Business Case Development and ROI Analysis Questions

Objective: Estimate incremental ROI over 6–12 months for adding Google Cloud generative AI to a support chatbot.

Data required:

• Baseline: current ticket volume, average handle time (AHT), deflection rate, CSAT, resolution rate, cost per agent, escalation rate.
• Model inputs: expected precision/recall of AI responses, latency, confidence thresholds, deployment costs (inference, storage), integration engineering effort.

Metrics to track:

• Operational: ticket deflection %, reduction in AHT, decrease in escalations, agent time saved (FTEs), cost per ticket.
• Business: CSAT/NPS delta, first-contact resolution improvement, revenue impact from retention.
• Technical: model F1/accuracy, false-assist rate, confidence calibration.

Time horizon & approach:

• 0–1 month: small closed beta measuring precision/confidence and A/B test UX.
• 1–3 months: pilot with 5–10% traffic — measure operational metrics and tune thresholds.
• 3–12 months: ramp and measure steady-state ROI.

ROI calc: (Savings from reduced agent cost + revenue retention uplift + efficiency gains - total AI cost) / AI cost. Use sensitivity analysis on deflection and false-assist rates.