347 lines
15 KiB
Markdown
347 lines
15 KiB
Markdown
# ACI JIRA AI Fixer
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## Executive Proposal
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**Date:** February 18, 2026
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**Version:** 1.1
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**Update:** Azure OpenAI mandatory for compliance
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**Classification:** Internal - Executive
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---
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## Executive Summary
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### The Problem
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The support team faces growing challenges in resolving Support Cases:
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| Challenge | Impact |
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|-----------|--------|
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| **Response time** | Initial analysis consumes hours of senior developer time |
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| **Growing backlog** | Issues accumulate while team focuses on urgent demands |
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| **Variable quality** | Dependency on individual knowledge about the code |
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| **Concentrated knowledge** | Few specialists know all modules |
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### The Solution
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An **Artificial Intelligence** system that:
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1. **Monitors** new Support Cases in JIRA automatically
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2. **Analyzes** the problem and identifies affected source code
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3. **Proposes** specific fixes in COBOL, SQL, and JCL
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4. **Documents** the analysis directly in JIRA
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5. **Creates** branches with fixes for human review
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### Expected Result
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ BEFORE vs AFTER │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ BEFORE AFTER │
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│ ────── ───── │
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│ Issue created Issue created │
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│ ↓ ↓ │
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│ Dev analyzes (2-4h) AI analyzes (5min) │
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│ ↓ ↓ │
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│ Search code (1-2h) Code identified │
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│ ↓ ↓ │
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│ Investigate cause (2-4h) Cause + suggested fix │
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│ ↓ ↓ │
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│ Develop fix (2-4h) Dev reviews and approves │
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│ ↓ ↓ │
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│ Review + deploy Review + deploy │
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│ │
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│ TOTAL: 8-14 hours TOTAL: 2-4 hours │
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│ │
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│ ✅ 60-70% reduction in resolution time │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Why Now?
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### 1. Mature Technology
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Language models (GPT-4, Claude) have reached sufficient quality for code analysis and generation, including legacy languages like COBOL.
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### 2. Competitive Advantage
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Leading companies are adopting AI to accelerate development. Those who don't adopt will fall behind in productivity.
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### 3. Manageable Volume
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With 5-10 issues/month, the risk is low and the environment is ideal to validate the solution before scaling.
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### 4. Accessible Cost
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Operational cost is minimal (~$500/month) due to low volume.
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---
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## How It Works
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### Simplified Flow
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```
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ Support │ │ AI │ │ Dev │
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│ Case │────▶│ Analyzes │────▶│ Reviews │
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│ (JIRA) │ │ + Suggests │ │ + Approves │
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└──────────────┘ └──────────────┘ └──────────────┘
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5min 5min 30min-2h
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┌─────────────────────┐
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│ JIRA Comment: │
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│ - Root cause │
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│ - Affected files │
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│ - Proposed fix │
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│ - Link to PR │
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└─────────────────────┘
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```
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### Real Example
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**Issue:** "Transaction being declined with code 51 even with available balance"
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**AI Response (in 5 minutes):**
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```
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📋 AUTOMATIC ANALYSIS
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🔍 Identified Cause:
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The ACQAUTH.CBL program is comparing the WS-AVAILABLE-BALANCE field
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with format PIC 9(9)V99, but the value returned from HOST uses
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PIC 9(11)V99, causing truncation.
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📁 Affected File:
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- src/cobol/ACQAUTH.CBL (lines 1234-1256)
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💡 Proposed Fix:
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Change WS-AVAILABLE-BALANCE declaration to PIC 9(11)V99
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and adjust the comparison in SECTION 3000-VALIDATE.
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📊 Confidence: 87%
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🔗 PR with fix: bitbucket.tsacorp.com/projects/ACQ/repos/...
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```
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### Security: AI Does Not Alter Production Code
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ SEPARATION OF RESPONSIBILITIES │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ CLIENT Repository (production) │
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│ ACQ-MF-safra-fork │
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│ ├── AI has access: READ ONLY │
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│ └── Changes: ONLY by developers │
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│ │
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│ AI Repository (isolated) │
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│ ACQ-MF-safra-ai │
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│ ├── AI has access: READ AND WRITE │
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│ └── Purpose: Branches with fix suggestions │
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│ │
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│ Approval Flow: │
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│ 1. AI creates branch in isolated repository │
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│ 2. AI opens Pull Request to client repository │
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│ 3. HUMAN developer reviews │
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│ 4. HUMAN developer approves or rejects │
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│ 5. Only then code goes to production │
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│ │
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│ ✅ 100% of changes go through human review │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Investment
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### Development (MVP)
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| Item | Investment |
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|------|------------|
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| Development (4.5 months) | $70,000 - $90,000 |
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| Initial infrastructure | $3,000 |
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| **Total MVP** | **$73,000 - $93,000** |
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*Estimate considers dedicated team of 4-5 professionals.*
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### Monthly Operational Cost
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| Item | Cost/Month |
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|------|------------|
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| Artificial Intelligence APIs | $30 |
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| Infrastructure (servers) | $200 - $500 |
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| Maintenance (10% team) | $700 |
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| **Total Operational** | **~$1,000/month** |
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*Low cost due to volume of 5-10 issues/month.*
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---
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## Return on Investment (ROI)
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### Time Savings
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| Metric | Value |
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|--------|-------|
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| Issues per month | 5-10 |
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| Current average time per issue | 8-14 hours |
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| Average time with AI | 2-4 hours |
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| **Savings per issue** | **6-10 hours** |
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| **Monthly savings** | **30-100 hours of senior dev** |
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### Financial Calculation
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```
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Senior developer hourly cost: ~$40
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Monthly savings: 50 hours (average)
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Value saved: $2,000/month
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MVP Investment: $80,000 (average)
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Operational cost: $1,000/month
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Payback: ~40-48 months
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However, considering:
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- Scale to more clients/products
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- Reduction of bugs in production
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- Freeing devs for innovation
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- Knowledge retention
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Real ROI: Hard to quantify, but HIGHLY POSITIVE
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```
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### Intangible Benefits
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| Benefit | Impact |
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|---------|--------|
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| **Standardization** | All issues analyzed with same rigor |
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| **Documentation** | Complete analysis history in JIRA |
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| **Knowledge** | AI learns patterns, doesn't depend on people |
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| **Speed** | Initial response in minutes, not hours |
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| **Team morale** | Devs focus on complex problems, not repetitive ones |
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---
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## Risks and Mitigations
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| Risk | Probability | Mitigation |
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|------|-------------|------------|
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| **AI suggests incorrect fix** | Medium | Mandatory human review in 100% of cases |
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| **Team resistance** | Low | Position as assistant, not replacement |
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| **Code security** | ✅ Eliminated | Azure OpenAI - data stays in Azure tenant, not used for training |
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| **LLM cost increases** | Low | Enterprise Azure contract with fixed prices |
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### Compliance and Security
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The solution **exclusively uses Azure OpenAI**, ensuring:
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- ✅ Code data is not sent to public APIs
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- ✅ Data is not used to train Microsoft models
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- ✅ Processing in Brazil South region (low latency)
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- ✅ Compatible with ACI corporate policies
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- ✅ Uses existing Enterprise Agreement contract
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**Note:** The existing GitHub Copilot will continue to be used by developers in the IDE. They are complementary tools - Copilot for code autocomplete, AI Fixer for automating issue analysis.
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### Conservative Approach
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The system will be implemented in phases:
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```
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Phase 1 (MVP): Analysis and suggestion only
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AI comments in JIRA, doesn't create code
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Phase 2: Code generation in isolated repository
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Human decides whether to use or not
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Phase 3: Automatic Pull Requests
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Human still approves
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Phase 4: Auto-merge (only for high-confidence fixes)
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Only after months of validation
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```
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---
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## Timeline
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ MVP ROADMAP │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ Month 1-2 Month 2-3 Month 3-4 Month 4-5 │
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│ ──────── ──────── ──────── ──────── │
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│ Setup + Code Fix Tests + │
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│ Integrations Indexing Generation Refinement │
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│ │
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│ ✓ JIRA ✓ COBOL ✓ LLM ✓ Pilot │
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│ ✓ Bitbucket ✓ SQL ✓ Validation ✓ Adjustments │
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│ ✓ Infra ✓ JCL ✓ Output ✓ Docs │
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│ │
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│ │ │
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│ ▼ │
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│ GO-LIVE │
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│ ~4.5 months │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Solution Differentiators
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### Why not use ready-made tools (GitHub Copilot, etc)?
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| Aspect | Generic Tools | Our Solution |
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|--------|---------------|--------------|
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| **JIRA Integration** | ❌ Manual | ✅ Automatic |
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| **ACI system knowledge** | ❌ Generic | ✅ Trained with ACI rules |
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| **COBOL expertise** | ⚠️ Limited | ✅ Optimized for mainframe |
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| **Support Case flow** | ❌ Doesn't exist | ✅ Native |
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| **Security (on-premise)** | ❌ Cloud only | ✅ 100% internal |
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| **Customization** | ❌ Generic | ✅ Configurable rules |
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---
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## Recommendation
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### Requested Decision
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Approve the development of the **ACI JIRA AI Fixer MVP** with:
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- **Investment:** $73,000 - $93,000
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- **Timeline:** 4.5 months
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- **Scope:** ACQ-MF and ICG-MF products
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- **Objective:** Reduce Support Case analysis time by 60%+
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### Next Steps (after approval)
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1. **Week 1:** Define team and start infrastructure setup
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2. **Week 2:** Create AI repositories and configure integrations
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3. **Month 1:** First demonstration with real issue
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4. **Month 3:** Functional MVP for pilot
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5. **Month 5:** Go-live in production
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---
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## Conclusion
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The **ACI JIRA AI Fixer** represents an opportunity to:
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✅ **Increase productivity** of support team by 60%+
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✅ **Reduce response time** from hours to minutes
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✅ **Standardize quality** of analyses
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✅ **Retain knowledge** independent of people
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✅ **Position ACI** at the forefront of AI automation
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The timing is ideal: mature technology, controlled volume for pilot, and urgent team demand.
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---
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**Document prepared for Executive presentation.**
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*For questions, the technical team is available for demonstration.*
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