When to Use AI (& When Not To)
AI solves specific problems. It fails at others. How to tell the difference before you've wasted six months.
The Reality
Everyone asks "can we use AI for this?" Not every problem needs AI. Sometimes a database query is better. Sometimes a rule beats a model. Sometimes a human is faster.
Good Use Cases
Classify emails, receipts, tickets into categories.
Why: Fuzzy input. Typos, variations. Subjective boundaries.
Example: "Spent £45 at Tesco on groceries" → Groceries.
Extract key info from long documents, emails, transcripts.
Why: Subjective. What's "key" depends on context.
Example: Extract date, merchant, amount from receipt.
Pull structured data from unstructured sources.
Why: Handles formatting variation. Learns from examples.
Example: Extract {date, merchant, amount, category} from receipt photo.
Find similar documents, past conversations, relevant examples.
Why: Understands meaning, not just keywords.
Example: "I lost my password" matches "forgot credentials".
Generate personalized copy, emails, explanations.
Why: Scale. Can't hire 100 writers. AI generates 10K variations.
Risk: Needs human review. Hallucinations happen.
Bad Use Cases
Don't use AI if you need the exact answer.
Example: "What's Q2 revenue?"
Why: Model guesses based on patterns. Query the database instead.
Don't use AI for "if A then B" decisions.
Example: "Apply 10% discount if order > £100".
Why: AI is probabilistic. Use a rule engine. Faster, cheaper, reliable.
Don't use AI for instant, high-stakes calls.
Example: "Approve this loan application?"
Why: Needs human review. Defeats the speed advantage.
Vision models improve, but hallucinate on uncommon objects.
Why: Real-world data is messier than training data. Reliability is low.
AI learns from patterns. No examples = can't learn.
Why: Will hallucinate. Use rules or humans instead.
Decision Framework
Compare AI against alternatives before committing:
- Handles fuzzy input
- Scales to millions
- Learns from examples
- Cost: £0.01–1/request
- Latency: 2–10s
- Millisecond latency
- No hallucinations
- Cheap (once built)
- Breaks on edge cases
- Hard to scale
- Exact answers
- < 100ms latency
- Cheap
- Structured data only
- No flexibility
- Perfect accuracy
- Understands context
- Expensive (£10–100)
- Slow (hours)
- Doesn't scale
Quick Checklist
- Is the answer objective? → Database or rules.
- Is the answer subjective? → Might be AI.
- Do you have >100 examples? → AI could work.
- Do you have 0 examples? → Use rules or humans.
- Speed critical (< 100ms)? → Not AI.
- Accuracy critical (>99.9%)? → Not AI.
- Costs low? → Maybe AI.
- Costs high? → Definitely not AI.
The Hybrid Approach
Best practice: Use AI where it excels. Use rules/databases everywhere else.
Example: Expense Categorization
- Known merchant? → Database lookup.
- Amount > £5,000? → Manual review.
- Unknown merchant? → AI categorization.
- AI confidence < 60%? → Flag for human.
Result: AI handles 70%. Rules handle 20%. Humans handle 10%. Cheaper and more reliable than AI-only.