How AI Is Revolutionizing Data Import, Export, Formatting, and Pricing Analysis

Introduction

One of the biggest hidden challenges inside pricing, procurement, and commercial operations is not always the pricing strategy itself—it is the enormous amount of time spent preparing and organizing data before any real analysis can even begin.

In many companies, analysts still spend hours:

• Importing files from ERP systems
• Cleaning spreadsheets
• Fixing broken formats
• Aligning columns
• Creating formulas
• Standardizing currencies and units
• Preparing pivot tables and dashboards

Only after all this work can the actual pricing review start.

From my personal experience working extensively with pricing and large industrial datasets, AI has become one of the most practical and valuable tools for simplifying this process. What previously required multiple tools, repetitive manual work, and significant Excel effort can now be handled much faster with AI-assisted support.

The biggest value is not just automation—it is reducing operational complexity and accelerating analysis preparation.

The Traditional Data Preparation Problem

In manufacturing, import/export, and pricing operations, data comes from many different sources:

• SAP exports
• Supplier price lists
• Customer files
• Freight reports
• Consumption reports
• ERP extracts
• Competitor webshops
• Inventory reports

The problem is that these files rarely arrive in a clean, usable structure.

Typical issues include:

• Wrong formatting
• Mixed currencies
• Broken formulas
• Different units of measure
• Inconsistent material descriptions
• Missing columns
• Duplicate entries
• Different decimal structures
• Date formatting issues

Before analysis even starts, analysts often spend hours preparing the data manually.

The Reality of Manual Excel Work

A large part of pricing analysis traditionally involves repetitive operational work such as:

• Copying and pasting data
• Creating lookup formulas
• Rebuilding broken references
• Reformatting supplier exports
• Matching materials manually
• Cleaning duplicate records
• Inserting trend formulas
• Aligning Power BI structures
• Building pivot tables

This work is necessary—but it consumes enormous amounts of time.

In some cases, analysts spend more time preparing data than actually analyzing it.

Where AI Changes the Game

AI is dramatically improving this process.

Instead of manually figuring out:

• Which formula to use
• How to structure the data
• How to align columns
• How to build calculations

AI can assist instantly.

1. Faster Data Import & Structuring

One major advantage of AI is helping organize imported datasets quickly.

For example, AI can help:

• Standardize column structures
• Detect incorrect formatting
• Suggest missing fields
• Align supplier exports
• Identify duplicated materials
• Separate mixed data types

This reduces preparation time significantly.

Instead of manually rebuilding structures every time, analysts can focus directly on the business logic.

2. Formula Creation Becomes Much Faster

One of the most practical benefits I personally experienced is how AI accelerates formula building.

Instead of:

• Searching forums
• Testing formulas repeatedly
• Building nested Excel logic manually

AI can quickly generate:

• IF formulas
• XLOOKUPs
• Trend analysis formulas
• Forecast calculations
• Margin calculations
• Power Query transformations
• DAX measures for Power BI

This is extremely valuable when working with large pricing datasets.

3. Simplifying Power BI and Data Modeling

AI also helps simplify Power BI preparation.

For example:

• Creating calculated columns
• Building measures
• Cleaning imported tables
• Transforming datasets
• Suggesting logic structures

Traditionally, this required significant technical expertise and debugging time.

AI accelerates the process considerably.

4. Reducing Errors in Manual Work

Manual formatting and formula handling create high risks of:

• Broken references
• Incorrect calculations
• Wrong margin outputs
• Missing data links
• Formula inconsistencies

AI helps reduce these risks by:

• Generating more structured logic
• Identifying inconsistencies
• Explaining formula behavior
• Suggesting corrections faster

This improves reliability.

5. Faster Competitive Price Analysis

In pricing environments, competitor comparisons often require:

• Importing webshop exports
• Matching materials
• Aligning descriptions
• Creating normalization logic

AI helps accelerate:

• Data matching
• Formatting alignment
• Apples-to-apples comparisons
• Trend calculations

This significantly reduces analysis time.

6. Easier Handling of Large Datasets

When dealing with thousands of materials, manual review becomes difficult.

AI helps:

• Filter anomalies
• Detect outliers
• Group patterns
• Highlight pricing deviations
• Identify missing records

Instead of manually checking thousands of rows, teams can focus directly on important commercial signals.

7. AI Helps Non-Technical Users Work Faster

Not every pricing professional is a technical developer or advanced data engineer.

AI bridges this gap.

It allows business users to:

• Create formulas faster
• Understand calculations
• Build reports more easily
• Structure datasets better

This increases operational efficiency across teams.

The Biggest Benefit: Time Savings

From my personal experience, one of the biggest advantages of AI is simply the amount of time it saves.

Tasks that previously required:

• Multiple tools
• Long formatting sessions
• Complex formula testing
• Repetitive cleanup work

Can now often be completed much faster.

This allows more time for:

• Actual analysis
• Decision-making
• Strategy discussions
• Commercial actions

But Governance Still Matters

AI accelerates data preparation—but validation remains essential.

Pricing teams must still verify:

• Formula accuracy
• Correct business logic
• Proper comparisons
• Consistent assumptions

AI improves speed—but human expertise ensures correctness.

The Future of Pricing and Data Analysis

The future is moving toward:

• AI-assisted Excel work
• Automated data preparation
• Faster ERP integration
• Smarter Power BI modeling
• Real-time pricing analysis

The role of analysts is evolving from:

• Manual data preparation

To:

• Decision-focused commercial analysis

Personal Reflection

In my experience, AI has become extremely useful for:

• Data import/export handling
• Formula generation
• Formatting support
• Power BI logic
• Quick pricing analysis
• Trend calculations

Instead of spending hours switching between multiple tools and rebuilding logic manually, AI helps simplify the process dramatically.

The result is not only faster work—but also more structured and scalable analysis.

Final Thought

The future of pricing analysis is not about replacing analysts with AI.

It is about reducing repetitive operational work so pricing professionals can focus more on:

• Strategy
• Commercial thinking
• Market analysis
• Better decisions

AI is becoming one of the strongest accelerators for modern pricing and data analysis environments.