Introduction

Financial services firms are adopting AI quickly, but there is a growing difference between using AI and getting value from AI.

The 2026 Cambridge Global AI in Financial Services Report found that 81% of surveyed financial services firms were adopting AI at some level. Yet 55% of industry respondents said measuring the value of their AI deployments was difficult. The technology is moving forward faster than the organizations using it.

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Consider an investment analyst preparing a quarterly portfolio review. They need to collect financial statements, research market conditions, compare performance, explain variances, and turn it all into a report for leadership or clients.

AI can help with nearly every step. But asking ChatGPT to summarize a document is not the same as having a reliable process that the whole team can repeat.

The real opportunity is to turn individual AI experiments into a capability the firm owns. That means training people on their actual work, developing shared workflows, and putting the right controls in place to protect sensitive information and maintain the quality of financial decisions.

The goal is not to replace financial judgment. It is to give your team more time to exercise it.

Understanding AI Readiness

What does it mean for a financial services team to be AI-ready?

An AI-ready team can use approved AI tools to complete real business tasks, understand where those tools can fail, and consistently produce work that meets the firm's standards.

This requires four components:

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   AI fluency: Employees understand what modern AI can and cannot do, and when to use it.

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   Practical workflows: Teams know how to apply AI to research, analysis, documentation, and reporting—not just general questions.

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   Security and governance: People have clear rules for client data, confidential information, approved tools, and human review.

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   Reusable capabilities: The best prompts, instructions, templates, and processes become shared AI skills rather than remaining with one employee.

The fourth component is often overlooked. If one analyst discovers a way to produce an excellent investment memo in half the time, the firm should not have to wait for every other analyst to figure it out independently.

This is how AI training begins to compound in value: one successful workflow becomes a team standard, then a reusable capability, and eventually an opportunity for automation.

Where AI Creates Value in Financial Services

Most firms do not need to begin with an expensive, fully autonomous AI system. The first opportunities are often the information-heavy tasks employees already perform every week.

1. Investment Research and Market Analysis

Analysts spend significant time reading filings, earnings transcripts, industry reports, and market commentary. AI can compare sources, extract relevant facts, and organize findings around a research question.

For example, an investment team could ask AI to compare the latest earnings calls of five public companies, identify changes in management guidance, and assemble a source-linked summary for review. The analyst still judges which developments matter and checks the underlying evidence.

2. Investment Memos and Due Diligence

Investment memos bring together financial information, company research, risks, and recommendations. Much of the work is repeatable, even when the underlying investment decision is not.

AI can help organize diligence materials, draft a consistent memo structure, identify unanswered questions, and connect claims back to documents. A senior professional then verifies the numbers, challenges the assumptions, and approves the conclusions.

3. Portfolio and Performance Reporting

Monthly and quarterly reports often involve gathering data from multiple systems, explaining performance, and preparing commentary for clients or internal committees.

AI can assist with first-draft narratives, variance explanations, and assembling supporting documentation. The underlying calculations should remain in controlled analytical systems and be reconciled before any report is released.

4. Client Communications

Wealth managers, advisors, and service teams can use AI to prepare meeting briefs, summarize approved research, and draft follow-up communications. The value is not simply writing faster; it is allowing advisors to spend more time understanding and serving clients.

Client-facing content still needs appropriate review, especially where advice, performance claims, or regulated communications are involved.

5. Internal Knowledge and Compliance Research

Employees frequently need to find answers across policies, procedures, product documentation, and internal knowledge bases.

AI-assisted search can help locate the right material and provide a cited answer instead of forcing employees to search through folders and documents manually.

It should point employees to the authoritative source, not quietly invent policy.

These use cases are consistent with the Cambridge report's finding that financial services AI deployment is concentrated in internal operations, with process automation and data management among the leading applications.

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Developing an AI-Ready Team

AI readiness is not a one-time workshop or a software purchase. It is a practical process for changing how work gets done.

Step 1: Identify the Highest-Value Workflows

Start with the team, not the technology. Ask employees where they spend the most time collecting information, reviewing documents, writing reports, and moving data between tools.

For each workflow, assess five questions:

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  How often is this task performed?

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  How many people perform it?

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  How much time does it take today?

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  What information and systems does it require?

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  What level of accuracy, oversight, and security does it demand?

The best initial opportunities are frequent, time-consuming, and easy to review. A research summary may be a good starting point. Automatically approving a credit decision is an entirely different level of risk.

Step 2: Train Teams on Their Own Work

General AI demonstrations create awareness. Hands-on training creates capability.

Instead of asking an analyst to practice on an unrelated example, have them work through a real task: researching an investment, interpreting a financial document, or building a first draft of a client report using approved or appropriately de-identified data.

Training should progress from the fundamentals to more advanced tasks:

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   Foundations: Asking useful questions, providing context, and checking AI outputs.

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   Applied workflows: Working with documents, spreadsheets, research, and structured reports.

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   Advanced capabilities: Building reusable AI skills, connecting approved data sources, and understanding when an agent or automation makes sense.

The difference becomes visible when an employee can independently complete a useful workflow the week after training—not just demonstrate a new tool during the session.

Step 3: Turn Good Workflows Into Reusable AI Skills

Financial firms already rely on standardized processes: investment committee memo formats, diligence checklists, portfolio review templates, and client reporting standards.

AI should build on these standards.

Take an investment memo. Rather than asking each analyst to start from a blank chat, create a reusable AI skill with:

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  The approved memo structure and instructions;

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  Required sources and financial metrics;

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  Rules for citations, assumptions, and missing information;

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  A checklist for verifying figures and conclusions;

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  A defined review and approval step.

Now the team has a consistent starting point. Senior professionals can improve the process once, and everyone benefits.

Over time, these skills can be connected to approved data systems and turned into more automated workflows.

Step 4: Establish AI Policies Before Scaling

Financial services teams work with confidential client information, material nonpublic information, sensitive financial data, and regulated communications. AI adoption has to account for these realities.

At a minimum, define which AI tools are approved, what information employees may enter, how outputs must be verified, who can approve external use, and how records are retained where required. Review vendor data-handling terms, access controls, and integration risks with the firm's security and compliance teams.

The rules should reflect the specific business and use case. For broker-dealers, FINRA Regulatory Notice 24-09 makes clear that existing securities rules continue to apply when firms use generative AI. The U.S. Treasury's Financial Services AI Risk Management Framework also provides a practical reference for developing appropriate AI risk controls.

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The objective is to give employees a safe way to move forward, with clear boundaries and accountability.

Step 5: Measure Results and Build the AI Roadmap

Finally, connect AI adoption to business outcomes. Measure time saved, quality of work, adoption across the team, and the cost of review and implementation.

Consider a team of 15 analysts. If each person saves an average of 2.5 hours per week across 46 working weeks, that creates 1,725 hours of additional annual capacity.

At an illustrative loaded labor value of $75 per hour, the equivalent productivity value is about $129,000 per year—before accounting for training, tools, review, and other costs.

This is not necessarily $129,000 in reduced expenses. The financial return depends on what the team does with the recovered capacity: cover more investments, improve research quality, serve more clients, or reduce actual operating costs.

Measure the baseline, test the workflow, and decide what to scale. This is the foundation of a credible AI roadmap.

The Next Step: From Training to Transformation

As AI tools become more capable, the advantage will not come from giving every employee access to the same model. Your competitors can do that too.

The advantage comes from combining your firm's industry expertise, proprietary knowledge, and established processes with AI capabilities your team knows how to use.

Over time, the most valuable workflows become standardized, connected to data, and automated where it makes business sense.

That progression matters: train your people, build shared AI capabilities, then automate the workflows that prove their value.

Conclusion

Financial services firms have an opportunity to move beyond isolated AI experiments and build capabilities that improve the way their teams operate.

Getting AI-ready starts with three practical moves: identify the work worth improving, train employees on real tasks, and turn what works into secure, repeatable processes. From there, firms can measure results and invest in the next level of automation with much greater confidence.

At SF AI Labs, we help financial services teams make that transition through hands-on training, AI strategy, and custom automations—so AI becomes a lasting advantage for the business.

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