FR EN
AEES

AEES Executive Certificate in Artificial intelligence and leadership

Decide where AI creates value and what you refuse to automate. This AEES certification attests artificial intelligence and leadership.

  • AssessmentProfessional assessment
  • Attempts2
  • PreparationIncluded
  • France
  • Canada
Description

Artificial intelligence and leadership

A vague familiarity with “Artificial intelligence and leadership” is no longer enough. This belongs with AI, data and digital. You lead AI, you govern data and you frame a digital change. People are hired to decide, not just to name the topic. This AEES certification is for professionals, managers and leaders who want to decide an AI use, judge what you do not automate and keep human accountability. No diploma is required. What is required is a working command of written French, and the will to show the subject on a real file, in a company, a public body or a partner organisation.

You are not joining a long degree. You take “Artificial intelligence and leadership” seriously enough to use it, and seriously enough for someone else to read it on a file. Everything happens online, around a job you already have. You prepare if you need to, then you sit a professional assessment. The AEES Executive Certificate, if it is issued, is proof in your name, and it can be checked. It holds in a CV, a move or a cooperation. It is a short specialisation, issued by a higher-education institution. You see at once what you are buying: a targeted title, not a course catalogue.

Afterwards you can carry “Artificial intelligence and leadership” into a team, a file or a new responsibility, and explain your choices to a manager, a funder or a partner institution. Others will see that you can decide an AI use, judge what you do not automate and keep human accountability, including outside your own organisation. If you succeed, AEES issues an AEES Executive Certificate in your name, with a unique serial number that can be checked in the public register. The award stays in your workspace. It shows a professional judgement you have demonstrated, useful for a CV, a new post or work in common.

Skills

What this certificate attests

The capabilities the assessment attests if you pass.

  • Decide an AI use
  • Judge what you do not automate
  • Keep human accountability
  • Anticipate bias, leakage and vendor lock-in
  • Decide who steers
  • Hold a frame when every function wants its own tool
ESCO classification — European Skills, Competences, Qualifications and Occupations

ESCO, the European skills vocabulary

This is the European Commission’s classification of skills, competences, qualifications and occupations. This certificate is linked to it through the skills below. Each link opens the official record.

Knowledge & research

Related scientific readings

Scientific readings related to this certificate.

Preparation included

To prepare for the assessment

After purchase, a preparatory course is available: 6 written modules, without an instructor, at your own pace. You are not required to follow it before opening the assessment. Each module sets out the notions, a commented case, the points to keep and a FAQ. It covers Business stake, Use case, Data and Risk.

When you are ready, you enter the assessment: a file already open, incomplete facts, conflicting views. You move through successive decisions. This is not a full taught programme with pedagogical supervision.

Structured written course

Notions, objectives and concrete examples, organised progressively.

Applied case

A commented professional situation to anchor the theory.

FAQ and preparation

Frequent doubts, then questions to go further on your own.

  1. Module 1, Business stake: tie AI to a value decision, not to a demo AI projects often start with an impressive proof of concept and end with unclear value, rising costs, and difficult trade offs. This module breaks that pattern. You will learn to frame AI from the first minute as a business decision with a stake you can explain, measure, and defend. The goal is not to show what a model can do, but to decide what your organization should do and under what conditions. A business stake is the value at risk or upside linked to a specific decision or workflow. It includes who owns the decision, the tolerable error, the economic logic, the constraints, and the conditions that would make you stop or scale. From the stake, you select an AI approach if and only if it moves a business metric at acceptable risk and cost. This framing keeps you away from demos that are impressive but irrelevant.
  2. Use case: choosing where to start, and where not to start Module 2 helps you pick your first and next AI use cases with confidence. You will learn to separate real opportunities from noise, to define boundaries that preserve human accountability, and to create a repeatable way to accept or decline proposals. The goal is a practical decision you can defend to your executive team, your board, and your customers. You will practice a simple method: start from a business outcome, evaluate feasibility and risk in parallel, and shape a small pilot with clear success and stop criteria. You will also learn how to hold a frame when every function wants its own tool, and how to avoid vendor lock-in and data leakage traps.
  3. Module 3: Data for AI Decisions Most AI projects fail quietly at the data layer. Not because the algorithms are poor, but because the organisation cannot supply the right data, at the right quality, with the right permissions, at the right time. This module helps you separate possibility from practicality. You will learn to ask focused questions that reveal whether you have enough to decide or only enough to dream. You will move from a use case idea to a clear view of what data is required, where it should come from, who owns it, and what it will cost to collect and maintain. You will check legal bases, leakage risks and supplier constraints before you commit money or reputation. You will also set evaluation baselines so that performance claims mean something against real business thresholds.
  4. Module 4/6, Risk: Anticipate Bias, Leakage and Vendor Lock-in Risk in AI is not abstract. It is the concrete set of ways an AI system can harm people, your company, your customers, or your mission. It also includes the strategic risks of being trapped by a vendor or exposing sensitive assets. In this module you will learn how to recognize these risks early, rank them, and choose practical mitigations that fit your context and budget. As a leader you do not need to code models, but you must set the frame: what not to automate, what to keep under human control, and when to say no. You will also need a common language with legal, security, compliance, and procurement. This module offers that language and a repeatable method that you can apply to any AI use case, whether it is a small internal assistant or a customer-facing product.
  5. Team: Steering, Control, and Ownership for AI Delivery AI succeeds when leadership clarifies who decides, who challenges, and who is accountable for outcomes. Without that clarity, teams ship pilots that cannot scale, risks go unmanaged, and functions argue over tools while deadlines slip. This module shows you how to set decision rights, assign control roles, and declare ownership for data, models, and outputs, so your team moves fast and stays safe. You will learn to translate abstract governance into concrete daily work. We focus on the team level: who holds the product vision, who validates and signs off, how to keep humans in charge of what matters, how to avoid vendor lock-in, and how to design cadences and documentation that stand up to internal and external scrutiny.
  6. Governance: Holding the Frame When Every Function Wants Its Own Tool AI products and models move faster than most corporate processes. If you do not define a common frame, every function will buy or build its own tools, create parallel standards, and fragment data and risk controls. Governance is how you hold a single frame without blocking useful work. It defines who decides, how decisions are made, and the safeguards that apply across the lifecycle. This module focuses on governance that is concrete and workable. You will design decision rights, approval paths, and operating rhythms. You will connect risk level to control strength, so low-risk tools can ship quickly and high-risk tools face deeper review. You will set guardrails that cut across functions: data boundaries, evaluation protocols, human oversight, incident response, and vendor exit plans.