Methodology
Agile AI Development Intake Process.
A structured, repeatable framework for envisioning, building, and sustaining AI-powered products — from first idea to production and beyond.
Product Development Roadmap
Nine stages across four phases.
Each stage is a focused unit of work with clear inputs, outputs, and ownership. Click any phase to jump to its detail below.
INTAKE
Stages 1–3Envision · Prioritize · Assign
Evaluate the idea, prioritize against the portfolio, and assemble the right cross-functional team before any technical work begins.
Explore phaseINITIATE
Stages 4–5Analyze · Refine
Gather the requirements that shape effective AI user stories and refine the full solution model using a business-canvas structure.
Explore phaseDEVELOP
Stages 6–8Design · Develop · Deliver
Design the architecture, build in disciplined 3-week sprints with full ceremonies, and deliver to production with validation at every step.
Explore phaseSUPPORT
Stage 9Support
Monitor model quality, retrain on cadence, audit responsible AI, and feed production insights back into the next intake cycle.
Explore phaseINTAKE
Evaluate, prioritize, and staff your AI initiative.
Stage 1
Envision
Every AI initiative begins with a clear articulation of intent. The Envision stage captures the core problem, the value hypothesis, and the organizational context before any technical work begins.
- 1Provide a short description of the AI product or capability you want to build.
- 2Which organizational group or customer segment will this AI solution serve?
- 3Will this solution generate revenue, reduce cost, or increase operational efficiency?
- 4Does this initiative align with specific organizational goals defined by corporate leadership? If so, list them.
- 5Who are the primary stakeholders for this initiative?
- 6Will the initiative be funded through the product stakeholder's budget, IT, or as a shared expense?
- 7Is this initiative associated with an immediate threat or risk — such as competitive displacement, regulatory compliance, or data security?
- 8Has a business/product owner been assigned? If so, who?
9. Which of the following best describes the nature of this initiative?
Stage 2
Prioritize
For IT and AI leadership to commit resources, a structured prioritization must take place. These questions help leadership assess the value, feasibility, data readiness, and urgency of the AI request.
- 1Does this initiative drive measurable revenue or efficiency gains?
- 2How broad is the organizational impact — does it benefit one team, a division, or the entire company?
- 3Does the initiative improve the overall customer or employee experience?
- 4Does this initiative align to an established annual corporate priority? If so, which one?
- 5Does this initiative address an imminent threat or competitive risk to the organization?
- 6Which teams are directly impacted?
- 7Does the requesting business unit have funding budgeted for this initiative?
- 8Do we currently have tools, models, or data pipelines that can partially meet the request?
- 9Has a product owner and AI subject matter expert been assigned?
Stage 3
Assign
When IT and AI leadership confirm the initiative's viability and prioritize it, a cross-functional team is assembled to begin the analysis phase.
Product Owner
Owns the AI product vision and business outcomes.
PM / Scrum Master
Manages delivery cadence and removes blockers.
Business Analyst
Translates business needs into AI requirements and user stories.
Solutions Architect
Designs the technical AI architecture — model selection, data pipelines, APIs.
Technical Team
Data Scientists, ML Engineers, MLOps Engineers, Data Engineers, QA.
Frameworks and tools used during the Initiate and Develop phases.
01
Phase · Project Initiation
Project Initiation.
Analyze requirements and refine the solution model — a structured, repeatable framework for translating intent into engineering-ready user stories before a single sprint begins.
Stage 4
Analyze
The enclosed framework guides the team in gathering all requirements needed to build effective AI user stories. Each dimension must be assessed before development begins.
Users
The humans who will interact with the AI — end users, admins, operators.
Interface
Channels and devices through which users access the AI (web app, API, chat, mobile).
Action
Capabilities the AI provides — generation, classification, summarization, prediction.
Data
Training data, retrieval corpus, or real-time data feeds the AI depends on.
Control
Guardrails, filters, and constraints the AI must enforce (safety, compliance, tone).
Environment
Infrastructure and deployment context (cloud, on-prem, edge, latency requirements).
Quality
Evaluation criteria: accuracy, hallucination rate, bias checks, latency, cost per query.
Stage 5
Refine
The Refine stage uses a business model canvas structure to help the product owner articulate the full picture of the proposed AI initiative before engineering begins.
Key Partners
Who are the key partners and data suppliers? Which key activities do partners perform?
Key Activities
What key activities does the AI initiative require (data labeling, fine-tuning, integration)?
Key Resources
What critical resources are required (data sets, GPUs, APIs, expert staff)?
Value Proposition
What value does this AI initiative deliver to customers or employees? What needs does it satisfy?
Personas
Is this for customers or employees? Which specific segment or role?
Ownership / Support
Who will own the AI model? Who is responsible for retraining and incident response?
Cost Structure
Will this reduce specific costs? What are infrastructure and operational costs of running the model?
Revenue Streams
Will this generate additional revenue? What percentage of overall revenue could it contribute?
Efficiency
Which workflows will be automated or accelerated? How much human effort will be saved?
DEVELOP
Design, build, and ship AI solutions in iterative sprints.
Agile AI Development
A proven methodology for model development, data pipelines, RAG, and fine-tuning.
This framework provides clients with a cross-functional AI Scrum team — data scientists, ML engineers, and MLOps specialists — handpicked for your specific use case. The team is dedicated to your priorities and focused entirely on delivering high-quality, working AI solutions that maximize innovation.
Fast & Flexible
Iterative sprints allow rapid experimentation and course-correction as model behavior is observed.
Reliable
Structured evaluation gates ensure AI outputs meet quality and safety standards before each release.
Cost-Effective
Predictable sprint structure enables accurate cost forecasting while maintaining agile velocity.
Stage 6
Design
Before a single model is trained or prompt is written, the Design stage establishes all architectural, experiential, and technical foundations.
- 1.Identify the target time frame and release milestones.
- 2.Identify the budget for model development and infrastructure.
- 3.Define quality standards and evaluation benchmarks (accuracy, latency, hallucination rate).
- 4.Translate all requirements into AI user stories.
- 5.Have the product owner prioritize user stories.
- 6.Build user and system personas (who uses the AI, in what context).
- 7.Estimate effort / T-shirt size each user story.
- 8.Document assumptions about data availability and model behavior.
- 9.Select model approach: fine-tuning, RAG, prompt engineering, or third-party API.
- 10.Create UX wireframes and conversation flows based on user stories.
- 11.Identify potential constraints (data privacy, latency, cost limits).
- 12.Document the full solution design (architecture diagram, data flow).
- 13.Create a proof of concept (POC) or strawman prototype.
- 14.Validate POC with the product owner and stakeholders.
- 15.Groom the backlog with the first three sprints of work identified and prioritized.
Initiating the AI Scrum Team
Structured onboarding before Sprint 1.
Before Sprint 1 begins, a structured onboarding sequence ensures the team, product owners, and stakeholders are fully aligned.
- 1
Create
Set up Azure DevOps (or equivalent) environment and provide client access.
- 2
Identify
Client identifies product owners, AI champions, and key stakeholders.
- 3
Train
Hold onboarding training for product owners and stakeholders on AI development process.
- 4
Consensus
Gain sign-off on approach, data governance agreements, and responsible AI commitments.
- 5
Planning
Execute 2-week analysis and sprint planning pre-work.
- 6
Backlog
Build backlog with 3–4 sprints of AI user stories (model behaviors, eval criteria, integrations).
- 7
Team
Assemble the full AI Scrum team.
- 8
Sprint 0
Project kick-off: introduce team to product owners; run a small set of low-risk user stories to calibrate.
- 9
Sprint 1
First full sprint: complete cycle executed with all ceremonies.
Stage 7
Develop — Sprint Cadence
Each sprint follows a rigorous 3-week cycle. Here is the day-by-day cadence.
Week 1
- •D1: Sprint Kickoff
- •D1: Finalize prompt and model design
- •D1: Start QA / evaluation test cases
- •D4: Joint team code and prompt review
- •D5: Complete evaluation test cases
Week 2
- •D6: Start iterative model testing
- •D9: Product Owner review of model outputs
- •D9: Consolidate any data or content changes
- •D10: Feature and prompt freeze
- •D10: Code review
Week 3 — Regression & Release
- •D11: Push to staging
- •D11: Begin regression and evaluation testing
- •D14: Finalize release candidate
- •D14: Go / No-Go decision
- •D14: Push to production
- •D15: Close sprint
Work Remediation (parallel)
- •D12: Ready to start remediation
- •D13: Dev and QA design session for bug fixes
- •D14: Sprint planning and estimation for next sprint
- •D14: Demo
- •D15: Retrospective
- •D15: Update and prepare backlog for next sprint
9-Week Engagement Cadence
A proven 9-week arc from requirements to production.
Analysis Sprint (Weeks 1–6)
- •Weeks 1–2: PBI assignment, requirements gathering
- •Weeks 3–4: PBI report out, continued requirements
- •Weeks 5–6: PBI planning session, transition to sprint planning
Dev Sprint (3-week cycle)
- •Week 1: Sprint kickoff, PO review, begin QA test cases, test-case lock
- •Week 2: PO review, dev code/scope freeze, code review, iterative testing
- •Week 3: Regression testing, demo / Go-No-Go, push to production
CADENCE
Recurring meetings and sprint ceremonies.
Sprint Ceremonies
Eleven recurring ceremonies that keep the AI Scrum team aligned.
Every sprint is governed by a set of recurring ceremonies that keep the team aligned, unblocked, and continuously improving. Click any ceremony for full detail.
Stage 8
Deliver
Delivery is not a single moment — it is a structured transition from sprint output to production AI with validation at every step.
- ✓A/B test AI output against baseline (existing system or prior model version).
- ✓Staged rollout: canary release to a small user group first.
- ✓Monitor real-world performance against evaluation benchmarks.
- ✓Obtain product owner and stakeholder sign-off.
- ✓Complete responsible AI checklist before full production promotion.
- ✓Hand off documentation to sustainment team.
SUPPORT
Monitor, maintain, and continuously improve in production.
Stage 9
Support
AI products are not static. Unlike traditional software, models can degrade over time as real-world data distributions shift. The Support phase is an active, ongoing commitment — not a helpdesk.
Model Monitoring
Track accuracy, hallucination rate, latency, and output quality in production. Alert on drift or degradation.
Scheduled Retraining
Define a retraining cadence based on data velocity and model risk. Trigger off-cycle retraining when drift thresholds are breached.
Responsible AI Audits
Periodic reviews of model bias, fairness, and alignment with organizational responsible AI policies.
Incident Management
Clear escalation paths for model failures, safety incidents, or unexpected outputs. Apply the same severity tiers as sprint triage.
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