I recently completed a certification at the University of Texas on AI for leaders, and it got me thinking about how the principles of AI training shares some similarities with leading a team. Both involve nurturing growth, correcting errors, and understanding the potential and limitations of those you're guiding. Instead of discussing prompts or automation today, I want to share with you how AI training parallels leadership lessons that can help guide your teams toward growth and success.

Lesson 1: More Information Isn't Always Better

Working with AI has reinforced something I've long believed about leadership: it’s important not to overwhelm systems - or people - with information. When training AI models, you provide data in structured, manageable batches to avoid what is called overfitting. This is where a model learns noise and irrelevant details in the training data too well, causing it to perform poorly on new, unseen data. It's remarkably similar to how effective teams operate.

Just as an AI model can't effectively process a flood of unstructured data, your team will struggle when overwhelmed with too much information or too many priorities at once. Clear communication and well-defined objectives work whether you're training algorithms or leading people.

Leadership Insight: Keep your communication straightforward and goal-oriented. Overloading your team with too much information can lead to confusion, burnout, and more importantly poor results. Instead, focus on providing the right information for them to take the next step confidently.

Lesson 2: Errors Are Data Points, Not Failures

When an AI makes mistakes, trainers don't blame the model. Instead, they analyze the underlying data, identify the issues, and adjust training to improve the model's performance. This perspective completely changes how you might view mistakes. Every error provides valuable data about gaps in the model's training or biases in your approach. The same principle applies to leadership.

When your team members make mistakes, treating these moments as valuable data points rather than failures creates an environment where innovation can thrive. Great leaders treat errors as a chance to learn, grow, and fine-tune strategies, which helps individuals and teams improve continuously.

Leadership Insight: Foster a culture where mistakes are embraced as part of the learning process. When your team sees errors as opportunities to iterate and improve, they become more willing to take calculated risks, leading to greater innovation.

Lesson 3: Growth Requires Iteration

AI models require time to learn, adapt, and improve. This involves multiple iterations where the model is trained, evaluated, and adjusted based on feedback to enhance its performance. Each iteration builds upon the last, gradually moving toward better performance. Trying to rush this process or skip iterations inevitably leads to subpar results. This mirrors team development perfectly.

Just as AI models need multiple training cycles, your team needs time to process new information, practice skills, and incorporate feedback. Quick fixes might show temporary improvements, but lasting change comes through consistent iteration.

Leadership Insight: Trust the process of gradual improvement. Create regular feedback cycles and celebrate incremental progress. Understanding that excellence comes through iteration helps maintain momentum during long-term development.

Lesson 4: Quality Input Determines Output Quality

With AI the idea of "garbage in, garbage out" is definitely important. The quality of training data directly determines the quality of results, regardless of how sophisticated the model is. High-quality datasets are accurate, diverse, and representative of real-world scenarios. Similarly, in your leadership, the "dataset" is your team.

The best leaders understand how to strategically place the right people in the right roles and projects to maximize their strengths and enhance overall team performance. The resources, training, and opportunities you provide your team - your "input data" - directly impact their performance and growth potential.

Leadership Insight: Curate your team thoughtfully. Match skills to tasks, and ensure that team members are set up for success by assigning them roles that play to their strengths. The right mix of talent and roles can transform outcomes. Invest in high-quality resources and training for your team. Just as AI needs quality data to perform well, your team needs proper tools and development opportunities to reach their potential.

Lesson 5: Embrace the Training and Growth Process

AI models need continuous retraining to adapt to new information and stay effective. This involves keeping the model updated with the latest data and adjusting its parameters to meet new challenges. Without this ongoing training, models experience "performance decay" - becoming less effective as conditions change. This mirrors what your team needs: continuous learning and development.

Markets change, technologies advance, and staying static means falling behind. Continuous growth isn't just about acquiring new skills—it's about adapting to changing environments and evolving needs. When you create an environment that fosters continuous learning, you help your team not only maintain their skills but also develop new capabilities that prepare them for future challenges.

Leadership Insight: Create systems for ongoing learning and development. Regular training, skill-sharing sessions, and opportunities to tackle new challenges keep your team adaptive and engaged. Growth is not a one-time event—it's an ongoing process.

Lesson 6: Hidden Biases Need Active Management

AI models can be affected by biased data, leading to flawed results. Biases can be subtle but significantly impact performance. We use bias detection algorithms and diverse training sets to combat this. In leadership, your unconscious biases can similarly affect decision-making and team dynamics, requiring active awareness and systematic approaches to ensure fairness.

Leadership Insight: Recognize and remove biases in your leadership approach. This might mean reconsidering who gets the most challenging projects or how you give feedback. Fair, unbiased leadership encourages a diverse range of voices and ideas, leading to better overall outcomes.

Lesson 7: Change Requires System-Wide Adaptation

In AI development, we monitor for "model drift" - when changing conditions cause a model's performance to decline. This requires adjusting not just the model but often the entire training system. Similarly, changes in team dynamics, market conditions, or individual situations can affect how well your team performs. Your effectiveness as a leader depends on being proactive, anticipating changes, and adjusting strategies accordingly.

Leadership Insight: Stay alert to changing conditions and be willing to adapt entire systems, not just individual processes. Regular reviews of team structure and workflows help maintain effectiveness as conditions evolve.

Conclusion: Leading Like an AI Trainer

As a leader, there's a lot you can learn from AI training — not because your team members are robots, but because the process of training AI teaches us valuable lessons about patience, adaptability, and empowerment. Your team is much like an evolving model - both require clear communication, opportunities to learn from mistakes, the right resources, and constant growth. By recognizing these parallels, you can build a more adaptive, resilient, and innovative group of people.

By the way, these aren't new insights - they're just confirmation of what effective leaders already know. The fact that these principles emerge in AI training suggests there are some universal truths about how systems - whether human or machine - adapt and grow.

The key difference, of course, is that while AI operates on data and algorithms, human teams bring creativity, emotion, and complex interpersonal dynamics to the equation. But seeing these principles at work in AI training has given me a new appreciation for why these leadership approaches are so effective.

What Next?

While the human element of leadership can never be reduced to algorithms, understanding these connections can help you become more thoughtful about how you approach both technology and team development.

Consider the leadership challenges you're currently facing with your team. Could treating them like a training problem help you see them differently? Perhaps you're dealing with information overload and need to restructure how you communicate. Maybe your team is struggling with a fear of failure, and viewing mistakes as valuable data points could help shift that mindset. Or you might be pushing for quick results when what your team really needs is time for proper iteration and growth.

Good leadership, like good training, is about creating the right conditions for success. When you focus on providing clear direction, embracing learning opportunities, allowing time for development, and actively addressing challenges, you create an environment where both your team and your results can thrive.