How Are Financial Services Firms Using AI to Make Analysts More Productive?

Financial services has specific constraints that make standard AI adoption advice wrong for this context.

The data sensitivity problem is real. Most enterprise AI tools in regulated industries operate under strict data governance requirements. Client names, deal terms, portfolio details, and internal financial data cannot go into consumer AI tools. The boundary between what's safe to process through AI and what isn't is genuinely unclear for many analysts, which drives one of two outcomes: no AI use at all, or AI use happening quietly and without oversight.

There's also a professional credibility problem unique to this industry. An analyst who produces AI-assisted work is accountable for every number and every claim in that output. The stakes of a factual error — in a client memo, a deal briefing, a market summary — are higher than in most other contexts. Analysts who haven't built a mental model for when to trust AI output and when to verify it are right to be cautious. The answer isn't to avoid AI. It's to understand where verification is required.

And then there's the positioning question. "Will I be replaced?" is more acute in financial services than in most industries because the efficiency gains from AI in research and analysis tasks are measurable and large. The analysts who figure out how to use AI well are positioning themselves as the people who can do what AI can't — relationship management, judgment under uncertainty, client trust — while using AI to handle the volume work.

Document summarization for research-heavy roles. Financial analysts regularly work with dense source material: earnings releases, regulatory filings, analyst reports, prospectuses. AI handles the synthesis well. The workflow: feed the document in, ask for a structured summary with key figures, verify the numbers against the source, use as the basis for your own analysis. The time savings on this task alone can be significant — and the risk of error is manageable because verification against the source is fast.

Meeting prep synthesis. Prior meeting notes, background on the company, key figures from recent filings, questions worth asking based on the agenda. AI assembles this faster than a manual search would. The analyst reviews and augments with relationship context AI doesn't have. This is the application that CoCreate has seen resonate most immediately with analysts at a major venture banking firm — it reduces time-to-ready before client meetings without touching any data that shouldn't move.

First-pass memo drafting. For recurring document types — deal memos, market summaries, weekly portfolio updates — AI can produce a structured first draft that the analyst then edits for accuracy and judgment. This works best when the document type has a consistent structure. The analyst provides the key inputs, AI produces the skeleton, the analyst handles everything that requires nuance or client-specific knowledge.

Model building acceleration. For sensitivity analyses, scenario modeling, and data formatting tasks, AI can write the underlying formulas, structure the worksheet, and document the logic. Analysts who know how to prompt for this save significant time on setup. The math still needs to be verified. The time savings is in structure, not in judgment.

The analysts most at risk from AI in financial services aren't the ones doing complex analysis. They're the ones whose primary value is information aggregation.

If your daily work is largely: find the data, format it, put it in the right template — AI does that faster. If your daily work is: build client relationships, make the judgment call when the model is ambiguous, develop the point of view that the client trusts you for — AI doesn't do that and won't soon.

The question every analyst should be asking: what does my work look like if AI handles the aggregation? If the answer is "I'd have time for the work that requires me specifically," that's a good position to be in. Build toward it deliberately.

CoCreate has been working with analysts at a major venture banking firm on exactly this question. The engagement started with discovery: 30-minute calls with individual analysts to understand which parts of their workflow were highest-repetition and lowest-judgment. The two that surfaced were meeting prep and first-draft memo generation.

Both workflows were built in front of the analyst team — every prompt decision explained, every tool choice walked through. Analysts left with documented processes they owned, not black-box tools they depended on. The emphasis was verification: when to trust the output, when to check, and what that checking process looks like in practice.

That last piece — building verification into the workflow — is what makes AI adoption credible in financial services. The tool is only as reliable as the process around it.

Analyst workflow builds in regulated environments are a core theme of CoCreate’s enterprise consulting services.

• How Do You Use AI Without Putting Client Data at Risk?
• What Should Enterprise Teams Automate with AI First?
• How Do I Run an Enterprise AI Pilot Program That Actually Works?