Artificial Intelligence in Coaching
- 1 day ago
- 6 min read
Curiosity, Concern, or Contribution?
Artificial intelligence (AI) is not a distant or merely possible concept in the coaching world.
It is here.
Now.
AI in Coaching: Here and Now
Over the past few years, discussions about AI in coaching have broadened and deepened. The questions being asked about AI reflect the same level of concern about any new technology which might upend the status quo: Will it remove value from a practice built on empathy and human connection? Will it compromise the essence of coaching? Will it displace coaches altogether? Could private information be inadvertently (or even deliberately) exposed? Will bias and skew become “baked in” to AI algorithms?
The research and the blogosphere conversations reflect both excitement and caution. Some see AI as “democratizing”—making coaching more accessible, affordable, and scalable. Others discuss the risks of overreliance, arguing that the nuances of human intuition, trust, and presence cannot be replicated by a machine.
Regardless of the views we all might have, the truth is that the technology is here, and the coaching profession needs to decide how best to engage with it.
What AI Does Well (and maybe not so well)
Some recent systematic reviews and other empirical studies point to AI coaching tools being effective for specific well-defined tasks such as goal setting and attainment, behavioral change processes and supporting psychological well-being. In fact, some of this research suggests that AI matches the efficacy of human coaches in these areas 1 2 3.
Perhaps unsurprisingly, AI currently appears to fall short of the mark in areas of coaching engagement which depend on deep engagement, highly individualized attention and the formation of a string coaching alliance 4 5.
I speak, teach and write extensively about the role of coaching alliance in behavioral change (and in a therapy framework, the complementing “therapeutic alliance”). Common Factors Theory 6 tells us that the factor which accounts for most of the beneficial variance in coaching and therapy outcomes is the client’s own resources, skills, abilities, beliefs, attitudes, values, supports, learnings, knowledge, motivations, networks, tools, role models and personal narratives. The next most influential factor is the coaching alliance. For me, it’s unsurprising that coaching can be so effective – coaching directly harnesses the most powerful factor in change! And… for AI to harness the power of common factors, it must be able to learn about the individual resources of the client at hand and not simply rely on a generic pool of skills and talents that individuals may have. (If you’d like to read more about common factors in coaching, the work of Erik de Haan is essential 7 8 9 10)
Within this broader discourse, there is a promising application for AI which stands out (to me at least!): AI as a training aid for coaches.
Simulation and Feedback for Coach Training
For trainee and newly certified coaches, practice is essential. Opportunities to coach “real” clients can be scarce – trainees typically harness existing networks which often means that family, friend and acquaintances offer assistance. And that’s the challenge – the “client” in this instance is engaging not because of their own need for personal change but because of a desire to support their friend or family member. In my experience as a coaching educator, this often leads to a situation where the client is “going easy” on the coach by being very compliant, or they’re acting as a challenging client, in the mistaken belief that this will aid development. (This is a little like a driving instructor giving a novice student challenges like controlling a car on an oily road or in the midst of heavy, high-speed traffic). Peer coaching, role-plays, and a small number of volunteer clients are helpful, but they typically don’t provide enough volume or variety to build strong skills quickly. Unlike aviation and medicine, where simulations are standard for training, coaching has lacked an equivalent. Until now.
Simulated coaching conversations with AI-generated “clients” are feasible and can offer a way to practice in a safe, low-risk environment. Coaches can rehearse, make mistakes, and try again—without worrying about harming a client relationship. Beyond role-play clients, AI can also be used to provide mentor like feedback. Frameworks already exist for assessing coaches against established competencies 11 12, and these same frameworks can be used to teach AI modules what they should be looking for in coaching performance.
The key benefits which I see in using AI to support coach training are that accessibility to practice is increased: coaches can practice anytime, anywhere, without needing to coordinate schedules; transcripts and recordings of simulated sessions can be deconstructed during supervision and/ or mentoring; and, there is a “failsafe” inherent in the process. It is impossible to precipitate distress or harm in another person using a simulation.
Of course, the AI simulation practice itself is of little value without an opportunity for reflection and learning which can be strengthened through a supervisory or mentor relationship. In this way, as far as I can tell at the moment, human coaches will always need to be a part of the process. This mirrors the aviation environment in which simulations can be used for training, and aircraft can be flown using the Autopilot function, but always with the presence and attention of a human pilot.
As more coaches test these tools, I believe attitudes will continue to shift. Beginning with the premise of AI not as a replacement for human clients or mentors, but as a supplement—the technology can be harnessed as a practice ground that strengthens the ability of coaches to show up fully in real conversations.
At the same time, the limitations are clear. Emotional nuance, intuition, and the lived complexity of client stories are not easily simulated. Feedback from AI is structured and consistent and currently also lacks the deep resonance of human mentoring.
Human-led Coaching, with AI as Support
The ultimate solution is likely one of integration. Coaches can use AI to gain more practice, accelerate learning, and receive structured feedback, while still engaging in real human interactions for the depth, empathy, and presence that define excellent coaching. Students can benefit from extra practice opportunities, and certified coaches can use AI mentors alongside human mentors to continue refining their skills and perspectives.
The broader coaching community continues (and should continue) to debate the role of AI. In my view, the training and development pathway already looks practical and promising. By situating AI as a practice partner and a means of providing mentor like feedback, coaches at every level can extend their learning without compromising the human essence of their work.
The future of coaching won’t be about coaches versus AI—it will be about coaches with AI. And those who learn to use these tools thoughtfully will find themselves better prepared, more confident, and ultimately more effective in serving their clients.
PS…I’ve received ethics approval to conduct a study on the feasibility and usability of AI in learning coaching skills. If you’re currently enrolled in the Wellcoaches Core Coach Training program, you’ll be shortly receiving an invitation to participate in the research.
Citations
1 Passmore, J., Olafsson, B., & Tee, D. (2025). A systematic literature review of artificial intelligence (AI) in coaching: insights for future research and product development. Journal of Work-Applied Management.
2 Terblanche, N., Molyn, J., De Haan, E., & Nilsson, V. (2022). Comparing artificial intelligence and human coaching goal attainment efficacy. PLoS ONE, 17.
3 Aggarwal, A., Tam, C., Wu, D., Li, X., & Qiao, S. (2023). Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. Journal of Medical Internet Research, 25.
4 Plotkina, L., & Ramalu, S. (2024). Unearthing AI coaching chatbots capabilities for professional coaching: a systematic literature review. Journal of Management Development
5 Bachkirova, T., & Kemp, R. (2024). ‘AI coaching’: democratising coaching service or offering an ersatz?. Coaching: An International Journal of Theory, Research and Practice, 18, 27 - 45.
6 Asay, T. P., & Lambert, M. J. (1999). The empirical case for the common factors in therapy: Quantitative findings. In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 23–55). American Psychological Association.
7 de Haan E, Duckworth A. The coaching relationship and other ‘common factors’ in executive coaching outcome. In: de Haan E, Sills C, eds. Coaching Relationships: Relational Coaching Field Book. Faringdon: Libri; 2012:185-196
8 de Haan E, Duckworth A, Birch D, Jones C. Executive coaching outcome research: the predictive value of common factors such as relationship, personality match and self-efficacy. Consulting Psychology Journal: Practice and Research. 2013;65(1):40-57.
9 de Haan E, Grant AM, Burger Y, Eriksson P-O. A large-scale study of executive and workplace coaching: the relative contributions of relationship, personality match, and self-efficacy. Consulting Psychology Journal: Practice and Research. 2016;68(3):189-207.
10 de Haan E, Gray DE, Bonneywell S. Executive coaching outcome research in a field setting: a near-randomized controlled trial study in a global healthcare corporation. Academy of Management Learning & Education. 2019;18(4):581-605.
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