# ACI JIRA AI Fixer ## Executive Proposal **Date:** February 18, 2026 **Version:** 1.1 **Update:** Azure OpenAI mandatory for compliance **Classification:** Internal - Executive --- ## Executive Summary ### The Problem The support team faces 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) 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 5-10 issues/month, the risk is low and the environment is ideal to validate the solution before scaling. ### 4. Accessible Cost Operational cost is minimal (~$500/month) due to low volume. --- ## 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 ACQAUTH.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/ACQAUTH.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.tsacorp.com/projects/ACQ/repos/... ``` ### Security: AI Does Not Alter Production Code ``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ SEPARATION OF RESPONSIBILITIES │ ├─────────────────────────────────────────────────────────────────────────────┤ │ │ │ CLIENT Repository (production) │ │ ACQ-MF-safra-fork │ │ ├── AI has access: READ ONLY │ │ └── Changes: ONLY by developers │ │ │ │ AI Repository (isolated) │ │ ACQ-MF-safra-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 ### Development (MVP) | Item | Investment | |------|------------| | Development (4.5 months) | $70,000 - $90,000 | | Initial infrastructure | $3,000 | | **Total MVP** | **$73,000 - $93,000** | *Estimate considers dedicated team of 4-5 professionals.* ### Monthly Operational Cost | Item | Cost/Month | |------|------------| | Artificial Intelligence APIs | $30 | | Infrastructure (servers) | $200 - $500 | | Maintenance (10% team) | $700 | | **Total Operational** | **~$1,000/month** | *Low cost due to volume of 5-10 issues/month.* --- ## Return on Investment (ROI) ### Time Savings | Metric | Value | |--------|-------| | Issues per month | 5-10 | | Current average time per issue | 8-14 hours | | Average time with AI | 2-4 hours | | **Savings per issue** | **6-10 hours** | | **Monthly savings** | **30-100 hours of senior dev** | ### Financial Calculation ``` Senior developer hourly cost: ~$40 Monthly savings: 50 hours (average) Value saved: $2,000/month MVP Investment: $80,000 (average) Operational cost: $1,000/month Payback: ~40-48 months However, considering: - Scale to more clients/products - Reduction of bugs in production - Freeing devs for innovation - Knowledge retention Real ROI: Hard to quantify, but HIGHLY POSITIVE ``` ### 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 | --- ## 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** | ✅ Eliminated | Azure OpenAI - data stays in Azure tenant, not used for training | | **LLM cost increases** | Low | Enterprise Azure contract with fixed prices | ### Compliance and Security The solution **exclusively uses Azure OpenAI**, ensuring: - ✅ Code data is not sent to public APIs - ✅ Data is not used to train Microsoft models - ✅ Processing in Brazil South region (low latency) - ✅ Compatible with ACI corporate policies - ✅ Uses existing Enterprise Agreement contract **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. ### Conservative Approach The system will be implemented in phases: ``` Phase 1 (MVP): 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 ``` --- ## Timeline ``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ MVP ROADMAP │ ├─────────────────────────────────────────────────────────────────────────────┤ │ │ │ Month 1-2 Month 2-3 Month 3-4 Month 4-5 │ │ ──────── ──────── ──────── ──────── │ │ Setup + Code Fix Tests + │ │ Integrations Indexing Generation Refinement │ │ │ │ ✓ JIRA ✓ COBOL ✓ LLM ✓ Pilot │ │ ✓ Bitbucket ✓ SQL ✓ Validation ✓ Adjustments │ │ ✓ Infra ✓ JCL ✓ Output ✓ Docs │ │ │ │ │ │ │ ▼ │ │ GO-LIVE │ │ ~4.5 months │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ ``` --- ## Solution Differentiators ### Why not use ready-made tools (GitHub Copilot, etc)? | Aspect | Generic Tools | Our Solution | |--------|---------------|--------------| | **JIRA Integration** | ❌ Manual | ✅ Automatic | | **ACI system knowledge** | ❌ Generic | ✅ Trained with ACI rules | | **COBOL expertise** | ⚠️ Limited | ✅ Optimized for mainframe | | **Support Case flow** | ❌ Doesn't exist | ✅ Native | | **Security (on-premise)** | ❌ Cloud only | ✅ 100% internal | | **Customization** | ❌ Generic | ✅ Configurable rules | --- ## Recommendation ### Requested Decision Approve the development of the **ACI JIRA AI Fixer MVP** with: - **Investment:** $73,000 - $93,000 - **Timeline:** 4.5 months - **Scope:** ACQ-MF and ICG-MF products - **Objective:** Reduce Support Case analysis time by 60%+ ### Next Steps (after approval) 1. **Week 1:** Define team and start infrastructure setup 2. **Week 2:** Create AI repositories and configure integrations 3. **Month 1:** First demonstration with real issue 4. **Month 3:** Functional MVP for pilot 5. **Month 5:** Go-live in production --- ## Conclusion The **ACI 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 ✅ **Position ACI** at the forefront of AI automation The timing is ideal: mature technology, controlled volume for pilot, and urgent team demand. --- **Document prepared for Executive presentation.** *For questions, the technical team is available for demonstration.*