AI in Finance: The Key Questions Finance Teams Should Be Asking

If you’re working in a Finance team you will have seen AI-powered tools become more prevalent, and you’ll have faced an increasing number of product demos and proposals promising automation, insight, and transformation. But not all AI tools are created equal, and the stakes for your data governance, compliance, and financial accuracy are high. 

So how should you evaluate new AI-driven finance tools or standalone AI systems? The best way is to start by asking the right information to help you make an informed decision. 

We’ve created a list of questions to ask with guidance to what the answers mean so you don’t get overwhelmed with technical terms and acronyms. 

1] What problem are we trying to solve, and how does AI support that goal?

Before diving into technical features, you should understand what business outcome you hope the tool can achieve. Whether it’s improving your forecasting accuracy, streamlining your invoice processing, or enhancing your spend analytics, AI should be a means to an end, not the end itself.

2] What type of AI is used, and how transparent is it?

AI is new to most of us, and it’s a mystery as to how it works and what it can do. Ask whether the tool uses machine learning, natural language processing, or other techniques, and what level of explainability it provides. You’ll want to know how the decisions that form the outputs are made and if there are ways to audit or review these AI-generated outputs. 

Simple Explanation: If the vendor says the tool uses natural language processing (NLP), that means it can interpret and respond to human language, such as analyzing invoice descriptions or understanding email content. This method could make the tool easier to use or more insightful, but it also means its accuracy will depend on how well the AI understands your specific financial terminology. 

If machine learning is mentioned, it means the tool improves over time by learning from data patterns. You'll want to know what data it's learning from, just your data or all data it has access to, and how those patterns are verified. 

"Explainability" refers to how easily humans can understand why the AI made a particular decision—this is crucial in finance where accountability matters. 

3] Where does the AI get its data, and how is our corporate data used?

This is critical. You’ll want to determine whether the tool uses your company’s financial data to train its models, and if so, how that data is processed, stored, and protected. You should ask: 

• Is data encrypted in transit and at rest? 

• Is our data shared with third parties or used to train models shared across clients? 

• Can we restrict how our data is used? 

Simple Explanation: If your corporate data is used to "train" the AI, it may help the tool work better for your organisation—but only if it's handled securely. Encryption means your data is coded so it can't be easily accessed by hackers. If data is shared across clients or stored overseas, that could introduce legal or privacy concerns. You’ll want assurances that your data won’t be used beyond your organisation without your explicit permission. Data "in transit" means it's moving between systems; "at rest" means it's stored somewhere—both need the appropriate protection. 

4] How does the tool handle data privacy and compliance?

For UK businesses, GDPR compliance is non-negotiable. You need to understand: 

• What personal data the AI processes 

• Where data is stored (e.g. UK, EU, US) 

• How data subjects can exercise their rights 

Simple Explanation: GDPR (General Data Protection Regulation) is a set of legal requirements that protects individuals' personal data in the UK and EU. If your AI tool collects any personal details, such as names or email addresses, it must comply with GDPR. This includes allowing people to see, correct, or delete their data. Knowing where the data is physically stored matters, too, as laws vary by country. 

5] What are the security measures in place?

Security isn't just an IT issue—it's a finance one too. You’ll need to ask about: 

• Access controls and authentication protocols 

• Regular security audits and penetration testing 

• Breach notification processes 

Simple Explanation: You need to know who has access to your financial data and how they prove their identity (authentication). Penetration testing is like hiring ethical hackers to find weaknesses before real ones do. Breach notifications ensure you’re informed quickly if something goes wrong. These measures are essential if the AI tool integrates with your financial systems. Access controls refer to the ability to restrict data based on roles or departments.

6] What is the total cost and ROI?

Beyond licensing fees, you’ll want to evaluate the vendor’s implementation, training, integration, and change management costs to ensure you can accurately calculate the ROI.

7] How easily can this integrate with our existing systems?

Ensuring compatibility with your ERP, accounting software, and reporting tools is critical to understanding the complexity and cost of any potential implementation and integration. You’ll want to ask whether the AI tool has APIs or pre-built connectors, and whether IT or third-party provider involvement is needed for integration. 

Simple Explanation: Integration means how well the new AI tool "talks to" your current systems. An API (Application Programming Interface) is like a bridge that allows two systems to exchange data. Pre-built connectors mean faster setup. The easier the integration, the smoother your implementation. Your IT team may not have the skills required to build custom connectors if that is a requirement.

8] Can we test and validate before full deployment?

Look for tools that offer sandbox environments or pilot programs. This allows your finance team to assess usability, accuracy, and relevance before a full rollout. 

Simple Explanation: A sandbox is a safe testing area where you can try out the tool without risking your actual financial data.

9] What support and training are provided?

AI is not a simple plug-and-play solution so you’ll need to ensure your team will be supported with onboarding, training, and ongoing assistance. Ask for documentation, access to account managers, and timelines for support response.

10] How will the vendor evolve the tool over time?

AI capabilities are developing rapidly. You’ll need to ask about the product roadmap, how updates are managed, and how clients are informed of changes. 

Simple Explanation: A product roadmap shows the vendor's plans for future updates and features. You'll want to know how often the tool improves and whether those updates disrupt your workflow. Transparency about future development helps you plan ahead.  

Final Thoughts 

AI presents powerful opportunities for finance teams—but only when deployed thoughtfully and securely. By starting with these critical questions, you can ensure that you are on track to selecting tools that align with your business goals, protect your sensitive data, and deliver real, measurable value to your team and your business.