LLM‑Powered Analytics Opportunities: Research Brief Template

Purpose

This brief helps teams evaluate an LLM-powered analytics opportunity quickly and rigorously so you can decide whether to pilot, how to pilot safely, and what success looks like. Focus on measurable outcomes, data privacy, realistic architecture options, and human oversight.

How to use this template

Fill each section concisely. Attach supporting artifacts (data inventory, architecture diagrams, cost estimates, stakeholder list). Use the example at the end as a starting point. This template is for scoping experiments — keep pilots small, time-boxed, and measurable.

1. Opportunity statement & decision context

Describe the problem you would like an LLM-assisted capability to solve and why it matters to the business. Include the primary decision(s) this capability should inform and the expected impact if successful.

• Opportunity name: (short descriptive title)
• Decision context: Who uses the output? What decision does it support?
• Current baseline: How is the decision made today? Time, accuracy, cost, manual steps.
• Desired outcome / hypothesis: Clear change you expect (e.g., reduce time-to-insight by 50%, reduce false positives in alerts by 30%, increase leads qualified by 15%).
• Primary stakeholders: List owners, data stewards, legal/privacy, IT, and end users.
• Estimated value (high-level): qualitative or ballpark cost/benefit estimate and time horizon.

2. Data inputs and privacy implications

List the data required, sensitivity, access path, and what transformations (de-identification, sampling) are necessary. Identify privacy, compliance, or contractual constraints up front.

• Data sources: (tables, events, logs, documents, CRM fields, sensor streams)
• Data types & sensitivity: PII, PHI, proprietary, public — attach data inventory if available.
• Minimum viable dataset for pilot: Small, representative subset and retention policy.
• Privacy mitigations: De-identification, aggregation, data minimization, synthetic data, query filtering, secure enclaves.
• Access controls & logging: Who can query, what is logged, redaction policies.
• Regulatory considerations: GDPR, HIPAA, sector-specific rules, vendor contractual restrictions (model training/retention).

3. High-level architecture options and guardrails

Outline feasible architectures and the tradeoffs (latency, control, cost, vendor dependency). Include required integrations and who is responsible for operations.

• Option A — Cloud-hosted LLM API: fastest to prototype, lower ops, but may have data sharing concerns. Guardrails: request/response filtering, prompt scrubbing, strict access controls.
• Option B — Hybrid with private inference or fine-tuned model: better privacy and control, higher cost/ops. Guardrails: model governance, change control, model testing, retraining plan.
• Option C — Retrieval-augmented generation (RAG) with vector DB + LLM: keeps sensitive context local while using LLM for synthesis. Guardrails: index encryption, chunking strategy, freshness controls, provenance tracking.
• Operational guardrails: Rate limits, cost caps, monitoring for drift, version pinning, human approval gates for high-risk outputs.

4. Evaluation metrics and human-in-the-loop plan

Define measurable success criteria and the role of humans during the pilot. Include both technical metrics and business/behavioral metrics.

Suggested metrics

• Quality: accuracy, precision/recall, F1 score, factuality/hallucination rate (measured on labeled samples).
• Utility: task completion rate, reduction in manual effort, time-to-decision, percentage of decisions changed by the assistant.
• Reliability: latency (p95), uptime, error rate, rate of unsupported queries.
• Cost: cost per query, estimated monthly spend at projected scale, infra and ops costs.
• Risk & safety: incidents of sensitive data leakage, number of flagged outputs needing correction, escalation frequency.
• Adoption & satisfaction: user acceptance, Net Promoter Score, task satisfaction surveys.

Human-in-the-loop (HITL)

Describe how humans validate outputs, who can override, and how feedback is captured.

• Approval levels (suggested: human-verified for high-risk outputs, assisted for routine outputs).
• Feedback loop: how corrections are logged, used for retraining, and surfaced to product owners.
• Escalation: clear path when model output is ambiguous or risky.

5. Pilot success criteria and rollout considerations

Make success criteria numeric where possible and include minimum thresholds to move from pilot to limited rollout.

• Pilot length: (e.g., 8 weeks)
• Sample size: number of users, queries, or cases required to evaluate metrics.
• Minimum thresholds to pass: e.g., factuality > 90% on sampled checks; time-to-insight reduced > 30%; user satisfaction > 4/5; cost per useful query < $X.
• Go/No-Go criteria: list conditions that block rollout (privacy breach, sustained low quality, unacceptable cost, regulatory disapproval).
• Rollout considerations: training, change management, performance monitoring, model/version governance, SLA commitments, vendor terms, and rollback plan.

Practical checklist before starting a pilot

• Data inventory and privacy review completed.
• Stakeholder alignment and sponsor assigned.
• Initial test dataset and labeled evaluation set prepared.
• Architecture and cost estimate agreed.
• Monitoring, logging, and incident response plan in place.
• Human reviewers identified and briefed.

Worked example (short)

Opportunity: Sales Insights Assistant to summarize weekly CRM notes and flag at-risk accounts.

• Decision: Account managers prioritize outreach to at-risk accounts.
• Data: CRM notes (text), recent transaction logs, support tickets (PII present — require de-identification).
• Architecture: RAG with vector DB holding de-identified docs + cloud LLM for summarization.
• Metrics: precision of at-risk flagging > 70%, time-to-summary reduced from 2 hours to < 15 minutes, user satisfaction > 4/5.
• Pilot: 6-week pilot with 10 account managers, weekly labeled checks, human reviewer in the loop before outreach.

Next steps

1) Complete this brief and attach data inventory. 2) Run a privacy & legal check. 3) Prepare a small labeled evaluation set (50–200 examples). 4) Select architecture for pilot. 5) Time-box pilot and assign owners.

Attachments & artifacts to include

• Data inventory / sample extracts
• Initial labeled evaluation dataset
• High-level architecture diagram
• Cost estimate and budget owner
• List of pilot users and reviewers

Use this template to move quickly from curiosity to a focused experiment. Keep pilots small, measurable, and governed — and record results so successful patterns can be scaled safely.