Introduction of AI to Learning and Development
November 2022 saw the release of ChatGPT. In most areas of business the 18 months since then has seen limited development or radical change. In the field of AI, however, we are experiencing a dizzying pace of iteration with each product and breakthrough building massively on the last. Organizations, especially L&D departments, are grappling to keep up with this rapid evolution. Despite the large amounts of discussion on the topic, a recent CIPD Learning at work study found just 5% of L&D professionals using AI tools in their day job.
Traditional L&D Methods Meet Early AI Applications
L&D departments are encountering challenges similar to those faced by educational systems worldwide, transitioning from ‘front-loaded’ education to lifelong learning. The rapid changes in required knowledge, skills, and behaviors in various sectors make planning educational content and learning paths increasingly complex. L&D departments are under pressure to adapt, often with reduced budgets and increased demands. As Chrysanthos Dellarocas writes in the Harvard Business Review:
“Traditional learning-and-development methods often fall short, being costly, ineffective, and unable to keep pace with rapidly evolving skill requirements.”
With the uncertainty surrounding official AI policies, many managers have turned to generative AI tools for tasks like emails, assessments, peer reviews, and social media posts. Throughout 2023, the focus on data security and privacy intensified as organizations worked to ensure AI was used safely and securely.
Current AI Applications in New Manager Training
AI usage in new manager training has concentrated around five key themes:
- Personalized learning experiences
- AI-driven content creation
- Virtual mentors and coaches
- Enhancing engagement
- Real-time progress tracking
The first theme, personalized learning experiences, offers significant opportunities due to AI's ability to provide tailored inputs and rapid outputs. AI tools can now consider user psychometrics, learning styles, career paths, and goals to deliver fully customized learning experiences. Traditional manager training often struggles with lack of personalization, but AI provides a solution by catering to diverse cohorts with varying levels, specializations, and goals.
AI-powered coaching and mentoring also represent a breakthrough, offering hyper-specific, on-demand learning experiences. These AI tools can synthesize the insights of top executives and subject matter experts, integrating them with specific organizational processes and values. This ensures coaching is both highly relevant and available exactly when needed, freeing manager training from the constraints of formal, scheduled sessions.
Potential AI Pitfalls for L&D Professionals
Despite its potential, AI comes with risks and challenges. One significant issue is AI hallucinations, as illustrated by the case of Mata v. Avianca. MIT Management explains:
“In this case, a New York attorney relied on ChatGPT for legal research. The federal judge noted that the opinion contained internal citations and quotes that were nonexistent. The chatbot fabricated them, claiming they were available in major legal databases.”
This highlights a major issue with AI: it’s not designed to ensure accuracy. AI’s reliability depends on its training data, and even accurate data can be miscombined, leading to errors. In new manager training, this can result in incorrect advice or fabricated scenarios, causing confusion and mistrust in AI tools.
Another critical concern is data security. AI systems require vast amounts of data to provide personalized and effective training, often involving sensitive employee information. Unauthorized access to this data could lead to severe breaches of confidentiality and misuse of personal information, with potential consequences like identity theft, corporate espionage, or reputational damage.
How L&D Can Leverage AI for Maximum Impact
To maximize AI’s benefits and mitigate risks, L&D teams should adopt a strategic approach. Firstly, it's essential to identify the specific problems AI solutions aim to address. Many L&D goals are still best achieved through human interaction and traditional methods, such as collaborative leadership training. Once AI goals are defined, policies and protections must be established to guard against negative side effects. Incorporating human oversight is crucial, with trainers and subject matter experts periodically reviewing and validating AI outputs.
L&D teams should prioritize high-quality training data to ensure optimal AI performance. Using diverse, representative, and high-quality datasets will enhance the effectiveness of AI in manager training. Additionally, implementing comprehensive encryption protocols for data in transit and at rest, along with strict access controls, will protect sensitive information. Adhering to data protection regulations like GDPR is essential for compliance and maintaining employee privacy.
By following these best practices, L&D teams can harness AI’s power to enhance training programs for new managers, ensuring a secure, accurate, and trusted implementation.
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