Five industries walked through — what AI-native looks like in the wild — step 8 of 9

Write process_debt_score(company) that takes a company profile (dict) and returns a dict with two fields:

• score: integer 0-100, higher means MORE process debt (bad)
• verdict: string, one of:
  • "AI-native ready" if score <= 20
  • "manageable debt" if score <= 50
  • "heavy debt" if score <= 80
  • "incumbent trap" if score > 80

Score the company on these signals (each adds points to the debt total):

• age_years > 10: add 25 (incumbent business — typical 20+ year history)
• data_scattered_across_tools >= 5: add 20 (CRM + Slack + email + spreadsheets...)
• tacit_knowledge_pct > 50: add 20 (more than half of company knowledge "in someone's head")
• paper_processes > 0: add 15 (any paper at all)
• headcount_per_revenue_million > 7: add 10 (overstaffed)
• documented_workflows_pct < 30: add 10 (mostly undocumented)

Two companies run. Expected output:

HVAC Inc:   {'score': 100, 'verdict': 'incumbent trap'}
StartupCo:  {'score': 0, 'verdict': 'AI-native ready'}