It’s 2026, but are your Excel skills still in 1993?
Written by First Intuition
In a recent First Intuition webinar, Microsoft MVP and qualified accountant Mark Proctor explored what “advanced Excel” should mean in 2026.
He demonstrated how modern tools can help finance teams reduce manual work, improve accuracy and spend more time analysing information rather than preparing it.
Rethinking ‘advanced Excel’
Around 75 per cent of finance and accounting job descriptions ask for advanced Excel skills. However, the examples employers commonly give, including VLOOKUP, IF functions, PivotTables and VBA, were all available, or had close equivalents, by 1993.
These features remain useful, but Excel has developed significantly since then. Tables, Power Query, Power Pivot, Dynamic Arrays, Power Automate, Python and Copilot have created new opportunities to automate processes that previously required repeated manual intervention.
The problem is therefore not a lack of technology, but the gap between the tools available and the skills finance teams have developed to use them.
Finance professionals need data skills
Finance professionals understand a task and the financial information required to complete it, however, the process of turning that information into a report is normally a data process.
Without an understanding of how data should be structured, teams can create spreadsheets that are visually clear but difficult to update, analyse and automate.
Mark explained that good data should have:
- A separate column for each attribute
- One consistent data type within each column
- A unique header describing each column
- A structure in which changing the order of rows or columns does not alter the meaning
This tabular format provides a much stronger foundation for calculations, analysis and automation.
Start by using excel tables
One of the simplest ways to improve an Excel process is to convert a standard cell range into a Table.
Unlike information placed directly onto a worksheet, a Table is recognised by Excel as a structured data object. It uses named column references and automatically expands as new information is added.
When data has been converted into a Table, the formulas update automatically which removes a manual task and reduces the risk of reports omitting recently added information.
Use Power Query to remove repetitive data preparation
Finance data rarely arrives in a perfectly structured Table. It may come from an accounting system, database, CSV file, SharePoint folder or several separate workbooks.
Power Query enables users to connect to these sources, combine information and reshape it into a consistent format. It records every transformation so the process can be repeated when the underlying data changes.
Mark showed how Power Query could:
- Import several monthly trial balance files from a folder
- Remove unnecessary rows
- Promote the correct column headings
- Extract the reporting date from each filename
- Apply the correct data types
- Merge the results with an account mapping table
- Load the finished data into an Excel Table
The potential time saving is significant. Research shared during the session found that Power Query users estimated savings of between 22 and 89 working days a year, with an average of 76 days.
Automate the complete reporting process
Power Query can automate the preparation of data, but Power Automate Desktop takes the process further.
Using its low-code, drag-and-drop interface, finance teams can build workflows that interact with Excel and other applications. For example, a single automated flow could include:
- Opened an Excel report
- Selected the relevant reporting date
- Refreshed the Power Query connections
- Updated the figures and charts
- Converted the report into a PDF
- Emailed it to the intended recipient
Similar workflows could generate and distribute reports to multiple cost centre owners, clients or other stakeholders. By designing the process from beginning to end, teams can replace a series of separate manual actions with one reliable workflow.
Know when to automate and when to use AI
Automation is deterministic: when it is correctly designed, it follows the same steps and produces a consistent result each time. AI is probabilistic: it interprets patterns and language, meaning its responses may vary.
This makes AI valuable for tasks that require judgement or pattern recognition, including items with different descriptions but similar meanings. However, results still needed to be reviewed by someone who understands the task. AI should be incorporated into a controlled process with clear checks, rather than used as a replacement for reliable automation or professional judgement.
Mark advised teams to build the automated process first and then identify where AI could add value within it. Asking AI to control the entire workflow may produce less consistent results.
Modern tools still require human understanding
AI can help users write Power Query code, troubleshoot problems or suggest ways to complete a task. However, it does not remove the need to understand the underlying tools.
If finance professionals are responsible for checking AI-generated outputs, they must have sufficient knowledge to recognise errors, inefficiencies and risks. As AI becomes more widely used, strong technical and data skills will become more important, not less.
It may be time for an Excel skills upgrade
Many finance teams are relying on the same skills and processes they have used for decades. Tools such as Tables, Power Query and Power Automate are already available to many users and can remove significant amounts of repetitive work. Copilot and other AI tools can then support more complex tasks where interpretation and pattern recognition are required.
Modern Excel skills are about understanding data, building connected processes and using the right combination of automation, AI and human judgement. For finance teams, that can mean faster reporting, fewer errors and more time to focus on the analysis and insight that add genuine value to the organisation.