The case for AI-assisted coaching supervision software to accelerate professional development of coaches
Scarcely believable advances in AI are making unthinkable advances in learning and development not just possible but essential for any business or individual seeking to stay relevant and competitive.
Coaching is now a well-established, recognised helping profession that has become an integral support and development intervention for people both in private (life coaching) and organisational (workplace coaching) settings. Numerous research studies (1) have shown that coaching works! In fact the research agenda has moved on to now try and understand why, when and how coaching works, a clear sign of a maturing profession.
As the demand for coaching grows, so of course is the number of people entering the coaching profession. We often hear that the coaching profession has grown significantly in recent years. To get a sense of just how significant this growth has been, I searched the archives and I found an ICF report from 2012. In 2012 ICF was present in 110 countries or territories, had just over 20,000 members with just shy of 10,000 of them credentialed. Fast forward to 2022: ICF is present in 159 countries or territories, has over 53,000 members, 44,000 of which are credentialled. And of course there are other coaching bodies such as EMCC, AC, and COMENSA that have also grown. Clearly coaching is on the up, which is good for coaches and clients as well as institutions who train new coaches. The challenge however is to ensure that coaches are trained well and that coach skill levels are not neglected at the cost of fast growth. We don’t want coaching to become the victim of its own success and we certainly don’t want to return to the “Wild West of coaching” (2)
To have good coaches we need good coach training, but coach training can be very expensive. One of the main cost elements of coach training is the trainers and supervisors. Coach trainers and supervisors are typically experienced coaches themselves and can command significant fees for their services. The average rate of executive coaching in the USA for example is well over $300 per hour (3). An important part of coach training is being observed in a coaching session by an experienced coach supervisor and receiving feedback on your coaching skills. Due to the high cost of coach supervisors these crucial observed coaching sessions are typically limited during coach training, potentially leading to coach-skills blind spots.
The challenge described above is not unique to coaching. In numerous industries the notion of optimising productivity and reducing cost has been studied for aeons. One approach to reduce dependence on expensive human skills is the use of Artificial Intelligence (AI). There are numerous definitions of AI and scholars have lively debates about which is most accurate. The definition I relate to most is that AI is a “simulation of human intelligence by machines”. Although AI has been around since the 1950s, it has received renewed interest in the last 10 years and we now see AI applied in numerous aspects of life from self-driving cars to robotics. If the ability of AI is improving steadily and its benefits of cost saving and scaling are becoming clear, we have to ask the question about the potential role of AI in coaching. What if we could create a “machine” that simulates a human coaching or human coach supervisor? Could that help us scale coaching and coach training?
AI in coaching is a relatively new domain, but it holds immense potential. For example I have created a number of AI coaching chatbots to coach people and support human coaches. My research shows AI chatbot coaches are remarkably efficient compared to human coaches (4)! A new frontier in AI coaching is in the domain of coach training: creating algorithms that can teach coaches how to improve their coaching session. In other words, creating an AI system that mimics the expert knowledge of a human coach supervisor and trainer at a vastly reduced cost.
A company that set itself this exact goal is Ovida.org. Ovida created a software platform that encapsulates human coach training and supervisor expert knowledge to analyse a coach’s coaching conversation. The platform tracks a number of known measures of “good coaching” such as open ended questions, coach versus client talk time, coach interruptions and question rate to name a few. The platform then provides feedback to the coach by tracking changes in these metrics and highlighting significant moments in the coaching conversation. This data and feedback allows coaches to become more aware of their praxis and design strategies to improve their coaching skills, all at a fraction of the cost of using a human supervisor or trainer. When I first discovered Ovida’s platform I was very impressed! As a coach training myself I immediately saw the potential for our students. As a researcher I was also intrigued to uncover the underlying principles of why this platform is so powerful.
In this series of articles I will attempt to explain in non-academic language the principles that support the use of AI-based expert system coach training such as Ovida. I hope this will help with the understanding of how coaches learn and grow and why it is possible to automate the training and supervision of some of these aspects. This is important because I personally have experienced the power of coaching. Imagine a world where there are many more well trained and highly skilled coaches. AI and expert system-based coach training platforms can make this a reality.
In the next article I will take a step back and discuss the process of learning - how do coaches learn and what are the important considerations and conditions for learning. This will set the scene for understanding how coaching training can be facilitated using software and AI tools.
References
(1) Athanasopoulou, A., & Dopson, S. (2018). A systematic review of executive coaching outcomes: Is it the journey or the destination that matters the most? The Leadership Quarterly, 29(1), 70–88. https://doi.org/10.1016/j.leaqua.2017.11.004
Blackman, A., Moscardo, G., & Gray, D. E. (2016). Challenges for the theory and practice of business coaching: A systematic review of empirical evidence. Human Resource Development Review, 15(4), 459–486. https://doi.org/10.1177/1534484316673177
Kombarakaran, F. A., Yang, J. A., Baker, M. N., & Fernandes, P. B. (2008). Executive coaching: It works!. Consulting Psychology Journal: Practice and Research, 60(1), 78–90. https://doi.org/10.1037/1065-9293.60.1.78
(2) Sherman, S., & Freas, A. (2004). The wild west of executive coaching. Harvard business review, 82(11), 82-93.
(3) Terblanche, N., Molyn, J., De Haan, E., & Nilsson, V. O. (2022). Coaching at Scale: Investigating the Efficacy of Artificial Intelligence Coaching. International Journal of Evidence Based Coaching & Mentoring, 20(2).
(4) Terblanche, N., Molyn, J., de Haan, E., & Nilsson, V. O. (2022). Comparing artificial intelligence and human coaching goal attainment efficacy. Plos one, 17(6), e0270255.
