April 7, 2023
"R-AI": The Case for Artificial Intelligence in Finance
What kind of return should investors expect on their firm's investment in AI? With close to half of all private equity investors already working on ways to use this transformative technology, it's important to stop and think about the motivating reasons for your strategy. The first article in our new series on AI in private capital dives into these questions, and sets the stage for our upcoming deep dive on the most effective applications of artificial intelligence in finance.

You are reading part 1 of our ongoing article series on AI in private capital markets. To learn more visit our page.

A notable yet often overlooked pattern in financial industries this that top firms tend to consistently outperform their peers.

What fewer people realize, is that the differences in returns accruing to private investment firms tend to increase as you move from the middle of the pack to the top of the industry. While there are different dynamics in VC vs. PE and other fields of finance, portfolio company outcomes are often such that a small number of investments generate the majority of returns, and the difference in performance between the top-tier firms and the median or lower-tier firms is not linear, but exponential.

Research shows that investments in proprietary assets or processes yield sustainably high returns that compound over time, which allow these top firms to maintain their performance. What this means for firms looking to defend their spot or get ahead, is that even a slight edge, or the smallest incremental addition to one’s “moat”, could sustain competitive advantage for years to come.

Private capital investors have sought to develop their competitive edge with anything from unique investment theses, to fine-tuned talent strategies, and increasingly with greater expenditure in data science and technology. Today and going forward, firms’ use of AI is going to be a similarly critical component of competitiveness, and, potentially, long-term advantage.

Some AI adoption is going to be table stakes for managers hoping to catch the next wave of productivity growth, evidence of which looks especially compelling for financial industries. But how companies adopt the new technology will determine the real winners. Because no single PE or VC shop has yet established themselves as the leading AI-driven firm, there is a massive opportunity to secure proprietary data, partnerships and capabilities in the early days of the race.

Firms that do so will be able to capitalize on what we call "R-AI": return on AI and investments in related technologies and processes.

These returns pay out not just in immediate financial terms, but also through increased efficiency and alpha generation that will lead to higher ROI and competitiveness across a number of dimensions for the foreseeable future.

According to Bain & Co., AI insights and process improvements at the firm level are one of the ‘big 3 shifts’ for AI in private capital (alongside portfolio company improvements and risk analysis).

Our thesis is that investments in AI will see multiplier effects, and more importantly, non-linear returns.

Let’s tackle these one at a time:

  1. Multiplier effects: Leveraging AI effectively tends to yield not just one, but multiple benefits for companies, some unexpected. Rolling out something like a new CRM might make an individual process or two more efficient. Firm-wide adoption of AI however, is more akin to giving everyone at the company a computer or access to the internet for the first time.The wider the deployment, the greater number of force multipliers are possible: when everyone from deal teams, to internal operators to firm leaders can access leading AI solutions, entirely new ROI-generating activities and skill development can happen. You might have a data scientist who can now effectively code for the first time, or an investment team that can review opportunities so much faster that it can competitively bid on a new class of deals. 57% of software developers are regularly using coding assistants. This is where next-gen productivity growth will be derived.
  2. Non-linear returns: AI often leads to outcomes like time savings, cost savings, and faster learning curves across functions. This frees up time, capital and resourcing that can be continuously reinvested into activities that generate additional IRR.For example, a deal team that is able to decrease their due diligence timeline by 50% using AI might reinvest that time into further improving the efficacy of the deal review process from sourcing to closing. This could yield further process improvements and time savings that can be further reinvested in reviewing more deals or developing new investment theses that lead to competitive advantage.

To be sure, not just any adoption of AI tooling will maximize benefits for firms. Firms that move quickly and establish defensible advantages will see yet further returns accrue, as they will have the ability to bid on more, higher-quality deals.

For most investors, developing operational partnerships with AI firms will be the most effective approach, maximizing the respective capabilities of each company in the partnership (i.e. investment + technology).

If done right, by partnering with a firm in its early stages, such alliances can be competitively advantageous. For example, early AI startups may have access to talent, knowledge and technology that has not yet been made broadly available to the market, and which rivals may eventually have to pay more for to later integrate into their own firm or portfolio companies.

Everyone will need to leverage AI in some capacity, but the right sort of investments and deployments will lead to the most interesting outcomes. What is right for one firm, may not be right for another. This is what we will tackle in our next blog post in the series.

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