I reviewed 50 "AI transformations" last quarter.
35 of them were just expensive ways to parse CSV files.
I'm not exaggerating. A Fortune 500 company paid $5 million for an "AI-powered intelligent document processing platform." You know what it actually did? Extracted numbers from PDFs and put them in columns.
The intern they fired could do it in Excel in 20 minutes.
But "Excel" doesn't get you a promotion. "AI" does.
The Evolution of Solving the Same Problem
Let me show you how we've solved the exact same business problem over 40 years:
1985: The Excel Era
=VLOOKUP(A2, Inventory!A:B, 2, FALSE)
```text
Cost: $100 Excel license
Time to implement: 1 hour
Maintenance: Your accountant
### 1995: The Database Revolution
```sql
SELECT quantity FROM inventory WHERE sku = ?
```text
Cost: $50K for Oracle licenses
Time to implement: 3 months
Maintenance: DBA you now need to hire
### 2005: The Web Framework Phase
```php
$inventory = $db->query("SELECT * FROM inventory")->fetchAll();
```text
Cost: $200K custom development
Time to implement: 6 months
Maintenance: Full dev team
### 2015: The Machine Learning Hype
```python
model = RandomForestRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
```text
Cost: $1M including data scientists
Time to implement: 12 months
Maintenance: ML Engineering team
### 2025: The AI "Transformation"
```python
response = gpt4.complete("What's our inventory for SKU-123?")
```text
Cost: $5M for "AI platform"
Time to implement: 18 months and counting
Maintenance: Entire AI department
**Same problem. Same solution. 50x the cost each decade.**
## Real Examples That Made Me Lose Faith
### The "AI-Powered Inventory System"
A retail chain hired consultants to build an AI inventory management system. Six months, $3 million.
The AI's breakthrough innovation? It predicted that stores near beaches sell more sunscreen in summer.
The previous system? A spreadsheet with a IF(MONTH()>=6, "Order more sunscreen", "Normal orders") formula.
The spreadsheet was more accurate.
### The "Intelligent Document Processor"
A law firm wanted to "leverage AI" to extract dates from contracts. They bought an enterprise platform, spent 4 months on integration.
The pattern they were looking for? Dates. In contracts. Which are always formatted as "January 1, 2025" or "1/1/2025".
A regex pattern: `\d{1,2}/\d{1,2}/\d{4}`. One line of code. 10 seconds to write.
Instead, they're paying $30K/month to OpenAI to sometimes correctly identify dates. Sometimes.
### The "Predictive Analytics Platform"
A SaaS company built an AI to predict customer churn. Complex neural networks, embeddings, the works.
Their breakthrough finding? Customers who don't log in for 30 days usually churn.
The previous method? Checking last_login_date in the database.
The AI's advantage? It cost 1000x more and was wrong 15% of the time.
## Why This Keeps Happening
### The Executive Translation Problem
What the business needs: "Show me which products are selling"
What IT hears: "Build a real-time analytics platform"
What consultants sell: "AI-powered demand forecasting system"
What gets built: $5M disaster that might tell you product sales, eventually
### The Résumé-Driven Development
Nobody gets promoted for maintaining spreadsheets.
Everyone gets promoted for "leading AI transformation."
I sat in a meeting where an executive literally said: "I don't care if it works better. I need to be able to say we're using AI."
### The Vendor Lock-in Special
Spreadsheet: $100, leave anytime
AI Platform: $500K/year minimum, 3-year contract, migration costs: your firstborn
Vendors know this. They're not selling solutions. They're selling lock-in with an AI marketing wrapper.
## The Three-Question Test
Before you replace anything with AI, ask:
### 1. Can Excel do this?
If yes, use Excel.
I'm serious. Excel has solved 90% of business problems since 1985. It still does.
### 2. Is the complexity in the logic or the scale?
- Complex logic, simple scale: Use a spreadsheet
- Simple logic, complex scale: Use a database
- Complex logic, complex scale: Maybe consider AI
99% of problems are simple logic, simple scale.
### 3. What happens when it's wrong?
- Spreadsheet wrong: Bob from accounting fixes it
- AI wrong: Nobody knows why, costs $100K to debug
## The Spreadsheet Liberation Movement
Here's my radical proposal: **Embrace the spreadsheet.**
Stop being embarrassed by simple solutions. Stop overengineering to impress people. Stop burning money on AI when VLOOKUP works fine.
I helped a startup cut their tech costs by 90% with one strategy: We moved everything possible back to Google Sheets.
- Customer tracking? Google Sheets
- Inventory management? Google Sheets
- Financial projections? Google Sheets
- Task management? Google Sheets
"But that doesn't scale!" the engineers screamed.
They were right. It scales to about 100,000 rows. Which is 99,000 more than most startups need.
## When AI Actually Makes Sense
I'm not anti-AI. I'm anti-stupid. Here's when AI genuinely adds value:
### Natural Language Processing at Scale
- Analyzing millions of customer reviews
- Real-time translation for global platforms
- Semantic search across huge document sets
### Pattern Recognition in Unstructured Data
- Medical imaging diagnosis
- Fraud detection in complex networks
- Anomaly detection in system logs
### Creative Generation
- First drafts of marketing copy
- Code completion for developers
- Design variations for A/B testing
Notice what these have in common? **They can't be done with formulas.**
## The Corporate Antibodies Problem
The biggest obstacle to spreadsheet sanity? Corporate antibodies.
Try suggesting Excel in a Fortune 500 IT meeting. Watch the horror on people's faces. "That's not enterprise-grade!" "What about governance?" "How does it integrate with our data lake?"
These are the same people who spent $10 million on a data platform that crashes weekly.
Excel has been running the world's finances for 40 years. It's enterprise-grade.
## Your Spreadsheet Liberation Checklist
1. **Audit your AI initiatives.** List every AI project.
2. **Apply the Excel test.** Could a spreadsheet do this?
3. **Calculate the ratio.** Cost of AI solution / Cost of Excel solution
4. **If ratio > 10x**, kill the AI project. No exceptions.
5. **Redeploy the budget.** Use the savings for actual innovation.
## The Success Stories Nobody Talks About
### The Insurance Company That Saved Millions
They killed their "AI claims processing system" and went back to Excel macros. Processing time went from 3 days to 3 hours. Accuracy improved 20%. Cost dropped 95%.
### The Retailer That Embraced Simplicity
Replaced their "demand forecasting AI" with a spreadsheet using last year's sales +10%. More accurate than the AI. Cost: one afternoon of an analyst's time.
### The Startup That Chose Sanity
Instead of building an "AI-powered CRM," they used Google Sheets with some Apps Script. Saved $200K. Used the money to hire salespeople. Revenue grew 300%.
## The Path Forward
The next time someone pitches you an AI solution, ask them to solve it in Excel first.
If they can't, maybe you need AI.
If they can, you just saved millions.
The dirty secret of enterprise AI? Most companies don't have AI problems. They have Excel problems. They have process problems. They have data quality problems.
AI won't solve any of these. It'll make them 100x more expensive.
## The Uncomfortable Truth
We're not in an AI revolution. We're in a complexity bubble.
Every generation thinks their technology is revolutionary. But most business problems haven't changed since the invention of commerce:
- Track inventory
- Calculate prices
- Predict demand
- Manage schedules
- Process transactions
These are spreadsheet problems. They were spreadsheet problems in 1985. They're spreadsheet problems now.
The only thing that's changed is how much we're willing to pay to avoid admitting it.
## Your Action Items
1. **Download your data.** Whatever AI system you're using, export the data to CSV.
2. **Open Excel.** Yes, really.
3. **Solve the problem with formulas.** Give yourself 2 hours.
4. **Compare the results.** I'm betting Excel wins.
5. **Calculate the savings.** Then buy yourself something nice with the budget you just freed up.
Because at the end of the day, your AI strategy isn't an AI strategy.
It's a spreadsheet strategy with venture capital funding.
And that's the most expensive Excel license you'll ever buy.