Executive Decision Guide

Selecting the right GenAI use cases.

Generative AI creates value through Creativity (new ideas and content), Automation (cognitive workflows), and Personalization (1-to-1 experiences). This hub helps leaders prioritize which initiatives to fund based on business value, feasibility, and risk.

Value

Revenue uplift, cost-to-serve reduction, cycle-time compression.

Feasibility

Data readiness, integration effort, operating model fit.

Risk

Privacy, compliance, reliability, safety and brand exposure.

GenAI Portfolio Map
A practical way to prioritize
Creativity

Accelerate ideation and content creation while keeping humans in the loop for brand and quality control.

Automation

Move from rule-based RPA to cognitive workflows that can read, write, summarize, and route.

Personalization

Generate tailored experiences in real time: dynamic offers, conversational assistance, and personalized journeys at scale.

Most successful programs start with a balanced set

Fund 1–2 low-risk “quick wins” (copilots and content), 1 medium-scope workflow automation, and 1 strategic differentiator (personalized experiences).

Tip: Standardize evaluation criteria and reuse an architecture pattern (model + retrieval + workflow + telemetry) to scale faster.

Three pillars of GenAI value

Use the pillars to organize opportunities and avoid an “everything is a chatbot” strategy.

Creativity

Generate first drafts, options, and new concepts—then refine with human judgment. Best for marketing, product, sales enablement, and knowledge work.

Good starting use cases
  • • Campaign concepts and copy variants
  • • Product requirement drafts and user stories
  • • Proposal/RFP responses with citations
Deep dive Human-in-the-loop

Automation

Interpret unstructured inputs (emails, PDFs, chats), reason over context, and orchestrate end-to-end workflows—beyond rigid rule engines.

Good starting use cases
  • • Document intake + extraction + routing
  • • Customer support summarization + drafting
  • • IT ops incident triage and runbook execution
Deep dive Workflow + controls

Personalization

Generate context-aware experiences per user, per moment—moving from segments to individualized content, offers, and journeys.

Good starting use cases
  • • Dynamic email and landing page generation
  • • Conversational commerce assistants
  • • Adaptive learning and onboarding
Deep dive Data + governance
Use Case Selection Framework

A pragmatic way to pick winners

Score candidates on Value, Feasibility, and Risk. Then select a portfolio that balances quick ROI with strategic differentiation.

Value (0–5)
  • • Direct revenue uplift or conversion improvement
  • • Unit cost reduction (cost-to-serve, analyst hours)
  • • Cycle-time improvement (days → hours)
Feasibility (0–5)
  • • Data availability + quality + access rights
  • • Integration complexity with systems and workflows
  • • Clear evaluation metrics and feedback loop
Risk (0–5; higher is safer)
  • • Regulatory exposure (PII, HIPAA, PCI, etc.)
  • • Hallucination tolerance and error cost
  • • Brand risk and model behavior controls
Decision matrix (example)
Customize for your enterprise
Quick Wins (High value, high feasibility)
  • • Drafting copilots with retrieval
  • • Internal knowledge search + summarization
  • • Meeting notes, action items, reporting
Automation Plays (Value via scale)
  • • Document processing pipelines
  • • Ticket triage + routing + drafting
  • • Exception handling over RPA
Differentiators (Strategic moat)
  • • Personalized product experiences
  • • Co-creation in product workflows
  • • New AI-native service offerings
Avoid / Defer (Low feasibility or high risk)
  • • High-stakes decisions without auditability
  • • Fully autonomous actions on critical systems
  • • PII-heavy flows without clear governance
Recommended selection output

A prioritized backlog with (1) target KPIs, (2) data + integration plan, (3) operating model and ownership, and (4) risk controls + evaluation plan.

KPIs

Quality, time saved, deflection, conversion, CSAT.

Telemetry

Prompt/response logs, evaluations, drift monitoring.

Adoption

Workflow fit, training, change management, UX.

Build a portfolio, not a one-off pilot

Executives win by standardizing capabilities and scaling repeatable patterns.

Reusable platform primitives
  • • Model gateway and cost controls
  • • Retrieval (RAG) + permissions
  • • Workflow orchestration + human review
  • • Evaluation, monitoring, and audit logs
Phase funding model
  • • Phase 1: Prototype + eval rubric
  • • Phase 2: Limited production + guardrails
  • • Phase 3: Scale + process redesign
  • • Phase 4: New product offerings
Ownership and operating model
  • • Business owner for outcomes + KPIs
  • • Product team for roadmap and UX
  • • Risk/legal for policy and reviews
  • • IT/security for platform + access

Governance and guardrails

The best programs treat GenAI as a new production dependency. Put controls in place early so you can scale safely.

Minimum viable controls
  • • Data classification + redaction for PII
  • • Grounding via retrieval and citations
  • • Human review for high-impact outputs
  • • Automated evaluations and incident playbooks
  • • Cost budgets, rate limits, and fail-safe modes
Risk posture by pillar
Creativity

Lower operational risk; focus on brand, IP, and review workflows.

Automation

Medium risk; require error handling, approvals, and auditability.

Personalization

Higher privacy risk; require consent, data minimization, and policy enforcement.

Rule of thumb: start with “assist” modes (draft/suggest), then graduate to “act” modes only when evaluation and controls are mature.

Next: pick your first 3 use cases

Select one creativity accelerator, one automation workflow, and one personalization differentiator. Use consistent KPIs and guardrails so you can scale without re-architecting.