How it works

• Workflow: IF condition met THEN action taken
• Example: IF payment received THEN update CRM + send receipt
• Multiple rules: IF condition A THEN action A, IF condition B THEN action B
• Works perfectly when conditions are clear and limited

Strengths

1. Simple to understand: Anyone can see the logic (IF/THEN)
2. Cheap to build: No AI training, just configure rules
3. Predictable: Same inputs ? same outputs every time
4. Fast to deploy: Can be built in days, not weeks
5. Easy to audit: Clear rules = clear compliance trail

When traditional automation works best

• Routine, repeatable processes (invoice routing, data entry)
• Clear decision criteria (if amount > ?830K, escalate to manager)
• Limited variations (most cases follow same pattern)
• Compliance-critical (audit trail important, predictability required)

Limitations

1. Doesn't handle exceptions: Payment is partial? Rule breaks.
2. Rule explosion: Every variation = new rule. 10 rules become 100.
3. Doesn't learn: Same situation next time, still requires same rule.
4. Brittle: Small variations break workflow (misspelled field, format change)
5. No context: Doesn't understand intent, just pattern matches

Cost profile

• Development: ?415K-20K (build rules)
• Maintenance: ?41,500-2K/month (add/fix rules as business changes)
• Per-process cost: Lower initial, but compounds as rules grow

Examples

• Email ? CRM routing (invoice, support ticket, sales inquiry)
• Invoice processing (amount > ?415K ? approval workflow)
• Lead scoring (5+ website visits + email opened = hot lead)

How it works

• Model trained on examples (1,000 past invoices)
• AI learns patterns (what makes an invoice an invoice?)
• New situation arrives: AI analyzes, understands context, makes decision
• No explicit rules written; AI infers from training data

Strengths

1. Handles exceptions: Partial payment? AI understands (context-aware)
2. No rule explosion: One AI model handles variations. No 100 rules.
3. Learns over time: AI improves as it sees more examples
4. Flexible: Can adapt to variations without re-programming
5. Contextual: Understands intent, not just pattern matching

When AI automation works best

• Complex decision-making (credit decisions, content moderation)
• High variation/exceptions (customer support, qualification)
• Adapting to changes (business evolves, AI adapts automatically)
• Scale (one AI handles volume that would need 100 rules)
• Judgment calls (subjective decisions that need nuance)

Limitations

1. Requires training data: Need 500+ past examples (traditional needs 0)
2. Less transparent: Why did AI decide X? ("Black box" sometimes)
3. More expensive: Development, training, infrastructure
4. Compliance complexity: Hard to explain decisions to auditors
5. Slower decisions: Sometimes (AI inference takes more processing)

Cost profile

• Development: ?1,245K-50K (gather data, train model, test)
• Maintenance: ?166K-5K/month (monitoring, retraining as patterns change)
• Per-process cost: Higher initial, but doesn't grow as volume increases

Examples

• Loan approval (credit score + income + history ? approve/deny + conditions)
• Support ticket routing (understand sentiment, urgency, category ? assign to best agent)
• Lead qualification (understand intent, pain, fit ? hot/warm/cold)

Schema

ComparisonChart, FAQPage, Guide

SEO Checklist

Title includes comparison
Meta mentions trade-offs + choosing
H1 asks "which approach"
Clear comparison table
Case study showing hybrid
FAQ with 8 questions
Internal links to services

AEO Optimization

Direct comparison (AI vs. traditional)
Decision criteria (when to use each)
Real example (insurance claims)