How To Know If An AI Project Is Worth Funding In Financial Services

Not every AI project deserves a budget, especially in financial services.

A strong demo can look useful in a controlled setting, then fall apart once it touches client data, research workflows, reporting processes, legacy systems, vendor review, or compliance requirements. That’s why firms need a better way to evaluate AI projects before they commit a budget.

AI spending is rising quickly. The World Economic Forum notes that financial services firms spent $35 billion on AI in 2023, with investment expected to reach $97 billion by 2027. As more capital moves into AI, the firms that benefit most will likely be the ones that fund specific workflow improvements rather than broad experimentation.

For asset managers, hedge funds, private equity firms, RIAs, and wealth managers, the funding decision usually comes down to five questions: What workflow does the project improve? How will value be measured? Is the data ready? Are the controls clear? Will the people inside the workflow actually use it?

Start With The Workflow

The weakest AI projects usually start with the tool. Someone sees a demo, tests a model, or asks how the firm can “use AI” across research, reporting, client service, or operations. That can lead to pilots that look interesting, but do not change much about how work actually gets done.

A better AI project starts with a specific workflow. “AI for research” is too broad. “Earnings transcript comparison for a defined coverage universe” is easier to evaluate. “AI for reporting” is too vague. “Portfolio company KPI intake and first-pass LP reporting drafts for one fund” gives the firm a clearer way to measure whether the project is useful.

The stronger the workflow definition, the easier it is to decide whether the project deserves funding. Good candidates are usually frequent, manual, document-heavy, and expensive to complete by hand. Research retrieval, LP reporting, client onboarding, compliance review, meeting prep, and data reconciliation are all stronger starting points than open-ended AI testing.

Make The Value Measurable

An AI project should be tied to a clear business outcome. That might mean fewer hours spent cleaning data, faster research retrieval, shorter onboarding cycles, fewer reporting errors, faster review, or less administrative work for senior staff.

Before approving budget, the firm should understand the current baseline. How long does the process take today? Who touches it? Where do delays happen? Where do errors show up? How much time is spent reviewing, reworking, or searching for information?

Without that baseline, the project becomes difficult to judge. A useful pilot should be able to answer simple questions: Did it save time? Did it improve accuracy? Did it make review easier? Did users trust the output enough to use it again?

If those questions cannot be answered, the project may still be interesting, but it probably needs more definition before it receives serious funding.

Check The Data First

Many AI projects do not fail because the model is weak. They fail because the data is scattered, inconsistent, or not ready to use.

A research assistant needs access to the right transcripts, filings, internal memos, and notes. A reporting workflow needs clean portfolio company data and approved templates. An onboarding assistant needs client intake materials, CRM data, custodial documents, and compliance records in a controlled system.

The GAO’s report on AI use in financial services notes that AI can support efficiency and customer experience, but also introduces risks around data quality, privacy, cybersecurity, and bias. Those risks become more important when a project depends on sensitive financial, investment, or client information.

If the source data is messy, AI will not fix the problem on its own. It may only move the same problem faster. Before funding a project, the firm should know where the data lives, who owns it, what can be accessed, what should stay restricted, and how sensitive information will be handled.

The NIST AI Risk Management Framework is useful here because it focuses on trustworthy AI across design, use, and review. For financial services firms, that means every AI project should be able to explain where the data comes from, how outputs are checked, and who is responsible for the final result.

Build Governance In Early

Governance cannot be treated as a final review step. The SEC’s 2026 Examination Priorities reference AI technologies, automated investment tools, trading algorithms, and alternative data. FINRA Regulatory Notice 24-09 also reminds firms that existing supervision, recordkeeping, and compliance obligations still apply when they use GenAI.

A project may have real value, but still create risk if it has unclear supervision, weak audit trails, unreliable outputs, or poor controls around client information. Before approving a budget, the firm should define what the system can access, who can use it, what it cannot do, where outputs are stored, and what requires human approval.

Vendor risk also belongs in the funding conversation. The federal banking agencies’ third-party risk guidance focuses on due diligence, risk management, and ongoing monitoring. Even outside banking, the same principle applies. Buying an AI product does not remove the firm’s responsibility to understand how that vendor handles data, controls, and ongoing risk.

Watch For Adoption Risk

A project can pass the technical test and still fail if the people inside the workflow do not trust it, need it, or want another system to check.

The best AI projects are built with the people who already own the work. Analysts, advisors, finance teams, operations teams, and compliance staff know where the process breaks. They also know which outputs are useful and which errors would create real problems.

Adoption risk should be considered before the budget is approved. If the tool forces users out of their normal workflow, adoption will be harder. If the output is not source-linked, review teams may not trust it. If the workflow owner is not involved, the project may never move beyond testing.

A fundable AI project should make existing work easier to complete, not create another disconnected tool for the team to manage.

A Simple Funding Test

A strong AI project solves a specific workflow problem, has measurable value, uses data the firm can govern, includes human review, and has a clear owner.

A weak project is usually broad, hard to measure, disconnected from daily work, dependent on messy data, and unclear on controls. A hedge fund might be better off starting with earnings review. A PE firm might start with portfolio KPI intake. An RIA might start with onboarding document review. A wealth manager might start with client meeting prep.

Before approving a budget, financial services firms should be able to answer one question clearly: if this AI project works, what process gets better, how will the improvement be measured, and what controls make the result safe to use?

If the answer is specific, the project may be worth funding. If the answer is vague, the project likely needs more work before the budget is committed.

FAQ

How Do You Know If An AI Project Is Worth Funding?

An AI project is worth funding when it solves a specific workflow problem, has measurable value, uses data the firm can access and govern, includes human review, and has a clear owner.

What Is A Good First AI Project In Financial Services?

A good first AI project is narrow, repeatable, and tied to an existing pain point. Examples include earnings review for hedge funds, LP reporting support for PE firms, client onboarding review for RIAs, and meeting prep summaries for wealth managers.

Why Do AI Projects Fail In Financial Services?

AI projects often fail because they start with a tool instead of a workflow. Other common reasons include messy data, unclear ownership, weak governance, poor adoption, and outputs that are difficult to verify.

What Should Firms Measure In An AI Pilot?

Financial services firms should measure time saved, error reduction, review quality, workflow speed, user adoption, source accuracy, and whether the system made the process easier to supervise.

Should Compliance Be Involved Before Funding An AI Project?

Yes. Compliance, risk, or legal teams should be involved early when an AI project touches client data, investment research, reporting, communications, or regulated workflows.