The AI Gap: Ambition vs Readiness

Over the past few months, we’ve met with dozens of investment teams across conferences and meetings. Almost everyone is excited about AI, but few feel ready to use it meaningfully. The enthusiasm is real. The readiness isn’t.

What we’re seeing is that firms don’t have an AI problem, they have a data problem. Their internal knowledge is scattered across emails, chat messages, analyst notes, models, meeting summaries, and PDFs. It’s fragmented, unstructured, and hard to use.

AI tools depend on clean, connected data. Without that, they fall flat. Large language models work because their training data is labeled and organized, which is what made a company like Scale AI valuable. Financial firms face a similar challenge: before they can apply AI to their research and decision-making, they need to get their own internal data in order.

That means knowing what exists, classifying it, adding context, and making it accessible in a consistent format. It’s not glamorous work, but it’s essential. Skipping it leads to impressive demos that never translate into real outcomes.

The firms we’re talking to know where they want to go. They’re not short on ideas. They want automation, faster research output, and better visibility across teams. But the message is consistent: until their internal data is structured and connected, they can’t get there.

Here’s what firms say they want to do once that foundation is in place:

1. Research Automation
Reducing manual coverage work through AI-driven alerts, check-ins, and decision nudges.

2. AI-Assisted Authoring
Accelerating writing and prep with contextual draft assistants and meeting builders.

3. Quant and Knowledge Integration
Merging models, research notes, and commentary into unified company views.

4. Insight Quality and Consistency
Using AI to spot blind spots, peer trends, and confirmation bias in analysis.

The pattern we’ve heard is clear: AI ambition is high, but readiness takes real work. Most firms are still in the middle of that transition, organizing what they already know so AI can finally add value.

~Ryan Erickson, Founding Executive

Building the Foundation for AI in Investment Research

We're doubling down on the 'boring stuff.' Like Scale AI did for training data, we're building the infrastructure for internal proprietary data in finance. Basis connects your internal tools, organizes and enriches the data, and listens for new activity in real time, giving your organization a unified, AI-ready knowledge layer. It's not glamorous work, but it's essential. And it's what separates impressive demos from real outcomes.

About Shift
Investment research shouldn't be this hard. Shift turns your firm's scattered knowledge into powerful insights with AI built for how you actually work. We're a team of builders and finance experts based in Charlottesville, VA.