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Using AI Analytics for Leadership Coaching

Dart board

Artificial intelligence (AI) is rapidly changing the field of leadership coaching. Analytic platforms provide for another data vector to improve leadership outcomes over time. It's not to say that existing analyses such as the WHO5, DISC, Myers-Briggs, or a host of other homegrown assessment tools don't do the job. But, the need isn't for more data, but for the right data. It's to say that we should begin to move beyond lagging indicators of performance to predictive measures of future performance.

For instance...

IBM provides Watson Talent Frameworks for talent recruitment and development. CCL provides a predictive analytics framework for assessing and predicting leadership performance.

These solutions are leveraging Supervised Machine Learning (ML) algorithms which are trained on historical data to predict future outcomes. Natural Language Processing (NLP) can be used to analyze text data, such as emails, social media posts, and chat transcripts. This data can be used to identify patterns in human behavior that can be used to predict future performance. And, data mining tools can identify patterns in very large datasets to predict coaching outcomes or future performance.

One of the most valuable benefits of AI-based analytics for leadership coaching is that it provides leaders with objective performance feedback, which complements traditional feedback methods from coaches, colleagues, and employees.

These analytics enable coaches to more precisely see how leaders are faring over time, whether it be peer benchmarking, goal attainment, or goal setting.

Immediate AI Applications for Leadership Performance

Here are some immediate areas of applicability.

Communication style

Analyzing written and verbal communication for tone of voice, written communication style, and sentence structure. Such an analysis can determine the emotional content of messaging, communication clarity, or amount of corporate speech.

Time management

Tracking time management is critical in identifying how efficient a leader is in dealing with a multitude of demands being foisted on them. For instance, do they stay on task and scan for high-priority emails or how do they manage interruptions or time-wasting activities?

Decision-making style

This includes analyzing the factors they consider when making decisions, the risks they are willing to take, and how often they make decisions that are successful. Conversely, identifying what drives a leader's thought processes which leads to poor decisions is essential to improve decision making which will have a direct health on corporate health and wellness.

Emotional intelligence

AI can be used to track leaders' emotional intelligence by analyzing their facial expressions, body language, and tone of voice. Identification of the range and types of emotions used, and their communication effectiveness is an essential element in determining the emotional context of communication. Self- and corporate awareness is a cornerstone of effective leader-to-employee communication. This in turn is essential for improving both employee satisfaction and their sense of empowerment.

Eggs with faces on them

Final thoughts...

Although machine-based analytics provide an additional layer of context in determining human performance, it is not a replacement for the intuitive leaps made during a coach-client conversation. It's a data vector, nothing more.


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