People & Leadership

How CFOs can get people on board with AI

AI is everywhere, but finance adoption often lags behind. The challenge? Adoption. Here's how CFOs can get C-suites, teams, and customers on board.

Artificial intelligence (AI) is everywhere. But adoption isn’t happening at the same rate for every use case.

For example, many IT functions have already been automated with intelligent tools and natural language processing (NLP) models are making it easier for users to find answers to technology questions.

But when it comes to finance, some businesses are often more reticent to leverage AI tools, often citing concerns around security, accuracy, and adoption.

In this article, we’ll offer actionable advice for you, as a finance leader, to help get C-suites, teams, customers, and other stakeholders on board with AI.

Here’s what we’ll cover

What is AI for finance?

How AI can benefit finance functions

Securing AI buy-in: Practical talking points by stakeholder segment

Final thoughts on AI for finance teams

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What is AI for finance?

AI for finance uses machine learning (ML) algorithms to power AI functions. These functions can help improve financial decision-making or streamline operations.

Tools might be used to predict market behaviors and reduce investment risk.

They can also help automate labor-intensive processes such as data entry.

At its most basic, artificial intelligence uses sets of machine learning algorithms to complete specific functions.

Where AI tools differ from typical IT processes is their ability to ‘learn’.

This is accomplished by providing AI tools with sets of rules and sets of data and then training these tools on how to answer specific questions.

Once AI tools can reliably answer questions using training data, they’re given access to larger data sets.

By applying what they’ve learned from training exercises and combining it with new data sets, AI solutions can effectively evolve over time.

From a finance perspective, this might take the form of a budget recommendation tool.

During training, the tool is given access to historic and current budget data along with a set of controlled market variables.

It is then asked to create a budget for conditions A, B, and C.

Once outputs are accurate and consistent, the tool is given access to larger datasets and asked to interpret them using trained budget rules.

While this new data may not align with the data provided, the processes ‘learned’ by the solution allow it to reach independent conclusions that can then be reviewed by staff for accuracy and viability.

Read more: The 22 best generative AI prompts for accountants

How AI can benefit finance functions

AI tools offer several benefits for finance functions, including:

Improved forecasting

Market and demand forecasting are challenging tasks. Multiple factors influence both markets and demands daily.

For example, issues with materials sourcing or logistics can impact market conditions. While changing consumer preferences and shared online experiences can cause rapid demand shifts.

As a result, many companies still rely on traditional forecasting techniques that involve some combination of historical data and personal experience.

AI helps address these forecasting issues by collecting, analyzing, and comparing data from multiple sources.

Consider consumer demand.

What if demand is high and data from the previous three months suggests a continued increase? There may be pressure on finance teams to approve large inventory investments.

AI may discover data that suggests a demand peak in two months, followed by a steady decline. AI can access larger data sets and has the ability to analyze these sets without human bias.

As a result, CFOs are better equipped to create new budgets.

Reduced error rates

Manual data entry leads to errors. It makes sense: human beings simply aren’t designed to continually enter and check data.

According to research data, humans entering data into spreadsheets have an error rate of approximately 650 per 10,000 entries, or 6.5%.

While this number can be reduced with the addition of consistency checks, this creates its own problem: more people are assigned the task of data entry when they could be working on something else.

AI tools can help address this error issue.

While these tools aren’t foolproof—their ability to successfully capture and enter data depends on how they are programmed and trained—they don’t suffer from human issues such as lack of attention, tiredness, or the demands of other tasks.

For finance teams, reduced error rates are especially helpful, since even a small mistake could end up costing $10,000, $100,000, or more depending on its impact.

Expanded visibility

The more teams know about past, current, and future finance conditions, the better.

The siloed nature of finance data, however, often makes it difficult for companies to see the big picture.

It’s a common occurrence: payroll, HR, accounting, development, and IT teams may each use their own instance of financial software, with each instance effectively existing in a walled garden.

While it’s possible to pull out and compare data, it requires significant effort on the part of teams and team leaders.

In addition, the time required to find and compare this data reduces its relevance—the longer it takes to get good data, the lower its value in decision-making.

Equipped with the right permissions and rules, AI tools can search multiple databases simultaneously to provide expanded visibility for finance teams.

This provides a more holistic view of business finance, in turn helping CFOs make better decisions.

Increased performance

AI can also help improve the performance of finance teams.

AI improves the efficiency of data entry and has the ability to quickly and easily find the information required.

Consider the rise of generative AI (GenAI) tools such as ChatGPT.

Public-facing versions of these tools are ideal for general market questions. Secure versions are now available for businesses to purchase and use.

These tools can be customized to securely search specific dataset.

This makes it possible for teams to ask complicated finance questions in plain language.

The result? Useful output with minimal effort.

For example, using a generative AI-powered assistant that tackles your to-do list, automates tasks, and recommends ways to help you make savings and drive improvements would make your finance team more efficient.

Worth noting? Better questions provide better answers. Improving these questions is a process known as prompt engineering.

Put simply, asking questions that include specific instructions and desired outcomes can improve AI output. This makes it easier for teams to find actionable data.

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Securing AI buy-in: Practical talking points by stakeholder segment

According to data from The Economist, 46% of financial firms believe that incorporating AI into their business process and products will help them better achieve business priorities.

And this isn’t just talk—85% of these companies have a clear strategy for implementing AI.

The challenge? No strategy is foolproof.

Even best-laid plans can get off-track if enterprises encounter unexpected roadblocks.

For example, technology teams might draft a plan for AI deployment, have it approved by C-suite executives, and secure the budget they need—only for the plan to go off-track when end users opt for familiar solutions over new AI tools.

While these tools may be demonstrably better, familiar functions have a powerful draw. This makes it an uphill battle to ensure AI adoption.

And end-users aren’t the only potential pain point in AI adoption. C-suite executives and finance teams also play a role in successful AI adoption.

To help navigate the road from AI discussions to effective deployment, we’ve put together a set of practical talking points for each stakeholder segment.

C-suite executives

C-suite executive buy-in is the first step in any AI project.

Without executive backing, finance teams won’t have the budget or time they need to effectively find, deploy, and integrate AI tools.

Securing this buy-in, however, is often difficult. This is because C-suite executives typically think in terms of business outcomes: “How much will a new technology cost, and what’s the potential impact on the business’s bottom line?”

If CFOs can’t answer these questions clearly, other C-suite executives may defer investment or outright refuse.

The result? Getting C-suite members on board means focusing on business outcomes, not technical details.

Don’t say

“New tool X will allow teams to easily search multiple databases and reduce data entry error rates. In addition, generative functions will make it possible to use natural language queries.”

While nothing about this statement is inaccurate, it offers little value for C-suite executives. This is because it focuses on what the technology can do, not what it can do for the business.

Instead, try

“Using new tool X, we may be able to save Y amount of money over Z amount of time. In addition, new tool X will reduce the overhead required for manual data entry, in turn saving time.”

This is a better approach because it puts AI in the context C-suites want to hear: money and time.

Finance teams

Finance teams are often the first line of end-users for AI.

New tools can help reduce the amount of effort required to do their jobs. It also makes it easier for teams to find the data they need.

The challenge? Effective adoption means convincing teams that new tools are worth making the move from familiar frameworks.

Don’t say

“New tool X is so much better than our current system. As a result, system Y will be retired after six months and all staff will be required to use the new tool.”

Objectively, the new tool is better than what you have—after all, that’s why you spent the money. But users familiar with systems are reluctant to give them up.

Sure, the new tool will enable them to do things they couldn’t do before.

However, they have to learn how it works. How it works may be very different from what they’re familiar with.

Instead, try

“New tool X includes features, A, B, and C that current tool Y doesn’t have. Over the next few weeks, we’ll give you access to new tool X so you can try it out, see how it works, and offer any feedback.”

This approach includes teams in the discussion and recognizes that change isn’t immediate.


Customers are often interested in AI but concerned about issues such as privacy and security.

As a result, it’s important to reassure them that their data is safe and will be used only for specific purposes.

Don’t say

“We are implementing tool X as of <date>. It will provide a better experience and more opportunities for success.”

This statement can make customers feel uncomfortable since they don’t know exactly how the tool works or what it does.

Best case scenario? They stick around but aren’t quite as committed to the company.

Worst case? They take their business somewhere else.

Instead, try

“As of <date>, we are beginning our rollout of tool X. This AI solution will help us better serve your needs with improved market forecasting and analysis, in addition to enhanced security. To learn more, see our website.”

This approach focuses on the customer: their concerns, their needs, and the potential benefits for their experience.

By giving being clear about what’s happening and giving them access to relevant data, you can help bring your customers on board.

Final thoughts on AI for finance teams

AI finance tools offer substantive benefits for businesses. But effective adoption is about more than the tool itself.

For CFOs to bring the C-suite, finance teams, and customers on board, they need to consider their audience and craft messaging that addresses concerns, highlights benefits, and makes people part of the process.