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FORMATION IA

IA pour les DSI et VP Ingénierie : Conception de l'Organisation Technique

Repartez avec une stratégie d'ingénierie augmentée par l'IA couvrant les talents, les outils et les investissements plateforme.

Format
programme
Durée
20–32h
Niveau
practitioner
Taille de groupe
6–20
Prix / participant
€4K–€7K
Prix groupe
€25K–€55K
Public
CTOs, VP Engineering, and Heads of Platform in mid-to-large technology-driven organisations
Prérequis
Active engineering leadership role (CTO, VP Eng, or equivalent); familiarity with software development lifecycle and team management

Ce qu'elle couvre

Ce programme de niveau praticien dote les DSI et VP Ingénierie des cadres et outils de décision nécessaires pour réorganiser leur fonction technique autour des capacités de l'IA. Les participants évaluent les outils de productivité développeur, construisent une thèse d'investissement plateforme IA et définissent des stratégies de talents alliant rôles IA-natifs et ingénierie traditionnelle. Le format combine des sessions en cohorte, des études de cas d'organisations techniques à grande échelle et un exercice de conception organisationnelle en guise de capstone. Les participants repartent avec une feuille de route prête à présenter au conseil.

À l'issue, vous saurez

  • Build a scored vendor evaluation matrix for AI developer tooling and present a justified buy/build/partner recommendation
  • Design an AI-augmented engineering org chart with defined team topologies, responsibilities, and headcount implications
  • Define a 12-month talent strategy that identifies roles to retrain, roles to hire, and roles impacted by AI automation
  • Construct a platform investment roadmap distinguishing foundational infrastructure from quick-win productivity tools
  • Produce a board-ready business case quantifying productivity gains, cost impacts, and risk factors of the proposed AI strategy

Sujets abordés

  • AI-assisted developer productivity: Copilot, Cursor, and beyond
  • Build vs. buy vs. partner: AI vendor stack evaluation frameworks
  • Platform engineering investments for AI workloads (MLOps, LLMOps)
  • Talent strategy: hiring, upskilling, and role redefinition in AI-augmented teams
  • Engineering org design models: platform teams, AI guilds, embedded AI squads
  • Measuring engineering effectiveness with AI tooling (DORA, SPACE metrics)
  • AI governance and security posture for engineering organisations
  • Budget modelling and board-level business cases for AI platform investment

Modalité

Delivered as a 4-week cohort programme with two 3-hour live virtual sessions per week, supplemented by async case study reading and a group capstone project. In-person intensive formats (2-day offsite) are available for executive leadership teams. Hands-on exercises account for approximately 50% of learning time. Participants receive a facilitated peer network of engineering leaders across non-competing industries. All materials, frameworks, and templates are provided digitally.

Ce qui fait que ça marche

  • Executive sponsorship: CTO champions the AI engineering vision explicitly, rather than delegating to a single AI team
  • Pilot-then-scale discipline: one high-visibility team adopts AI tooling with defined success metrics before org-wide rollout
  • Role clarity: new AI-adjacent responsibilities (prompt engineers, AI platform engineers) are formally defined in job architecture
  • Regular feedback loops: quarterly engineering effectiveness reviews that tie AI tool adoption to DORA and SPACE metric trends

Erreurs fréquentes

  • Treating AI tooling adoption as a bottom-up engineering choice rather than a strategic org-design decision with budget and governance implications
  • Deploying AI developer tools without updating engineering metrics, leading to misleading productivity signals
  • Underestimating talent strategy change — assuming existing engineers will self-organise around new AI workflows without structured upskilling
  • Selecting AI infrastructure vendors based on technical demos alone, without evaluating data residency, compliance, and long-term TCO

Quand NE PAS suivre cette formation

This programme is not the right fit if the organisation has fewer than 10 engineers or has not yet shipped a production product — foundational engineering practices should be established before redesigning the org around AI augmentation.

Fournisseurs à considérer

Sources

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