If you were to hire the first employee for a new finance team tomorrow, you wouldn't start with a CFO. You'd build from the bottom of the organizational chart—first hiring someone who can handle the foundational work of reconciliation, invoicing, payroll, and handoffs across systems. These mundane tasks keep the business running and provide a useful reference point for thinking about your first AI employee.

When integrating AI into the finance function, operations are often the area where enterprise teams have yet to fully tap into automation opportunities. The first wave of AI applications focused on the analytics layer, but the biggest gains lie elsewhere—in another capability of the underlying models: the ability to actually complete tasks accurately and on time, regardless of workload.

The Role of AI in Corporate Finance

Today's AI is remarkably capable. The LLMs used daily possess PhD-level understanding of accounting, financial reporting, and internal controls, and excel at generating written material. Because of this, many early finance AI pilots target analytics and reporting: the custom dashboards teams dream of, or intelligent chatbots that can answer questions based on internal data and knowledge bases. These tools are useful in themselves, but they don't necessarily lighten the team's load.

AI that truly makes a difference in finance organizations actually does the work—processing incoming invoices and finance queries 24/7, tirelessly reconciling thousands of transactions against the general ledger, or completing those enterprise-specific, cross-system processes that, lacking APIs or off-the-shelf integrations, could only be handled manually.

This is where the value of AI agents in finance lies. The same intelligence that powers chatbots can be directed to execute workflows, operating within the same systems your team uses. It logs in, navigates tools like a person, reads screen content, drafts journal entries, and sends emails. You give it training, context, and access, just as you would a new employee, and it completes work the way a team member would. The difference is that it isn't limited by the hours in a day or the number of tasks a single person can handle simultaneously.

Why Now?

For much of the past year, AI wasn't quite ready for finance. The trade-offs were too crude: fast but prone to hallucination, innovative yet difficult to control. Many teams defaulted to a wait-and-see approach.

But in the last six months, we've reached a new inflection point. The underlying models have become more accurate, more consistent, and finally auditable enough to take on real financial work. The tools surrounding the models have also caught up, adding layers of evaluation, guardrails, and audit trails.

Finance-grade execution is now applicable to the operational work that consumes the most time in enterprise teams: executing the same process thousands of times without drift, extracting data from systems that weren't designed to talk to each other, catching errors or mismatches in massive datasets, handling workloads that human teams can't keep up with, and consistently following the rules.

The current question is how to bring it into your team and responsibly entrust it with real work.

Making Your First AI Hire

Once you accept that your first AI employee should belong in operations, a series of practical questions arise. The first is where to start.

Start with one workflow, not ten. Choose a single high-volume, cross-platform process, such as bank reconciliation, receivables inbox triage, or month-end data extraction, and run it end-to-end. Resist the urge to automate everything at once.

Your instinct might be to build a solution in-house. Internal teams may be highly motivated, the work may seem simple, and the technology for building custom internal tools is more accessible than ever. But building AI tools that scale and produce consistent results requires expertise beyond what most finance or IT teams currently possess. The first prototype runs well on clean data, but then edge cases emerge, and soon what you've built requires more engineering effort than anyone anticipated. The work you wanted to outsource comes back in the form of software maintenance—which isn't their job.

For most enterprise teams, a more sustainable path is finding a trusted partner to handle the building and maintenance. When evaluating AI during procurement or a pilot, assess it as you would a new employee. The right agent will behave like a smart junior colleague. You should be able to give it instructions and tool access, and expect it to complete work at the level of someone with three to five years of experience or better.

Your first workflow might not be glamorous, but it will help the team build confidence and open up new possibilities. What else you can delegate is limited only by your imagination.

Learnwhat Woodrowcan take off your team's plate.

Sidharth Kakkar is the founder of Woodrow, an AI agent for finance and operations that provides the accuracy and control enterprise teams need. Explorewoodrow.aito see how Woodrow fits into your workflow.