Editor's note:Siqi Chen is the CEO of Runway, a financial planning software company based in San Francisco, California. The views expressed in this article are solely those of the author.

With the buzz around artificial intelligence growing louder, chief financial officers are feeling the pressure. A recent survey by American Express found that three-quarters of financial leaders view digital transformation as a strategic priority. The research notes that this trend stems from pressure to adopt innovative technologies like AI in finance and beyond.

Today, the question is no longer whether CFOs need AI, but how long they can hold out without it.

However, CFOs need not fear AI itself. Contrary to some exaggerated claims, AI will not replace your job. Quite the opposite: it is here to help you perform your duties faster and more wisely. AI can assist in drafting reports, alerting you when actual data deviates from forecasts, and even flagging potential risks. But the final decision-making power remains in your hands.

For CFOs who ignore AI, the real risk lies in being replaced by a financial leader who has learned to embrace the technology.

That said, it must be clear that AI is not a magic tool. Its effectiveness depends largely on how well it is used. Here are key strategies for unlocking AI's true value in the finance department:

Lay the groundwork before moving forward

AI is not a plug-and-play solution. You need to build a solid foundation for it to operate on.

First, clean up your financial data. Data must be organized, consolidated, and easily accessible. Otherwise, AI will only add complexity rather than create clarity. This is like hiring a CFO without providing any background information—it's hard to make a meaningful impact.

If data is messy or scattered, AI tools will only produce useless results. Conversely, if data is well-organized, AI becomes a powerful assistant.

The key is to ensure measures are in place to reconcile differences between the ERP system and subsidiary ledgers, unify account definitions across business units, and consolidate data into a single source of truth.

Once these foundational tasks are complete, AI can truly deliver value: identifying patterns you might miss, uncovering overlooked insights, improving forecast accuracy, and supporting smarter, data-driven decisions.

But if you introduce AI too early, before data is ready, you'll end up with expensive and confusing outputs.

Deploy AI deliberately

Start small, but aim high. Try using AI for cash flow forecasting or revenue projections to achieve quick, visible wins.

Also, keep in mind: AI should enhance your processes, not replace your judgment. You should first have a solid understanding of the business, then leverage AI to communicate that understanding to the rest of the team.

If the team over-relies on AI, or lets it substitute for thinking, they will lose the nuanced insights that come from human understanding. Make sure team members are equipped to validate AI outputs and deliberately use AI to optimize strategy.

AI tools are built on large language models, which means they are trained on text rather than numbers. Think of AI as having human-like intuition—good at understanding and generating language, but not particularly strong at math. Therefore, AI can make financial models easier to understand and access, but it won't truly replace the models themselves.

Make AI invisible (in a positive way)

AI should not be something you have to "talk" to at all times. Most people view AI as a tool accessed through chat interfaces like ChatGPT, which is feasible but has clear limitations.

What you need is AI that doesn't just respond when prompted, but also works quietly in the background, providing assistance without disrupting your workflow.

AI should be deeply integrated into your systems, providing insights during the work process rather than after the fact. The goal is to make AI as natural an extension of your existing way of working as possible.

To achieve this, look for AI tools that can connect deeply with your data sources, gain full business context, and do so without interrupting your workflow.

Manage the human factor

Assemble an AI oversight team with members from finance, IT, legal, and risk departments to establish clear AI usage policies and maintain overall consistency. Ensure your team receives AI training to help them understand AI's capabilities and limitations, so they can validate its outputs. Work closely with the CIO or CTO to ensure the technology stack supports your AI goals.