Andrew Ng: AI's biggest benefit is making sense of unstructured data

Unlocking Unstructured Data: The Most Practical AI Win for Customer Support Leaders

Your tickets, chats, emails, call transcripts, and knowledge articles hold answers you're already paying for-but can't use at scale. The most useful application of AI right now is turning that unstructured mess into fast answers, clear insights, and better decisions.

If you run support, this isn't theory. It's the clearest path to lower handle time, higher CSAT, and fewer escalations-without hiring your way out.

What counts as unstructured data in support

• Tickets, chat logs, and email threads
• Call recordings and transcripts
• Help center articles, PDFs, internal docs, release notes
• Community posts and CRM notes

Why this matters to your KPIs

• FCR: Agents surface the right fix on the first try with semantic search and suggested replies.
• AHT: Summaries, auto-tagging, and intent classification cut switching and manual work.
• CSAT: Consistent answers and faster resolution times.
• Deflection: Better article relevance reduces ticket volume.
• QA/compliance: Automated checks and redaction reduce risk.

Core techniques in plain language

• Semantic search and embeddings: Find the passage that means the same thing as the question, not just keywords.
• Retrieval-Augmented Generation (RAG): Pull facts from your docs, then draft an answer grounded in that evidence. Original RAG paper
• Summarization: Turn long threads and calls into concise briefs with next steps.
• Classification and tagging: Auto-apply product, issue type, priority, and sentiment.
• Topic mining: Spot patterns driving volume and churn.

High-impact use cases you can ship this quarter

• Agent assist: Draft replies with citations from your knowledge base and past solved tickets.
• Internal semantic search: One search across tickets, articles, and release notes with snippet previews.
• Ticket and call summarization: Standardized summaries posted back to the CRM.
• Auto-tagging and routing: Intent + sentiment + priority to route to the right queue.
• Help center optimization: Identify content gaps and stale articles from search failures.
• QA automation: Score calls and tickets for policy adherence and tone.
• PII redaction: Remove sensitive data before model usage.

30-60-90 day rollout plan

• Days 0-30: Pick two use cases (agent assist + summaries). Inventory data sources. Define KPIs (AHT, FCR, CSAT, deflection). Set up PII redaction. Create a gold set of 100 real queries for evaluation.
• Days 31-60: Stand up semantic search + RAG on a small corpus. Pilot with 10-20 agents. Capture feedback, relevance scores, and time saved. Tighten prompts and chunking. Add guardrails and citations.
• Days 61-90: Expand to more teams and data sources. Integrate with CRM/helpdesk macros. Roll out QA and analytics dashboards. Formalize governance and an update process for content.

Minimal tech stack (build or buy)

• Connectors: Pull tickets, articles, and transcripts from your helpdesk, CMS, and call platform.
• Preprocessing: Chunk docs, normalize formats, redact PII, add metadata (product, date, region).
• Embeddings + vector store: Store text embeddings for semantic retrieval.
• LLM layer: Use RAG to draft answers with citations; enforce templates and tone.
• Orchestration and guardrails: Prompt templates, timeouts, content filters, and approval flows.
• Monitoring: Track quality (groundedness, relevance), usage, latency, and costs per ticket.
• Buy vs. build: Many helpdesk suites now include these features; if you build, start with one use case.

Governance and risk you can't ignore

• Data privacy: Redact PII before indexing. Enforce data residency and retention policies.
• Security: Least-privilege access to sources. Encrypt data at rest and in transit.
• Quality: Require citations for every drafted answer. Block unsupported claims.
• Human-in-the-loop: Agents approve before sending. Audit logs on by default.
• Evaluation: Maintain a test set; measure groundedness and factuality with each update. See NIST AI RMF for structured risk controls.

Metrics that prove ROI

• AHT: Target 15-30% reduction from summaries, search, and better routing.
• FCR: 5-15% lift from better suggestions and content coverage.
• Deflection: 10-20% lift after improving article relevance and coverage.
• QA coverage: From spot checks to near 100%, with automated scoring.
• ROI model: (Time saved x fully loaded hourly rate x volume) - (tooling + infra + change management).

Common pitfalls (and the fix)

• Messy content: Outdated articles lead to bad answers. Assign owners and review cycles.
• Too much, too soon: Start with one team and one workflow to build trust and score quick wins.
• No evaluation set: Without it, quality drifts. Keep a living test set and compare before/after.
• Prompt sprawl: Standardize templates and version them.
• Shadow data: Centralize connectors; block personal file uploads into the index.

Team you need

• Support lead: Owns KPIs, change management, and enablement.
• Content owner: Maintains articles and release notes.
• Data/ML engineer: Pipelines, embeddings, vector store, and evals.
• QA/compliance partner: Policy checks, redaction, audit.

Where to skill up fast

• Explore practical courses by role: Complete AI Training - Courses by Job
• Level up prompts and automation: Prompt Engineering Guides

Bottom line

If you do nothing else with AI this year, make unstructured data usable. Start with agent assist and ticket summaries, prove the metrics, then expand. The compounding effect on cost, speed, and customer trust is hard to beat.