Course : EXIN BCS Artificial Intelligence Foundation, EXIN certification
Practical course - 2d
- 14h00 - Ref. AIE
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 | Situate the role and place of AI in organizations |
| Identify and describe the characteristics of an intelligent agent |
| Present the main contributions and benefits of AI in different usage contexts |
| Explain the data-driven learning process and its functional, software and hardware dimensions |
| Illustrate the human-machine collaboration made possible by AI, in particular through machine learning. |
Intended audience
Anyone wishing to understand the principles and uses of artificial intelligence in an organization: developers, project managers, product, data or security managers.
Prerequisites
None.
Certification
L’examen consiste en un QCM de 1 heure comprenant 40 questions.
Un score minimum de 65 % est requis pour réussir l’examen.
Le passage de l’examen a lieu en ligne et en anglais sur le site EXIN.
Course schedule
1 Introduction to artificial intelligence
- Definitions: AI, machine learning, generative AI, robotics.
- Main approaches to AI: symbolic, statistical, connectionist.
- Essential concepts: algorithm, model, training, inference.
- AI types: weak (narrow) and general (AGI).
- Historical development and outlook.
2 Societal, environmental and ethical impacts
- Effects of AI on employment, skills and work organization.
- AI's carbon footprint and how to reduce it: eco-design, digital sobriety.
- Ethical principles: fairness, explicability, transparency, respect for privacy.
- Governance and regulatory compliance (RGPD, AI Act).
3 Technologies and machine learning
- Overview of related technologies: cloud, IoT, robotics.
- AI value chain: collection, preparation, training, deployment, supervision.
- Machine learning fundamentals: supervised and unsupervised learning.
- Model management: overlearning, validation, performance metrics.
- Introduction to generative AI and language models (LLM).
4 Data, quality and governance
- The central role of data in AI systems.
- Quality criteria: accuracy, completeness, freshness, traceability.
- Governance: key roles (data owner, data steward) and responsibilities.
- Data risks and mitigation measures.
- Good visualization and interpretation practices.
5 AI framing and opportunities
- Identify relevant use cases according to company strategy.
- Analyze the value, feasibility and risks of an AI project.
- Build a business case and an IA business case.
- Estimate costs, benefits and return on investment.
- Build a pilot plan or MVP (proof of concept).
6 Project management and AI governance
- Roles and stakeholders: sponsor, business, data scientist, MLOps, legal, DPO.
- Choose the right management approach (agile, iterative, experimental).
- Governance: usage policies, AI committee, model supervision and monitoring.
- Post-deployment monitoring: drift, quality of service, equity.
7 Evolving professions and increasing skills
- New roles: AI product manager, ML engineer, ethics officer, data steward.
- Impact on professions and organizations.
- Skills enhancement and reskilling/upskilling strategies.
- The augmented human: collaboration between AI and human expertise.
8 Case studies and feedback
- Analysis of real cases from different business sectors.
- Key success factors: quality data, strong sponsorship, business adoption.
- Common causes of failure: unclear framework, data debt, lack of ownership.
- Lessons that can be transferred to participants' AI projects.
PARTICIPANTS
Anyone wishing to understand the principles and uses of artificial intelligence in an organization: developers, project managers, product, data or security managers.
PREREQUISITES
None.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Anyone wishing to understand the principles and uses of artificial intelligence in an organization: developers, project managers, product, data or security managers.
PREREQUISITES
None.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Dates and locations
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