Résistance à l'IA

L'IA est une technologie relativement nouvelle pour beaucoup de gens, même s'ils y sont exposés dans leur vie quotidienne (par exemple, par des recommandations de produits). Par conséquent, les employés et autre personnel de l'entreprise peuvent être réticents à sa mise en œuvre ou à son utilisation.

Cet exercice fait partie du cours

<cours>Implémentation IA en entreprise</cours>

Exercice interactif pratique

Transformez la théorie en action avec l’un de nos exercices interactifs

Commencer l’exercice

Cet exercice fait partie du cours

<cours>Implémentation IA en entreprise</cours>

DébutantNiveau de compétence

4.8+

Commencer le cours gratuitement

Learn what AI is, how it has evolved, and how it is different from other non-AI data solutions. In this chapter, you will begin building a framework for how to implement AI into your business, including key elements to an AI strategy, phases for implementation, and basic technical design. Finally, you'll learn about the importance of Responsible AI governance throughout the entire process.

Exercise 1: Overview of AI Exercise 2: Do you know AI?Exercise 3: The phases of an AI solution Exercise 4: 10,000 ft view: how does AI work?Exercise 5: Still know AI?Exercise 6: Generative AI vs. Other AI Exercise 7: Components of an AI solution Exercise 8: Responsible AI Exercise 9: Why is Responsible AI important?Exercise 10: Common Principles of Responsible AI Exercise 11: Assessing risk for a new AI solution

You now know what AI is and how to think about it responsibly. It's time to gain a better sense of its benefits, limitations, and use cases in business. In this chapter, you'll do just that! You'll understand the value AI can bring to an organization as well as the areas it lacks in. You'll go through some high-level use cases across different departments in a business. Finally, you'll learn about principles for choosing a great use case for an AI solution.

Exercise 1: The benefits of AI for a business Exercise 2: Pillars of AI Value Exercise 3: Business value or nice to have?Exercise 4: Limitations of AI Exercise 5: Identify limitations of AI Exercise 6: Limitation or benefit?Exercise 7: Human-in-the-loop Exercise 8: Use cases for AI Exercise 9: Common business use cases Exercise 10: AI advantage in a scenario Exercise 11: Identify a use case for AI Exercise 12: Principles of a good use case Exercise 13: Classify use cases Exercise 14: Deconstructing a task

With use cases in mind, it's time to build a proof of concept! This is a small project that will determine the feasibility and estimate further business value if fully implemented. In this chapter, you will learn the phases of a POC, what makes them successful, and the important components to think about regarding technical infrastructure and data. Finally, you'll learn about the types of roles required for your POC project team.

Exercise 1: Getting started with a POC Exercise 2: Phases of a POC Exercise 3: What makes a successful POC?Exercise 4: Questions to clarify goals Exercise 5: Data Exercise 6: Importance of data Exercise 7: Profiling and evaluating data Exercise 8: Biased data Exercise 9: Infrastructure Exercise 10: Questions about the POC infrastructure Exercise 11: The AI development environment Exercise 12: Buy or build?Exercise 13: Building the project team Exercise 14: Types of roles Exercise 15: Responsibilities

In this final chapter of the course, you will learn the final steps of an AI solution implementation - assessing the POC, scaling, and aligning culture and skills. You will understand the areas in which a POC can be evaluated as well as what are good indicators for moving forward with scaling the solution. You will go over the big components to focus on for scaling to a full implementation, including requirements for culture and upskilling folks in your business.

Exercise 1: Mesure de la preuve de concept Exercise 2: Qu’est-ce qui fait le succès d’une preuve de concept ?Exercise 3: Classification des métriques dans un scénario de commerce électronique Exercise 4: Évaluation de la preuve de concept Exercise 5: Composants pour la mise à l'échelle Exercise 6: Équilibrer la valeur et la faisabilité Exercise 7: Prendre une décision sur la mise à l'échelle Exercise 8: Considérations pour une mise en œuvre complète Exercise 9: Domaines d'intervention pour une mise en œuvre complète Exercise 10: Surveillance et dérive du modèle Exercise 11: DevOps, MLOps ou conformité ?Exercise 12: Adoption et qualification Exercise 13: Raisons de l'absence d'avantages financiers Exercise 14: Résistance à l'IA

Exercice actuel

Exercise 15: Avantages pour la gestion du changement, la culture et l’amélioration des compétences Exercise 16: Conclusion