How Addepar is Using Data and AI to Transform Investment Management

Key Takeaways

1. Alternatives Are Driving Structural Complexity - As allocations to private markets increase, so does the operational and analytical burden required to manage them effectively.
2. Data Infrastructure Is the Real Constraint - The ability to aggregate, normalize, and analyze alternatives data, not access to new tools, is the limiting factor.
3. AI Is an Output, Not a Starting Point - Technology is only as effective as the data and systems it sits on top of.
4. Fragmented Systems Break Down in Alternatives - Managing private markets across disconnected tools creates inefficiencies and limits decision making.

AI Is Not the Starting Point in Investment Management

Eric Poirier has focused his career on a core challenge in investment management: bringing structure and clarity to fragmented financial data. As CEO of Addepar, he leads a platform that underpins how many of the world’s largest and most complex portfolios are tracked and analyzed. Today, Addepar supports more than $8 trillion in assets across over 1,300 clients globally, including institutions, family offices, RIAs, and private banks.

At Thirdpath, we also leverage Addepar within our own infrastructure, which made this discussion directly relevant to how we think about data, systems, and decision-making.

The focus was not on what AI might enable in theory, but on what actually determines whether it works in practice.

The Problem Isn’t Technology

Most firms are not short on tools. They are short on structure.

“AI is only as good as the data it sits on top of. If the data is incomplete or inconsistent, you’re not really improving anything—you’re just accelerating bad inputs.”

In alternatives, this challenge becomes more pronounced. Data is spread across managers, formats, and reporting standards, making it difficult to build a consistent view of the portfolio.

“You’re not solving for intelligence first. You’re solving for organization. Getting the data into a place where it actually makes sense is the hard part.”

Without that foundation, layering in AI tends to create more noise than insight.

Where Things Actually Break Down

As complexity increases, gaps in infrastructure become harder to ignore.

“At a certain point, you just can’t piece it together anymore. You have different systems telling you different things, and no real way to reconcile them cleanly.”

What begins as a workable setup gradually turns into a fragmented stack across reporting, operations, and investment workflows. The issue is not just inefficiency. It is a lack of confidence in the numbers themselves.

“If you don’t trust the data, you’re not going to trust anything built on top of it. That’s where everything starts to break down.”

AI as a Result, Not a Strategy

The distinction is straightforward but often overlooked.

“AI is an output. It’s not where you begin. It’s what becomes possible once the foundation is already in place.”

When systems are integrated and data is structured, AI can improve speed, automate workflows, and surface insights more effectively. Without that, its impact is limited.

“Everyone wants to jump to the advanced layer. But if the inputs aren’t right, the outputs won’t be either. It doesn’t fix the underlying problem.”

The differentiator is not who adopts AI first, but who has built the infrastructure that allows it to actually work.

A More Integrated Approach

Technology is increasingly less about adding new tools and more about how everything connects.

“It’s not about having more systems. It’s about having systems that actually talk to each other and operate off the same data.”

As that integration improves, technology becomes less visible but more embedded in the investment process itself, supporting better decisions without adding operational friction.

Closing Thought

AI is often positioned as the driver of change. In reality, it reflects something more fundamental.

“If the foundation isn’t there, none of it really matters. The technology doesn’t fix that—it just exposes it faster.”

For investors operating in increasingly complex portfolios, the focus is shifting toward building systems that can support scale, consistency, and better decision-making over time.

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