# JIRA AI Fixer ## Executive Proposal **Date:** February 2026 **Version:** 1.1 **Classification:** Product Documentation --- ## Executive Summary ### The Problem Support teams face growing challenges in resolving Support Cases: | Challenge | Impact | |-----------|--------| | **Response time** | Initial analysis consumes hours of senior developer time | | **Growing backlog** | Issues accumulate while team focuses on urgent demands | | **Variable quality** | Dependency on individual knowledge about the code | | **Concentrated knowledge** | Few specialists know all modules | ### The Solution An **Artificial Intelligence** system that: 1. **Monitors** new Support Cases in JIRA automatically 2. **Analyzes** the problem and identifies affected source code 3. **Proposes** specific fixes in COBOL, SQL, and JCL 4. **Documents** the analysis directly in JIRA 5. **Creates** branches with fixes for human review ### Expected Result ``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ BEFORE vs AFTER │ ├─────────────────────────────────────────────────────────────────────────────┤ │ │ │ BEFORE AFTER │ │ ────── ───── │ │ Issue created Issue created │ │ ↓ ↓ │ │ Dev analyzes (2-4h) AI analyzes (5min) │ │ ↓ ↓ │ │ Search code (1-2h) Code identified │ │ ↓ ↓ │ │ Investigate cause (2-4h) Cause + suggested fix │ │ ↓ ↓ │ │ Develop fix (2-4h) Dev reviews and approves │ │ ↓ ↓ │ │ Review + deploy Review + deploy │ │ │ │ TOTAL: 8-14 hours TOTAL: 2-4 hours │ │ │ │ ✅ 60-70% reduction in resolution time │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ ``` --- ## Why Now? ### 1. Mature Technology Language models (GPT-4, Claude, Llama) have reached sufficient quality for code analysis and generation, including legacy languages like COBOL. ### 2. Competitive Advantage Leading companies are adopting AI to accelerate development. Those who don't adopt will fall behind in productivity. ### 3. Manageable Volume With typical support volumes, the risk is low and the environment is ideal to validate the solution before scaling. ### 4. Accessible Cost Operational cost is minimal, especially with free/low-cost LLM options available. --- ## How It Works ### Simplified Flow ``` ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Support │ │ AI │ │ Dev │ │ Case │────▶│ Analyzes │────▶│ Reviews │ │ (JIRA) │ │ + Suggests │ │ + Approves │ └──────────────┘ └──────────────┘ └──────────────┘ 5min 5min 30min-2h ┌─────────────────────┐ │ JIRA Comment: │ │ - Root cause │ │ - Affected files │ │ - Proposed fix │ │ - Link to PR │ └─────────────────────┘ ``` ### Real Example **Issue:** "Transaction being declined with code 51 even with available balance" **AI Response (in 5 minutes):** ``` 📋 AUTOMATIC ANALYSIS 🔍 Identified Cause: The AUTH.CBL program is comparing the WS-AVAILABLE-BALANCE field with format PIC 9(9)V99, but the value returned from HOST uses PIC 9(11)V99, causing truncation. 📁 Affected File: - src/cobol/AUTH.CBL (lines 1234-1256) 💡 Proposed Fix: Change WS-AVAILABLE-BALANCE declaration to PIC 9(11)V99 and adjust the comparison in SECTION 3000-VALIDATE. 📊 Confidence: 87% 🔗 PR with fix: bitbucket.example.com/projects/PRODUCT/repos/... ``` ### Security: AI Does Not Alter Production Code ``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ SEPARATION OF RESPONSIBILITIES │ ├─────────────────────────────────────────────────────────────────────────────┤ │ │ │ CLIENT Repository (production) │ │ Product-Client-Fork │ │ ├── AI has access: READ ONLY │ │ └── Changes: ONLY by developers │ │ │ │ AI Repository (isolated) │ │ Product-Client-AI │ │ ├── AI has access: READ AND WRITE │ │ └── Purpose: Branches with fix suggestions │ │ │ │ Approval Flow: │ │ 1. AI creates branch in isolated repository │ │ 2. AI opens Pull Request to client repository │ │ 3. HUMAN developer reviews │ │ 4. HUMAN developer approves or rejects │ │ 5. Only then code goes to production │ │ │ │ ✅ 100% of changes go through human review │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ ``` --- ## Investment ### Pricing Models | Model | Description | Price | |-------|-------------|-------| | **SaaS** | Hosted, managed by vendor | $2,000 - $5,000/month | | **On-Premise License** | Self-hosted, perpetual | $50,000 - $100,000 one-time | | **Enterprise** | Custom deployment + support | Contact for quote | ### ROI Calculation ``` Senior developer hourly cost: ~$40-80 Average time saved per issue: 6-10 hours Monthly savings (10 issues): $2,400 - $8,000 SaaS payback: Immediate positive ROI Enterprise license payback: 12-24 months ``` ### Intangible Benefits | Benefit | Impact | |---------|--------| | **Standardization** | All issues analyzed with same rigor | | **Documentation** | Complete analysis history in JIRA | | **Knowledge** | AI learns patterns, doesn't depend on people | | **Speed** | Initial response in minutes, not hours | | **Team morale** | Devs focus on complex problems, not repetitive ones | --- ## Deployment Options ### Option 1: SaaS (Recommended for Quick Start) ``` ✅ Fastest time-to-value (days, not months) ✅ No infrastructure to manage ✅ Automatic updates ✅ Included support ``` ### Option 2: On-Premise (For Compliance Requirements) ``` ✅ 100% data stays in your infrastructure ✅ Air-gapped option (no internet required) ✅ Full control over updates ✅ One-time license cost ``` ### Option 3: Hybrid ``` ✅ You host, we manage ✅ Balance of control and convenience ✅ Flexible pricing ``` --- ## Security and Compliance ### LLM Provider Options | Provider | Data Location | Compliance Level | |----------|---------------|------------------| | **Azure OpenAI** | Your Azure tenant | Enterprise | | **Local (Ollama)** | Your servers | Air-gapped | | **OpenAI API** | OpenAI cloud | Standard | | **OpenRouter** | Various | Development | ### Compliance Features - ✅ Data segregation by client/product - ✅ Complete audit trail - ✅ Configurable log retention - ✅ 100% on-premise deployment option - ✅ Air-gapped deployment available - ✅ No code sent to public training datasets --- ## Risks and Mitigations | Risk | Probability | Mitigation | |------|-------------|------------| | **AI suggests incorrect fix** | Medium | Mandatory human review in 100% of cases | | **Team resistance** | Low | Position as assistant, not replacement | | **Code security** | Configurable | Choose Azure/local for compliance | | **LLM cost increases** | Low | Multiple provider options | ### Conservative Approach The system is designed for phased adoption: ``` Phase 1: Analysis and suggestion only AI comments in JIRA, doesn't create code Phase 2: Code generation in isolated repository Human decides whether to use or not Phase 3: Automatic Pull Requests Human still approves Phase 4: Auto-merge (only for high-confidence fixes) Only after months of validation ``` --- ## Implementation Timeline ``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ IMPLEMENTATION ROADMAP │ ├─────────────────────────────────────────────────────────────────────────────┤ │ │ │ Week 1-2 Week 3-4 Week 5-6 Week 7+ │ │ ──────── ──────── ──────── ──────── │ │ Setup + Code Business Go-Live + │ │ Integrations Indexing Rules Refinement │ │ │ │ ✓ JIRA ✓ COBOL ✓ Modules ✓ Production │ │ ✓ Bitbucket ✓ SQL ✓ Validation ✓ Adjustments │ │ ✓ Portal ✓ JCL ✓ Testing ✓ Support │ │ │ │ │ │ │ ▼ │ │ LIVE │ │ ~5-7 weeks │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ ``` --- ## Solution Differentiators ### Why JIRA AI Fixer? | Aspect | Generic Tools | JIRA AI Fixer | |--------|---------------|---------------| | **JIRA Integration** | ❌ Manual | ✅ Automatic | | **Domain knowledge** | ❌ Generic | ✅ Configurable business rules | | **COBOL expertise** | ⚠️ Limited | ✅ Optimized for mainframe | | **Support Case flow** | ❌ Doesn't exist | ✅ Native | | **Deployment options** | ❌ Cloud only | ✅ SaaS, on-prem, or air-gapped | | **Customization** | ❌ Generic | ✅ Fully configurable | --- ## Next Steps ### To Get Started 1. **Schedule Demo** - See JIRA AI Fixer in action with your data 2. **Pilot Program** - 30-day trial with limited scope 3. **Full Deployment** - Production rollout with support ### Contact - **Email:** sales@yourcompany.com - **Demo Request:** https://jira-ai-fixer.yourcompany.com/demo --- ## Conclusion **JIRA AI Fixer** represents an opportunity to: ✅ **Increase productivity** of support team by 60%+ ✅ **Reduce response time** from hours to minutes ✅ **Standardize quality** of analyses ✅ **Retain knowledge** independent of people ✅ **Choose your deployment** - SaaS, on-prem, or air-gapped The timing is ideal: mature technology, flexible deployment options, and proven ROI. --- **JIRA AI Fixer - Intelligent Support Case Resolution** *Ready to transform your support workflow?*