AI, data & software instructor
Meriam Mbindyo
Instructor for AI, data, DevOps, Agile and software modules, with experience across Paris-based IT and business schools.
Artificial intelligenceMachine learningData mining
Module content
Course description
YEAR 2: ADVANCED AI, LLMs & ENTERPRISE DEPLOYMENT
Program: Applied Artificial Intelligence for Business & Enterprise Systems
Semester 4 (36h):Corporate AI Implementation & Deployment
Program Structure
This course is part of a two-year academic program comprising four semesters.
This course corresponds to: Semester 4 of 4.
Each semester includes 12 instructional sessions, with each session lasting 3 hours, for a total of 36 hours per semester.
The complete program represents 144 hours of structured instruction, combining lectures, applied workshops, and project-based learning.
Primary anchors (from our catalogue):
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Corporate AI Implementation & Deployment
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MCP & n8n – Workflow Automation (https://linguistic-communication.com/courses/mcp-n8n-workflow-automation-and-api-integration/)
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SD-40 – AI Programming in HTML/CSS/JS (conceptual UI layer) (https://linguistic-communication.com/courses/htm-css/)
Semester intent
Show how AI actually survives inside companies: governance, integration, and scale.
Course Overview
This capstone semester focuses on how AI systems are deployed, governed, and scaled within real organizations. Students learn how AI moves from pilot projects to enterprise systems, addressing challenges related to architecture, data governance, security, operations, and accountability.
The course emphasizes organizational design, vendor strategy, workflow automation, and AI risk management rather than technical development. Students analyze how AI systems integrate into existing IT and business environments and how failures are detected, managed, and corrected.
The semester culminates in a comprehensive enterprise AI deployment project suitable for executive or board-level presentation.
Assessment structure
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Workshop 1: Enterprise AI architecture & governance
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Workshop 2: AI risk & operations simulation
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Final Project: Full enterprise AI deployment blueprint
COURSE SCHEDULE – SEMESTER 4
| Session | Focus | Weekly Session Content |
|---|---|---|
| 1 | Enterprise AI Reality | From pilots to systems |
| 2 | AI Architecture | Cloud, hybrid, on-prem |
| 3 | Data Governance | Compliance & control |
| 4 | AI Security | System & LLM risks |
| 5 | Workflow Automation | MCP & n8n use cases |
| 6 | AI Interfaces | Business-facing AI tools |
| 7 | Workshop 1 | Architecture & governance |
| 8 | LLMOps / MLOps | Monitoring & rollback |
| 9 | Evaluation & Audits | Trust & accountability |
| 10 | Vendor Strategy | Buy vs build |
| 11 | Workshop 2 | AI incident simulation |
| 12 | Final Capstone | Enterprise AI deployment defense |
Target Audience
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Final-year business and digital strategy students
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Future AI project managers and consultants
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Professionals overseeing AI initiatives or vendors
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Students preparing for enterprise or consulting roles
Class reference: SEM4/AI
Form Updated on: 22/01/2026 (Version 1)
Last Modified on: 22/01/2026
Program Information:
This program is continuously updated to reflect the latest AI tools, business applications, and regulatory frameworks.
Brief pédagogique en français
YEAR 2: Corporate AI Implementation & Deployment est présenté ici en version synthétique française afin que les équipes pédagogiques puissent évaluer rapidement l'intérêt du module.
Le module s'inscrit dans la famille Analytics et intelligence artificielle. Il peut être adapté au calendrier de l'école, au niveau Tous niveaux, au volume horaire 36 h et aux modalités d'évaluation prévues.
Objectif d'intervention
Ce module vise à relier les outils data, IA et automatisation à des usages professionnels concrets.
Livrables et activités possibles
- cas d'usage, prompts, scénarios d'automatisation ou analyses data
- évaluation critique des résultats, limites et risques
- communication claire des choix techniques et business
Adaptation école
LC peut ajuster le déroulé, la langue d'enseignement, les supports, les exercices et les critères d'évaluation selon la promotion, le diplôme, le niveau d'autonomie attendu et les contraintes de planning.
Pour une version détaillée du syllabus en français, LC confirme le programme final après cadrage du niveau, des heures, du calendrier et des livrables attendus.
