The most common question from companies hiring an AI delivery team is: "What will they actually do each week?"
It is a fair question. AI development is unfamiliar territory for most organizations, and the combination of rapid iteration, model experimentation, and continuous deployment looks nothing like traditional software projects. A Gantt chart from a waterfall-era project manager will not survive first contact with an AI build.
This article provides a transparent, week-by-week breakdown of how a dedicated AI delivery team operates across a 6-week engagement. It covers what each team member does, what gets shipped, what the client sees, and where the risks live.
The Team
A dedicated AI delivery team for a 6-week build typically has three core members:
- •UX Designer (25-50%) for customer-facing products
- •DevOps/Platform Engineer (25%) for complex infrastructure requirements
| Role | Focus | Time Allocation |
|---|---|---|
| Lead Product Engineer | Architecture, core implementation, AI agent orchestration, code review | 100% |
| Product Strategist | Requirements, stakeholder management, acceptance testing, sprint planning | 100% in Weeks 1-2, 50% in Weeks 3-6 |
| ML/AI Engineer | Model selection, prompt engineering, RAG pipeline, evaluation, production hardening | 100% |
Week 1: Discovery and Alignment
What the Team Does
Product Strategist:
Lead Product Engineer:
ML/AI Engineer:
What the Client Sees
- •Tuesday stand-up: Team shares initial findings and asks clarifying questions
- •Friday demo: Presentation of Problem Statement, Technical Feasibility Assessment, and Model Evaluation Report. Discussion of go/no-go decision for each proposed feature.
- •Problem Statement Document
- •Technical Feasibility Assessment
- •Model Evaluation Report
- •Draft User Story Map
Deliverables
Week 2: Architecture and Prototype
What the Team Does
Product Strategist:
Lead Product Engineer:
ML/AI Engineer:
What the Client Sees
- •Tuesday stand-up: Architecture walkthrough and discussion of technical decisions
- •Friday demo: Working prototype that handles real data. Not polished, not production-ready, but functional. Client provides direct feedback.
- •System Architecture Document
- •Working prototype
- •Sprint plan for Weeks 3-6
- •Cost model (projected monthly operating costs at production scale)
Deliverables
Week 3: Core Build -- Sprint 1
What the Team Does
This is the first full build sprint. The team follows a daily rhythm:
Morning (30 min): Stand-up. Each person states: what they completed yesterday, what they are working on today, and any blockers.
Day: Focused build work. The Product Engineer implements application features. The ML Engineer refines the AI pipeline. Both use AI coding agents to accelerate implementation.
End of Day: Code review. Every pull request is reviewed before merge. Tests must pass. The CI pipeline catches issues before they reach staging.
Typical Week 3 output:
What the Client Sees
- •Daily: Access to a staging environment updated on every merge
- •Tuesday stand-up: Progress update with live demo of completed features
- •Friday demo: Formal demo of all completed features. Client tests the staging environment directly and provides feedback.
- •Scope creep: Client sees the product taking shape and wants to add features. The Product Strategist's job is to capture requests and defer them to the backlog without disrupting the current sprint.
- •Data surprises: Real production data reveals quality issues not caught in Week 1. The ML Engineer must adapt the pipeline to handle real-world data messiness.
Risks at This Stage
Week 4: Core Build -- Sprint 2
What the Team Does
Second build sprint. Focus shifts from "get it working" to "get it working well."
Typical Week 4 output:
ML Engineer focus areas:
What the Client Sees
- •Daily: Staging environment with all MVP features functional
- •Tuesday and Friday: Demos and feedback sessions
- •End of week: Feature-complete MVP. All core user stories pass acceptance criteria.
Week 5: Production Hardening
What the Team Does
The product works. Now it needs to work reliably, securely, and at scale.
Lead Product Engineer:
ML Engineer:
Product Strategist:
What the Client Sees
- •Monday: Full walkthrough of the production-ready system
- •Wednesday: User acceptance testing session with real stakeholders
- •Friday: Go/no-go decision for production launch. All parties review: test results, security scan, monitoring dashboards, and runbooks.
- •Load test results
- •Security scan results
- •Monitoring dashboards
- •Runbooks and documentation
- •User guide and training materials
Deliverables
Week 6: Launch and Handoff
What the Team Does
Days 1-2: Staged Rollout
Days 3-4: Monitoring and Stabilization
Day 5: Handoff
What the Client Sees
- •Day 1-2: Production system live with real users
- •Day 3-4: Daily reports on production metrics
- •Day 5: Formal handoff meeting with complete documentation
- •Production system live and serving traffic
- •Complete technical documentation
- •Architecture decision records
- •Handoff guide for ongoing maintenance
- •Recommended improvement roadmap
Deliverables
Communication Cadence Summary
| Touchpoint | Frequency | Format | Duration |
|---|---|---|---|
| Stand-up | Daily | Video call or async (Slack) | 15 min |
| Sprint demo | Twice weekly (Tue/Fri) | Video call with screen share | 30-45 min |
| Staging access | Continuous | Client can test any time | -- |
| Status report | Weekly (Friday) | Written summary | -- |
| Stakeholder update | Bi-weekly | Formal presentation | 30 min |
What Makes This Work
Three things make a 6-week AI delivery engagement successful:
1. Senior team. Every person on the team has production experience. There is no ramp-up period, no learning on the job. The team is productive from Day 1.
2. Locked scope. The discovery sprint (Weeks 1-2) produces a clear scope that does not change during the build (Weeks 3-6). New ideas go in the backlog, not the current sprint.
3. Continuous visibility. The client sees working software from Week 2 onward. There are no "we'll show you something at the end" surprises. If something is going wrong, the client sees it early enough to course-correct.
This is not a methodology for every project. It works for scoped AI features and products with clear requirements and a committed client. But when those conditions are met, it consistently delivers production-ready AI products in a timeline that traditional development cannot match.