If you were hiring the first person for a new finance team tomorrow, you wouldn't start with the CFO. You'd build up from the bottom of the org chart. You'd hire someone who could do the grunt work: reconciliations, billing, payroll, and cross-system handoffs. This is the grind that keeps the business running, and it’s also a helpful way to think about your first AI hire.
When it comes to integrating AI into finance, operations is often an untapped automation opportunity for enterprise teams. The first wave of AI adoption has been focused on the analytical layer of the function, but the biggest gains sit somewhere else, in a different capability of the underlying models entirely: The ability to actually get the work done, accurately and on time, no matter the workload.
Where AI belongs in enterprise finance
AI today is incredibly capable. Your everyday LLM has a PhD-level understanding of accounting, financial reporting, and controls, and it's an expert at generating written material. That's why so many early AI pilots in finance have been aimed at analysis and reporting: the custom dashboards your team has dreamed of for years, or the slick chatbot that can answer questions out of your internal data and knowledge base. These tools are useful in their own right, but they don't always reduce the burden on your team.
The AI that pulls its weight in your finance org will actually do the work—managing inbound invoices and finance queries 24/7, tirelessly reconciling thousands of transactions against the GL, or completing the multi-system processes unique to your business that have only ever been handled by people because no API or off-the-shelf integration was ever going to cover them.
This is where AI agents earn their keep in finance. The same intelligence that powers a chatbot can be pointed at executing a workflow, operating inside the same systems your team works in. It logs in, navigates your tools the way a person does, reads what's on the screen, drafts journal entries, and sends emails. You give it the training, context, and access you'd give a new hire, and it does the work the way a member of your team would. The difference is that it isn't constrained by hours in the day or by how many things one person can take on at once.
Why now?
For most of the last year, AI wasn't ready for finance. The tradeoffs were too rough: it was fast but hallucinating, innovative but ungovernable. A wait-and-see approach was the default for many teams.
But in the last six months, we’ve hit a new inflection point. The underlying models have become more precise, more consistent, and finally auditable enough to do real finance work. The tooling around them has also caught up, with new layers of evaluation, guardrails, and audit trails.
Finance-grade execution is now available for the kind of operational work that drains the most time from enterprise teams: completing the same process thousands of times without drift, pulling data out of systems that weren't built to talk to each other, catching errors or mismatches across massive data sets, handling workloads no human team can keep up with, and following rules the same way every time.
The question now is how to bring it into your team and give it real work, responsibly.
Making your first AI hire
Once you accept that the first AI hire belongs in operations, a handful of practical questions follow. The first is about where to begin.
Start with one workflow, not ten. Pick a single high-volume multi-platform process, like bank reconciliations, A/R inbox triage, or month-end data pulls, and run it end-to-end. Resist the urge to automate everything at once.
Your instinct may be to build a solution in-house. Internal teams may feel motivated, the work looks straightforward, and the technology to build bespoke internal tools has gotten more accessible. But building AI tools that scale and produce consistent results takes more expertise than most finance or IT teams have today. The first prototype works with a clean data set, but then edge cases start appearing, and soon the thing you built requires more engineering effort than anyone planned for. The work your team wanted to offload comes back as software maintenance, which isn't what they were hired to do.
For most enterprise teams, the more sustainable path is to find a trustworthy partner who can handle the building and upkeep. During the buying process or pilot, evaluate AI the way you'd evaluate a new hire. A suitable agent will behave like a smart junior teammate. You should be able to give it instructions and access to your tools, and expect it to get the work done at or exceeding the level of someone with three to five years of experience.
Your first workflow may not be glamorous, but it will help your team build confidence and open up new possibilities. The only limit on what else you can delegate is what you can imagine.
Find out what Woodrow can take off your team's plate.
Sidharth Kakkar is the Founder of Woodrow, an AI agent for finance and operations, built with the accuracy and controls enterprise teams demand. Explore how Woodrow fits into your workflows at woodrow.ai.
