AI in Finance

The four kinds

The four kinds of AI in finance

Most use cases fall into one of four categories. Knowing which one a vendor is selling makes the evaluation shorter.

Adoption

How to adopt AI without losing the audit trail

Sequence the portfolio, run a governed pilot, write the policy before the second use case needs it.

Measurement

What AI in finance is worth

Tangible return pays for the project. Strategic return is why the second project gets funded.

Go deeper

Books, courses, and talks on AI in finance

Questions

Questions finance leaders ask about AI

What does AI in corporate finance actually look like day-to-day?

It depends on the function, but the through-line is removing manual, repetitive work so finance teams can focus on analysis and strategy. In accounts payable, that might mean intelligent invoice matching that flags anomalies instead of making someone eyeball every line. In FP&A, it could be a forecasting model that pulls actuals automatically, detects trends in revenue drivers, and produces a rolling forecast without the usual spreadsheet gymnastics. Treasury teams use ML for cash-flow prediction. Controllers use NLP to scan contracts for revenue recognition triggers. The common thread: AI handles the data wrangling and pattern recognition, and your people handle the judgment calls.

Where should a finance team start with AI?

Start with a process that is painful, repetitive, and data-rich. Month-end close tasks, variance analysis, expense categorization, and cash-flow forecasting are all strong candidates. The goal is to pick something where the ROI is obvious and the risk is low, so you can build confidence and momentum before tackling bigger strategic initiatives. Glenn typically recommends a 90-day pilot focused on one workflow.

How is AI in finance different from traditional automation or RPA?

RPA follows rigid rules: “if this cell equals X, copy it to column Y.” It breaks when the format changes. AI and machine learning work differently. They learn patterns from data, handle variability, and improve over time. RPA is great for structured, predictable tasks. AI excels where data is messy, volumes are high, or the process requires judgment-like decisions such as anomaly detection, natural-language processing of contracts, or dynamic forecasting. In practice, the best implementations layer AI on top of RPA to get the benefits of both.

Do I need a data science team to use AI in finance?

No. A lot of modern AI tooling is designed for business users, with no-code or low-code interfaces. That said, you do need someone who understands both the finance workflows and the data. Glenn’s approach bridges that gap: he helps finance leaders evaluate tools, clean up data pipelines, and implement solutions without requiring a dedicated ML engineering team. For more complex use cases like custom predictive models, he can bring in specialized resources.

What kind of ROI can we expect from AI in finance?

ROI varies by use case, but the patterns are consistent. Process automation (invoice processing, reconciliation) typically delivers 40 to 60 percent time savings. Predictive analytics (forecasting, cash management) often improves accuracy by 20 to 35 percent. Risk and anomaly detection can catch issues worth multiples of the implementation cost. Glenn helps clients build a business case with realistic projections before any investment is made.

What are the biggest risks of AI adoption in finance?

The top risks are poor data quality feeding bad models, lack of organizational buy-in leading to shelfware, over-reliance on AI outputs without human validation, and regulatory or compliance blind spots. None of these are reasons to avoid AI. They are reasons to approach it with a structured methodology. Glenn’s engagements are specifically designed to mitigate each of these risks through proper scoping, governance, and change management.

Will AI replace finance professionals?

No, but it will reshape what finance professionals spend their time on. The transactional, data-gathering, and reconciliation work that consumes 60 to 70 percent of most finance teams' time is exactly what AI automates well. That frees people to do more analysis, business partnering, and strategic work. The finance professionals who learn to work alongside AI will be significantly more valuable. The ones who refuse to adapt will find their roles shrinking.