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.

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.

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.

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

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.