AI in Tech: Beyond Expectations, Into Execution

AI in Tech: Beyond Expectations, Into Execution
Speaker: Nouamane Cherkaoui, Directeur de la Transformation et de la Stratégie at BPCE SI

This presentation by Nouamane Cherkaoui (Director of Transformation & Strategy at BPCE SI) provides a roadmap for integrating Generative AI across the entire software development lifecycle (SDLC).

It explores the evolving impact of generative AI (GenAI) on software development throughout the application lifecycle. Although GenAI tools offer promising efficiency improvements, they are still maturing and face challenges specific to each organization and its coding practices. Organizations are increasingly incorporating GenAI into their developer workflows to enhance productivity, while also needing to recognize the strengths and limitations of these tools for effective software development.

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Conference Details:
Conference: GenerationAI Paris 2025, part of FOST (Future of Software Technologies)
Theme: Enterprise GenAI-readiness with the API mindset
Date: 1 - 3 December 2026 • CNIT Forest – Paris
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Transcript

1. Leveraging GenAI to support tech activities December2025 AI in Tech: Beyond Expectations, Into Execution Nouamane Cherkaoui
2. MARKET | DIFFERENT LEVELS OF USAGE OF GENAI IN SOFTWARE DEVELOPMENT Description Level Data Usage GenAI as an assistant to improve productivity Pre-trained models with external data Autocomplete and chat during development GenAI as a pair programmer to modify or create features External and some internal data Code generation based on the entire data a context GenAI as a programmer to automate development External and internal data and processes Implementation through declarative programming
3. MARKET | GENAI SHOULD IMPROVE PRODUCTIVITY THROUGHOUT THE APPLICATION LIFECYCLE, IMPACTING DEVELOPMENT ACTIVITIES ACCORDING TO TASKS, BUT THE MATURITY OF THE TOOLS IS NOT YET AT ITS MAXIMUM. Requirements & User journeys Design/ Prototyping Development Testing Deployment Maintenance/ Support Humans Define requirements, write prompts to get epics & user stories, validate epics & user stories Write design prompts in natural language, validate the prototype Build architecture & complex code functions (e.g., integrations), write prompts for code generation Write prompts for test cases, oversee & validate results before providing feedback Push releases, validate Gather analytics with prompts, validate bugs Gen AI Generate first draft and refine epics & user stories Generate & refine prototype, generate visuals & UI Automate code generation and optimize performance Generate test cases, execute automated testing, identify bugs & code vulnerabilities Streamline workflow, automate & deploy releases, enhance adoption through native language user support Generate analytics, identify & fix bugs, human free user support Tools Maturity 15-25% 15-25% 30-50% 10-20% Time spent "Run" step
4. TECH - USE CASES | GENERATIVE AI USE CASES FOR IT DEVELOPMENT AND OPERATIONS ​ AI.4.Dev – Development Use Cases ​ AI.4.Ops – Operations Use Cases Design & Testing • AI-assisted generation of user stories and rapid prototyping • Creation of functional test cases and manual or automatic execution of these tests Augmented Developer • AI-powered code generation and optimization across multiple programming languages (JS, C++, COBOL, etc.) • Creation of associated unit tests to ensure the required quality level Version Upgrading • Upgrade of software components to newer versions of the same technology • Reduction of tech debt from obsolete components (e.g., Angular 9) Code Migration • Rewriting of source code using a new technology • Preservation the original functionality without modification Documentation reverse-engineering • Detailed analysis of existing systems to understand and document their functioning and their architecture Knowledge Mining & Documentation Generation • Enrichment of documentation databases by cross-referencing incidents, changes, and application data • Automatic creation of application documentation (e.g., flow mapping) Enhanced Support & Corrective Maintenance • Automated responses to recurring questions already covered in technical documentation • AI-assisted ticket generation filtering of false incidents (~30%), and improved ticket assignment Predictive Maintenance & Performance Optimization • AI-driven analysis of application logs and performance data to identify optimization opportunities and predict service limits • Intelligent automation of CI/CD pipelines, QoS monitoring, and performance scaling for continuous improvement Robo-Advisory for Production & Application Maintenance • Automated log analysis for end-to-end incident diagnosis across services, code, and data • AI-driven resolution strategies and automatic incident remediation Risk & Security Analysis • Behavioral analysis & access management to control user activities • AI-enhanced security through correlation analysis, fraud detection, and vulnerability identification
5. Thank you ! "The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn."