You have an AI idea. Customers want results, not demos. This playbook covers discovery, prototyping, launch, and post-launch iteration—with concrete deliverables for each phase so your team knows exactly what to do.
Phase 1: Problem Discovery
- Customer interviews: Conduct five 45-minute calls. Capture workflows, language, and existing workarounds.
- Outcome mapping: Document desired outcomes, constraints, and success metrics in a one-page brief.
- Data audit: Inventory available data, privacy considerations, and labeling requirements.
- Feasibility matrix: Score ideas on impact vs. effort vs. risk. Pick one primary workflow to automate.
Phase 2: Prototype
The goal is learning. Build the smallest experience that proves value. Recommended artifacts:
- Wizard-of-Oz demo: Use no-code tools or manual workflows. Validate output quality with humans in the loop.
- Prompt design doc: Store prompts, context windows, and evaluation criteria in version control.
- Evaluation dataset: Collect 50-100 representative examples. Define success/failure thresholds.
- Stakeholder review: Present learnings to product, engineering, legal, and support.
Phase 3: Build the V1
Definition of Done
- Production-ready architecture diagram and threat model.
- Evaluation pipeline integrated into CI/CD.
- Guardrails: rate limiting, abuse detection, fallback responses.
- Launch checklist covering docs, support training, pricing, and analytics.
Team Roles
- Product manager: drives discovery and launch narrative.
- Tech lead: orchestrates architecture, reliability, and integration.
- AI operator: maintains prompts, evaluation datasets, and observability.
- Customer success partner: sources beta users and feedback.
Phase 4: Launch & Iterate
After release, the work shifts to measurement and continuous improvement.
- Track activation, retention, and quality metrics tied to customer value.
- Schedule weekly evaluation reviews with product + engineering.
- Implement feedback widgets and capture qualitative insights.
- Maintain a roadmap of high-impact enhancements based on usage data.
Phase 5: Scale Responsibly
- Localization: Expand datasets, prompts, and evaluation to new languages/markets.
- Platformization: Expose APIs/SDKs for partners if demand exists.
- Governance: Formalize ethics, privacy, and audit processes.
- Cost optimization: Optimize model selection, caching, and infrastructure commitments.
Timeline Overview
Weeks 0-4
Discovery + Wizard-of-Oz prototype. Decision: proceed or pivot.
Weeks 5-10
Build evaluation pipelines, secure data approvals, architect V1.
Weeks 11-16
Implement, test, and launch to beta customers. Measure relentlessly.
Weeks 17+
Iterate, scale, expand to new personas. Revisit strategy quarterly.
Key Deliverables Checklist
- Problem brief & customer interviews
- Prompt design doc & evaluation dataset
- Architecture + threat model
- Launch checklist & support playbook
- Post-launch dashboard & cost tracker
Closing Thought
Successful AI products emerge when teams move deliberately: understand the customer, validate with lightweight prototypes, invest in reliability, and iterate with data. Follow this roadmap and your idea will graduate from hackathon novelty to a product customers rely on.