Due diligence isn’t just a core risk or differentiator – speaking to several fund managers working more heavily with technology investment targets, it’s also getting more complex as the scope of DD expands. Whereas it traditionally may have covered financials, commercials and legal, teams are now regularly diligencing IT, cybersecurity and ESG, all of which is relatively data intensive and can often be better handled by AI systems.

Given the dominance of Office in the financial industry, it is hard to dismiss the case outright that Microsoft will offer compelling AI solutions for its core user base. While it is easier to say that Google’s Gemini will likely play a role elsewhere, Microsoft does seem intent on pushing AI adoption among its traditional customers like investment and consulting firms. In addition, we are already seeing the most adoption among private finance firms of OpenAI and Microsoft AI products.

CIOs are thinking a lot about generative AI these days. Often, they are the final decision-makers when it comes to adopting new technologies like large language models in context of private equity and other financial firms. We sat down with tech executives at several leading PE shops to learn more about how they are approaching these strategic decisions.

Today, leading large language models (LLMs) will attempt to generate advanced mathematical visualizations, walk you through the steps of an analysis, and derive insights from datasets. While many mathematical queries made to leading chatbots still result in hallucination, failure mode, and similarly frustrating outcomes, the realm of usable tools and response types is expanding. If you ask OpenAI, their next release is supposed to be even more promising.

This month, we interviewed PE analysts – those who spend day in and day out sifting through documents, evaluating companies and modeling investment scenarios. While some of the most intense speculation around AI has to do with where it might replace knowledge work, our conversations with teams of analysts reveal a different story.

In the early days of commercial LLMs, capabilities were defined by models' inherent architecture and the way they made use of their underlying training data. When you asked a question to a chatbot, it would famously tell you that it only had knowledge going up to 2021. Additionally, chatbots could only process and remember so much information in a prompt. You had to be very specific, clear and consistent in your prompting if you wanted the same in your outputs. Even then, what LLMs could generate was limited by the information they had access to.

Innovation in traditional industries like consulting, law and finance is no task for the faint of heart. While most investors and financial professionals agree that change will eventually come to their function as AI progresses, to embrace it and push for it is another mindset entirely.

Our Enterprise buying guide walks you through the primary classes of AI applications and how to understand them.

For our Agents of Change series, we speak with real decision-makers at investment firms to understand the impact AI is having on finance roles today. Our series kicks off with a profile of the Private Equity Associate, a top destination for some of the most talented investors in private capital. Read on to discover how these leaders are pushing for technological change in the finance industry, and the gaps AI companies need to bridge in order to make a difference.

We'll walk you through what to think about as you're deploying AI solutions at your investment firm.

Interested in how PE firms are using generative AI to improve their investment processes *today*? In the final article of our "Return On AI" series, we give you a peak under the hood of how we think about solving efficiency challenges in the due diligence space, and how investors we talk to think about implementing similar systems.

How exactly should private equity investors be thinking of using ai? It's clear that within a few years, most firms will have adopted this transformative technology, but how it is deployed will determine who establishes the largest competitive advantage. The second article in our series on AI in private capital further explores the most effective areas for investors to deploy machine learning and more.

AI adoption just reached an all-time high among financial firms. Funds are doing everything from building their own models to testing solutions up and down the stack. What does this stack look like, and where should you start? We talked to dozens of investors and solution providers to find out. Learn more in our latest blog post below.

We just published a deep dive of the AI finance market on our blog, breaking down the components of the market map we published last week. Learn more about some of the leading companies in the space and how buyers are navigating it in our latest article.

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.

Introducing our brand new article series, Capital AI. You can find us every other week here, or on the Keye Linkedin page.