Customer experience outsourcer. Stock down 95%.
TTEC provided outsourced contact center operations and CX consulting, charging clients per human agent per hour. As AI chatbots and autonomous agents proved capable of resolving most routine support tickets, clients began making smaller commitments while evaluating in-house AI capabilities. Revenue guidance fell 5-8% year-over-year. The CEO's bid to take the company private was withdrawn. The stock collapsed from $110 to ~$5, a 95% decline that mirrors the structural death of the human-agent-as-a-service model.
The per-seat billing model for outsourced customer service is collapsing. AI agents handle unlimited concurrent conversations at near-zero marginal cost, making human-agent-by-the-hour pricing obsolete.
Peak: $110/share, strong post-pandemic CX outsourcing demand
Stock declines 52% as AI chatbot adoption accelerates
'Transitional year' — stock drops another 77%, CEO's go-private bid fails
Revenue guidance declines 5-8%, stock hits $5.50
Effectively a zombie company; clients building AI agents in-house
Replace outsourced contact center operations with AI-first customer service. Build autonomous agents that handle 80%+ of inquiries, keep a small expert team for complex cases, and eliminate the per-seat outsourcing model entirely.
Inventory all outsourced support functions and their monthly cost
Categorize by complexity: routine (AI), moderate (AI + human review), complex (human only)
Deploy AI agents for routine tier — typically 60-80% of all tickets
Build escalation workflows that route complex cases to a small internal team
Wind down outsourcing contracts as AI resolution rate improves
Reinvest savings into product improvements that reduce support demand
Analyze our current outsourced support operation: Monthly ticket volume: {{volume}} Current cost: {{cost}}/month Top ticket categories: {{categories}} Average resolution time: {{resolution_time}} Create a migration plan to AI-first support: 1. Which ticket categories can AI handle on day one? 2. Expected AI resolution rate by category 3. Staffing plan: how many human agents still needed? 4. Month-by-month cost reduction timeline 5. Risk mitigation for the transition period
Review these {{count}} AI agent conversations and evaluate: 1. Resolution accuracy (did the AI solve the problem?) 2. Tone appropriateness (friendly, professional, empathetic) 3. Escalation decisions (should it have escalated? Did it escalate unnecessarily?) 4. Information accuracy (any hallucinations or wrong info?) 5. Overall CSAT prediction (1-5) Flag any conversations that need human review. Conversations: {{conversations}}