Governance of artificial intelligence: human aspects that should not be overlooked
In this series of articles on artificial intelligence, we have seen how organizations can initiate AI projects (1) and what organizational levers need to be activated for successful implementation (2). In this third installment, we address a fundamental pillar of artificial intelligence governance: the human aspects, in particular training and change management.
Train employees in artificial intelligence
Adopting artificial intelligence requires training tailored to all levels of the organization. Some companies are even creating internal AI academies that include online or in-person training, hands-on workshops, mentoring, and study visits.
A differentiated training strategy
The most successful programs divide their training into four main areas:
Leadership
Leaders must understand the principles of AI, its impact on roles and processes, and know how to prioritize opportunities. They must also support the necessary cultural change.
Analytics
This section is aimed at technical experts (data scientists, engineers, architects) and covers data governance, analytical skills, and AI model development.
Data translators
These profiles, often from administrative roles, learn how to link operational challenges to AI solutions. They are taught the technical fundamentals and best practices of analysis.
End users
Frontline employees receive practical training in new AI tools. Professionals in strategic functions, such as marketing or finance, can take advanced training courses that incorporate real-world use cases.
Establish a culture of sustainable change
Deploying AI in an organization often takes between 18 and 36 months. To maintain the momentum of transformation, leaders must support consistent and engaging AI governance.
Lead by example
Leaders must get directly involved, undergo training, and adopt a learning mindset. They encourage experimentation, value lessons learned from failures, and ask questions that stimulate reflection:
« What data supports this decision? How often are we right? »
Assign responsibility to operational units
Too often, AI projects are led by technical teams. However, it is the business units that must take responsibility for the results. A shared scorecard can help track adoption rates, deployment speed, and concrete impacts.
Monitor adoption and continuously adjust
Comparing the results obtained with and without AI is an excellent way to demonstrate the value of the tool. This promotes acceptance. It is also essential to adapt interfaces and integration into everyday tools (e.g., dashboards) to facilitate use.
Align incentives with AI objectives
AI initiatives can fail if employee incentives are misaligned. For example, if the tool recommends liquidating inventory without discounts, but managers are rewarded for total sales, the system will be rejected.
It is crucial to review incentives so that they encourage the adoption of AI tools without compromising other business objectives.
Conclusion: Human-centered AI governance
Organizations that successfully integrate artificial intelligence into their DNA are those that invest in their human capital. They:
- Train their teams appropriately
- Encourage interdisciplinary collaboration
- Align their structures and incentives with new operating models
Thanks to structured and human governance of artificial intelligence, AI is becoming a powerful lever for empowerment, decentralized decision-making, and sustainable transformation.
These components—training, change management, unit accountability, and monitoring—are not linear, but feed into each other in a virtuous cycle. This is what enables organizations to evolve with agility and resilience in an ever-changing digital environment.
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FAQ – Governance of artificial intelligence
What is artificial intelligence governance?
AI governance refers to the set of mechanisms, processes, and practices put in place to oversee the development, implementation, and responsible use of artificial intelligence within an organization.
Why train all employees in AI?
Because the adoption of AI depends on people. Training tailored to each profile (management, technical, operational) ensures a shared understanding, reduces resistance, and facilitates the adoption of tools.
What is the average duration of an AI project in a company?
The duration can vary considerably depending on the nature of the project, the digital maturity of the organization, and the resources available. Some pilot projects can be completed in a few months, while company-wide transformations can take several years. The key is to move forward in a structured manner, adapting to the specific context of the organization.
What are the risks if incentives are not aligned with the use of AI?
Poorly designed incentives can hinder adoption, create internal tensions, and lead to project failure. Effective AI governance adjusts these levers from the outset.