AI Implementation Plan Template
A detailed implementation plan template for taking an AI project from approved concept through to production deployment. Covers work breakdown, resource allocation, technical milestones, testing, and go-live procedures.
Overview
What's included
Work Breakdown Structure
Work Breakdown Structure
Project name: Project lead: Start date: Target go-live:
Phase 1: Discovery & Data (Weeks 1-3)
| # | Task | Owner | Duration | Dependencies | Status |
|---|---|---|---|---|---|
| 1.1 | Finalise requirements with business stakeholders | 3 days | — | ||
| 1.2 | Map data sources and access requirements | 2 days | 1.1 | ||
| 1.3 | Conduct data quality assessment | 5 days | 1.2 | ||
| 1.4 | Build data extraction pipeline | 5 days | 1.2 | ||
| 1.5 | Clean and prepare training dataset | 5 days | 1.3, 1.4 | ||
| 1.6 | Define model evaluation criteria | 2 days | 1.1 |
Phase 2: Development & Training (Weeks 4-7)
| # | Task | Owner | Duration | Dependencies | Status |
|---|---|---|---|---|---|
| 2.1 | Set up development environment | 2 days | 1.5 | ||
| 2.2 | Develop baseline model | 5 days | 2.1 | ||
| 2.3 | Iterate on model performance | 10 days | 2.2 | ||
| 2.4 | Build API / integration layer | 5 days | 2.2 | ||
| 2.5 | Develop UI / user interface | 5 days | 2.4 | ||
| 2.6 | Conduct bias and fairness testing | 3 days | 2.3 |
Phase 3: Testing & Validation (Weeks 8-9)
| # | Task | Owner | Duration | Dependencies | Status |
|---|---|---|---|---|---|
| 3.1 | Unit and integration testing | 3 days | 2.4, 2.5 | ||
| 3.2 | Performance and load testing | 2 days | 3.1 | ||
| 3.3 | User acceptance testing (UAT) | 5 days | 3.1 | ||
| 3.4 | Security review | 3 days | 3.1 | ||
| 3.5 | DPIA completion (if required) | 5 days | 2.6 |
Phase 4: Deployment & Go-Live (Week 10)
| # | Task | Owner | Duration | Dependencies | Status |
|---|---|---|---|---|---|
| 4.1 | Deploy to staging environment | 1 day | 3.3, 3.4 | ||
| 4.2 | Go/No-go decision meeting | 1 day | 4.1 | ||
| 4.3 | Deploy to production | 1 day | 4.2 | ||
| 4.4 | Monitor launch (intensive first week) | 5 days | 4.3 | ||
| 4.5 | Post-launch retrospective | 1 day | 4.4 |
Resource Plan
Resource Plan
Team Structure
| Role | Name | Allocation | Phase(s) |
|---|---|---|---|
| Project Lead | % | 1-4 | |
| Data Engineer | % | 1-2 | |
| ML Engineer / Data Scientist | % | 1-3 | |
| Software Engineer | % | 2-4 | |
| QA / Test Engineer | % | 3 | |
| Business Analyst | % | 1, 3 | |
| Design / UX | % | 2 | |
| DevOps / Infrastructure | % | 2, 4 |
External Resources
| Resource | Provider | Duration | Cost |
|---|---|---|---|
| weeks | £ | ||
| weeks | £ |
Infrastructure Requirements
| Resource | Specification | Monthly Cost | Duration |
|---|---|---|---|
| Cloud compute (training) | £ | months | |
| Cloud compute (inference) | £ | Ongoing | |
| Storage | £ | Ongoing | |
| AI platform / API | £ | Ongoing |
Budget Summary
| Category | Budget | Actual | Variance |
|---|---|---|---|
| People (internal) | £ | £ | £ |
| People (external) | £ | £ | £ |
| Technology | £ | £ | £ |
| Infrastructure | £ | £ | £ |
| Contingency | £ | £ | £ |
| Total | £___ | £___ | £___ |
Go-Live Readiness Checklist
Go-Live Readiness Checklist
All items must be marked complete before the go/no-go decision:
Functional Readiness
- All acceptance criteria met and signed off by business owner
- Model accuracy exceeds minimum threshold: %
- UAT completed with no critical or high-severity defects outstanding
- User documentation and training materials delivered
- End users have been trained on the new system
Technical Readiness
- Production environment provisioned and tested
- CI/CD pipeline deploying to production environment
- Database migrations completed and verified
- API endpoints tested under expected load
- SSL/TLS certificates installed and valid
- DNS and routing configured correctly
Operational Readiness
- Monitoring dashboards configured with alerts
- On-call rota defined for first 2 weeks post-launch
- Rollback procedure documented and tested
- Incident response plan in place
- Support team briefed on new system
Compliance Readiness
- DPIA completed and approved (if applicable)
- Privacy notice updated
- AI governance review completed and approved
- Data processing agreement in place with vendors
Communication Readiness
- Internal announcement prepared
- Customer communication prepared (if applicable)
- FAQ document created for support team
Go / No-Go Decision
Decision date: Decision: Go / No-Go Conditions (if conditional go): Approved by:
Instructions
How to use this template
Adapt the work breakdown to your project
The template shows a 10-week timeline. Adjust durations based on your project complexity, team size, and data readiness.
Assign owners to every task
Every task needs a named owner. Unowned tasks are the most common cause of project delays.
Track dependencies carefully
Note which tasks block others. Use these dependencies to identify the critical path and manage delays proactively.
Use the go-live checklist rigorously
Do not skip checklist items under time pressure. A failed launch is more expensive than a delayed one.
Run a post-launch retrospective
Within 2 weeks of go-live, gather the team to discuss what went well, what could be improved, and lessons for future AI projects.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
Simple implementations (e.g. deploying a pre-built AI service) can take 4-6 weeks. Custom AI projects typically take 10-16 weeks. Complex enterprise implementations may take 6-12 months.
Data quality is the most common cause of implementation failure. Invest in thorough data preparation and quality checks before model development begins.
An iterative approach (agile-inspired) works best for most AI projects because model development is inherently experimental. However, key milestones like data readiness and go-live should be planned in advance.
Use a formal change request process. Assess the impact on timeline, budget, and resources before agreeing to any scope change. The project lead should have authority to reject changes that jeopardise delivery.
Define a minimum viable accuracy before development starts. If the model cannot meet this threshold, options include: collecting more or better training data, trying alternative approaches, reducing scope, or deciding not to proceed.
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