How to implement AI in your finance team: 8-step roadmap

AI is no longer a novelty in finance – it's a practical way to reclaim time, reduce errors and elevate your team's impact. Yet most finance teams haven't made the leap from experiments to embedded workflows. This guide draws from The State of AI in Finance 2025 report to provide a pragmatic, low-risk roadmap you can start today.

Why AI, why now

Finance teams find themselves in a curious position. While 85% of CFOs are optimistic about AI's potential, 61% still haven't implemented it into their workflows. The culprits? Unclear ROI, limited team skills, and the time required to implement new systems.

Meanwhile, early adopters are pulling ahead – cutting close times, automating AP/expenses, accelerating reporting, and improving forecast quality.

Your objective isn't wholesale transformation. It's a sequence of focused wins that compound.

An 8-step roadmap

Based on conversations with finance leaders who've successfully implemented AI, here are the key takeaways consolidated into one cohesive plan.

The scope for AI enhancement in finance can easily become overwhelming. There are potential efficiency gains and process improvements everywhere.

Step 1: Perform an initial assessment

The first step is to map your processes, identify pain points, and assess your data. Use tools like Miro to visualise your existing workflows and uncover opportunities for automation. Look for your quickest wins and biggest pain points.

Anne-Claire began by interviewing team members to understand their challenges. Payroll entries were consuming unnecessary time due to repetitive, manual tasks—a perfect candidate for automation. She was able to compile a shortlist of challenges to tackle, with a mix of relatively quick wins and big-picture overhauls.

"Our overall objective was to automate manual tasks to increase team efficiency and efficiency in our processes. To reduce the month-end close time from 10 to five days, to automate financial reporting, to eliminate 80% of manual work, and to cut invoice processing time from three days to one day." These are all perfectly realistic aims with the help of AI tools.

Output: a prioritised backlog with effort vs impact. Pick one "quick win" to start.

Step 2: Start with a discrete use case

Particularly if you're building your own AI solution, don't bite off too much at once. Generative AI can indeed solve a wide range of common finance issues. But you're far better off making major improvements to one key issue, than vague and messy enhancements across the board.

This was Gabriella's biggest lesson: "If I could do it again, I'd start with a smaller, more focused use case to demonstrate quick wins. You want to build confidence in the AI capabilities first, before scaling to more complex workflows."

Start with one of your most pressing challenges – ideally involving high amounts of manual repetition and review. Once that first issue is solved, simply rinse and repeat.

Step 3: Clean your data

All the experts we spoke with emphasised the importance of data cleaning before launching your AI model. Hallucinations are a key concern, as we've seen. But so is the quality of data you put into your AI tools and models. "Conduct a data audit to assess the quality of your data," says Anne-Claire. "Because AI, to work well, needs clean, accessible and organised data."

Paul Jun says that "setting up your data structures in the right way from day one is immensely helpful and prevents the problem – kind of like technical debt – from creeping up in the first place. An ounce of prevention is worth a pound of cure."

The one silver lining for those who have piles of disorganised data: these tools can also help clean them up. AI tools are faster – and often better – than humans at reformatting and validating large swathes of data.

Step 4: Find the right tools

Which AI tools you'll implement is obviously a chief concern. Much depends on your team's skill levels, the scope of the issue(s) you're trying to solve, and the time and energy you want to put into implementation.

Key questions to ask include: How quick and easy is it to set up and use; What use cases can handle, and will you need multiple tools for different cases; Is it scalable? Will you run into issues with the amount of data and users you need?; How easily does it integrate with your existing finance stack?; How secure is your data? Will sensitive information be used to train its models further?

For Gabriella's team, usability was crucial. "Our finance team is not technical, so we chose tools that everyone was able to use."

Usability and "feasibility" were also important to Veronika's team at Everphone. But integrations were also a priority. "Vertex AI provided seamless integration with our existing Google Cloud infrastructure." If you can integrate new tools without disrupting your existing systems, that's a clear win.

Step 5: Run a pilot

Anne-Claire recommends beginning with a single, measurable project and testing the results. This meant automating payroll entries with Google Scripts, cutting the process from one day to just 15 minutes. This quick win built momentum for broader AI adoption.

A pilot also leaves plenty of room for feedback and insights from others. For Veronika, moving too quickly meant the team missed some opportunities. "Allocating time to gather more feedback on initial prototypes might have improved usability and satisfaction from the outset."

Anne-Claire also suggests finding your "AI champions" – a few carefully selected people who can receive advanced training and help scope out projects, before rolling them out to the whole team.

You can't hope to upskill a whole team overnight, with new tools that nobody has used or understands. These AI champions can test out training initiatives first.

Step 6: Scale across teams and geographies

Assuming the pilot succeeds, you can now roll AI out to other teams and regions. As is always the case for internal change, this will require some hand holding and plenty of help from your AI champions.

Anne-Claire organised "learn and develop" sessions to share results and empower teams to adopt automation in their workflows.

As CFO, you need to be clear and firm about the importance of these new ways of working. Communicate the desired goals: to be more impactful and waste less time on manual tasks. And reassure those who struggle that a learning curve is normal, and this will become natural in time.

There's a real benefit to employees here too. Confidence with AI tools will soon be a prerequisite to working in finance. Team members who embrace the training today set themselves on a strong career path in the future.

Step 7: Monitor, optimise and improve

AI implementation doesn't end at rollout. Regularly review your tools and processes to ensure they're delivering value. Stay informed about emerging AI technologies to keep your team ahead of the curve.

If you're working with custom models, these will constantly need to be improved. In fact, Anne-Claire suggests the eventual need for an "AI automation analyst" in most finance teams – someone who can keep improving efficiency and accuracy in your systems. This may not be necessary for out-of-the-box solutions, but could certainly be beneficial in future.

"It's changing very quickly, and every day you have new functionalities and new tools. Organise monthly or quarterly reviews to ensure your process continues to deliver value."

Step 8: Set clear data and security guidelines

Organisations are rightly nervous about exposing their data to large language models. Your company should have a clear policy about this already, including for non-finance employees.

For the finance team, set clear boundaries about what should and shouldn't be shared with tools. Double-check the terms and conditions and, if possible, invest in in-house tools or paid plans that are contractually prohibited from sharing your data further.

Veronika has implemented a range of measures at Everphone. "We're developing a granular permission system that ensures users access only variables and data sources they're allowed to. The AI model also asks you what you want to achieve with the request, and if it thinks the request might be used wrongly, it asks the user's manager for permission."

"Our Google AI ecosystem doesn't save or use our prompts for learning, complying with data security laws in Europe. Lastly, we log user activity and responses for later auditing and reviewing."

Related: Will AI steal my finance job?

What good looks like in 90 days

• Weeks 1–2: Process and data assessment; choose a single high-impact use case; define baseline metrics.

• Weeks 3–6: Data cleaning; tool configuration; build pilot; implement validation; train a small cohort.

• Weeks 7–8: Run pilot; capture metrics; remediate edge cases; decide on rollout.

• Weeks 9–12: Expand to one more entity or process; formalise SOPs; publish results; schedule reviews.

Example KPIs to track

• Time saved per cycle (e.g., AP processing time, month-end close days).

• Touchless rate (percentage processed without human intervention).

• Error/rework rate and audit findings.

• SLA adherence for data requests.

• Finance business partnering time (hours reallocated to analysis).

Tooling patterns that work

• Out-of-the-box for AP and expenses: Robust platforms like Spendesk can deliver instant wins with built-in extraction, coding and matching.

• Co-pilot for FP&A and reporting: Natural-language SQL and Python to accelerate data pulls and scenario modelling.

• Scripted bridges: Light Python or Apps Script to connect spreadsheets and systems where vendor integrations don’t exist yet.

• Document intelligence: Contract/receipt parsing to feed revenue schedules and policy checks.

Common pitfalls to avoid

While the roadmap above provides a clear path forward, finance teams often stumble on predictable obstacles. Here are the most common mistakes to sidestep:

• Trying to do everything at once: Too many parallel use cases dilute impact.

• Dirty inputs: Skipping data standardisation leads to brittle outputs.

• Shadow AI: Unapproved tools with unclear data terms increase risk.

• Set and forget: Models, prompts and processes drift — review them.

Final thoughts

Implementing AI in finance is a series of pragmatic steps, not a single leap. Start small, validate rigorously, and scale with governance. The outcome is a team that closes faster, explains results better and spends more time on decisions than data wrangling.