Finance + AI = higher salaries. How to combine financial and AI skills?
According to reports by the World Economic Forum, digital skills are now one of the three most important factors influencing salaries in the financial sector. The ability to work with data analysis tools, reporting automation and predictive modelling allows specialists to earn salaries that are up to 30-40% higher than those with a traditional financial profile.
However, it is not about replacing financial knowledge with technology. It is about combining them. The finance employee of the future is not a programmer, but a person who understands how to use AI and data in decision-making. They can interpret the results of a predictive model, assess the risks of process automation or plan an investment budget taking into account the costs of implementing algorithms.
The new role of the financier is that of a "data translator" – someone who connects the world of numbers and business with the possibilities of technology. What skills are most in demand today?
5 competencies that increase the market value of a specialist
- Data literacy, i.e. the ability to understand and interpret data.
- Knowledge of BI tools (e.g. Power BI) that automate reporting and visualise results.
- Predictive analysis and working with AI models – understanding how forecasts can support financial planning, as well as the conscious use of ready-made models.
- An agile approach to projects – faster testing of solutions and responding to changes, as well as innovation in action.
- Prompting for finance – writing precise queries to AI tools, creating templates, e.g. for describing transactions, summarising contracts and controlling expenses.
Nowadays, the development of financial competencies cannot be separated from technology. AI and automation are becoming a natural part of a financier's work , but it is still people who give them meaning. The greatest value lies not in knowing the algorithm, but in the ability to apply it in a business context, emphasises Kamila Izdebska, Head of BPP Professional Education in Poland.
Companies are increasingly investing in data analysis and AI training for their finance departments. It is not about creating programmers, but about expanding competencies so that the team can use the power of technology in their daily decisions. In practice, this means less tedious analysis in Excel and more time for interpreting results and making strategic recommendations.
For employees, this means an opportunity for faster promotion and higher earnings. LinkedIn Learning research shows that people who combine financial knowledge with digital skills are promoted faster than their colleagues with a traditional profile. For organisations, this means more efficient processes and better investment decisions.
Good practices to start with
- Start with small wins. Choose one process that regularly takes up time – e.g. invoice control, expense reporting or cash flow analysis – and test simple automation. The key is a quick result that will demonstrate the value of AI in practice.
- Create a knowledge repository. Collect effective prompts and solutions in one place – e.g. in a team spreadsheet or on SharePoint. Each new case is an inspiration for future projects.
- Establish rules for responsible use of AI. Specify what data can be processed, how to validate model results, and when human intervention is required. This builds trust in the technology and minimises the risk of errors.
- Include AI in the decision-making cycle. Let predictive analytics be one of the elements of the budget planning or forecasting process. AI is not meant to replace your experience – it is meant to help you verify it with data.
- Learn to visualise. An AI report is only as valuable as the decisions it facilitates. Instead of multiplying charts, focus on one question: what does this mean for the business?
The future of finance will not belong exclusively to analysts or IT specialists. The winners will be those who can think across disciplines – who understand numbers, data and people.