The finance professional of 2027 doesn't exist yet

Key takeaways

• 74% of finance leaders have made AI integration a top priority. The main barrier is not the technology. It's having people qualified to use it.

• 53% cite limited team experience as their biggest barrier to scaling AI. Budget and tooling rank lower.

• Teams developing AI capability from within will outpace those waiting for the external talent market to catch up.

In January 2026, McKinsey replaced a cohort of junior graduates with AI systems. Every business publication ran the story. Analysts debated displacement rates. Graduates reconsidered career paths. Commentators mapped which tasks a model could already outperform a first-year analyst on.

The CFOs in our Forrester study face a different pressure. They want AI running their finance functions. They cannot find the people to manage it.

Think about the hire you'd want to make by 2027. Someone who can read a balance sheet and debug a pipeline. Someone who asks why the forecast looks wrong, then writes the code to find out. Someone who understands what happens when you plug AI into a month-end close. And someone who's already thought through what the auditors will ask six months later.

Post that description and two types of candidates apply. The accountants who've never opened a terminal. The data scientists who've never sat through a month-end close. Both bring real skills. Neither brings the whole picture.

74% of finance leaders rank AI integration as a top priority for the next 12 months. They put it alongside financial controls, audit readiness, and compliance. Ask any group of CFOs who they're trying to hire, and they describe the same person. The talent pipeline hasn't produced that person yet.

Unless stated, all statistics referenced come from "Building An AI-Ready Finance Function", a commissioned study conducted by Forrester Consulting on behalf of Airwallex, June 2026.

The job spec nobody can fill

Every other finance team has posted a version of the same ad. Ask the head of finance at a consumer goods company in Singapore or a SaaS business in London and they describe the same role, the same gap, the same fruitless shortlist. Leaders in our Forrester study raised the same problem.

A bigger salary won't make the talent pipeline move any faster. Universities are only now adding AI to finance curricula. The graduates who'll arrive fluent in both disciplines are still sitting their second-year exams. You can't hire professionals who haven't qualified yet.

As AI moves from isolated experiments into connected workflows, the main challenge shifts from the technology to whether your team is ready to run it. 53% of leaders cite their team's limited experience running AI-enabled finance processes as the primary barrier. 29% point to resistance to change and low adoption among their finance teams. While the industry was comparing vendors, the constraint moved from the software to the people.

Skills now have a six-month half-life

38% of finance teams have only basic AI literacy, or no formal view of what skills need to change at all.

The prompt library your team built last spring makes the point. The model under it has had two upgrades since. A chunk of those prompts now produce worse answers than a plain question would. Hire for today's skill requirements and they're outdated before the first week is done. Every quarter compounds the distance.

40% of finance leaders report difficulty building a business case to go beyond short-term productivity gains. That is a circular trap. Without AI-fluent people on the team, the investment case doesn't get made. Without the investment, the team doesn't develop the fluency. Only 15% of organisations have broken that cycle with an enterprise-wide AI talent strategy. The rest know the window is narrowing. Most are still waiting for certainty before they act.

Develop the professional you can't recruit

The people who already reach for AI when they hit a problem are the ones to back. Think of the senior analyst who keeps asking how the model reached its number, or the controller who taught herself to prompt over a quiet fortnight in August. Those people are already on your payroll.

The Head of Commercial Finance at an Australian ecommerce company described the shift. Training that was once roughly 80% technical accounting and 20% softer skills has reversed, with a much heavier emphasis on AI usage, effective prompting, and critical thinking. The in-house route works because people who already understand how your finance function operates can deploy AI faster than an external hire who knows the technology but not the context it has to work in. Firdevs Abacioglu, Head of Data Science and AI at Airwallex, made this case recently, arguing that the most effective AI leads come from your data science team, because they already possess the institutional knowledge as well as relevant skills.

43% of finance leaders expect AI capabilities to be delivered through vendor platforms, with internal teams focused on orchestration, integration, and control. That split makes in-house fluency more valuable, not less. The people doing the orchestration need to understand what they're orchestrating.

Pick that controller. Pair her with a data analyst for a quarter. Give her one workflow to own from start to finish, with the authority to change how it runs. Do that this quarter and you're building your 2027 hire while your competitors are still booking the introductory webinar.

Your infrastructure decides how fast your people learn

People learn on the systems they use every day. A team on one connected platform learns faster than a team switching between seven tools that don't share data. Every extra login is another place where information gets lost.

Fragmented systems slow these people down. Picture your treasury analyst in London. She spots a better way to flag duplicate supplier payments. On a connected platform, she changes the workflow that afternoon. In a fragmented setup, she files an IT ticket and waits three sprints, and by the time anyone picks it up, everyone's forgotten why she raised it. Repeat that delay across a year of small fixes, and the fragmented team ships a fraction of what the connected team does.

This is the case for putting everything in one place. When your payments, FX, accounts and spend data sit on one platform, the people you're training work from one clean dataset instead of piecing several together first.

Start with the people you've got

The shortcut everyone wants doesn't exist. The ready-made hybrid who can pass the technical screen and already knows why Q3 always throws the anomaly isn't on the market. The fastest path runs through your own team.

Pick two or three people who already reach for AI when they hit a problem. Give each of them one finance workflow to own end to end, on a platform where they can change how it works without filing a ticket. Do that this quarter and you'll have people running AI across close, forecasting, and reconciliation long before the market produces the candidate you've been waiting for.

More than that, the people you develop in-house understand your business in a way a new hire never would on day one. A recruited unicorn knows the technology. Your promoted analyst knows the technology and knows why the London entity closes two days late and why the Q3 variance always traces to the same supplier. That combination of technical skill and institutional knowledge can't be purchased from the external market. It compounds inside the organisation that builds it first, and competitors can't simply recruit their way to the same position.

Source: Unless stated, all statistics referenced in this article come from "Building An AI-Ready Finance Function", a commissioned study conducted by Forrester Consulting on behalf of Airwallex, June 2026.