For those of you who keep up with our insights and articles, you’ll have seen, recently that my colleague Edward Vorley, recently shared his thoughts on how AI is becoming FP&A’s ‘wingman’ (read it here)

Over recent weeks, like Edward, I’ve had the chance to speak with quite a few US and North American-based finance leaders deeply involved in AI adoption across PE-backed and large corporate environments – in fact, my daily conversations often attune their direction towards the topic of AI supporting finance teams.  

 

To get the opinion of US operators and senior professionals in in PE-backed businesses, I spoke with a series of senior finance professionals whose insights revealed a pragmatic, evolving picture of AI’s impact and the skills finance teams will need to thrive.  

 

My key takeaway: AI is no longer a ‘distant prospect’ for finance functions. It’s here, reshaping how teams operate, supporting the skills they need, not just in transactional activity but with financial controls and business analysis. 

 

What’s more, finance teams need to keep an eye on staying up to speed with AI tools and invest in their personal development to embrace elements of AI support into their day-to-day routines and tasks.

 

There are some common themes and messages are occurring from within finance functions– but what’s clear is that people are – and need to be curious about AI - and they can benefit by dedicating time to understand it and develop their skills sets to make both transactional and transformational impact within the function.

 

“AI adoption isn’t just about technology; it’s about people and continuous learning,” Ashish Poudel, former Group Finance Director of Volaris, told me during one of our recent conversations.

 

“In finance, skills sets need to be constantly evolving – it's no longer enough to master traditional accounting – teams must be AI literate, able to craft effective prompts, critically assess AI outputs and use these insights in decision making. 

 

“This requires an analytical mindset and technical curiosity.”

 

 I also spoke with Ryan Waring, CFO of Californian-based drone inspection software company Zeitview. 

 

 

He suggested that teams should undertake continuous development and learning around the value and capabilities of AI and emphasised the importance of embedding AI into the regular rhythm of finance teams. 

 

His key recommendations for finance professionals include:  

 

·       Dedicating at least 30 minutes each week specifically to AI learning

·       Engaging with varied resources such as articles, demos, and new AI tools

·       Fostering a culture where continuous AI education is part of everyday work life

·       Taking a view that ongoing upskilling as essential to staying current and confident in AI deployment

·       Aiming to avoid feeling overwhelmed by rapid change through steady, incremental learning

 

For Ryan, this steady commitment helps finance teams confidently leverage AI instead of being “daunted by its fast evolution”. 

 

Anna Tiomina, is a former CFO based in Texas, USA, who now specialises in AI advice and training to other businesses.

 

She believes finance professionals without an AI understanding are already losing out in the race for the next role:

 

“I see companies who are valuing AI literacy, who are not willing to hire people who don't embrace the technology - even if you don't know how to use it [AI] now, you need to be able to learn quickly,” she said.
 

 

Fund side

From the fund side, the message is somewhat more nuanced in terms of AI adoption.

 

From my conversations with a handful of top 50 global financial portfolio operators within PE funds my key takeaways were that whilst AI tools promise enormous efficiency gains, adoption is not straightforward and the case for its implementation needs to be solid.

 

Change management is a huge part of this; many finance professionals have deep expertise in legacy processes and can be resistant to adopting AI without clear demonstration of value.  

 

AI adoption must be framed around solving specific pain points whether it’s speeding up reporting, improving forecasting accuracy, or automating repetitive reconciliations, one senior operator told me.

 

He said that when teams see real benefits early, they become champions of AI but if AI feels like an abstract ‘tech project,’ adoption stalls. 

 

Where time pressure and resource constraints are intense, AI initiatives must be “tightly scoped, with clear business cases and measurable outcomes”, he emphasised.

 

He said that it’s tempting to try and automate everything at once, but incremental wins build momentum and trust with investors.

 

AI is the enabler. Not the replacement.

Encouragingly for us humans – and as executive search consultants (!)- across conversations, a recurring insight is that AI is a performance enabler, not a replacement for the professional.

 

Whilst AI can automate data processing, generate forecasts, and identify anomalies, human oversight remains critical – a theme reflected in the recent blog written by my colleague, Edward Vorley.

 

Finance leaders I’ve spoken to with audit experience suggested that the future audit or finance team member will use AI to highlight issues.

 

But humans must interpret results, ask the right questions, and need to make judgement calls – including the required checks and sign-off processes.

 

In my conversation with CFO, Ryan Waring, he agreed; and highlighted the need for teams to become “AI literate but not AI dependent.”  

 

He explained that investing time in learning about AI helps finance professionals understand its limitations as well as its power.  

 

“This balanced understanding ensures AI is used as a tool to augment human judgement, not replace it,” he emphasised.

 

Through continued discussions these are the themes that come up adding, AI provides insights faster, but strategic decisions require human context.  

 

Finance teams need to balance trusting AI outputs with their own professional scepticism. 

 

CFO Anna Tiomina also agrees. She said AI is a ‘teammate’ not a replacement.

 

“This is not a very distant future and the same way as you decide if you trust a person, you decide if you can trust AI and there is of course a way to make it more reliable.

 

“At the end of the day, if you are signing off something, you are ultimately responsible for it.”

 

The next steps for AI and finance leaders.

So, the finance function’s transformation through AI is well underway, but it’s a journey, not a sprint.

 

Leaders I spoke with are pragmatic; they understand the challenges and are prioritising people and skills alongside technology – most leaders feel AI tools are ‘team mates’ not replacements for experience.

 

For PE-backed and corporate finance teams alike, the key to optimising AI lies in embracing a mindset of continuous learning, being deliberate about adoption, and maintaining a healthy balance between AI’s capabilities and human insight and oversight.

 

Here’s my practical steps for finance leaders considering finding the appropriate ‘blend’ of AI and talent in their finance function. 

 

·       Build AI learning into the weekly routine. As Ryan recommends, even 30 minutes per week dedicated to AI learning can accelerate upskilling and reduce fear of new technology. 

 

·       Focus on targeted use cases. Experience shows that starting with well-defined pain points and small wins generates momentum and trust. 

 

·       Invest in hybrid skillsets. Teams need to combine strong finance fundamentals with data analytics and AI tool proficiency. 

 

·       Foster a culture of curiosity and continuous improvement. Encourage teams to experiment with AI tools, share learnings, and challenge assumptions. 

 

·       Maintain human oversight and professional scepticism. AI outputs are powerful but not infallible. Finance professionals must remain critical thinkers and decision makers.