AI in Finance: What’s Working, What Isn’t, and What Still Requires Human Judgement

AI in Finance: What’s Working, What Isn’t, and What Still Requires Human Judgement

Introduction

Artificial intelligence is increasingly positioned as a major shift in how finance teams operate. In practice, its impact is more targeted.

AI is already being used across a number of finance processes, particularly in areas involving large volumes of data and repetitive tasks. At the same time, many core aspects of finance remain dependent on human judgement, interpretation, and communication.

The real shift is not that AI is replacing finance roles, but that it is changing where time is spent — reducing manual workload while increasing the importance of insight, interpretation, and decision support.

Across the market, adoption has accelerated rapidly, but results remain mixed. While many organisations are embedding AI into finance processes, the value delivered often depends on data quality, integration, and how effectively teams adapt their ways of working.

In many cases, the challenge is not access to AI, but understanding how to apply it effectively within existing finance processes and broader finance transformation initiatives. The organisations seeing the greatest value are not necessarily those using the most AI, but those applying it with the clearest operational purpose.

Where AI Is Already Being Used in Finance

AI is most effective in areas where processes are:

• repetitive
• data-heavy
• time-sensitive

In finance teams, this typically includes:

Reporting and Data Consolidation

AI tools are increasingly used to automate the collection and consolidation of financial data, reducing time spent on manual processes and improving reporting speed and accuracy across finance operations.

Forecasting Support

AI can assist in identifying patterns and trends within historical data, supporting more dynamic forecasting and modelling within financial planning and analysis (FP&A) teams.

Transaction Monitoring and Anomaly Detection

In areas such as accounts payable and expense management, AI can help identify inconsistencies or unusual patterns across large datasets.

Process Automation

Routine activities such as reconciliations, data entry, and standard reporting workflows are becoming more streamlined through automation, allowing finance teams to focus more on analysis and business partnering.

How AI Is Being Applied Across the Finance Function

The impact of AI varies across different areas of the finance function, depending on the nature of the work.

• Financial Controllers are seeing increased automation in reporting, reconciliations, and financial control processes
• FP&A teams are using AI to enhance forecasting, scenario modelling, and performance analysis
• Finance Analysts benefit from faster data processing and improved access to real-time insights
• Finance Directors are leveraging more timely and accurate information to monitor performance and support strategic planning
• Chief Financial Officers are increasingly focused on using outputs to inform decision-making, capital allocation, and long-term business strategy

This shift is not limited to senior leadership. It is affecting the wider finance function, from analysts and reporting teams through to Financial Controllers, Finance Directors and CFOs, with each role evolving alongside the technology supporting it.

What AI Improves — and What It Doesn’t

AI is improving efficiency across finance functions, but its impact is not uniform.

What AI Improves

• speed of data processing
• accuracy in repetitive tasks
• ability to analyse large volumes of data

What AI Does Not Replace

• commercial judgement
• stakeholder communication
• decision-making in uncertain or complex environments

Finance roles still require interpretation. Data alone does not provide direction without context, particularly in environments where business performance, risk, and strategy are closely interconnected.

Why AI Isn’t Delivering Consistent Value Across Finance Teams Yet

Despite rapid adoption, the effectiveness of AI in finance varies significantly between organisations.

Data Quality

AI models rely on clean, structured data. Many organisations continue to face challenges in maintaining consistent and reliable datasets across systems.

System Integration

Integrating AI tools into existing finance systems can be complex and resource-intensive, particularly in more established organisations with legacy infrastructure.

Skills and Capability Gaps

While tools are advancing quickly, many finance teams are still developing the skills required to fully utilise AI outputs effectively. The ability to interpret outputs, challenge assumptions, and apply insight commercially remains critical.

Lack of Clear Application Strategy

In some cases, AI has been introduced without a clear view of where it adds the most value. Without alignment to finance processes and business objectives, adoption can remain surface-level rather than transformative.

Oversight and Accountability

AI-generated outputs require validation. In regulated or high-risk environments, finance professionals remain responsible for accuracy, governance, and decision-making.

How Finance Roles Are Shifting — From Reporting to Decision Support

The impact of AI is less about replacing roles and more about redefining them.

There is decreasing emphasis on:

• manual processing
• repetitive reporting
• data preparation

And increasing emphasis on:

• interpreting outputs
• supporting business decisions
• business partnering with non-finance stakeholders

Finance is moving further away from data production and closer to decision support, reflecting the broader shift in how finance functions operate within modern organisations.

For a broader view on how finance roles are evolving, see our article on What Businesses Now Expect from Strategic Finance Leadership.

What This Means for Finance Professionals

As AI becomes more embedded within finance teams, expectations are evolving across all areas of the finance function.

There is increasing value placed on:

• commercial awareness
• the ability to translate data into insight
• confidence in decision-making environments
• communication with non-finance stakeholders

Professionals who can combine technical understanding with business context are becoming more central to how finance teams operate, particularly in environments where finance plays a key role in shaping business strategy and performance.

This shift is also influencing hiring decisions, with businesses placing greater emphasis on individuals who can operate commercially and work alongside evolving finance systems.

Conclusion

AI is becoming part of the finance function, but it is not redefining it entirely.

Its greatest impact lies in improving efficiency and supporting analysis, while the core value of finance roles — judgement, interpretation, and decision support — remains unchanged.

As adoption continues to develop, the most effective finance teams will be those that combine technological capability with strong commercial understanding across every level of the function.

For a broader overview of finance hiring and leadership recruitment, explore our Finance Recruitment Agency London services.

As finance teams continue to evolve, having the right capability in place becomes increasingly important. Learn more about Harper May and how our specialist finance recruitment team supports businesses hiring finance talent across London and the UK for modern, data-driven environments.