Private Equity

Most PE firms now have access to powerful AI tools. ChatGPT Enterprise, Microsoft Copilot, or both. The licenses are paid. The tools are available. And yet, when you look at how they are actually being used, it is almost always the same story:

• Drafting first versions of emails
• Summarizing documents that someone manually copied and pasted in
• Answering general knowledge questions
• Cleaning up writing or generating templates

This is not a criticism of the teams using them this way. These are genuinely useful applications. But it represents maybe 5-10% of what these tools can actually do.

The reason is simple: out of the box, ChatGPT and Copilot do not know anything about your business. They do not know your portfolio companies. They do not know your deal pipeline. They do not know your fund's investment thesis, your historical performance data, or the market intelligence you subscribe to.

Every time someone on your team needs actual business context, they have to leave the AI, open another system, find the data, copy it, paste it back, and then ask their question. The friction is high enough that most people just do not bother.

The shift is not about making AI "smarter." The underlying models are already remarkably capable. The shift is about giving them access to the information that matters for your work.

When a PE firm's AI environment is properly connected to their data sources, the kinds of questions that become possible are fundamentally different:

The difference is not just convenience. It is a fundamental change in what the tool can do for you. In the first column, AI is a writing assistant. In the second column, it is an analyst with access to your data who can answer real questions about your business.

The technical work involves connecting your AI platform to the data sources your team already uses. For a typical PE firm, this might include:

The integration work is done once. After that, your team simply uses the same ChatGPT or Copilot interface they already know - the difference is that now it can actually answer questions about your business.

We handle the technical complexity: secure API connections, authentication, data formatting, permission controls, and testing. Your team does not need to learn anything new or change how they work. They just start asking better questions.

If connecting data sources is so valuable, why does it not come standard?

The honest answer is that OpenAI and Microsoft are platform companies. They build powerful general-purpose tools and make them available to everyone. They are not in the business of understanding the specific systems a Nordic PE firm uses, how their deal flow process works, or which FactSet endpoints matter for their investment analysis.

That last-mile work - understanding your specific context, connecting to your specific systems, and configuring things so the AI actually knows how to use the data - requires someone who understands both the technology and your business.

This is exactly what we do.

Connecting your AI to business data is valuable on its own. But it also unlocks something bigger: the ability to automate entire workflows, not just answer questions.

Once the AI has access to your systems - not just to read data, but potentially to take actions - you can start building what the industry calls "agents": AI that does not just respond to prompts, but actively works through multi-step processes on your behalf.

For a PE firm, this might look like:

• Automated deal screening: New CIMs arrive, the agent extracts key metrics, compares them against your investment criteria, and flags the ones worth a closer look - with reasoning attached.
• Portfolio monitoring alerts: Instead of waiting for monthly reports, an agent watches for variances against forecast and surfaces issues as they develop, with relevant context pulled automatically.
• Due diligence preparation: Before a first meeting, an agent compiles everything your firm knows about a target - prior touchpoints, relevant comps, news mentions, competitive landscape - into a briefing document.

This is not science fiction. The building blocks exist today in both ChatGPT Enterprise and Microsoft Copilot. But they only work if the AI is already connected to your data. The integration work we describe above is the prerequisite that makes process automation possible.

We typically recommend starting with the data connections - let your team get comfortable asking questions and seeing real value - and then exploring automation use cases once you have a clearer picture of where the leverage is.

A typical engagement looks like this: