/analyze-ai-applicability

Purpose

Analyze AI applicability using empirically-validated methodologies:

Occupation mode: Score occupations using O*NET IWAs and Microsoft Research data
Process mode: Analyze processes using APQC PCF framework
Activity mode: Evaluate specific work activities against IWA success rates

Syntax

/analyze-ai-applicability <target> [--mode <occupation|process|activity>] [--industry <name>] [--output <path>]

Parameters

Parameter	Type	Required	Default	Description
`target`	string	Yes	-	Occupation name, process, or role to analyze
`--mode`	string	No	occupation	Analysis type: occupation, process, activity
`--industry`	string	No	cross-industry	Industry for PCF analysis
`--output`	string	No	stdout	Output file path
`--detailed`	flag	No	false	Include IWA-level breakdown
`--roi`	flag	No	false	Include ROI projections
`--headcount`	integer	No	-	Headcount for ROI calculations
`--hourly-cost`	float	No	50	Hourly cost for ROI calculations

Examples

Occupation Analysis

# Analyze Customer Service Representative
/analyze-ai-applicability "Customer Service Representative" --mode occupation

# With ROI projections
/analyze-ai-applicability "Customer Service Representative" --roi --headcount 100 --hourly-cost 45

# Detailed IWA breakdown
/analyze-ai-applicability "Sales Representative" --detailed --output sales_analysis.md

Process Analysis

# Analyze customer service process using PCF
/analyze-ai-applicability "Manage Customer Service" --mode process --industry banking

# Cross-industry process analysis
/analyze-ai-applicability "Process Accounts Payable" --mode process

Activity Analysis

# Analyze specific work activity
/analyze-ai-applicability "Respond to customer inquiries" --mode activity

# Multiple activities
/analyze-ai-applicability "Document preparation, Data analysis, Customer communication" --mode activity

Batch Analysis

# Analyze multiple occupations
/analyze-ai-applicability "occupations.csv" --mode occupation --output results.xlsx

# Department-wide analysis
/analyze-ai-applicability "Customer Support Department" --mode occupation --roi --headcount 50

Output

Standard Output (Occupation Mode)

─────────────────────────────────────────────────────────
AI APPLICABILITY ANALYSIS: Customer Service Representative
─────────────────────────────────────────────────────────

Occupation: Customer Service Representatives (SOC 43-4051)
Employment: 2,858,710 workers nationwide
O*NET Data Level: Data available (30.1)

AI APPLICABILITY SCORE: 0.408

┌─────────────────┬─────────┬──────────────────────────────┐
│ Metric          │ Value   │ Interpretation               │
├─────────────────┼─────────┼──────────────────────────────┤
│ Coverage        │ 72.0%   │ % of activities AI addresses │
│ Completion      │ 90.1%   │ Success rate on those tasks  │
│ Scope           │ 59.1%   │ Depth of capability          │
│ Overall Score   │ 0.408   │ Top 10% of occupations       │
└─────────────────┴─────────┴──────────────────────────────┘

TOP IWAs BY AI SUCCESS:
1. Respond to customer problems (92% completion)
2. Provide information to customers (91% completion)
3. Explain policies/procedures (89% completion)
4. Gather information from sources (88% completion)

AUTOMATION TIERS:
  Tier 1 (Full Automation):    4 IWAs (45% of role)
  Tier 2 (Supervised):         3 IWAs (35% of role)
  Tier 3 (Human-Assisted):     2 IWAs (15% of role)
  Human Essential:             1 IWA (5% of role)

RECOMMENDATION: High AI applicability. Priority deployment target.

Detailed Output (with --detailed)

IWA-LEVEL BREAKDOWN:

┌────────┬──────────────────────────────────────┬────────┬──────────┬─────────┐
│ IWA ID │ Activity                             │ Import │ Complete │ Tier    │
├────────┼──────────────────────────────────────┼────────┼──────────┼─────────┤
│ 4.1.1  │ Respond to customer problems         │ 4.8    │ 92%      │ Tier 1  │
│ 4.1.2  │ Provide information to customers     │ 4.5    │ 91%      │ Tier 1  │
│ 4.1.4  │ Explain policies/procedures          │ 4.2    │ 89%      │ Tier 1  │
│ 3.1.1  │ Prepare informational materials      │ 3.8    │ 88%      │ Tier 1  │
│ 2.4.1  │ Resolve customer complaints          │ 4.0    │ 78%      │ Tier 2  │
│ 1.1.1  │ Gather information electronically    │ 3.5    │ 90%      │ Tier 1  │
│ 2.2.1  │ Make decisions and solve problems    │ 3.2    │ 80%      │ Tier 2  │
│ 5.2.1  │ Develop customer relationships       │ 3.0    │ 65%      │ Tier 3  │
└────────┴──────────────────────────────────────┴────────┴──────────┴─────────┘

Weighted AI Applicability: 85.3%

ROI Output (with --roi)

ROI PROJECTION:

Inputs:
  Headcount: 100 FTEs
  Hourly Cost: $45
  Annual Labor Cost: $9,360,000

Projections:
  Productivity Gain: 47.2%
  Hours Saved/Year: 98,176 hours
  Annual Value: $4,417,920

  Platform Cost (est): $240,000/year ($200/user/mo)
  Net Annual Benefit: $4,177,920
  ROI Multiple: 18.4x

Recommendation: Strong ROI - proceed with deployment

What Happens

Parse Target: Identify occupation, process, or activity
Map to Taxonomy: Match to O*NET SOC codes or PCF categories
Retrieve Data: Get IWA importance weights and AI success rates
Calculate Scores: Compute Coverage × Completion × Scope
Tier Assignment: Categorize activities by automation potential
ROI Projection: Calculate value if --roi flag provided
Generate Report: Format output as specified

Success Output

✅ COMMAND COMPLETE: /analyze-ai-applicability

Analysis Summary:
- [x] Target: Customer Service Representative
- [x] Mode: occupation
- [x] O*NET code: 43-4051
- [x] IWAs analyzed: 12

Results:
- [x] AI Applicability Score: 0.408
- [x] Automation Tier Distribution: 4/3/2/1
- [x] Weighted Completion Rate: 85.3%
- [x] Coverage: 72.0%

Output: analysis_csr.md

Failure Indicators

❌ Occupation not found in O*NET database
❌ PCF category not found (process mode)
❌ <50% IWA mapping success
❌ Missing importance weights
❌ Invalid industry specified
❌ Output file write failed
❌ Score calculation error (NaN or negative)

When NOT to Use

Analyzing physical labor roles (low AI applicability known)
Individual performance assessment (this is role-level)
Real-time task routing (use process mining)
Comparing specific AI products (methodology, not product)
Non-US occupations (O*NET is US-focused)
Making hiring/firing decisions directly

Troubleshooting

Issue	Solution
Occupation not found	Use SOC code directly, or try alternate title
Low confidence score	Check if occupation has O*NET data available
Score seems too high	Verify IWA mappings; check for physical tasks
Score seems too low	Consider enterprise AI context (higher than consumer)
Missing ROI data	Provide --headcount and --hourly-cost
PCF category not found	Use cross-industry PCF as fallback

Skills: ai-occupation-applicability, process-classification-framework, work-activity-ai-analysis
Agents: workforce-transformation-advisor
Data: O*NET 30.1, APQC PCF v7.4, Microsoft Research study

Completion Checklist

Target occupation/process identified
Mapped to O*NET SOC code or PCF category
IWA data retrieved and validated
AI success rates applied
Score calculated correctly
Automation tiers assigned
ROI calculated (if requested)
Output generated in specified format
Results validated against expectations

Anti-Patterns

Anti-Pattern	Problem	Solution
Score absolutism	Treating 0.40 vs 0.38 as different	Use tiers, validate with pilots
Ignoring context	Generic scores without enterprise adjustment	Apply industry/enterprise multipliers
Automation-first	Recommending layoffs based on scores	Frame as productivity/capacity gain
Physical role analysis	Scoring warehouse workers	Skip low-score roles (known pattern)
Single metric focus	Only looking at overall score	Review Coverage, Completion, Scope separately

Verification

# Verify O*NET data available
python3 -c "
from coditect.onet import lookup_occupation
occ = lookup_occupation('43-4051')
print(f'Found: {occ.title}')
print(f'IWAs: {len(occ.iwas)}')
"

# Validate score calculation
/analyze-ai-applicability "Customer Service Representative" --detailed | grep "Score"
# Expected: ~0.408 ± 0.02

# Check IWA mapping
/analyze-ai-applicability "Customer Service Representative" --mode activity --detailed

Generated by: CODITECT Command Generator Source: AI Applicability Skills Suite Generated: 2026-01-23

Purpose​

Syntax​

Parameters​

Examples​

Occupation Analysis​

Process Analysis​

Activity Analysis​

Batch Analysis​

Output​

Standard Output (Occupation Mode)​

Detailed Output (with --detailed)​

ROI Output (with --roi)​

What Happens​

Success Output​

Failure Indicators​

When NOT to Use​

Troubleshooting​

Related​

Completion Checklist​

Anti-Patterns​

Verification​

Purpose

Syntax

Parameters

Examples

Occupation Analysis

Process Analysis

Activity Analysis

Batch Analysis

Output

Standard Output (Occupation Mode)

Detailed Output (with --detailed)

ROI Output (with --roi)

What Happens

Success Output

Failure Indicators

When NOT to Use

Troubleshooting

Related

Completion Checklist

Anti-Patterns

Verification