{"id":51806,"date":"2025-08-15T09:00:14","date_gmt":"2025-08-15T13:00:14","guid":{"rendered":"https:\/\/wp266329.wpdns.ca\/?p=51806"},"modified":"2025-08-18T10:42:42","modified_gmt":"2025-08-18T14:42:42","slug":"artificial-intelligence-governance-human-aspects","status":"publish","type":"post","link":"https:\/\/eficio.ca\/en\/blog\/artificial-intelligence-governance-human-aspects\/","title":{"rendered":"Artificial Intelligence Governance \u2013 Human Aspects"},"content":{"rendered":"\t\t
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Governance of artificial intelligence: human aspects that should not be overlooked<\/h1>\t\t\t\t<\/div>\n\t\t\t\t
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In this series of articles on artificial intelligence, we have seen how organizations can initiate AI projects (1)<\/a> and what organizational levers need to be activated for successful implementation (2)<\/a>. In this third installment, we address a fundamental pillar of artificial intelligence governance: the human aspects<\/strong>, in particular training<\/strong> and change management<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Alain Marchildon<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
President<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Train employees in artificial intelligence<\/h2>

Adopting artificial intelligence requires training tailored to all levels of the organization. Some companies are even creating internal AI academies<\/strong> that include online or in-person training, hands-on workshops, mentoring, and study visits.<\/p>

A differentiated training strategy<\/h3>

The most successful programs divide their training into four main areas:<\/p>

  1. Leadership<\/b><\/b><\/h5>

    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.<\/p><\/li>

  2. Analytics<\/b><\/h5>

    This section is aimed at technical experts (data scientists, engineers, architects) and covers data governance, analytical skills, and AI model development.<\/p><\/li>

  3. Data translators<\/h5>

    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.<\/p><\/li><\/ol>

    End users<\/b><\/h3>

    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.<\/p>

    Establish a culture of sustainable change<\/h2>

    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.<\/p>

    Lead by example<\/h3>

    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:<\/p>

    \u00ab What data supports this decision? How often are we right? \u00bb<\/p><\/blockquote>

    Assign responsibility to operational units<\/h2>

    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.<\/p>

    Monitor adoption and continuously adjust<\/h2>

    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.<\/p>

    Align incentives with AI objectives<\/h2>

    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.<\/p>

    It is crucial to review incentives so that they encourage the adoption of AI tools without compromising other business objectives.<\/p>

    Conclusion: Human-centered AI governance<\/h2>

    Organizations that successfully integrate artificial intelligence into their DNA are those that invest in their human capital. They:<\/p>

    • Train their teams appropriately<\/li>
    • Encourage interdisciplinary collaboration<\/li>
    • Align their structures and incentives with new operating models<\/li><\/ul>

      Thanks to structured and human governance of artificial intelligence, AI is becoming a powerful lever for empowerment, decentralized decision-making, and sustainable transformation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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      These components\u2014training, change management, unit accountability, and monitoring\u2014are 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.<\/p>

      Need strategic support to implement AI in your organization?<\/h2>

      Our team can help you build customized AI governance<\/strong> and equip your teams at every stage of the transformation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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