AI adoption in accounting and finance is picking up speed. Through our conversations with leaders, we’ve learned how companies are integrating AI into existing workflows — and how that’s changing hiring decisions.
Where Most Companies Stand Today
Most of the companies we speak with fall into one of four phases. Each one comes with different challenges and talent needs.
The Discussion Phase
“We know we need to do this, but haven’t started.” These companies recognize the importance of AI, but haven’t had the resources or bandwidth to act.
The Building Phase
Some are building internal capabilities specifically for AI adoption. They’re carving out resources and headcount to explore this strategically rather than reactively.
The Expansion Phase
These organizations have established teams and are now hiring to scale AI across all departments (including accounting and finance). They’ve moved past proof of concept and are ready to operationalize.
The Outsourcing Phase
Other companies are using consulting firms to determine their needs and lead implementation. They’re leveraging external expertise to accelerate adoption without building internal capacity first.
Questions To Ask for Each Phase
The questions you’re facing depend on where you are in the adoption process.
If You’re in the Discussion Phase:
What would it take to get started?
The first step is figuring out your biggest barrier. Bandwidth, budget, or direction? If it’s bandwidth, what can you deprioritize or delegate to make room? If it’s budget, what’s the minimum investment to get started? If it’s direction, conversations with people who’ve done this (or advisors like us) can help.
If You’re in the Building Phase:
Do you have someone who can bridge AI and accounting?
You need someone who understands both AI capabilities and accounting and finance workflows well enough to bridge the two. Look for someone on your team who’s curious about technology and deeply familiar with your processes. If no one fits that description, you’re looking at either upskilling someone promising or bringing in new talent.
Once you identify that person (or realize you need to hire), 2 more questions become critical.
What skill gaps exist between your current team and what AI adoption requires?
Your answer here will determine whether you need to hire, train, or do both. The bigger the gap between current capabilities and what AI adoption requires, the longer your timeline and the more resources and budget you’ll need.
How are you preparing your existing team for the transition?
Upskilling existing staff may make more sense than bringing on new talent, but it requires an investment of time, training, and patience. Are you willing to commit the resources to make it happen? And if so, who on your team has the aptitude to learn?
If You’re in the Expansion Phase:
Where is the talent you need actually coming from?
We’re seeing talent emerge from Big 4 firms or consulting practices who’ve worked on AI implementation projects. Some have led adoption efforts at their organization. And increasingly, early-career professionals are entering the market with exposure to these tools from their education.
But make no mistake- these individuals will be very difficult to find. This isn’t a checkbox hire where you can post a job description and sort through dozens of applicants. The talent pool is limited, and competition for it is fierce.
New online courses and certificate programs show an emerging focus on learning these skills. The pipeline is building, but it’s not yet robust. Companies that wait for the “perfect candidate” to materialize may find themselves waiting a very long time.
If You’re in the Outsourcing Phase:
What happens when the consultants leave?
Make sure you’re building internal knowledge alongside external expertise, or you’ll remain dependent on outside firms indefinitely. The goal should be capability transfer, not just project completion.
How will your team maintain and evolve what gets built?
The handoff matters. Without internal ownership and understanding, AI implementations can stall once consultants leave. Your team needs to be able to run with what they inherit.
What We’re Seeing Work
The companies making the most of AI are starting small, proving value, then scaling. They’re testing this new tech on internal processes and implementing software and systems with built-in AI and automation rather than trying to layer AI onto outdated infrastructure.
In terms of tools, Microsoft Copilot keeps coming up in our conversations with leaders. They like that it integrates with systems they already use and includes data controls that prevent proprietary information from leaving their organization.
They’re also realistic about what AI should and shouldn’t do. It speeds things up, but it doesn’t replace critical thinking. The best approaches prioritize human oversight for accuracy and decision-making.
Final Thoughts
Every organization is at a different point in AI adoption, facing different talent challenges.
Through our partnerships with accounting and finance leaders, we’ve seen what works at each stage and why companies struggle to get the right people in place. Our recruiters know where the talent is and what it takes to hire them.
Want more insights on AI adoption? Curious what we’re seeing in the market? Let’s connect! Give us a call at (952) 278-1800 or fill out this form.