Limitations and When to Get Help
AI is a powerful data analysis assistant, but it has important limitations. Knowing these helps you use AI effectively and know when to involve a data professional.
AI Limitations in Data Analysis
1. File Size Limits
Limitation: Most AI tools have upload limits (ChatGPT: ~100MB, Claude: varies by file type).
Workaround: For large datasets, upload a representative sample or summary statistics instead.
2. No True Statistical Validation
Limitation: AI can calculate statistics, but it may not apply proper statistical tests for significance.
When to worry: When making important decisions based on small differences or patterns.
Solution: Ask AI to explain confidence levels, or have a statistician validate critical findings.
3. Potential for Hallucination
Limitation: AI might make up patterns or misread your data, especially with complex datasets.
Solution: Always verify surprising findings by checking the underlying data.
4. Can't Access External Data
Limitation: AI can't pull in data from other systems, databases, or live sources.
You'll need to: Manually export data before each analysis session.
5. No Persistent Memory (Usually)
Limitation: AI doesn't remember previous conversations or maintain an updated dataset.
Solution: Re-upload your data and provide context at the start of each new session.
When to Involve a Data Professional
1. High-Stakes Decisions
If decisions involve significant money, legal implications, or safety, have a professional validate the analysis.
- Investment decisions over $100K
- Regulatory compliance reporting
- Healthcare or safety-related data
- Legal evidence or audit materials
2. Complex Statistical Analysis
AI can do basic statistics, but these scenarios need an expert:
- Predictive modeling and forecasting
- A/B test analysis and experiment design
- Causal inference (proving cause and effect)
- Statistical sampling design
- Regression analysis with multiple variables
3. Sensitive Data
Some data should never be uploaded to external AI tools:
- Personal health information (HIPAA)
- Financial records with personal identifiers
- Customer data with PII (names, addresses, SSNs)
- Proprietary business secrets
- Data covered by NDA or regulatory requirements
Instead: Use anonymized or aggregated data, or work with an internal data team.
4. Production Systems
AI-generated insights shouldn't directly feed into:
- Automated decision systems
- Customer-facing reports without human review
- Financial statements or official filings
- Systems that affect people's lives or livelihoods
5. Unexplained Anomalies
If AI finds something surprising that you can't explain, investigate further before acting.
Building an AI + Human Workflow
The best approach combines AI efficiency with human judgment:
- AI first pass: Upload data, get initial insights and patterns
- Human review: Validate findings against your domain knowledge
- AI refinement: Ask follow-up questions, create reports
- Expert validation: For high-stakes findings, get professional review
- Human decision: Use AI insights to inform, not replace, your judgment
Questions to Ask Before Trusting AI Analysis
Before acting on AI-generated insights:
- Does this finding make sense given what I know about the business?
- Have I verified this with spot-checks of the raw data?
- How many data points support this conclusion?
- What could cause this pattern other than what AI suggested?
- What's the worst that could happen if this analysis is wrong?
Key Takeaway
AI is an excellent first-pass analyst and report writer. But for high-stakes decisions, sensitive data, or complex statistical questions, combine AI efficiency with human expertise and professional validation.