Identify 3 high‑impact, low‑complexity processes, define success metrics and create a 6–8 week experiment backlog.

How do I measure ROI?

Combine operational KPIs (AHT, SLA, defect rate) and business KPIs (conversion, NPS) with implementation and run‑time costs.

What risks must be governed?

Privacy, security, bias, hallucinations, third‑party dependency; apply RAI policies, technical guardrails and human‑in‑the‑loop review.

Apply Azure AI to boost customer experience, operational efficiency and growth.

↘︎ AHT

Shorter handling times through assistants and automation.

↑ NPS

Higher satisfaction with timely, on‑point answers.

↗︎ Margin

Optimize pricing, promos and data‑driven recommendations.

Self‑service, triage, knowledge with citations and human hand‑off.

Quality inspection, inventory, OCR, safety and retail analytics.

Ticket classification, entity extraction, summaries and sentiment.

STT, TTS and translation for contact centers and multilingual content.

Recommendations, ranking and anomaly detection on KPIs.

Phase	Output	Duration
1. Discovery	Use‑case backlog, KPIs, risks	2 weeks
2. Pilot	Measured PoC with real users	4–6 weeks
3. Industrialization	Pipelines, monitoring, go‑live	4–6 weeks

Recommended training: AI‑900 · AI‑102 · DP‑100

Policies, transparency, audit, human review, bias management.

Data locality, encryption, secret management, least‑privilege access.

Quality/cost monitoring, SLOs, incident response and rollbacks.

How do I avoid model hallucinations?

Use RAG with enterprise sources, controlled prompts, confidence thresholds and fallbacks to search/human agent.

What’s the minimum team?

Product owner, data/ML engineer, security, process SME; involve legal/compliance for regulated cases.

Which metrics should we track?

Time to resolution, deflection rate, conversion, NPS, AHT, model accuracy/precision‑recall, cost per outcome.