AI In Accounting and Finance
What's real, what's noise and what's next
A practical look at where finance teams stand with AI, how to move from experimentation to strategy, and how to build trust while creating capacity for higher-value work.
Key insights from the eBook:
- AI adoption is still in its early stages: 27% of finance teams aren't using any AI features, highlighting the need for a more strategic approach to adoption.
- Trust and skills are major barriers: Strong data foundations, clear governance and human oversight are essential to using AI responsibly.
- AI is an opportunity to build capacity, not cut costs: Automating routine tasks can free finance professionals to focus on analysis, strategic planning and higher-value work.
- Measuring ROI is critical: 64% of surveyed CFOs aren't tracking AI ROI, making it difficult to understand its impact or justify further investment.
- Start small and scale with purpose: Assess existing tools, identify high-impact opportunities and measure results before expanding AI adoption.
AI Adoption In Finance
Being ready for AI isn't about adopting every new tool. It's about understanding where you are today, identifying the gaps and knowing what needs to happen next.
Where should you start with AI in finance and accounting?
- Assess what your team is already using
- Check AI features built into your existing software
- Identify opportunities for AI in your finance function
Explore using AI for finance and accounting to:
- Streamline invoice processing by extracting invoice data and reducing manual entry.
- Speed up month-end by automating checks and identifying discrepancies.
- Improve financial analysis by exploring trends and investigating variances in your numbers.
- Strengthen oversight by flagging unusual journals or supplier payments for review.
Start with the areas that offer the greatest potential value, then measure the results to understand what's working and where to invest further.
Find out how ready you are for AI in relation to your peers with our AI readiness checklist.
AI Trust & Governance
Three steps to building trust in AI
- Get your data in order: Inconsistent financial data won't fix itself with AI. Poor-quality inputs can lead to unreliable outputs.
- Protect sensitive information: Understand where your data goes, who can access it and how your chosen tools handle it.
- Establish clear rules: Define where AI can support your team and which decisions require human oversight.
Ask the right questions before choosing a vendor
A polished demo won't necessarily show you what using an AI tool will look like six months down the line.
Before committing, ask:
- Pricing: How will costs change as usage grows, and what drives consumption?
- Data security: Where is your data stored, and who can access it?
- Integration: How will the tool work with your existing finance systems and workflows?
- Exit: Can you retrieve your data in a usable format if you decide to switch providers?
But if AI becomes more capable of handling finance tasks, there's another question to consider: what does this mean for the people currently doing the work?
Treat AI like a really good new starter. You wouldn’t follow their advice blindly without checking.
Differentiate between AI hype & reality
Will AI Replace Finance Jobs?
The simple answer is no. AI will help evolve the role rather than end it. But for finance leaders, the question is how you use the capacity it creates.
From data entry to strategic decision-making
Automating invoice matching could free up 30% of a Financial Controller's time. The opportunity here is to develop your team's skills and retain their expertise, helping them move into more analytical roles.
This is where a 10x mindset comes in. Instead of asking how AI can help your team do the same work faster, ask what they could achieve with the extra capacity.
Measure the return, not just the cost
AI can appear more cost-effective than increasing headcount, but software costs can grow with usage. To understand whether the investment is worthwhile, start by
- Defining what you want AI to achieve
- How you'll measure success through AI
The goal is to understand the value of the capacity it creates, and how that translates into better decisions and business outcomes.
The skill set for finance professionals is shifting. Emotional intelligence, data visualisation, and asking the right strategic questions are replacing technical accounting entry.
Looking Ahead: What Finance Leaders Should Do Now
Here are 3 conversations you can have with your team tomorrow morning:
Where are we already using AI?
Before introducing new tools, find out what your team is already using, including AI features built into your existing software.
Which tasks could we improve first?
Start with one specific use case where AI could reduce manual work and give your team more time to focus on analysis and decision-making.
Where do we need human oversight?
Agree on the checks and controls needed before introducing AI into a process. Review where human insight is needed.
Conclusion:
The goal isn't to adopt AI simply to follow a trend. It's to use it strategically, with the right safeguards in place, to give your finance team greater capacity to focus on higher-value work, contribute meaningful insights and drive better business decisions.
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