with portfolio companies about ballooning marketing budgets and slowing sales performance. Old DCF models that assumed a zero-interest rate environment drove excessive spending in portfolio companies with high growth expectations, because the value of a dollar projected to be earned in five years was nearly equal to the dollar spent today in hopes of increasing market share.  

In our new era of moderating expectations with a higher cost of capital, AI offers the hope of outsized value creation without excessive capital investment. This source of alpha requires technology transformation beyond cloud deployments and enterprise resource planning systems. For most portfolio companies–whether a 7-year-old enterprise SaaS provider or a 70-year-old distribution business–measurable value creation opportunities can be found within their go-to-market (GTM) strategy. For example, AI can be leveraged for lead management optimization to model, test, and deploy lead scoring metrics to rapidly capture insights and respond to inbound leads more effectively. AI can also significantly reduce the cost of language translation for content creation across sales, marketing, and customer service. To illustrate, the cost to translate a 100-page technical manual from English into French, Spanish, German, and Chinese can be reduced from $24,000 to $9.45 using Claude3 Opus, which is a cost reduction of 2,539x. AI can analyze contracts and purchase orders to identify excessive or redundant spending in marketing and sales departments. Hundreds of thousands to millions of dollars can be identified and reclaimed from unused SaaS accounts, redundant software, and old service contracts. And, before deciding how to redeploy these found resources, AI can model the potential outcomes of new growth initiative expenditures under multiple scenarios. 

Finally, let’s not forget about liquidity events. Optimizing how funds monetize the exit is the final phase of the investment journey. For private capital funds, liquidity options have become more diverse with secondary markets and continuation vehicles; however, driving alpha in private market liquidity remains challenging with uncertain IPO windows. Returning to a higher interest rate environment after more than two decades makes the time value of money loom larger when projecting the holding period before exit. Moreover, unforeseen changes in competitive markets and the macroeconomic environment can significantly alter the ideal exit window.

Funds that employ a disciplined review of each investment at regular intervals–based on a systematic analysis of portfolio company performance, competitive market assessment, capital market sentiment, scenario risk modeling, and liquidity event options–can better prepare management teams to strategically plan for a desirable exit. This systematic analysis is a heavy lift, demanding significant time from management teams, operating advisors, and investment professionals. It is easy to cut corners and “go with your gut,” leading to missed opportunities or worse. AI is well-suited to time-consuming and repetitive systematic analyses, so that funds can focus on developing actionable insights that investors and management teams need to drive exit alpha.

Ultimately, investment funds that efficiently build deal flow, close investments, add value to their portfolio companies, and execute well-timed liquidity events will generate alpha to their fund and LPs. Fund managers who embrace AI capabilities in these core functional areas will improve their teams’ capacity to put money to work and outperform their peers.

NextAccess partners with investors and their portfolio companies’ management teams to create value through performance improvement and operational efficiency as well as market differentiation and growth strategies. We incorporate AI into GTM solutions as a force multiplier to break down bottlenecks, accelerate time-to-market, and reduce complexity to drive growth more efficiently. Simply put, we believe AI can unlock value creation both within an investment fund and amongst their portfolio companies.