AI is becoming part of the everyday toolkit of finance teams. From analysing financial data and preparing reports to identifying trends and automating repetitive processes, AI tools are changing how professionals approach tasks that once required hours of manual work.

For finance professionals, keeping up with this shift does not necessarily mean becoming a programmer or Machine Learning specialist. It means developing the practical skills needed to work confidently with AI, understand its outputs, and apply it where it can create real value.

So, which AI skills matter most for finance professionals in 2026?

1. AI literacy and understanding AI capabilities

The first step is knowing what AI can realistically do.

Finance professionals increasingly work with generative AI tools that can summarise information, analyse datasets, generate explanations, identify patterns and assist with everyday decision-making. Understanding these tools at a practical level makes it easier to identify suitable use cases and recognise their limitations.

AI literacy also means knowing when human judgement is essential. Financial decisions can have significant business implications, so AI-generated outputs should be reviewed, challenged and validated before they influence a decision.

2. Prompting for financial workflows

The quality of an AI output depends heavily on the quality of the instructions behind it.

Effective prompting is therefore becoming a useful skill for finance professionals. Instead of asking an AI tool a broad question, professionals can provide relevant context, define the desired output and establish specific criteria for the analysis.

For example, AI can assist with:

• Summarising financial reports and management information
• Explaining changes in KPIs or financial performance
• Comparing different scenarios
• Creating first drafts of financial communications
• Identifying unusual movements in datasets
• Structuring information for presentations or reports

The goal is to develop prompts that produce useful, consistent and easy-to-review results.

3. Data literacy

AI does not eliminate the need to understand data. In many cases, it makes data literacy even more important.

Finance professionals should be comfortable working with structured datasets, identifying inconsistencies, understanding basic data relationships and assessing whether information is suitable for analysis.

This includes knowing how to:

• Check data quality before using an AI tool
• Interpret charts, trends and financial indicators
• Recognise anomalies or missing information
• Distinguish correlation from meaningful business insight
• Question conclusions that do not align with the underlying data

Strong data literacy helps finance professionals use AI as part of a reliable analytical process rather than treating its output as an answer in itself.

4. AI-assisted analysis and forecasting

Financial analysis involves finding patterns, understanding performance and considering what may happen next. AI can support each stage of this process.

Professionals can use AI-assisted tools to explore historical data, identify recurring patterns, compare scenarios or accelerate the preparation of forecasts.

For example, when analysing revenue or costs, AI could help surface unusual changes across periods or highlight relationships worth investigating. The finance professional then brings the business context needed to determine what those patterns actually mean.

This combination of analytical tools and financial expertise can make forecasting and scenario planning more efficient.

5. Automation of repetitive finance tasks

Finance teams deal with many recurring processes: reporting, data preparation, reconciliations, document processing, recurring calculations and information gathering.

Understanding how to identify tasks suitable for automation is becoming an increasingly valuable skill.

The most useful starting point is often a process that is repetitive, rule-based and time-consuming. AI and automation tools can help reduce manual steps, organise information and support workflows that previously required repeated intervention.

This can give finance professionals more time to focus on analysis, planning and business partnering.

6. Responsible and secure use of AI

Finance professionals often work with sensitive business and financial information. As AI becomes more integrated into their workflows, understanding responsible AI use becomes essential.

Professionals should know what information can safely be entered into an AI tool, how data may be handled, and when an organisation's internal systems or approved AI solutions should be used instead.

They should also be aware of risks such as inaccurate outputs, bias, unsupported recommendations and data privacy issues.

Responsible AI use is ultimately about combining technological capabilities with appropriate professional judgement.

7. Communication and AI-assisted storytelling

Finance professionals frequently need to turn complex analysis into information that executives, managers and other teams can understand and act on.

AI can support this process by helping structure reports, summarise findings, prepare presentation content and adapt technical analysis for different audiences.

However, effective communication still depends on understanding the audience and the business context. Finance professionals who can combine analytical thinking with clear communication will be better positioned to turn AI-assisted insights into meaningful business conversations.

8. Critical thinking and validation

As AI tools become more capable, knowing how to evaluate their outputs becomes just as important as knowing how to use them.

A finance professional should be able to ask:

• Does this result make sense?
• Are the underlying assumptions reasonable?
• Is important information missing?
• Can the conclusion be supported by the available data?
• What could explain a different result?
• What are the potential business risks?

AI can accelerate analysis, but financial expertise remains essential for interpreting results and making informed decisions.

Building AI skills around your existing role

Finance professionals do not need to master every AI technology available. A more effective approach is to connect learning with the tasks already part of their working routine.

Start with one recurring challenge. Perhaps preparing a monthly report takes too long, analysing a dataset involves repetitive steps, or turning financial findings into management-ready content requires significant manual work.

Explore how AI could support that process, test the workflow carefully, evaluate the results and gradually expand into other use cases.

Over time, these small improvements can develop into a broader set of AI skills that support productivity, analysis and decision-making.

Prepare for the next stage of finance work

AI is becoming an increasingly practical part of professional workflows across industries, including finance. Professionals who understand how to use these tools effectively can improve the way they analyse information, automate routine work and communicate insights.

For finance professionals, the opportunity lies in combining existing financial expertise with practical AI capabilities.

Ready to build practical AI skills?

The Practical AI for Professionals course by Big Blue Data Academy is designed to help professionals understand and apply AI tools in their everyday work. Through practical learning and real-world applications, participants can develop the confidence to use AI more effectively and identify opportunities to improve their workflows.

Explore Practical AI for Professionals and start building the AI skills you need for the workplace of today.