AI tools are everywhere, but do they actually solve your finance team's real problems?
Thereâs no shortage of AI buzz in finance right now. Every week brings a fresh batch of tools promising to automate, accelerate, or completely rethink the way your team works. Some sound revolutionary. Others⌠less so.
But if youâre an in-house finance leader, the real question isnât âShould we be using AI?â Itâs âWhere would it actually help?â
Before you jump on the latest app, take a breath. Hereâs how top finance teams should be thinking about AI: not as a magic fix, but as a practical tool for specific bottlenecks.
Start with the pain, not the tool
The smartest way to begin with AI isnât to chase the most-hyped app. Itâs to look inward and ask:
Where does my team consistently lose time, accuracy, or sanity?
- Is it in the month-end close: Where manual rework and reconciliations eat up days?
- Is it in reporting: Where commentary is always last-minute?
- Is it in AP: With invoice processing still heavily manual?
Once youâve mapped the real operational bottlenecks, then it becomes easier to ask: âCould AI help here?â
Clean data beats clever tools
Hereâs something we donât hear enough in the AI hype cycle:
Garbage in, garbage out:
If your data and processes are a mess, AI wonât fix it, itâll just automate the mess.
Think of AI as a multiplier. If your data is clean and your workflows are clearly defined, AI can simplify, speed up, and even improve your processes. But if your Chart of Accounts is inconsistent, your tracking categories are misused, or your intercompany balances donât balance, the output will reflect that.
Before you dive into new tools, itâs worth spending time getting your foundations right. Standardise your COA. Fix your mapping. Tighten up month-end. AI wonât replace this work, but it will reward it.
A few questions to guide your starting point
If youâre trying to figure out where to begin, here are a few prompts worth running through:
- Where do we rely on repetitive, rules-based processes?
These are prime candidates for automation or augmentation.
- What do we always run out of time for?
If your team consistently rushes commentary, board packs, or reconciliations, AI could buy back some hours.
- Where do problems hide until it's too late?
Think cash flow gaps you don't spot until week 3, expense anomalies that surface in board meetings, or vendor duplicates that slip through until year-end. AI excels at pattern recognition that human eyes miss.
What to look for when evaluating an AI tool
Not all AI tools are created equal and flashy demos can hide real-world gaps. When youâre assessing a tool, look for:
- Problem fit: Does it solve your specific pain point or is it a generic solution in search of a problem?
- Ease of integration: Can it connect smoothly with your current stack (especially Xero)?
- Explainability: Can the tool clearly show how it got to a result or suggestion? Can you easily understand and clearly communicate how the tool got to a result or suggestion?
- User control: Are you still in the driverâs seat or does the tool make decisions you canât adjust?
- Time to value: Can you pilot it quickly and measure impact within a month or two?
AI should feel like a co-pilot, not a black box. If itâs hard to trust, hard to explain or hard to use, itâs probably not the right fit (yet).
Look for early signals and safe places to experiment
Once youâve cleaned up your processes and identified a pain point, here are some categories where AI is starting to deliver real value. These arenât endorsements, just examples of where the market is heating up:
- Bookkeeping + Coding: Tools like Jenesysâ Jack and Booke.ai are generating early interest for their ability to assist with transaction classification and bookkeeping clean-up.
- Multi-Entity: Tools like Mayday can automatically match transactions for intercompany reconciliations and flag any inconsistencies in the Chart of Accounts across your group.
- Narrative Reporting + Commentary: Platforms such as Joiin and Syft Analytics are exploring AI-generated insights and commentary layered on top of your month-end numbers.
- AP Automation: Apps like Derive and Payhawk act like an extra set of hands, doing the review work youâd otherwise have to do manually.
These tools work best on top of strong processes. They're not designed to sort out messy data or poor practice, they're designed to make good teams faster.
The goal isnât to be early, itâs to be effective
You donât need to overhaul your stack or roll out new tools overnight. The best teams are starting small: picking one task, trying one tool, and evaluating the outcome.
Itâs less about leading the AI charge and more about making smart, incremental improvements to the way your team works.
If you want to hear how other finance teams are actually using AI (beyond the marketing hype), join The Stack Exchange. It's a free community where finance professionals share what's really working in their day-to-day operations.
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