The Frightening Truth About ChatGPT Deployments in Equity Research

Everywhere we go, we’re hearing the same story from buy-side and research teams. It starts with excitement: a new AI project, a ChatGPT deployment, or a promising proof of concept. But a few months later, the results vanish into thin air.

The experiment that started full of potential becomes another ghost in the tech stack.

Here are the scariest reasons why:

1. Security and compliance barriers. Private research and model data cannot safely leave the firm, yet most deployments rely on public clouds. That ends many projects before they begin.

2. Workflows lost in translation. Analyst work is not a simple chat. It is structured, iterative, and collaborative, but generic AI tools cannot see the full picture.

3. Models that do not understand models. Large language models can write summaries, but they cannot interpret the relationships or logic that drive financial models.

4. Connectors without context. Many tools promise to retrieve documents, but they only surface files without meaning. A haunted search bar.

This is not about blaming the models. OpenAI and Anthropic are working hard to make AI more enterprise-ready, with efforts such as OpenAI’s Company Knowledge, Anthropic’s financial services announcement, and Sam Altman’s comments about AI’s growing role on Wall Street.

But even the most powerful models cannot exorcise the real problem: disorganized data and disconnected workflows.

To make AI truly work inside an investment firm, you need more than access to an API or chat interface. You need:

• Data that is clean, labeled, and permissioned correctly.

• Interfaces designed for financial professionals, not prompt engineers.

• A team that understands both finance and how to get the most out of large language models.

Many firms are now building their own private deployments, but the biggest challenge is the lack of expertise. Without the right foundation, even the best AI ends up as another ghost project.

And the risk of ignoring this is growing. Firms that treat AI as a nice-to-have will find themselves haunted by inefficiencies that competitors are already solving. Those that build on AI-ready data, structured workflows, and internal capability will move faster, uncover insights sooner, and quietly gain an advantage.

As one head of research told us, “We don’t have an AI problem. We have a data and workflow problem.”

The next wave of AI in finance will not be about which model wins. It will be about who learns how to use them effectively, and who gets left in the dark.

~Ryan Erickson, Founding Executive

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