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AIDLC Adoption Scenarios and Validation Plans

36 min read
AI documentation guide

This document presents fictional adoption scenarios for financial services, manufacturing, the public sector, and fintech. Organizations, timelines, costs, incidents, and outcomes are illustrative inputs, not anonymized customer evidence or validated results. Use the scenarios to formulate review questions and validation plans.

How to use the numbers

Before/After tables demonstrate calculations with assumed values. To assess an actual adoption, define the comparison period, sample size, workload scope, staff and tool costs, and quality criteria, then measure the results. The improvements and timelines below are not guarantees.

Case Study Framework​

Each case is organized with the following structure:

1. Context         — Industry, project scale, organizational structure
2. Challenges      — Problems that needed solving
3. AIDLC Approach  — What strategy was used for adoption
4. Quantitative Assumptions — Before/After metrics
5. Lessons Learned — What was learned

Case 1: Financial Company A — Legacy Monolith Migration​

1.1 Context​

ItemDetails
IndustryFinancial Services (Asset Management)
Project ScaleSmall (~KRW 800 million, 6 months)
Organizational StructureWaterfall-based, strong role silos (planning/dev/QA separated)
Tech StackLegacy Monolith(Java) → MSA(Spring Boot + K8s) transition
Team Composition1 PM, 1 Architect, 5 Developers, 2 QA Engineers

1.2 Challenges​

Business Challenges:

  • 20-year-old legacy system with no documentation
  • Loss of tacit knowledge due to domain expert departures
  • Strengthened regulatory requirements (financial supervisory guidelines)

Technical Challenges:

  • Codebase of over 2 million lines of monolithic code
  • Unclear business logic boundaries
  • Lack of MSA transition experience

Organizational Challenges:

  • Team accustomed to waterfall processes
  • Perception that "AI is just an assistive tool"
  • Handoff delays between roles (planning → dev → QA)

1.3 AIDLC Approach​

Phase 1: AI Coding Assistant Introduction (1 month)

Goal: Developers become familiar with AI tools
Tool: Amazon Q Developer
Activities:
  - Practice refactoring existing code
  - Automated unit test generation
  - Pilot automated code review

Phase 2: Mob Elaboration Introduction (2 months)

Goal: AI-led requirements analysis
Strategy:
  - Weekly Mob Elaboration sessions (full team participation)
  - Practice Intent → Unit decomposition with AI Agent
  - Build initial ontology (financial domain terminology)

Phase 3: Ontology-based Refactoring (3 months)

Goal: Transform legacy code to DDD structure
Strategy:
  - AI analyzes existing code → extracts Aggregates
  - Ontology-based domain modeling
  - Gradual migration (Strangler Fig pattern)

The phase durations above (1, 2 and 3 months) are planned durations; they add up to the 6-month project duration in 1.1. The "4.8 months assumed" in 1.4 is an illustrative value that assumes some phases finish earlier than planned or that adjacent phases overlap. This scenario does not define the actual duration of each phase or the overlapping periods.

1.4 Quantitative Results​

Illustrative assumptions: These values are not measurements. Unless indicated otherwise, improvement percentages are relative to the baseline.

MetricBeforeAfterImprovement
Requirements Analysis TimeAverage 3 weeksAverage 1.5 weeks-50%
Code Review Time4 hours/dev/week2.2 hours/dev/week-45%
Initial Defect Rate8.5 per 100 LOC5.9 per 100 LOC-31%
Development Schedule6 months planned4.8 months assumed-20%
Test Coverage52%81%+56%

Cost Benefits:

  • Project cost: KRW 800 million → assumed spending KRW 680 million (KRW 120 million savings)
  • Opportunity cost savings from early completion: approximately KRW 200 million (new customer acquisition)

1.5 Lessons Learned​

✅ Success Factors:

  1. Gradual Adoption — Phased transformation (Phase 1 → 2 → 3), not big bang
  2. Mob Elaboration — Cross-organizational collaboration ritual that broke silos
  3. Ontology Investment — Formalizing domain knowledge is key to long-term ROI

⚠️ Resistance Points:

  1. Security Personnel Shift-Left Resistance

    • Problem: QA team pushed back saying "AI is taking away our role"
    • Solution: Redefined QA → Test Strategy Designer role, AI automates repetitive testing
    • Result: Increased QA satisfaction (increased strategic work proportion)

  2. Clash with Waterfall Culture

    • Problem: Culture perceiving "requirements changes" as failure
    • Solution: Intent → Unit decomposition transformed requirements changes into natural refinement process
    • Result: Requirements changes increased, but quality improved

  3. PM Authority Anxiety

    • Problem: Anxiety that PM role would diminish if AI proposes plans
    • Solution: Redefined PM as "AI proposal validator + business context provider"
    • Result: PM focused more on strategic decision-making

Review perspective:

Define responsibilities first and measure collaboration outcomes for each workload.

Case 2: Manufacturer B — Smart Factory Operations Platform​

2.1 Context​

ItemDetails
IndustryManufacturing (Automotive Parts)
Project ScaleMedium (~KRW 3 billion, 12 months)
Organizational StructureAgile Scrum (3 parallel teams)
Tech StackIoT(MQTT) + Real-time Data(Kafka) + K8s + TimescaleDB
Team Composition1 PM, 2 Architects, 12 Developers, 3 SREs

2.2 Challenges​

Business Challenges:

  • Real-time production data processing (500K events/second)
  • Predictive maintenance requirements (99.5% accuracy target)
  • Brownfield environment (must integrate with existing legacy)

Technical Challenges:

  • Complex domain (4 Subdomains: production, logistics, quality, maintenance)
  • Real-time processing vs cost efficiency tradeoffs
  • Lack of K8s operations experience

Organizational Challenges:

  • High inter-team dependencies (production ↔ quality ↔ maintenance)
  • Language gap between domain experts and dev teams
  • SRE burnout from repeated night deployments

2.3 AIDLC Approach​

Phase 1: Ontology-based Domain Modeling (2 months)

Goal: Establish common language between domain experts ↔ dev teams
Strategy:
  - Identify 4 Subdomains (Core/Supporting)
    - Core: Production scheduling, Predictive maintenance
    - Supporting: Quality inspection, Logistics coordination
  - Build ontology (34 Entities, 18 Aggregates)
  - Create Ubiquitous Language dictionary (125 terms)

Phase 2: AgenticOps Feedback Loop (6 months)

Goal: AI-based autonomous operations
Strategy:
  - ADOT → AMP → Grafana observability stack
  - AI Agent anomaly detection (60% MTTR reduction)
  - Auto-scaling (Karpenter + KEDA)

Phase 3: Harness Strengthening (4 months)

Goal: Ensure operational reliability
Strategy:
  - Circuit breakers (IoT device failure isolation)
  - Retry budgets (Kafka reprocessing limits)
  - Output gates (Predictive maintenance alert validation)

2.4 Quantitative Results​

Illustrative assumptions: These values are not measurements. Unless indicated otherwise, improvement percentages are relative to the baseline.

MetricBeforeAfterImprovement
Error Rate8.3%1.2% (31-day avg)-85%
MTTRAverage 45 minAverage 18 min-60%
Incident Auto-Recovery Rate0%38%New
Operating CostKRW 32 million/monthKRW 24 million/month-25%
Predictive Maintenance Accuracy91.2%99.1%+7.9%p

In these illustrative assumptions, the predictive maintenance accuracy of 99.1% falls short of the 99.5% target stated in section 2.1, so meeting the target requires further validation.

Cost Benefits:

  • Operating cost savings: KRW 96 million annually
  • Prevented unexpected production downtime: KRW 280 million annual loss prevention
  • Payback: Calculate the incremental adoption cost and benefit separately. The assumptions above do not establish payback within one year.

2.5 Lessons Learned​

✅ Success Factors:

  1. Initial Ontology Investment

    • Spent 2 months on domain modeling only → key to long-term ROI
    • Ubiquitous Language dictionary reduced inter-team communication time by 70%

  2. AgenticOps Feedback Loop

    • Operational data → Ontology evolution → Better predictions
    • Successfully built self-improving system

  3. SRE Shift-Right

    • Transitioned SRE role from "incident response" to "harness design"
    • Reduced night responses → resolved burnout

⚠️ Resistance Points:

  1. Domain Expert Resistance to Ontology Participation

    • Problem: "We run factories, not define IT terminology"
    • Solution: Explained the work as recording workplace terminology and operating rules, then showed how those definitions support production, quality, and maintenance tasks
    • Result: Domain experts took ownership of ontology evolution

  2. Real-time Processing vs AI Inference Latency

    • Problem: AI model inference time violated real-time requirements (50ms target → assume 200ms was observed)
    • Solution: Edge AI + Cloud AI hybrid (urgent → Edge, complex → Cloud)
    • Result: Assume 95% is processed at Edge with 60ms average latency; the 50ms target still requires further work

Review perspective:

Scope domain modeling and evaluate comprehension, rework, and maintenance costs together.

Case 3: Public Agency C — Large-scale Informatization Project​

3.1 Context​

ItemDetails
IndustryPublic Sector (Central Government Agency)
Project ScaleLarge (~KRW 5 billion, 18 months)
Organizational StructureMulti-layer governance (client + general SI + 3 subcontractors)
Tech StackOn-premises K8s + Open-weight model(GLM-5) + PostgreSQL
Team Composition2 PMs, 3 EAs, 30 Developers, 5 Security Audit Team

3.2 Challenges​

Business Challenges:

  • Data residency requirements (no overseas transfer)
  • Personal Information Protection Act compliance (sensitive information handling)
  • Multi-layer governance (5-step approval process)

Technical Challenges:

  • Cloud usage restrictions (on-premises priority)
  • Sending sensitive data to external LLM APIs is prohibited; general work also requires policy approval
  • Lack of open-weight model operation experience

Organizational Challenges:

  • How to coordinate 30 developers?
  • Code quality variance across subcontractors
  • Security audit response (every milestone)

3.3 AIDLC Approach​

Phase 1: Hybrid LLM Strategy (3 months)

Goal: Data residency compliance + cost reduction
Strategy:
  - Sensitive information processing → GLM-5 on on-premises Kubernetes with vLLM
  - General business → An approved cloud model only when permitted by policy
  - Automated routing with LiteLLM Gateway

Phase 2: Governance Framework Construction (6 months)

Goal: Enterprise-wide AI quality management
Strategy:
  - 3-layer governance framework
    - Policy Layer: Security·compliance policies
    - Process Layer: Review process, approval workflows
    - Tool Layer: AI code review, security scanning
  - Subcontractor code integration verification (independent AI agent)

Phase 3: Declarative Deployment Automation (9 months)

Goal: Reduce deployment lead time
Strategy:
  - GitOps (ArgoCD)
  - AI auto-generates Helm Chart + Terraform
  - Automated security audits (Trivy, Falco)

3.4 Quantitative Results​

Illustrative assumptions: These values are not measurements. Unless indicated otherwise, improvement percentages are relative to the baseline.

MetricBeforeAfterImprovement
LLM API CostKRW 18 million/month assumedKRW 12.6 million/month assumed-30%
Security ComplianceManual evidence checks (15 working days/cycle)Automated evidence checks (1 working day/cycle)-93%
Deployment Lead TimeAverage 5 daysAverage 1.5 days-70%
Code Quality Variance±35% by subcontractor±8% after integration verification-77%
Security VulnerabilitiesAverage 23 per milestoneAverage 4 per milestone-83%

The "Security Compliance" row above refers to automated scans such as Trivy and Falco. Automated scanning speeds up evidence collection and vulnerability detection, but it does not by itself assure regulatory compliance. Compliance also requires mapping to control items, owner approval, and audit evidence.

Cost Benefits:

  • Project cost: KRW 5 billion → assumed spending KRW 4.8 billion (KRW 200 million savings)
  • LLM cost savings: KRW 64.8 million annually
  • Security audit response time savings: approximately KRW 120 million

3.5 Lessons Learned​

✅ Success Factors:

  1. Hybrid LLM Strategy

    • On-premises + Cloud combination achieved both security and cost efficiency
    • Transparent routing with LiteLLM Gateway

  2. Governance Framework

    • Elevated AI quality management from "recommendation" to "mandatory process"
    • Significantly reduced quality variance across subcontractors

  3. Executive Sponsorship

    • Full support from client CIO → minimized resistance
    • Top-down message that "AI is essential, not optional"

⚠️ Resistance Points:

  1. Initial Failure Starting Without Governance Framework

    • Problem: Months 1-2, subcontractors used different AI tools → chaos
    • Solution: Month 3 introduced governance framework, standardization
    • Lesson: Large projects must build governance first

  2. On-premises Open-weight Model Operation Difficulty

    • Problem: vLLM installation, GPU resource management, model version management complexity
    • Solution: Specialist review, applied EKS GPU Node Strategy
    • Result: Stabilized by month 3

Review perspective:

The more organizations participate, the more clearly you must define the shared review criteria and the exception-approval path.

Case 4: Fintech Startup D — AI Agent Incident Case​

4.1 Context​

ItemDetails
IndustryFintech (B2B SaaS)
Project ScaleStartup (4 developers)
Organizational StructureAgile (Kanban without Scrum)
Tech StackNode.js + OpenAI GPT-4 + Sendgrid
Team Composition1 CTO, 3 Developers

4.2 Incident Overview​

Fictional incident scenario: uncontrolled retries

Assume an email-drafting agent retries 847 times during one task. The values below are inputs for analysis, not a reported incident:

  • $2,200 in LLM API costs (4x budget)
  • 14 incomplete emails sent to customers (credibility hit)
  • 3 hours of service outage (manual intervention required)

4.3 Root Cause Analysis​

Surface Cause:

  • AI agent continued to fail while generating email drafts → unlimited retries

Root Cause (Harness Absence):

Harness PatternImplemented?Result
Retry Budget❌ None (unlimited retries)847 retries
Timeout❌ NoneLoop ran indefinitely
Output Gate❌ None14 incomplete emails sent
Circuit Breaker❌ NoneContinued trying after 847 failures
Cost Limit❌ NoneNo alert until $2,200 charge

This scenario does not identify whether the initial failure arose from the model, prompt, or code. Investigate request logs and input/model revisions, and verify that repeated failures cannot cause unbounded spending or external messages.

4.4 Redesign with Harness Applied​

The YAML below is a design-review example, not executable configuration for a particular SDK. Implement each limit in the actual execution path. Use idempotency keys and send approval for email, and atomically track shared budgets and attempts across concurrent jobs.

Retry Budget Configuration:

retry_budget:
  max_attempts: 3            # Limited 847 → 3
  backoff: exponential       # 1s, 2s, 4s
  budget_limit: 10           # 10 times per hour

Timeout Configuration:

timeout:
  request: 30s              # 30 seconds per request
  total: 300s               # 5 minutes total operation

Output Gate:

output_gate:
  validators:
    - email_completeness    # Required field validation
    - syntax_check          # HTML syntax validation
    - pii_detection        # PII masking
  action_on_failure: reject

Circuit Breaker:

circuit_breaker:
  failure_threshold: 5      # Open after 5 failures
  timeout: 60s             # Half-Open after 60s

Cost Limit:

cost_limit:
  per_request: 0.50         # $0.50 per request
  per_hour: 10.00          # $10 per hour
  alert_threshold: 0.80    # Alert at 80%

4.5 Post-Redesign Results​

MetricFictional incident inputValidation condition
API attempts per taskUnbounded retriesVerify that max_attempts: 3 includes the initial call and is not exceeded
Cost$2,200Measure spending limits and termination under normal, failing, and concurrent load
Incomplete emails14 sentVerify blocking on validation failure, duplicate requests, and missing approval
Service downtime3 hoursMeasure recovery with fault injection and compare with the agreed objective

Redesign alone does not establish a cost of $8.40, zero incomplete emails, or zero downtime. Acceptance requires execution records, evaluation scope, failure cases, and residual-risk review.

4.6 Lessons Learned​

✅ Key Lessons:

  1. Importance of Harness Engineering

    • Verify that execution controls limit harm regardless of the initial failure cause
    • "Agents aren't hard; harnesses are"

  2. Harness Proposed as a Recommended Baseline

    • This guide proposes the following 5 harness patterns as a recommended baseline even for startups (not an absolute requirement; adjust to the risk of the work):
      • Retry budget
      • Timeout
      • Output gate
      • Circuit breaker
      • Cost limit

  3. Harness Verification Before Production

    • Verify failure paths for retries, external messages, and spending limits before deployment
    • Pre-verify harness with Chaos Engineering

⚠️ Anti-patterns:

❌ "Small project, harness can wait"
❌ "GPT-4 is smart, it'll handle it"
❌ "If it works in testing, production should be fine"

✅ Correct Approach:

✅ Harness is essential regardless of project size
✅ Even with the best model, it's dangerous without harness
✅ Harness Chaos Test essential before production

Review perspective:

Include both model evaluation and execution-control validation in release decisions.

Common Failure Patterns and Lessons​

The following patterns and questions support review of the fictional scenarios above. They are not frequency estimates or causal findings from a sample of real projects.

Failure Pattern 1: Big Bang Adoption​

Symptoms:

  • "Let's go full AIDLC from the next project"
  • Skipping Phases 1-4 and jumping straight to AI Agent adoption

Result:

  • Team expects AI to be "magic" → disappointment
  • Conflict with existing processes → chaos
  • Regression to traditional methods after 3 months

Lesson:

  • AIDLC requires gradual adoption; the Phase 1 → 2 → 3 → 4 order is not an official standard but a sequence proposed by this guide
  • Decide each Phase's end conditions and observation period according to the scale of the work

Failure Pattern 2: Tools Only, Methodology Ignored​

Symptoms:

  • "We bought Q Developer, productivity will double"
  • Adopting tools only without ontology, harness engineering

Result:

  • Code generation speed increased but quality declined
  • Technical debt explosion after 6 months
  • Spread of sentiment "AI doesn't help"

Lesson:

  • Tools are means, methodology is essence
  • Evaluate domain understanding and execution controls before expanding adoption

Failure Pattern 3: Expansion Without Measurement​

Symptoms:

  • "We adopted AI, so it must have improved, right?"
  • No Before/After metric measurement

Result:

  • Cannot prove actual benefits
  • Loss of executive sponsorship → budget cuts
  • Team members also don't feel the benefits

Lesson:

  • Define measurement metrics first before AIDLC adoption
  • Minimum metrics: development speed, defect rate, code review time, cost

Failure Pattern 4: Bottom-up Attempt Without Executive Sponsorship​

Symptoms:

  • Dev team voluntarily tries AIDLC
  • Executives remain hands-off: "Team will handle it"

Result:

  • Blocked when organizational change needed (role redefinition, process changes)
  • Budget shortage (on-premises GPU, LLM API costs)
  • Stopped within 6 months by resistance forces

Lesson:

  • AIDLC is an organizational transformation project
  • Executive full sponsorship essential (especially for large projects)

Failure Pattern 5: Production Deployment Without Harness​

Symptoms:

  • "Works well in dev environment, production should be fine"
  • Deploying AI Agent without harness design

Result:

  • Incidents similar to Fintech case (cost explosion, data corruption)
  • Customer trust decline
  • Emergency rollback → AI adoption halted

Lesson:

  • Harness is a prerequisite for production deployment
  • Retry budget, timeout, output gate, circuit breaker, cost limit essential

Success Factor Summary​

Review these five areas when building an adoption plan:

ItemReview AreaDescriptionEvidence
1Gradual AdoptionPhased transition Phase 1→2→3→4, Advance after the phase validation criteria are metValidation plan and results
2Ontology InvestmentAssess the required domain scope and its maintenance costValidation plan and results
3Harness EngineeringImplement 5 essential harness patterns, verify before production deploymentValidation plan and results
4Executive SponsorshipFull support from CIO/CTO level, authority for organizational changeOwners and review records
5Measurement-based ExpansionMeasure Before/After metrics, data-driven expansion decisionsOwners and review records

Additional Success Factors:

  • Role Redefinition: Clarify roles as AI collaborators (see Role Redefinition)
  • Mob Elaboration: Cross-organizational collaboration ritual breaking silos
  • Independent Verification: Code generation agent ≠ Verification agent
  • Hybrid Strategy: On-premises + Cloud LLM combination

Next Steps​

If you've gained concrete insights on AIDLC adoption through these case studies, refer to the following documents:

Adoption Planning:

Technical Deep Dive:

References​

AIDLC Methodology​

External References​