The CFO’s Dilemma: Managing Change in AI Implementation for Finance

Change management isn’t easy, especially when you’re dealing with company-wide improvements such as AI implementation. Despite this, many organisations in the United Kingdom are now exploring how they can use AI to make finance and accounting processes more efficient.  

Related: Accounting Automation: A Comprehensive Guide for Modern Firms 

The real dilemma for a CFO is how they can introduce this innovation to their finance team without disrupting operations, overwhelming the team, or risking compliance issues.  

CFOs understand that this shift is a bit challenging because aside from making tweaks on their processes, their finance and accounting teams also have to adjust to new systems, workflows, and expectations. This also calls for new skills, training, and ongoing support to help the team understand how AI fits into their daily responsibilities. 

The article covers: 

a. Understanding AI implementation in finance 

b. What are the key benefits of implementing AI in finance?   

c. Challenges in AI implementation for finance 

d. Regulatory considerations and compliance  

e. Key compliance consideration for finance Teams   

f. How CFOs can lead an effective AI implementation 

Understanding AI Implementation in Finance

In finance, AI implementation involves a strategic change that goes beyond simply adopting AI-powered software. It affects processes, team responsibilities, and decision-making.  

Finance teams can use AI tools to automate repetitive tasks, detect anomalies, and provide support, all of which can free up their time and focus to higher-level tasks that require their judgement and insight. 

Below are some of the typical applications of AI in Finance: 

a. Accounts payable and receivable automation

Using AI in finance allows accountants to automatically match invoices, flag discrepancies, and reduce manual effort that is mostly prone to errors. 

b. Budgeting and forecasting

AI can analyse historical trends to predict cash flow revenues, and expenses more accurately, helping the people around it to have a better insight. 

c. Anomaly detection and risk management

Finance teams can also use AI tools to detect unusual transactions, inconsistencies, or patterns that may signal risks, errors, or possible fraud. 

d. Financial reporting 

Month-end processes and real-time reporting are tedious tasks that require precision. AI tools can organise large sets of financial data, highlight inconsistencies, and generate reports in real-time, giving finance teams more time to review insights rather than manually preparing them. 

What Are the Key Benefits of Implementing AI in Finance? 

After understanding how AI is being implemented in finance, the next question is: what benefits can it actually bring to the organisation? For many CFOs, the value of AI becomes clearer when they see how it improves efficiency, accuracy, decision-making, and overall financial visibility.  

Below are some of the key advantages that AI can offer to modern finance teams. 

1. Increased Efficiency and Cost Reduction

AI can take over routine, time-consuming tasks, such as invoice matching, data entry, reconciliation, and report preparation. 

Having these tasks offloaded from the team, your finance professionals can focus on higher-value work like analysis, strategy, and supporting business decisions. This not only speeds up processes but can also help reduce operational costs over time. 

2. Enhanced Accuracy and Better Risk Management

Another way AI tools can be beneficial is that they can help you improve accuracy and reduce risk in how your team processes daily tasks. With AI supporting activities like data entry, reconciliations, or financial reporting, there is far less room for manual mistakes that often occur in these areas. 

3. Improved Decision-Making Capabilities

On top of that, AI can help you analyse historical data, spot trends, and provide predictive insights. This gives you a clearer picture of cash flow, revenue patterns, and potential risks. 

Below are some of the examples, AI can help your finance teams: 

• Forecast cash positions more accurately 

• Model different financial scenarios before making decisions 

• Identify spending patterns that may signal inefficiencies 

• Provide real-time dashboards that support faster, evidence-based decisions 

With these insights, your finance function can move from reactive reporting to forward-looking planning, making it easier to guide the organisation in the right direction. 

4. Scalability and Flexibility for Growing Operations

As organisations grows, transaction volumes increase. To make it less stressful to your team, you must use tools such as AI to lighten the load, handle growing workloads, process large amounts of data quickly, and support new operational demands without putting additional strain on your finance staff. 

5. Better Use of Workforce Skills

Lastly, since routine tasks are mostly handled by AI, your finance team can now focus on work that requires their skills and judgment. So, instead of spending hours on data entry, reconciliations, or checking spreadsheets, they can shift their time toward analysis, strategy, and supporting leadership decisions. This not only improves productivity but also boosts morale because your team is doing more meaningful, high-value work. 

Challenges in AI Implementation for Finance 

Even with all the benefits of AI, implementing it in finance is not without its challenges. Many organisations in the UK and CFOs often face practical issues that can slow down or complicate the transition. 

Here are some of the most common challenges finance teams encounter when introducing AI into their workflows: 

a. Data Quality Issues

One of the biggest hurdles in AI implementation is data quality. AI tools rely on clean, accurate, and well-structured data to work properly. If the data fed into AI models incomplete or inaccurate, the output becomes unreliable. This can lead to errors in reports, incorrect insights, or misleading forecasts, issues that can create more problems instead of solving them. 

To address this, finance chiefs should begin with a data readiness assessment within the organisation to help identify gaps, inconsistencies, and areas where data needs to be cleaned or standardised.  

By improving the quality of data early on, the finance team can set a strong foundation that ensures AI produces reliable, useful, and actionable insights. 

b. Integration with Existing Systems

Integrating tools with their existing systems can be another challenge when implementing AI. 

Legacy software like older ERP platforms, accounting systems, or core financial databases is usually not built to support modern AI capabilities.  

These systems may lack the flexibility, APIs, or real-time data connectivity needed for AI tools to function properly. And when this happens, finance teams may struggle with data silos, delays, or incomplete information flowing between systems. 

In fact, according to KPMG’s AI in Finance report, many UK companies identify system compatibility and integration as a common barrier to successful AI adoption. These technical limitations make it harder for CFOs to get reliable, real-time insights from AI tools. 

To overcome this challenge, organisations may need to explore options such as: 

• Using middleware or APIs that bridge old and new systems 

• Upgrading outdated systems or parts of the IT infrastructure to support real-time data flows 

• Running AI tools alongside legacy platforms before full integration 

Doing these steps will help you ensure that AI technology works properly with your existing finance systems and reduces the risk of disruptions during daily operations. 

c. Staff Readiness and Adoption

One of the biggest challenges for finance leaders is making sure their team is ready to use AI. Afterall, even the best AI tools in the market won’t deliver results if the people using them are unsure, overwhelmed by the change or concerned especially if the team worries that automation might replace their roles. 

A good practice is to introduce AI within the team to allow them to adjust to new workflows, dashboards, and decision-making strategies. 

One way to do this is to focus on clear communication and upskilling. When employees understand how AI supports their work, reduces manual tasks, and allows them to take on more meaningful responsibilities, they are far more likely to adopt it confidently. 

To help the team adapt, here are some practical steps you can take: 

1. Provide training sessions and hands-on workshops

Give your team time to learn the new tools before they become part of their daily routine. Having a short, practical workshops can help them understand how AI works and how it will support their tasks. 

2. Communicate the purpose and benefits clearly

People are more open to change when they understand why it’s happening. As their finance leader, it is only right to explain how AI reduces routine work, improves accuracy, and helps the team focus on higher-value tasks instead of manual ones. 

3. Encourage questions and feedback

Create a space where employees can voice their concerns or ask for clarification. This helps identify gaps in understanding and prevents frustration as the new system rolls out. 

4. Highlight how AI enhances roles,not replaces them

Reassure the team that AI is designed to support their work, not remove their responsibilities. When people see that the technology helps them make better decisions and reduces repetitive tasks, adoption becomes much easier to implement within the organisation. 

By taking these steps, you can be sure that your team will have a better understanding and overview on why they must embrace AI in their current processes. 

Regulatory Considerations and Compliance

On top of operational and technical challenges, you must also think about regulatory compliance when implementing AI in the finance processes.  

This is especially important for organisations that operate in or work with partners in the European Union, where the EU AI Act is now in effect and sets the sets world’s first comprehensive legal framework for artificial intelligence. 

What is EU AI Act? 

This is a regulation imposed to ensure that artificial intelligence system used in the EU are safe, transparent, and aligned with fundamental rights. It introduces a risk-based approach where AI systems are categorised according to the level of risk they pose from minimal to high risk with corresponding obligations for providers and users. 

Why it Matters for CFOs? 

As finance function handles mostly sensitive personal and financial data, it is understandable why compliance is such an important part of AI adoption. AI systems used in finance must follow strict rules on transparency, data protection, and responsible decision-making especially when these tools can directly affect reporting accuracy, audit trails, and stakeholder trust. 

Read: Accounting Process Improvement: How to Reduce Inefficiencies 

Key Compliance Consideration for Finance Teams 

When using AI in finance, staying compliant isn’t just a legal requirement, it’s part of protecting your organisation, your clients, and your team. Below are some of the important areas CFOs and finance teams need to pay attention to: 

1. Understanding Your AI Risk Level

Under the EU AI Act, AI tools are usually grouped by risk. This means not every system your team uses will fall under the same category. For example, tools that help with credit scoring, fraud detection, or identity verification are usually considered high-risk, meaning they come with stricter rules.  

This is why knowing where your AI tools fall in this framework helps you understand the level of oversight needed within your organisation. 

2. Transparency and Clear Documentation

Transparency is everything, especially when you’re dealing with sensitive financial information. Finance teams should have a clear understanding of how an AI system works, what data it uses, and how it arrives at certain outputs.  

This is important not only for day-to-day decision-making but also when auditors, regulators, or stakeholders ask for explanations. Having proper documentation in place also makes it easier to trace decisions, review potential issues, and ensure the AI tool is being used responsibly. 

3. Data protection and privacy

With a large amount of personal, confidential, and financial data that finance department are working on, protecting that information needs to be a top priority. This is why, any AI tool used in finance must follow strict data protection standards to ensure sensitive records are not exposed, misused, or accessed without permission.  

This includes having proper controls in place for data storage, encryption, access levels, and retention. For CFOs, this also means ensuring that any third-party AI vendor complies with GDPR and other applicable privacy regulations. 

4. Human oversight is still required

AI can support decision making, but it should never replace human judgement in finance. In fact under the EU AI Act, AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.  

For finance teams, this means reviewing AI-generated insights, validating the numbers, and making the final call on anything that affects compliance, reporting, or risk. Doing this also shows clear accountability, ensuring that every AI-supported action can be traced back to a responsible person who can verify the accuracy and integrity of the output. 

This layer of human oversight not only strengthens compliance but also builds trust across the organisation, especially when integrating new technology into critical financial workflows. 

5. Vendor and Third-Party Compliance

Lastly, if you’re using AI tools from external providers, it’s not enough to assume they are compliant. This is why finance chiefs must always do their due diligence and check whether vendor systems meet the requirements of the EU AI Act, provide proper documentation, and have clear policies around data usage, model training, and data storage. 

Read Next: The Importance of AI Literacy in the Finance and Accounting Industry 

How CFOs Can Lead an Effective AI Implementation

Now that we’ve covered the benefits, challenges, and compliance requirements, the next question is: How can finance leaders actually make AI work in their organisation? 

Below are some of the practical steps CFOs can follow to guide the transition in a way that feels organised and manageable for the entire finance team. 

a. Start with a clear AI Roadmap 

Instead of adopting AI tools all at once, finance chiefs should begin by identifying where AI can create the highest value within the process. This often includes areas like reconciliations, reporting, forecasting, or risk detection. 

A good roadmap should outline: 

• Which processes will be automated first 

• What resources are needed (data, systems, people) 

• Expected timelines 

• Success measures and checkpoints 
  

This ensures the team knows exactly what to expect and avoids any confusion during rollout. 

b. Prioritise high-impact processes first 

AI adoption becomes much easier when teams see early wins. One way to do this is to begin with tasks that deliver quick, measurable improvements such as reducing manual data entry or shortening month-end deadline. 

These early gains help build confidence across the team and show that AI is not a threat, but a tool that supports their work. 

c. Prepare the Team Through Ongoing Training 

Training shouldn’t happen once and then stop. Instead, it should be a continuous part of the implementation process so the team can adapt confidently as new tools and workflows are introduced. 

CFOs can build stronger team readiness by: 

• Providing regular, easy-to-follow training sessions 

• Allowing hands-on practice with new AI features 

• Offering clear guidance on updated processes 

• Addressing concerns early to build trust and comfort 
  

When employees understand the technology and feel supported, they become more confident and engaged, which leads to better adoption across the finance function. 

d. Strengthen Data Foundations Before Moving Forward 

AI is only as good as the data you feed it. That’s why, as a CFO, you often need to invest time in improving data accuracy, consistency, and structure to ensure the system delivers reliable insights. 

This may include:  

• Cleaning financial data  

• Organising historical files  

• Setting naming or formatting standards  

• Removing duplicate or outdated records 

By doing this, you are strengthening the foundation of your AI system. 

e. Consider External Support When Needed 

Lastly, AI implementation doesn't have to be done entirely in-house. You still have the option to turn to external partners to provide support for back-office tasks while the finance team focuses on strategy and decision-making. 

Outsourcing can help with areas like: 

• Data preparation and cleansing 

• Routine accounting and reconciliations 

• Compliance checks and documentation 

• Reporting and analytics 

By working with experienced outsourcing partners, you can offload some of the workload from your internal team, maintain operational efficiency during the transition, and ensure AI tools are applied effectively. This allows your finance professionals to focus on higher-value activities that drive strategy and insight. 

THE BOTTOM LINE 

Without proper preparation, even the most advanced tools can create confusion and slow down the very processes they are meant to improve. 

However, if you take the time to plan, train the team, and prepare your data and systems, you can get the most value from AI. 

Ready to Future-Proof Your Finance Operations? 

Are you looking to integrate AI into your finance processes? Partnering with a finance and accounting outsourcing provider that stays up-to-date with the latest AI and accounting technology trends may just be what you need. 

If you want to learn more, contact us today, or visit our website to know more about our scalable solutions tailored to your company’s needs. You may also download our The Rising Frontier: Harnessing the Power of Business Analytics whitepaper today and learn how we can be your reliable outsourcing partner.