Why you need this
Every organization is exploring AI, but most initiatives fail before they start—not from lack of technology, but from poor planning. Teams jump into implementation without defining success metrics, assessing feasibility, or understanding true costs. The result: wasted budget, frustrated stakeholders, and AI projects abandoned halfway through.
The problem: AI projects differ from traditional software projects. They involve data quality assessments, model selection trade-offs, accuracy thresholds, and ongoing maintenance costs that teams often discover too late. Without structured planning, projects either never get approved or fail during deployment.
This planner solves that. It provides a systematic framework for scoping AI initiatives from concept to deployment, helping you ask the right questions, assess feasibility realistically, and build stakeholder buy-in with comprehensive project plans.
Perfect for:
- Product managers scoping AI features for existing products
- Engineering leaders evaluating technical feasibility and resource needs
- Business leaders making build-vs-buy decisions for AI capabilities
- Consultants helping clients define and plan AI initiatives
What's inside
Project Definition Worksheet
Goal Clarity Template:
- Defines specific, measurable outcomes (not just "use AI")
- Identifies the problem being solved and why it matters
- Establishes success metrics (accuracy, speed, cost reduction, revenue impact)
- Documents current baseline performance for comparison
Use Case Validation:
- Tests if AI is the right solution (vs. rules-based systems, analytics, or process change)
- Identifies available alternatives and why AI is superior
- Assesses data availability and quality requirements
- Confirms stakeholder alignment on goals
Feasibility Assessment Checklist
Technical Feasibility:
- Data requirements: volume, quality, labeling needs, privacy constraints
- Model complexity: can existing models work, or custom development needed?
- Infrastructure: compute requirements, latency constraints, integration points
- Accuracy thresholds: what precision is acceptable vs. required?
Business Feasibility:
- Cost-benefit analysis: development, infrastructure, maintenance costs vs. expected value
- Timeline reality check: typical AI project phases and duration
- Risk assessment: what happens if accuracy is lower than expected?
- Change management: how will users adapt to AI-assisted workflows?
Organizational Readiness:
- Skill inventory: AI/ML expertise in-house or need external help?
- Data governance: policies for training data, model outputs, user privacy
- Compliance requirements: regulatory constraints, explainability needs, audit trails
Cost Estimation Template
Development Costs:
- Data collection, cleaning, and labeling (often 60-80% of project effort)
- Model development or API costs (OpenAI, Claude, Google, custom)
- Integration with existing systems
- Testing and validation
Ongoing Costs:
- API usage at projected scale (per-request costs add up fast)
- Infrastructure and compute (especially for custom models)
- Monitoring and maintenance
- Model retraining as data shifts
- Support and incident response
Hidden Costs to Account For:
- Failed experiments and iteration cycles
- Security and compliance reviews
- User training and change management
- Technical debt from quick prototypes
Timeline & Milestone Planner
Typical AI Project Phases:
- Discovery (2-4 weeks): Problem definition, feasibility study, stakeholder alignment
- Data preparation (4-8 weeks): Collection, cleaning, labeling, validation
- Prototype (2-4 weeks): Initial model or API integration, accuracy testing
- Development (6-12 weeks): Production system, integration, error handling
- Testing (4-6 weeks): User acceptance, edge case validation, performance tuning
- Deployment (2-4 weeks): Gradual rollout, monitoring, iteration
Milestone Definition:
- Establishes decision gates (go/no-go checkpoints)
- Defines deliverables for each phase
- Sets realistic timelines with buffer for unknowns
- Plans for iteration based on learnings
Risk Identification Matrix
- Data risks: Insufficient volume, poor quality, privacy concerns, bias
- Technical risks: Model accuracy below threshold, latency issues, scalability problems
- Business risks: User adoption, competitor moves, regulatory changes
- Mitigation strategies: Fallback plans, phased rollouts, pilot programs
How to use it
- Feature planning — Assess whether AI will genuinely improve your product before committing engineering resources
- Budget proposals — Build comprehensive cost estimates that include hidden expenses executives often overlook
- Vendor evaluation — Compare build vs. buy decisions systematically with realistic total cost of ownership
- Stakeholder alignment — Document assumptions and trade-offs upfront to prevent scope creep and misaligned expectations
Example planning scenario
Project: AI-powered customer support chatbot
Using the planner:
- Goal: Reduce tier-1 support tickets by 40%, improve response time from 4 hours to instant
- Success metric: 70% accuracy in resolving common issues, < 5% escalation to humans for mishandled requests
- Data assessment: 50K historical support tickets available, need labeling for intent (4 weeks, $8K)
- Cost estimate: GPT-4 API at projected volume = $1,200/month, engineering time = $45K, maintenance = $500/month
- Timeline: 14 weeks from kickoff to limited beta
- Risk: Accuracy below 70% → Mitigation: Start with limited issue types, expand gradually based on performance
Outcome: Clear project scope with realistic costs and timeline. Stakeholders approve with understanding of constraints.
Want to go deeper?
This planner covers project fundamentals. For comprehensive guidance on AI implementation:
- Guide: AI at Work Basics — Best practices for AI tools in professional settings
- Guide: When to Use AI Tools — Choosing the right use cases for AI
- Guide: Responsible AI Deployment — Ethical and safe AI implementation
- Glossary: Evaluation — Understanding AI model assessment
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Share with your organization or clients
- Customize for your industry and project types
- Integrate into project management frameworks
Just include this attribution:
"AI Project Planner" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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