It’s been over 18 months since I last examined the impact of Artificial Intelligence (AI) on the finance function, and whilst AI is becoming more prevalent in people’s everyday lives, its impact on business remains less obvious. With the government releasing its AI Adoption Research paper last month, now seems a great time to revisit this topic and assess progress.

2026 Artificial Intelligence research

The Government’s research suggests that the adoption of AI tools among UK SMEs is increasing, although many businesses remain at an early stage in their AI capabilities. Only around one in six UK businesses used AI tools as of mid-2025, and micro-businesses are significantly less likely to adopt them than larger organisations. For many SMEs, the most common barriers to AI transformation include limited internal expertise, uncertainty around cost and return on investment, and concerns around governance and data management.

AI presents both challenges and opportunities for senior leaders. As AI adoption grows across businesses, CFOs are increasingly expected to play a central role in evaluating where innovative technologies can increase efficiency, reduce costs and deliver the greatest value in their business models.

Within SME finance teams, this may include streamlining routine processes to reduce manual effort, such as data processing, inputting invoices and reporting. For CFOs, it’s about the speed and accuracy of financial insights to support strategic decision-making and inform future business strategy.

As adoption continues to rise, the finance department’s role is likely to evolve further. Rather than focusing primarily on manual processes, finance leaders will be expected to combine financial expertise with a clear understanding of technology, helping organisations adopt AI responsibly while maintaining appropriate controls, oversight and data integrity.

CFOs and FDs need to continue to understand and keep up to date with developments in automation, data analytics, and other AI technologies to ensure their businesses do not fall behind competitors and take advantage of these technological developments.

AI in the finance function: How far have we come?

Finance functions

The finance department often includes many routine, manual tasks that can be easily automated. From a bookkeeping perspective, AI is already transforming many routine processes that sit at the heart of the finance function, including:

• Tools that use optical character recognition can analyse invoices and receipts, extracting key data to automate processing and posting to accounting systems.
• Machine learning can also help apply the correct nominal codes to transactions based on historic patterns, improving consistency and reducing manual input.
• AI applications can review bank feed transactions, match them against recorded entries and highlight discrepancies for further review, while also supporting automated communications such as generating emails to issue or chase invoices.
• AI is increasingly used in managing and tracking receivables, helping finance teams monitor outstanding invoices, predict payment behaviour, and automate credit control processes.
• In addition, AI can analyse financial data to identify unusual patterns that may indicate potentially fraudulent activity and use historical trends to support cash flow forecasting and predictions of future trading performance.

It is the finance function’s responsibility to assess and, where suitable, embrace new technologies to maximise efficiency and improve information quality. Whether this is automating routine processes or using AI to provide a more in-depth analysis of a business’s finances and predict future outcomes, finance professionals can apply a range of tools to their daily tasks to help a business develop.

Finance professionals: The CFO/FD

The role of finance leaders in today’s companies continues to evolve, and leveraging technology is a key part of the CFO role. It is worth reiterating that AI tools can:

• Assist with financial planning and analysis.
• Increase the speed of reporting, such as forecasts and scenario planning, to assist future business growth and business strategy.
• Increase productivity and efficiency.
• Provide insights to detect risks and fraud.
• Making data-driven decisions.

AI tools can also assist with more routine activities, which often take up a significant amount of a finance professional’s working day.

Routine admin

AI can also assist Finance Directors with many of the administrative tasks that take up valuable time in a typical working day. Generative AI tools can quickly summarise lengthy reports, board papers, contracts, or regulatory documents to extract key points and decisions without reviewing every page in detail.

AI can also help draft email responses, adapting tone and wording to match an individual’s writing style, significantly reducing time spent managing a high volume of correspondence. In addition, AI tools can support note-taking during meetings, summarise discussion points and produce action lists, or help draft presentations, reports and internal communications. By streamlining these routine tasks, AI enables business leaders to spend less time on administration and more time on strategic planning, analysis and supporting wider business decision-making.

Beginning and developing your automation and AI journey

If an AI digital transformation is to succeed in any business, the business as a whole must have a clear AI strategy in place which aligns across all departments.

Implementing AI can be undertaken in phases.

Initially, focus your automation & AI journey by listing all the things departments repeat regularly and mapping your key processes to better understand your AI and automation needs. Then look for the right tools to offload or automate some of these tasks and processes.

The next stage may be to move away from more operational tasks and focus on more strategic areas where AI can support. These may be areas such as financial planning and reporting, scenario planning, risk management, or fraud detection.

AI and automation challenges

Effectively integrating AI and automation into finance teams remains challenging. The following will be challenges you will have to consider and overcome:

• Lack of understanding or trust in these technologies.
• Data quality, data silo’s and how to integrate systems.
• Skill gaps in the team around programming AI, data analytics, data science, AI technology, etc., as AI continues to develop rapidly.
• Potential overreliance on AI without an understanding/expectation of results.
• Staff fear for their future roles.
• Overcoming data bias.
• Concerns over data security, with some systems holding limited access to data, whilst others use it as part of their machine learning.
• Ethical and regulatory concerns.
• Explainability.

AI in financial institutions & accountancy

Recent UK government and regulatory reports highlight how quickly the AI shift is accelerating across the financial sector. Surveys by the Bank of England and the Financial Conduct Authority show that around 75% of financial services firms are already using AI, with a further 10% planning to adopt it within the next three years. At the same time, policymakers are increasingly focused on both the opportunities and risks associated with AI in financial decision-making, including data governance, transparency and systemic risk.

Accountancy firms are also increasingly embracing AI to enhance the services they provide to clients and to assist with auditing processes where large volumes of data/transactions need to be analysed.

Developments in this sector continue apace, and with ever-increasing abilities of software to supplement a business’s finance team, it’s certainly time to review your current use of AI and how it can be further harnessed to aid your business.

Summary

AI models and automation are rapidly advancing, in turn transforming how finance functions operate. Finance teams play a key role in adopting new technologies and leveraging AI and automation now and in the future.

Digital technologies in finance have the capacity to bring significant benefits to businesses, including the ability to process vast amounts of data, automate and speed up processes and tasks, improve productivity, reduce human error, reduce risk, and enable easier and more accurate financial forecasting and modelling.

AI systems can deliver these benefits to both the finance function and the business as a whole. Still, businesses must remember that, in its current form, AI will not fully replace human intelligence, as it still has limitations, including bias and the lack of people’s critical thinking and decision-making capabilities.

Key stakeholders and teams will need to adapt, learn new skills, and shift their day-to-day focus from mundane, repetitive tasks to more strategic work. Artificial intelligence is helping CFOs and FDs develop those strategic insights, and by harnessing real-time insights and predictive analytics, finance professionals and their teams will play an even more central role in driving business growth.

Glossary of AI terminology

Machine learning

Machine learning is a subset of AI that focuses on developing algorithms and models to help machines learn from data and predict behaviours and trends.

Predictive analytics

Predictive analytics uses technology to predict what will happen in a specific time frame based on historical data and patterns.

Generative AI

Whilst less important to a finance function and currently used more in areas like marketing, generative AI learns patterns and structures to generate something similar or new. Used more currently to generate things like content, pictures, art, product designs, etc.

Copilot

AI Copilots act like virtual assistants that you can use to ask questions, explore data and discover insights.

Deep learning

Deep learning is an artificial intelligence (AI) method that trains computers to process data in ways inspired by the human brain. Deep learning models can recognise complex pictures, text, sounds, and other data to produce accurate insights and predictions.

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