Data analysis is changing so fast, right? For years, we all relied on spreadsheets and manual cleaning, but 2026 is officially the year where “work smarter, not harder” actually means something. This article will show you how to power query data with AI so you can stop wasting hours on tedious tasks. If you are tired of complex formulas, stay with us to discover some game-changing tools. Let’s dive in and see how Varmeta can help you transform your workflow!
What Is Power Query?
Power Query is a data transformation and data preparation engine developed by Microsoft. In technical terms, it is an ETL (Extract, Transform, Load) tool. It allows you to connect to hundreds of different data sources, like Excel files, SQL databases, or even web pages, and then clean that data up before it goes into a report.
The core functionality
- Data connection: It acts as a pipe, pulling info from everywhere so you don’t have to copy-paste manually.
- Transformation: You can use it to filter rows, remove duplicates, or split columns.
- Repeatability: Once you set up a “query,” you can just hit refresh next month, and it performs all those cleaning steps again automatically.
Here is the catch: while Power Query has a nice interface, it is actually powered by a language called M. If you want to do anything truly custom or complex, you have to learn the M syntax or use DAX for calculations in Power BI. For most humans who just want to know their monthly sales growth, this feels like learning ancient Greek just to order a pizza. This is also one of the main reasons why many businesses are now looking for easier ways to query data with AI instead of relying on traditional data transformation workflows.
Why You Should Look For An Alternative To Power Query?
Power Query is powerful, sure, but it’s starting to show its age in a world that demands speed and flexibility. If you’ve ever felt “locked in” or frustrated by a “Data Format Error” you couldn’t explain, you’re experiencing the very reasons why people are hunting for an alternative to Power Query.
The Learning Curve is a Mountain
As mentioned, the M and DAX languages are not intuitive. You can spend weeks watching YouTube tutorials and still feel like a beginner. In 2026, most teams don’t have the luxury of “training time.” They need tools that work on day one without requiring a 40-hour certification.
Rigid and Isolated Structures
Power Query often feels like it’s trapped in a bubble. It was built for a world of “isolated” spreadsheets rather than the collaborative, cloud-first workspaces we use today. Sharing queries between team members can be clunky, and if the data source changes slightly, the whole “rigid” structure often breaks, leaving you to hunt through dozens of steps to find the error.
The “Microsoft Tax” and Pricing
To get the best features, especially the new AI capabilities like Power BI Copilot, you have to pay up. Big time. For enterprise-grade AI features in the Microsoft ecosystem, organizations often have to jump to high-tier “Fabric” capacities, which can start at over $10,000 per month. For small to medium-sized businesses, it’s a brick wall.
What Does It Mean To “Query Data With AI”?
Forget everything you know about clicking “Merge Queries” or “Group By.” To query data with AI is to treat your database like a colleague you can talk to. It is a fundamental shift from visual manipulation to intent-based analysis.
Natural Language is the New SQL
Instead of writing a line of code like SELECT * FROM sales WHERE region = ‘North’, you simply type: “Show me all sales from the North region last month”. AI-native platforms use Large Language Models (LLMs) to interpret that sentence, look at your data’s schema (the column names and types), and generate the result instantly. This is why more businesses are choosing to query data with AI rather than relying on traditional SQL workflows.
Context-Aware Intelligence
True AI querying isn’t just a chatbot. It’s context-aware. These tools “read” your data headers and even the first few rows to understand what you’re talking about.
If you ask for “Revenue,” the AI knows which column represents money and which represents quantity, often even correcting for different currency formats or messy date strings (like YYYY-MM-DD) without you having to ask. It is about lowering the barrier to entry, making sophisticated data work accessible to everyone, not just the IT department.
Real-World Applications Of AI-Powered Data Queries
To truly understand the power of an alternative to Power Query, we need to look at how these tools perform in the wild. Moving beyond manual button-clicking allows teams to solve complex data headaches in seconds. This is where the ability to query data with AI starts delivering measurable business value.
AI-Powered Data Cleaning & Transformation
This is perhaps the most immediate “win” for any team. Traditional tools require you to manually parse tricky formats, such as dates stored as strings in a YYYY-MM-DD HH:MM format.
Instead of writing ten different transformation steps or remembering exact format codes, you can simply tell the AI: “Convert the ‘transaction_time’ column to a datetime object and create a new column for the day of the week”. The AI handles the heavy lifting, instantly generating and executing the correct code while highlighting new changes, like a new column, in green.
Automating Sales and Financial Reports
Finance and sales teams often struggle with “data silos”—information scattered across different CSVs, ERP systems, or cloud warehouses. AI-native tools can merge these disparate sources by identifying common keys and creating a unified view without a single manual “VLOOKUP”.
For instance, you can import a monthly sales CSV, visually filter for specific regions and dates, and generate a pivot table to aggregate sales by product category in under a minute. The most valuable part is that the AI generates a reusable script, making the next month’s report purely automatic. This is one of the most practical ways businesses query data with AI to streamline reporting workflows.
Predictive Analytics for Business Growth
Beyond just looking at the past, the rise of Agentic AI allows businesses to look forward. A retail business can query historical data to predict next month’s demand or identify customers likely to churn.
AI agents guide users through building production-ready models in minutes, identifying seasonal patterns and providing explainable forecasts. This allows teams to manage inventory and marketing campaigns effectively without needing a PhD in data science.
Top 6 AI-Powered Alternatives To Power Query In 2026
As we move into 2026, several platforms have emerged as leaders in the race to replace traditional ETL tools. Each offers a unique spin on how to query data with AI, catering to different technical needs and business sizes.
| Tool | Best For | Key Differentiator | Connectivity & Scale |
| Analyst Intelligence | Cloud Data Prep | Translates plain English into SQL on Google BigQuery. | Google Sheets, BigQuery, and 3rd party sources. |
| Anomaly AI | Ad-hoc Answers | AI-first analysis with visible SQL for every answer. | GA4, BigQuery, Snowflake, MySQL, and large Excel files. |
| Mito AI | Python Transition | A spreadsheet that automatically generates Python code. | Operates inside Jupyter Notebooks and Streamlit. |
| Veezoo | Self-Service BI | Multilingual natural language search with no SQL needed. | Direct queries to Snowflake, Redshift, and BigQuery. |
| Julius AI | Messy CSVs | Conversational analysis specialized for statistical tasks. | Excel/CSV uploads and major DB connectors (Pro tier). |
| Pecan AI | Predictive Tasks | Automates model building for churn and demand forecasting. | Connects directly to CRMs, data warehouses, and DBs. |
Analyst Intelligence
This platform is a powerhouse for those who want the scale of a data warehouse without the complexity. Analyst Intelligence translates plain English directly into SQL that runs on Google BigQuery.
It bridges the gap between the familiar Google Workspace and enterprise-grade processing. Instead of being locked into a proprietary language like M, you get a collaborative environment where an AI chatbot helps you clean and transform data at scale, making it easier than ever to query data with AI.
Anomaly AI
If your goal is to get answers quickly rather than building a permanent, pretty dashboard, Anomaly AI is the “AI-first” choice. It functions like an on-demand data analyst. You can connect your databases or spreadsheets and ask questions in natural language. What sets it apart is transparency. It shows you the exact SQL code it generated to get the answer, ensuring you can trust the results and maintain data integrity.
Mito AI
Mito AI is often described as a “spreadsheet that speaks Python.” It’s a perfect alternative to Power Query for those who are comfortable in a grid-like environment but want the power of code. Every visual action you take, like filtering a column or joining two tables, is automatically converted into Python code by the AI. This makes it an incredible tool for teams looking to transition from Excel-based workflows to more robust data science practices.
Veezoo
Veezoo takes a “search-first” approach to business intelligence. Unlike Power BI, which requires you to set up a dashboard before you can see any insights, Veezoo allows you to start querying immediately. It is designed for non-technical users who need to explore data independently. You ask a question, and Veezoo visualizes the answer in the most appropriate format, whether that’s a bar chart, a map, or a pivot table.
Julius AI
Julius AI excels when dealing with “messy” file-based data. It functions much like a specialized version of ChatGPT but with a focus on statistical rigor and data manipulation. You can upload a disorganized CSV, and through a conversational interface, ask it to perform complex statistical tests or clean up specific data anomalies. This makes it an excellent option for teams that want to query data with AI without spending hours preparing datasets manually. It’s an ideal tool for researchers and analysts who work with varied, ad-hoc datasets.
Pecan AI
For businesses that want to look into the future, Pecan AI focuses on Predictive Analytics. It uses “Agentic AI” to automate the entire process of building predictive models. It can automatically write the SQL needed to prep raw data and then build models to forecast things like customer churn or lead scoring. It’s about moving from “what happened?” to “what will happen?” without needing a dedicated data science team.
Conclusion
The days of being “stuck” on one side of the data chasm, unable to write code but needing more power than a standard spreadsheet, are finally over. Whether you are an Excel guru or an aspiring data scientist, the ability to query data with AI democratizes analysis for everyone.
Power Query will always have its place in legacy Microsoft systems, but the future belongs to targeted, workflow-integrated tools that deliver practical productivity gains. Stop wasting hours on manual cleaning and start getting answers.
At Varmeta, we specialize in helping businesses integrate AI-powered solutions that actually make an impact. Let’s work together to turn your messy data into a strategic advantage. Reach out to Var-meta today!
FAQs
1. Can I really do data analysis without knowing SQL or DAX?
Yes, but with limits. AI-native tools like Julius AI and ThoughtSpot translate plain English into queries for trends and forecasting. However, about 52% of businesses already use no-code platforms successfully at scale.
2. What is the best free alternative to Power Query?
Looker Studio is the best fully free option with no limits on reports or users. For a more “AI-first” experience, the free tiers of Anomaly AI or Julius AI are great starting points.
3. Do these AI tools connect to live databases?
Absolutely. Most enterprise-grade alternatives like Veezoo, Analyst Intelligence, and Anomaly AI connect directly to Snowflake, BigQuery, MySQL, and PostgreSQL.
4. Why do some “no-code” tools still require technical setup?
Many visual builders (like Tableau) eliminate SQL but still require a “semantic layer” setup so the tool knows what your metrics mean. AI-native tools try to skip this by interpreting your intent on the fly, which is faster but can have accuracy trade-offs.
5. What happens when my data outgrows spreadsheets?
Once you hit the 10-50MB limit, you need to transition from file-based uploads to database connections. Choosing an alternative that supports both (like Julius AI Pro) ensures a smooth transition.