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Six Ways L&D Can Take the Lead in AI Adoption

by | Mar 12, 2024

The past year and a half have seen a surge in generative AI adoption, with OpenAI, Google, Microsoft, and other technology companies rapidly introducing many new and ever-evolving AI tools. In this competitive landscape, organizations are eager to leverage AI for efficiency and to gain a competitive edge. While many leaders are focused on the technical and operational aspects of AI adoption, there are many moving pieces that ought to be considered such as organization’s culture, policies, human oversights, staff’s attitudes and readiness as I wrote about on the AI Adoption Framework. These and many other areas are where Learning and Development (L&D) professionals can move beyond traditional training roles and become strategic partners in AI adoption.

1. Leading the AI Experimentation

Don’t wait for a complete roadmap. L&D can spearhead pilot projects with various AI tools, documenting the process for future reference and lessons learned. This includes “red teaming”, which involves simulating an adversarial attack on the AI technology. Think of it as playing devil’s advocate – L&D professionals can partner with a technical team to co-test the AI’s limits, identify potential vulnerabilities, and refine the technology for company-specific scenarios. Additionally, L&D can develop prompts for internal models, and fine-tune them for optimal performance.


2. Ensuring Human Oversights on AI Outputs

AI systems, while powerful, require human judgment to validate and audit their outputs. L&D teams can play a significant role in establishing oversight mechanisms that ensure AI-generated recommendations or decisions are accurate, minimize hallucination and privacy violation, and are aligned with organizational values. Furthermore, L&D can educate and support other business units on effective oversight techniques, and to build a culture of responsible AI use.


3. Examining and Evaluating AI Pedagogy

Effectively leverage AI for educational purpose goes beyond choosing and using a tool. Technology can embody specific learning practices, and it is important to be aware and closely examine these underlying assumptions. AI (or for that matter, any form of technology) doesn’t always make education better. We need to ask questions such as “what educational philosophy and practices does the tool implicitly promote? Does the tool align with established best practices and proven learning theories/models? L&D professionals are well-positioned to pose these questions and to review these tools with a critical lens.


4. Advocating for Data Policy and Governance

AI outputs are only as good as the data they’re trained on. L&D can support the establishment of data governance frameworks. This involves advocating for policies that address ethical concerns, mitigate bias in AI models, and ensure data privacy.  Engaging in dialogues about bias detection, algorithm transparency, and user control over personal data are essential steps. L&D can also lead training sessions to equip employees with the skills to identify and address potential biases in AI outputs.


5. Engaging in the Procurement Process

L&D brings a diverse perspective to the table by considering the needs and concerns of various stakeholders, including end-users. They can conduct market research with a focus on solutions that address real-world business problems and user pain points. When evaluating AI applications, L&D can ask critical questions about data requirements and usage from an end-user standpoint. This could involve understanding how data collection will impact user privacy and ensuring the AI solution is transparent and easy for employees to adopt. L&D’s role is to bridge the gap between the technical aspects of AI and the human experience, ensuring chosen solutions align with the organization’s long-term goals and have a solid business case for implementation.


6. Helping Prepare Staff for the Changing Nature of Work

The impact of AI on the workplace is a moving target, but L&D can prepare the workforce. By collaborating with business leaders and staying informed about current and emerging job landscapes, L&D can identify future skill needs and develop training programs to equip employees with the skills to complement, manage, and collaborate effectively with AI. Consider utilizing the AI Literacy Competency Framework I have created as a starting point. This framework outlines key areas of AI knowledge, from foundational concepts to advanced applications.


AI adoption presents a transformative opportunity, and L&D can contribute to the process in multiple ways. By taking a proactive approach, L&D professionals can bridge the gap between technology and human capital, fostering a culture of trust and continuous learning, and ultimately maximizing the potential of AI within the organization.