5 Things Every PE Investment Professional Needs To Get Right About AI

Map the work before you pick up the tool

Knowing which parts of your process AI should augment and which parts require your own judgment, experience, and direct involvement is a primary skill to develop.

The first step is to understand what the final work product needs to look like and then map the work backwards from there to figure out where AI fits. Test the tools, understand the limitations, know which prompts produce useful output and which produce noise, and then show the rest of the team what good looks like.

It does not require deep technical knowledge. It requires curiosity, a willingness to experiment, and the judgment to know when AI is helping and when it is getting in the way.

If you are that person you will be disproportionately valuable.

Collapse the iteration cycle

The traditional PE workflow is painfully sequential. A junior produces a draft, a senior leaves comments, the junior actions them, it goes back up for another round. AI compresses that cycle from both directions.

When you integrate AI into the workflow, a junior can produce a stronger first draft. A senior with AI can execute a fix directly instead of writing comments explaining what they want and hoping it's interpreted correctly.

Once you start working this way, the friction disappears, even when the task sits outside your core skill set. The loop between intent and execution gets shorter every time you do it.

Learn to show rather than ask, to work dynamically and immediately rather than passing things up and down, and you'll find your capabilities expanding in ways that carry well beyond any single deal.

Know where AI will accelerate parts of the deal process, and where it doesn't

AI does not need to be used for every task. A task that you could have done yourself in five minutes may cost you real time and tokens and still only get you 85 percent of the way there.

The key is not to try to use AI for everything but rather to recognize which tasks it genuinely accelerates and where it doesn't add value. That calibration is a learned behavior, and it compounds.

Speed is the obvious benefit, but even the best models can miss context, mis-weight information, or surface a technically correct answer that is analytically misleading.

Learning to move faster with AI while deliberately slowing down to apply your own experience at the right moments is a new way of working, and it takes practice.

Use the best AI tool for the type of workflow you're in

General-purpose LLMs are powerful, and they underpin platforms like ToltIQ, but there are specific tasks where they are the wrong tool in a standalone capacity.

Two key areas where general-purpose LLMs fall short at deal scale:

• Maintaining context across a full VDR, especially for larger document sets
• Supporting real-time team collaboration across the workflow

If these feel familiar, it's because they are structural limitations of general-purpose tools.

Understanding where the ceiling is and knowing when to use a purpose-built vertical solution for due diligence workflows, will help you move faster and more accurately through the process.

Understand the security implications of the tools you use

Data isolation, retention policies, model training, and potentially exposing MNPI are valid and serious concerns for any deal professional.

Before you upload any deal documents to any AI model or platform, you should know the answers to critical questions including:

• Does the platform retain my data after I'm done, and if so, for how long?
• Is my data being used to train the underlying model?

If you don't want to be the one who breached an NDA by loading deal docs into an insecure LLM chat, make sure you are confident in the answers you get to these questions.