Artificial intelligence (AI) has a permanent place in the learning and development (L&D) world. L&D departments are at the forefront of AI adoption, with many learning leaders using the tool as their personal assistant, from planning to outlining and creating assessment questions, to learning personalization and tracking.
AI is a key factor for increased productivity and seamless operations; yet it can also be an organizational risk. These risks can significantly impact business operations, reputation and compliance. To mitigate these risks, L&D professionals must know when and how to appropriately use AI in their initiatives. This requires differentiating cases where using AI may be high risk vs. low risk.
Otherwise, the original goal of boosting productivity could be overshadowed by unwanted consequences that are costly and damaging to the business. This article evaluates the differences between high-risk and low-risk scenarios for using AI in L&D, and key considerations for balancing risk and opportunity.
High-Risk Scenarios for AI in Training
High-risk scenarios for AI involve decisions that can significantly impact employees’ career paths, compliance and organizational culture. These situations require close human monitoring and oversight. Discrepancies or biases can result in unfair outcomes, legal consequences and harm to employees. Tom Whelan, Ph.D., director of research at Training Industry, shares, “For as amazing as it is, all the skills it can expand on and all the tasks it can help accelerate, it is still subject to hallucinations and other inaccuracies.”
In high-stakes scenarios, AI cannot be fully relied on as a backup. This doesn’t mean AI should be completely avoided, but it should be used with caution and clear intent. Learning leaders must continuously review AI outputs to identify and mitigate biases and errors.
Theodor Panayotov, co-founder and CEO of Ethermind, emphasizes the importance of human oversight in high-stakes scenarios involving AI, such as in emergency response and safety training. He cautions against relying solely on AI in situations where human lives depend on accurate and ethical decision-making. AI should complement, not replace, human judgment in these critical situations.
In training and development, high-risk scenarios involving AI can include:
1. Compliance training modules and assessments.
Risks: Using AI in compliance training poses the risk of relying on outdated or biased information, potentially providing inaccurate regulatory guidance. AI also struggles with nuanced legal and ethical considerations, which may leave employees unprepared for complex scenarios. Additionally, sensitive data involved in compliance training could be compromised if AI systems lack robust security measures.
2. Critical skill-building tied to safety protocols (e.g., in health care or construction).
Risks: AI systems may provide inaccurate or incomplete training, increasing the risk of errors in high-stakes environments. Safety protocols often require quick decision-making in life-threatening situations, which depends on human intuition and judgement. AI cannot account for the complexity of real-world variables, potentially overlooking hazards and resulting in unsafe practices.
3. Performance evaluations and promotions.
Risks: If AI is trained on biased data, such as historical performance metrics that favor specific demographics, it could perpetuate unfair practices, leading to discrimination claims and loss of employee trust. For example, women or minorities may be overlooked for promotions due to biases embedded in the AI model.
4. Security and privacy in data handling.
Risks: AI tools often rely on personal data to tailor learning experiences. However, if these tools fail to secure sensitive information or unintentionally expose employee data, such as health conditions or past performance records, it could lead to data breaches, violate privacy regulations and damage the organization’s reputation and employee trust.
Low-Risk Scenarios for AI in Training
A low-risk scenario for AI involves using it for administrative tasks, such as scheduling or organizing training materials. Since these tasks do not directly affect employee performance or safety, the potential for significant negative outcomes is minimal.
AI can streamline these processes, allowing L&D professionals to focus on more strategic work. However, it’s important to regularly update the AI-powered system to prevent errors that could lead to inefficiencies.
Examples of low-risk scenarios for AI include:
- AI-generated quizzes for non-critical soft skills. AI can quickly create quizzes tailored to specific soft skills like communication or teamwork. This allows learners to practice at their own pace and ensures consistent assessment and regular feedback for continuous improvement.
- Automated administrative tasks. AI can automate scheduling, reminders and progress tracking, freeing L&D teams to focus on more strategic tasks. This reduces human error, ensures timely communication and enables smooth coordination of multiple training programs.
- Chatbots assisting with course selection. AI-powered chatbots can guide employees through available training options, answering questions in real time and offering personalized course suggestions based on role, skills and career goals. This reduces the need for human intervention and enhances the user experience.
- Feedback analysis. AI can quickly analyze large volumes of qualitative feedback, identifying key themes and trends that human reviewers might miss. This enables L&D teams to make data-driven decisions and improve training programs more effectively.
- Personalized learning recommendations. AI can recommend courses based on learners’ prior performance, preferences and career objectives, providing a customized learning experience. This can support employee growth by offering learning opportunities aligned with their professional goals.
4 Key Considerations for Balancing Risk and Opportunity
Managing risk is a top concern for businesses. Learning leaders must be able to distinguish between high- and low-risk scenarios. Effective risk management begins with senior leadership having a clear understanding of the available tools and environments in which AI can be safely implemented. Understanding the role of humans in these processes is essential to mitigating risks and maximizing AI’s potential.
When integrating AI into your training processes, consider these four key principles to balance risk and opportunity:
- Human oversight: Ensure that critical decisions remain under human control. “The underlying issue is accountability,” says Dr. Whelan. “You can’t fire an AI for mistakes it makes, you can’t make it empathize with situations … it’s circuitry, it doesn’t care. So, the responsibility rests on us. If AI makes an error, we’re accountable for that.” In any risk scenario, senior leaders are ultimately responsible for the outcomes.
- Transparency: Clearly communicate when and how AI is used in your training programs to company stakeholders. In a survey, 86% of workers and 74% of leaders say an increasing focus on trust and transparency is important within an organization. Transparency can promote ethical AI use, keeping everyone accountable for how and when they use it. Transparency can also help mitigate any risks and prevent errors, such as a data leak or hallucinating.
- Testing and evaluating: Test AI applications in low-stakes areas before scaling them to high-risk contexts. Dr. Whelan recommends using a risk framework, such as the one developed by Training Industry, that categorizes risk into four categories: (1) clients, (2) employee workforce, (3) tangible organizational resources and (4) intangible organizational assets. The greater the potential threats, the higher the stakes involved. With a risk management framework, learning leaders can gain a holistic view of how AI can affect the business and the risks associated within each of the four categories. With this information, L&D leaders can create a plan of action to safeguard the business.
- Ethical AI use: Establish guidelines to prevent bias and ensure fairness in AI-driven decisions. Align AI practices with your organization’s values and diversity, equity, inclusion (DEI) goals to promote ethical and equitable outcomes.
Conclusion
AI’s integration into L&D can offer opportunities to improve efficiency and personalize learning experiences. However, L&D professionals must be mindful of risks, especially in high-stakes scenarios like compliance training and performance evaluations. Identifying when AI can safely enhance operations, such as in administrative tasks or personalized recommendations, is key. By applying strategies like human oversight and ethical AI practices, organizations can maximize AI’s benefits while minimizing potential risks.
