Complete Tutorial on AI Spreadsheet Automation, 2026 7 Tips to Manage Data Without Learning Functions

Complete Tutorial on AI Spreadsheet Automation, 2026 7 Tips to Manage Data Without Learning Functions

Many people get a headache as soon as they open spreadsheet software and want to quit when they see a long list of function names. In fact, in 2026, processing data has quietly changed. In the past, you had to remember how to write VLOOKUP, how many levels of IF nesting, and the parameter order of SUMIF. Now more and more spreadsheet tools have built-in AI assistants. As long as you explain the desired results clearly in plain English, it can help you complete the formulas, charts, and summaries. This tutorial does not talk about boring syntax, but takes you from the actual scenario to see how an ordinary person who cannot write functions can rely on AI to do daily data work quickly and decently.

How AI has changed the way we use tables

The core obstacle in using tables in the past was that you had a clear need in your mind, but you didn't know which function or combination to use to achieve it. There is a technical threshold in between. After AI came in, this threshold was significantly lowered. You can now directly say in the dialog box "Help me mark the duplicate customer names in this column", and the tool will understand your intention and automatically generate the corresponding operation or formula. Behind this is usually a large language model that translates natural language into table instructions. In other words, you are responsible for thinking clearly about what you want, and AI is responsible for implementing it into specific actions. The biggest significance of this change is not to make decisions for you, but to hand over those mechanical, repetitive, and grammar-checking links to the machine, so that you can focus more on what the data itself illustrates. Of course, it is not a panacea. The more vague the requirements, the easier it is for the results to deviate. Therefore, learning to describe the requirements clearly has become a key ability to use tables in the new era.

Tip 1: Use natural language to generate formulas

This is the easiest trick to use. Suppose you have a sales table and want to calculate the total sales of each region as a percentage of the entire company, but you can't remember which function to use. At this time, you don't need to read the help document, just tell the AI ​​assistant your idea, such as "calculate the proportion of the amount in each row to the total of column D in column E, and display it as a percentage." AI will understand this description, generate a formula with an absolute reference for you, and fill it in the corresponding cell. Many tools will also explain the logic of this formula. You can understand what it does at a glance, and you can even change it yourself in a similar scenario next time. The advantage of this ability is that it completely resolves the embarrassment of "I know what to count but don't know how to write it." What needs to be reminded is that you must check whether the generated formula range and reference are aligned with your real data area. AI will occasionally get the column number or row range wrong, especially when the header position is irregular.

Tip 2: Data cleaning can be done in one sentence

The data obtained in the real world is often dirty: some people write dates in various formats, some people put units after numbers, there are extra spaces before and after names, and the same city is written by several names. In the past, cleaning these required half a day of work by sorting, replacing, TRIM, and various nested functions. Now you can directly describe the cleaning target to the AI, such as "unify this list of phone numbers into pure numbers and remove the horizontal bars and spaces in the middle", or "unify all 'Beijing City', 'Beijing' and 'BeiJing' into 'Beijing'". The tools will process them in batches according to the rules you specify, and some can even preview the results for you to confirm they are correct before applying them. The time saved in this step is often the most considerable, because data cleaning is inherently the most tedious part of analysis. However, there is a pitfall here that you need to be particularly careful about: when it comes to fields that cannot make mistakes, such as amount, ID number, and order number, be sure to back up the original data first, then let the AI ​​do it, and randomly check a few lines to prevent it from clearing out the zeros or symbols that should be retained.

Tip 3: Automatic classification and summarization

When you are faced with thousands of rows of sales and want to know the totals for each month, each product line, and each salesperson, manually creating auxiliary columns and then dragging the formula is slow and easy to mess up. AI is very handy in this matter: you just need to say "summarize the sales amount by month and product category and make a grouped statistical table", and it will identify the key fields in your table and generate grouped summary results. For people who are not familiar with pivot table operations, this is equivalent to skipping a large learning cost. Some tools will directly help you create a new table, with rows representing categories, columns representing months, and totals filled in at the intersections. This kind of summary is most suitable for periodic work such as monthly reviews and inventory counts. It should be noted that the prerequisite for summary is that your original data columns are clearly defined and there are no subtotal rows or blank rows mixed in. Otherwise, AI may include these impurities, causing the total to be inconsistent. Developing the habit of reconciling the sum with the original sum will make you feel more at ease.

Tip 4: Let AI generate charts for you

Turning data into a graph that can tell a story is something that many people find difficult. It requires not only choosing the right graph type, but also adjusting the axis and color matching. With AI, you can eliminate most of the manual work. For example, if you select a piece of sales data and tell it, "Help me draw a trend line chart using these months' data and mark the highest point," the tool will be able to determine which column is suitable for the horizontal axis and which column for numerical values, and automatically generate a chart. If you are not sure whether to use a bar chart or a pie chart, you can also ask it "Which chart is more suitable for displaying this set of data?" It will usually give suggestions based on whether the data is based on trends, proportions, or comparisons. This allows people who don't understand visualization principles to make decent graphs. Of course, charts are just a means of expression, what really matters is the conclusion behind the chart. Sometimes the chart type selected by AI may not be in line with your purpose of expression. You have to know it in mind. If necessary, change the chart yourself. Don't completely leave your judgment to it.

Tip 5: Perspective analysis and looking at data from multiple angles

Pivot data is the most powerful but also the most frustrating function in tables. Just dragging fields to the rows, columns, and value areas makes many people dizzy. AI has made this easier. You can directly describe the dimensions from which you want to view the data, such as "I want to see the number of transactions and transaction amounts of each salesperson in different quarters." AI will understand the dimensions and indicators you mentioned and help you put together the perspective structure. If you want to look at it from a different angle, just say "Change to group by customer industry" and it will be reorganized. This kind of back-and-forth interaction is particularly suitable for the exploration stage. When you haven't figured out how to cut the data yet, you can ask the AI ​​to change several combinations to see which angle best explains the problem. It should be noted that it is critical to choose the right counting and summing methods in the perspective results. For example, if you want the number of deduplicated customers, the AI ​​may give you the number of records by default. You have to confirm this difference in details yourself, otherwise the conclusion will be very different.

Tip 6: Batch processing of repeated operations

There is a lot of repetitive work in the office, such as applying the same format to hundreds of worksheets, extracting and merging certain columns from a batch of files, and labeling each row according to rules. In the past, these were either manually clicked, or you had to be able to write macros. Now you can tell your requirements to AI and let it process a batch of data at once. To give a common example, you want to automatically label each order as "Large Order", "Medium Order" and "Small Order" according to the amount. As long as you clearly explain the classification criteria, AI can label the entire column in batches. Another example is to merge multiple tables with the same structure into one master table, and you can leave it to it even if you describe it clearly. Batch processing best reflects the labor-saving value of AI, because it will not make mistakes or be lazy because of repetition. But precisely because it is a batch process, once the rules are not clearly stated or are misunderstood, errors will occur in batches. The safe approach is to take a small part of the data for a test run first, confirm that the logic is OK, and then proceed with the full amount, so that it is easy to roll back even if something goes wrong.

Tip 7: Let AI write scripts and macros

When the requirements are too complex to be satisfied by ordinary operations, such as regularly pulling data from multiple sources, performing a series of processes, and then exporting reports, this used to be a job that could only be done by programmers or people who knew VBA and scripts. Now you can have AI write scripts or macros for you even if you don’t know how to program at all. You explain the process clearly in Chinese step by step, and AI will generate the corresponding code. You can just paste it into the script editor of the form tool and run it. When you encounter an error, send the error message back, and it can also help you locate and modify it. This is equivalent to giving ordinary people an assistant who can write code. However, the threshold for this method is slightly higher than the previous ones, because running the script means you have to understand a little bit what it is doing, especially when it involves deleting and overwriting data. It is recommended to run it on a copy first and understand each step before using it on the official data. For people who have never been exposed to code, you can ask AI to add comments on key steps and learn while using it, and gradually they will no longer be afraid.

The respective AI capabilities of Excel, WPS, and Feishu Sheets

Several mainstream spreadsheet tools have been working in the direction of AI in the past two years, but their focus is different. According to public information, Microsoft Excel has integrated capabilities such as generating formulas, interpreting data, and generating charts into the interface through its AI assistant, which is suitable for people who already use Excel heavily. WPS has also added AI functions to the form, targeting a large number of domestic office users. Common needs such as intelligent generation of formulas and data analysis can usually be covered. Feishu Forms relies on its collaborative and multi-dimensional form features to tightly integrate AI with online collaboration and automated processes, making it suitable for teams to process data together and build lightweight business processes. The specific open range of AI functions of each tool and whether a specific version or subscription is required. Each company's policies are different and will be adjusted. It is recommended to refer to the latest official instructions. Which one to choose depends more on what your team is already using and where the data exists, rather than just whose AI is more fancy. The one that can be integrated into your existing workflow is the one that is most useful to you.

The most common pitfalls when using AI to process forms

After using these techniques, there are several pitfalls that almost everyone will encounter. If you know them in advance, you can avoid detours. The first is over-trust in the numbers calculated by AI. The results it gives look very professional, but the model sometimes misunderstands your intentions, or makes up for it when the data is ambiguous, resulting in very subtle errors in the results. The second is that the demand description is too vague. The more general you are, the easier it is to give you an answer that seems reasonable but is actually incorrect. The third is to ignore the quality of the original data. The data itself has errors, duplications, and blank lines. No matter how fast the AI ​​calculates, it will only calculate garbage faster. The fourth is when it comes to privacy or sensitive data, you should pay attention to whether the tool you use will transmit the data to the cloud, and the bottom line of compliance and confidentiality cannot be relaxed. After all, AI is a very capable assistant, but it is not responsible for the results. The person responsible is always you. Develop a habit: Check all the figures that will be used to make decisions, report externally, or affect money by yourself. This step can never be omitted.

FAQ

If you don’t know any functions, can you really use AI to handle tabular data?

Most daily needs are ok. Common scenarios such as generating formulas, cleaning data, making summaries and charts can now be completed by AI through natural language descriptions, and you do not need to remember specific function syntax. But the premise is that you must clearly state your needs and know whether the results are reasonable. It is easy to make mistakes if you just accept the order without thinking at all.

Are the formulas and results generated by AI reliable? Do they need to be checked?

Be sure to check. AI performs well when the understanding is clear and the data is organized, but it may make mistakes when it encounters vague requirements or dirty data, and the mistakes are often hidden. Figures involving amounts, key indicators, and foreign exchange reports must be checked by yourself. It is a good habit to compare the sum of the original data with the results given by AI.

Which one should I choose among Excel, WPS and Feishu forms?

It mainly depends on what your team is already using and where the data is stored. According to public information, these models are all adding AI capabilities. Excel is suitable for heavy Excel users, WPS covers a large number of domestic office scenarios, and Feishu Forms has characteristics in team collaboration and automated processes. The specific function opening range is subject to the latest official instructions of each company.

Using AI to process forms, is my data safe?

Depends on the tool you use and how it handles data. Some AI functions will transfer data to the cloud for processing. Pay special attention to compliance requirements when privacy or sensitive information is involved. If necessary, first desensitize or operate on a local copy. Backing up important data before taking action is something that should be done under any circumstances.

I don’t know how to write code, can I ask AI to help me write scripts or macros?

Can. You explain the process you want to implement step by step in Chinese, and AI can generate the corresponding script or macro code. You can paste it into the script editor of the tool and run it. If you report an error, you can also send it back for help to correct it. However, it is best to test on a copy of the data before running the script, especially operations involving deletion or overwriting. It is safer to understand the logic before using it on the official data.

When it comes to data, tools are getting smarter all the time, but what is really scarce may always be the people who are willing to stop and take a second look and ask, "Is this number correct?"