Adopting AI for Finance Functions

Practical Use Cases.

Author: Buki Obayiuwana

Artificial Intelligence has become the latest industry focus — once it was macros, then robotic process automation. Now, AI promises to enhance the efficiency of numerous finance functions, yet many organisations remain hesitant to embrace it.

Caution is understandable. While some Finance teams often rely on manual, time-consuming, repeatable processes when things go wrong, the consequences can erode trust in finance but also damage the reputation of the organisation. This is every CFOs worst nightmare. However, whilst proceeding with caution, there is room for AI to deliver rapid value through better accuracy, faster throughput, and strategic insight by targeting high-volume, structured workflows, UK finance functions can unlock efficiency, reduce risk, and reallocate capacity towards analysis and advisory work, rather than administrative work.

Current landscape of AI in UK Finance departments

Finance teams across UK SMEs, public sector bodies, and financial institutions are increasingly aware of AI’s potential. Yet adoption is often uneven, pilot projects at best. Many systems remain manual, processes fragmented, and compliance risk high – data is also a major barrier.

This nascent stage presents opportunity and CFOs who are struggling to balance regulatory requirements, and constrained resources can benefit most from systematic AI strategies, starting small and scaling effectively.

Use case one: Save time daily with conversational everyday AI out of the box

Before diving into more advanced AI use cases, it’s worth remembering that AI is already at your fingertips if you use Microsoft 365. Many finance teams overlook the fact that Excel, Outlook, and Teams now have AI-assisted features built in with minimal extra licensing or integration needed. Many of our clients are already at this stage but a handful are still learning how to use these tools effectively.

Examples for finance teams include:

Using Excel Copilot to instantly summarise large datasets, build pivot tables and highlight trends. You can even ask it to suggest formulas based on natural language prompts like, “Show me total expenses by department for the past six months.”
Using Outlook AI to draft follow-up emails to suppliers based on invoice queries.
Using Outlook AI to summarise long chains of correspondence so you can act faster.
Using Outlook AI to also summarise your diary to enable you populate timesheets quicker, with the right approach to diary management – a personal favourite of ours.
Teams Meeting Recaps can automatically capture and summarise finance planning meetings, flagging key decisions and next steps without manual note-taking.
Use Copilot to scan all your emails, messages and meeting for the week, identify and prioritise all the actions you need to take. You can ask for this to be presented either as a table or with details.

The possibilities are endless. These are small but powerful ways to build AI confidence in the team, and they deliver value almost immediately with the right training and support to enable teams use them responsibly.

AI can make mistakes so human-in-the-loop remains a critical step.

Use case two: Linked and embedded AI agents in Finance and Accounting packages

The integration of AI is steadily gaining momentum, as evidenced by developments at Microsoft. Many accounting and Enterprise Resource Planning (ERP) platforms are also swiftly and subtly embedding AI capabilities, transforming how financial data is processed, analysed, and leveraged across organisations.

Xero now offers predictive cash flow tools.
Sage Intacct and Dynamics 365 Finance provide anomaly detection in transactions.
QuickBooks uses AI to auto-categorise expenses.
Oracle Netsuite is promoting its embedded AI agents.

We have also observed the growth in the number of Accounting Tech companies offering AI enabled solutions to augment existing capabilities. We are watching these closely and while there are many good ideas out there, we believe that our clients should carefully assess these opportunities in the context of their Finance Systems Architecture and roadmap.

As in use case one, many of these in-built “ready-made” features can often be switched on with minimal setup support, making them an ideal starting point before moving to more advanced, customised AI solutions. This however requires investment in training, change management and adoption support and ideally a group of empowered and enabled champions within teams supported by a team of experts behind the scenes to keep them abreast of changes and opportunities.

Use case three

Case Study one: Automated invoice processing and accounts payable- c.25% reduction in effort and cost

We use AI tools to extract key data from invoices and flag discrepancies or missing information, allowing human reviewers to focus only on exceptions rather than every entry. This team reduces average review time from 20 to 30 minutes per invoice to less than five minutes. Over months, the time saved adds up, enabling quicker supplier payments and reallocating staff to more strategic finance work.

Case Study 2: Cash flow forecasting and scenario planning

We use AI-powered forecasting models to predict future cash flow based on historical data. This helps businesses manage liquidity more effectively and allow finance teams to quickly run “what-if” scenarios, enabling faster and more accurate planning compared to manual methods.

Case Study 3: Automated Financial Reporting & Management Accounts

We can also use AI to draft narrative commentary from data. After pulling figures from ERP or Business intelligence (BI) systems, we use AI to generate written summaries: revenue analysis, variance explanations, key trends. We can provide the AI tool with relevant sector, business or strategic context to make the narrative specific to the context for which it is written, this leads to a much better and richer output and with the right boundaries, reduces the risk of hallucinations. Therefore, rather than spending hours writing commentary, a first draft can be ready in minutes. This can then be reviewed and edited as needed but saves time having to craft the right words.

These systems link numbers with qualitative insights automatically — highlighting outliers, comparing performance across periods, and identifying variance drivers, allowing finance leadership to make better-informed decisions faster.

Case Study 4: Clear down a balance sheet in minutes instead of hours

Microsoft Copilot 365 users now have access to enhanced functionality and finance teams and accounting firms should spend time familiarising themselves with the new functionality and how it might help. As with anything AI related, an expert eye is required for oversight.

You can use the Researcher agent to clear down a balance sheet. It will read numbers, write off the intercompany balances and perform the capital reduction. What is most important is explainability of its thought process. It will list all the steps it is taking, and users can observe this in real time. It will also note any legal implications. For our client, this activity took 10 minutes rather than the typical hours it would normally have taken.

Case Study 5: Drafting narrative commentary and insights from data

This next one depends on the level of integration with your ERP. Assuming this is relatively manual, after pulling figures from your ERP or BI systems, you can ask AI to generate coherent written summaries. To make this truly productive and useful, it is important to ensure it has access to relevant information about your business context, products, structures, strategy, goals and so on.

You can ask for revenue analysis, variance explanations and key trends. This means that rather than spending hours writing the first draft of your written commentary, a first draft can be ready in minutes for your review and iteration.

You can further enhance the quality of your draft using embedded AI tools in Word, PowerPoint or Microsoft.

Steps to getting started in a UK context

When constructing a building, you don’t start with the roof, you start with the cornerstone. The critical piece that sets the alignment and stability for everything else. Without it, the structure risks, leaning, cracking or even collapsing.

Adopting AI for finance works in the same way. Before you “build” with AI, you need to lay your cornerstones. The essential safeguards that will hold the structure together.

AI governance and risk management framework: Just as the cornerstone determines the building’s alignment, your governance framework ensures AI is aligned with your strategy, values and risk appetite. You need to ensure you have a robust AI governance framework in place.

For many, this will be a matter of assessing what you currently have in place with regards to your Enterprise risk management frameworks, and adapting it based on the ways you think you might want to use AI.

You need to assess a broad range of risks and put the necessary reinforcement in place. For example, assess your organisation and finance systems landscape and roadmap and ensure that any solution choices are aligned to keep things cost effective.

In addition, your AI programme will need risk assessments, bias testing, and monitoring controls to handle operational and ethical “load-bearing” challenges.

You need to ensure your choices keep you compliant with GDPR and Data protection requirements. Work with your technology team and external advisors to ensure you have the right safeguards in place to protect client data, company data and ensure regulatory compliance.

For example, understanding data residency and ensuring that your AI models process data within UK/EU compliant servers and follow internal data governance.

Without the cornerstone, your AI adoption will be like a building that looks fine at first until stress and time reveals its cracks. Once these are in place, you can then start with pilots in high volume workflows as well as in day-to-day use.

Good practices

Keep the human in the loop: Look at your review workflows and ensure that where AI is used, you have established review flags for humans to verify. Start small and build trust and accuracy before enabling full automation. We expect that as regulation and legislation evolves, this will become a key requirement.
Continuous monitoring and feedback: put a mechanism in place to feedback any cleaned and validated use cases and prompts to improve learning and performance over time.
Security: data and information security is paramount. Ensure that any models you use and host are in secure cloud environments and ideally in your own tenant and in compliance with applicable laws and regulations. If in doubt, ask.
Effective governance: Ensure you define and communicate roles and responsibilities, escalation paths, audit trails and regular reviews.

Conclusion

Adopting AI in finance can feel daunting, but it doesn’t have to be complex or costly. It’s about starting where the pain is greatest, whether that’s repetitive tasks, compliance challenges, or delayed decision-making.

If this sounds familiar, we’re here to help. Our teams support you at any stage of your AI journey, leveraging existing tools or building tailored solutions using enterprise-ready technology. We focus on laying the right foundations, not tech for tech’s sake, starting with one workflow, proving it works, and scaling from there.

To explore your AI risks and opportunities, please get in touch for a free assessment.