A CFO's Guide to Protecting your Company from the Dangers of AI

As we continue to integrate artificial intelligence (AI) into several aspects of our daily lives, the benefits it presents are unparalleled – cutting the time it takes for us to perform routine tasks, giving us intelligent insights to help with decision-making and helping optimize how we work and/or play.   

With these numerous benefits, it is only natural for companies to find a way to integrate them into their own company’s processes.   

Here’s what we’ll cover: 

1. Is It Safe to Use AI in Business?
2. Risk Associated in Integrating AI
3. Real-Life Case Studies: What CFOs can learn

4. Strategies for Mitigating AI Risks

5. Questions CFOs Should Ask Before Implementing AI

Is It Safe to Use AI in Business? 

A study by PYMNTS Intelligence revealed that more than 61% of small businesses and start-up companies in the US openly use AI for automating routine tasks. This includes bookkeeping and documentation to improve their company’s efficiency and to reallocate their people’s efforts towards other value-adding tasks.  

However, the use of AI comes with reservations for some: Is it safe? Aren't there risks? The simple answer? Yes, using AI for company processes will always present a certain amount of risk.   

However, it should not completely discourage you from using AI. You should instead adopt an adaptive mindset and know what to look out for and what to do to be able to fully maximize the use of AI along with reducing any risks.   

From data privacy concerns to ethical dilemmas and operational challenges, the potential dangers of AI can impact a company’s financial health, reputation, and long-term viability. With this, our comprehensive guide will help CFOs identify these risks and implement strategies to mitigate them effectively.  

Read: AI and Accounting Ethics: Ensuring Transparency and Accountability  

What Are the Risks that Come with Integrating AI? 

To better understand the risks associated with AI, here are some key challenges to consider: 

1. Data Privacy and Security  

Remember, the winning feature of AI is its intelligence – its ability to provide simulated answers and information that replicates human intelligence.  

To do this, AI systems use extensive datasets to learn and make accurate predictions, meaning, our information is used for their training.   

However, this dependency on data poses severe privacy and security risks. Inadequate data protection can lead to breaches, exposing sensitive information and resulting in substantial financial losses as well as ruining trust between your clients and your company. Failing to address data protection risks can erode client trust and compromise regulatory compliance. 

2. Subjective Results  

While AI has the ability to replicate human insights and intelligence, it is important to take into account its biggest limitations – its knowledge relies solely on the information that you feed it.  

Meaning, if you feed it biased and subjective training data, it can lead to limited and even discriminatory outcomes. These kinds of outcomes can have a detrimental effect if you use AI for front-end tasks such as client monitoring and communications. A as bias in AI system outputs can undermine business credibility and lead to operational inefficiencies. 
  

3. Operational Disruptions   

AI integration in the long run proves to be a helpful tool in increasing your company’s efficiency and optimizing your operational processes.  

However, these outcomes can only be achieved if you are able to integrate AI use smoothly. If the integration is sudden, unprepared or lacks the necessary resources to support, it may cause disruptions that halt your operations – affecting your service delivery and that can result in your company incurring financial losses.  
 

With this, it is the role of your company’s management team – including you, to make sure that before you integrate any AI software into your operational processes, you conduct thorough testing and validation before deployment.   

Real-Life Case Studies: What CFOs can learn 

The following are real-life case studies that illustrate the potential pitfalls of AI implementation and offer valuable lessons for CFOs aiming to safeguard their organizations: 

1. A Tech Giant’s AI Chatbot Went Rogue

In 2016, a leading tech company launched an AI chatbot called Tay on Twitter. The idea was for Tay to learn from users and have casual conversations. But within hours, people fed it harmful messages, and Tay started posting offensive and controversial content. The next day the company had to shut it down. 

Lesson: AI learns from the data it’s given. Without proper monitoring and filters, it can go off track fast. With this, CFOs need to ensure that their organizations have a clear understanding of what data is being used to train AI systems and how it’s being used. They should also have processes in place for monitoring and filtering out harmful content. 

2. An E-commerce Hiring Bias

An e-commerce company uses an AI-powered software to help with recruiting potential candidates for their job openings. However, the system became biased as it preferred men over women because it learned from past data worth of résumés submitted to the company — most of them from men. As a result, the system filters out woman in its database. 

Lesson: AI can inherit biases from historical data. As a CFO, you should make sure that the organization is using AI models with transparency and fairness to avoid legal and reputational risks in the long run. 

3. An Airline Company Ordered to Pay for Chatbot’s Misinformation

In February 2024, a major airline was ordered to compensate a passenger after its chatbot provided incorrect information. 

Following his grandmother’s passing in November 2023, Jake Moffatt asked the airline’s virtual assistant about bereavement fares. The chatbot told him he could book a regular-priced ticket from Vancouver to Toronto and request a refund within 90 days. Based on this, he purchased a one-way ticket for CA$794.98 and a return flight for CA$845.38. 

When Moffatt later applied for the refund, the airline denied his request, stating bereavement discounts don’t apply after purchase. He then filed a complaint, arguing that he was misled. 

The airline claimed it wasn’t responsible for its chatbot’s errors, but one tribunal member disagreed. They then ruled that the company failed to ensure its AI assistant provided accurate information and ordered it to pay Moffatt CA$812.02, including CA$650.88 in damages. 

Lesson: AI chatbots are an extension of a company’s customer service. If they give wrong information, the company is still liable. With this, CFOs should ensure that all tools, including AI, are carefully managed to prevent misinformation that can lead to legal and financial consequences. As the saying goes, customer trust is a company’s most valuable asset—once lost, it’s difficult to regain. 

Strategies for Mitigating AI Risks 

Now that you’re aware of the risks, here are some key strategies that CFOs and business leaders can do to minimize AI challenges within the organization: 

1. Develop a clear understanding of AI technology and its limitations.  

One way to minimize AI risk is to make sure your organization have better understanding about its uses and limitation, as it will help you make informed decisions about which processes to automate and which ones should remain manual.  

For example, if your organization is considering using AI to automate customer service, you should keep in mind that AI can only answer programmed questions.  

It won’t provide the same level of personalized responses as a human representative. By knowing these limitations, you can set realistic expectations and avoid potential setbacks.  

2. Prepare contingency plans for AI disruption

The best way to ensure successful AI-driven integration is to establish contingency plans and ensure human intervention remains consistent.  

Keep in mind that the goal of AI integration is not to replace humans with machines but to provide better tools and information that make employees more effective.  

With a solid backup plan, you can minimize system disruptions and ensure AI delivers real value to your organization  

3. Create a culture of continuous learning 

Keeping up with AI advancements helps you and your team stay ahead of industry trends. By providing access to training programs, workshops, and conferences, you can ensure your employees remain knowledgeable about AI’s role in your business. 

You can also encourage AI exploration by sharing weekly updates on your internal website. This keeps your team informed about new technologies and how they can be applied within your organization. 

Not only does this strengthen your leadership, but it also gives you an advantage in nurturing your team. 

4. Implement Strict Data Security Policies

Lastly, if your organization is keen on integrating any new technology such as AI, then be sure to have a strict data security policy in place from where data is stored, accessed, and shared.  

You can also consider having a dedicated IT team in your organization to make sure your AI systems comply with security best practices and regulatory requirements.  

This way, it will help protect your organization from any potential data breaches and ensure that the AI integration is done in a secure manner. 

Key Questions CFOs Should Ask Before Implementing AI 

Now that we’ve covered AI risks, real-life case studies, and strategies to mitigate potential challenges, the next step is making sure AI adoption is a well-thought-out decision.   

Before integrating AI into your finance operations, finance chiefs should ask themselves these important questions to ensure it aligns well with their company’s goals and risk management framework. 

1. What business problem are we solving with AI? 

Before anything else, integrating AI in your processes should have a clear purpose not just for the sake of keeping up with the trends.  

Are you going to use it to improve financial forecasting, detect fraud, automate repetitive accounting task or enhance decision making? By defining your needs, you can ensure AI delivers value in your organization. 

2. How do we measure AI success? 

Now that you define what your organization needs, the next part is how do you measure its success?  Will the success be measured by cost savings, faster processing time or reduced fraud cases?  

Having these key performance indicators (KPIs) early on, will help you assess AI’s impact over time. 

3. What are the potential risks and how will we address them? 

As outlined above, every AI systems has risks —whether it's data security concerns, regulatory compliance issues, or biased decision-making.  

So, as a finance chief, you should be able to identify the risks AI might bring to your company. Will it expose sensitive financial data? Can it make unfair decisions?  

Once you know the risks, you can set up protections like data security measures, compliance checks, and bias detection tools. 

4. How does AI align with our long-term financial strategy? 

While AI can bring cost savings in the long run, initial implementation can be expensive. Because of this, CFOs should be able to weigh the financial investment against the expected returns of this tool. Consider not only the cost of AI software but also training and maintenance.  

Will AI reduce manual work and cut down operational costs? Conducting a detailed cost-benefit analysis will help you determine whether AI is a smart investment for your organization. 

The Bottom Line 

In the end, we understand why there are reservations associated with using AI for your company’s processes.  

However, you must also keep in mind that having company processes that are up to date with the current technological landscape of your industry determines factors such as client trust and preference, longevity and success.   

With this, it may be best to not completely shut down the idea of AI but instead look into how you can combat the potential risks that come with AI use.  

Read Next: Advantages and Negative Impacts of AI in Accounting and Finance  

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This post was first published on 16 September 2024 and edited on 12 March 2025 for relevancy and comprehensiveness.  

Edited by: Angelica Garcia