Artificial intelligence (AI) can be a valuable aid for training managers, but human judgment remains essential. Learning and development (L&D) leaders are ultimately responsible for workplace decisions and must know how to evaluate AI outputs and oversee their use.

A training manager’s responsibilities span the entire training process, from identifying training needs and designing programs to overseeing instructional quality, managing delivery and resources, supporting workplace application, evaluating effectiveness and maintaining required training records.

AI can act as a sounding board, helping training managers explore options, synthesize information and develop scenarios. While it can serve as a “cognitive amplifier,” it can’t replace a comprehensive training framework. Effective use of AI still requires sound instructional design and human guidance.

For training managers, a more effective approach is to use AI to ask better questions rather than simply find answers. This means questioning AI-generated recommendations, recognizing its limitations and taking responsibility for the decisions that follow. Learning professionals can achieve this by combining purposeful instructional design with human guidance and meaningful assessment.

These principles also apply to the leaders who participate in training programs, who need to use AI while maintaining their own judgment and oversight. The following practices illustrate how L&D professionals can incorporate AI into leadership training to help managers develop these skills.

1. Begin with managerial capability.

Identify what managers need to do better. Identify training needs and translate these into observable learning objectives. A manager should be able to identify what evidence is missing, compare alternatives, make a choice and justify it and recognize when additional expertise is required, giving training a clear purpose and establishing standards that AI can assist with.

2. Distinguish personalization from learning.

AI can adjust explanations, generate relevant examples and respond immediately to questions. However, personalization alone is not a measure of success. Build a structured framework that includes instruction, practice, feedback and application. Managers should be able to explain concepts independently and transfer their reasoning to unfamiliar situations.

3. Require thinking before prompting.

Train managers to analyze a situation before consulting AI. They should first identify the decision they need to make, the relevant stakeholders, any constraints and their initial assumptions. Then, they can use AI to explore alternative approaches, challenge their assumptions or identify factors they may have overlooked.

This sequence keeps managers actively involved in reasoning, rather than relying on AI to make decisions for them. Before reviewing AI-generated recommendations, managers should establish criteria for evaluating potential solutions and consider their likely consequences. They can then assess AI’s suggestions against those criteria, explaining which ideas they retained, which they rejected and what evidence influenced their judgment.

4. Teach verification as a practical skill.

Managers should exercise repeated practice in detecting plausible but unsupported information. In leadership training programs, consider including exercises with fabricated citations, incomplete evidence, misleading conclusions or overlooked context. Have participants verify important claims against approved sources and consult qualified peers when necessary. An AI explanation should also be examined critically: fluency and apparent certainty are insufficient grounds for acceptance. Teach managers to distinguish information they have checked from assumptions they still need to test, especially when decisions could materially affect employees or customers.

5. Rehearse oversight through realistic scenarios.

Use simulations to address challenges around staffing, budgets or customer complaints. Let AI propose a course of action, then introduce conflicting information or changing circumstances. Ask managers when they would continue, seek review, escalate or stop.

Effective AI oversight requires time, relevant knowledge, access to evidence and the authority to intervene.

6. Clarify responsibility and protect information.

Each training exercise should specify who makes the final choice, who reviews and who contributes. Using company AI policies and regulations as a guide, set limits for authorized tools and acceptable data.

For instance, instead of entering private customer or employee data into unapproved systems, use anonymized scenarios. Teach proportional documentation for important decisions, including the AI’s involvement, the checks that were done, the decision owner, the unsolved uncertainties and the justification. Training managers need to learn how to ask questions, acknowledge areas of uncertainty and consult experts.

7. Assess independent performance and review impact.

Integrate AI-supported tasks with activities that allow managers to demonstrate their own knowledge and judgment. Ask them to justify a recommendation, explain why they dismissed an alternative or navigate an unfamiliar situation without AI.

After training, assess whether managers apply these skills in workplace decisions. Evaluate decision quality, appropriate escalation and efficiency. Gather feedback from managers, facilitators and affected colleagues, then refine scenarios and guidance based on areas for improvement. AI can strengthen managerial development when learning has a clear purpose and human oversight remains active.

Ultimately, training professionals must model the responsible use of AI in their own work while creating opportunities for learners to practice sound judgment, exercise oversight and explain the decisions they are accountable for implementing.