Here’s how to use artificial intelligence in coaching – without losing human connection
Writing: Lisa Turner
Artificial intelligence is forcing a rethink across leadership development. For organizations that invest heavily in coaching, the question is no longer whether AI will play a role, but how to use it without degrading the very human capacities that coaching is meant to develop.
The most important issue is not whether AI replaces coaches. It is whether organizations understand coaching well enough to know which parts of it can be supported by AI, and which parts must remain fundamentally human.
AI is exceptionally good at replicating defined processes. It can scale structure, consistency and pattern recognition at a level no human system can match. But it can only amplify what already exists. When the underlying methodology is clear, AI strengthens it. When the methodology is vague or poorly understood, AI amplifies confusion and surface-level language.
For leaders responsible for commissioning coaching, this distinction matters more than the technology itself.
Understanding and scaling what works
I’m an engineer who spent years dealing with undiagnosed complex post-traumatic stress disorder (CPTSD) from a traumatic past. Confronted by questions of how the brain develops, I did what engineers do: I worked the problem. I took apart every personal development process I could find. I wanted to know exactly what worked, why it worked, and how.
I then spent two decades teaching and refining the frameworks I developed. When AI showed up, I turned my existing processes into AI tools. The AI became a way to deliver methodologies I’d tested over decades. The value was never in the technology itself: it was in the clarity of the underlying system. I’m not using AI to replace what I do – I’m using it to scale how I deliver it.
That distinction is now critical for organizations making decisions about AI-
enabled coaching.
Why some coaching approaches struggle with AI
A large proportion of coaching relies on what psychologists call unconscious competence. Practitioners develop strong intuition through experience, but are often unable to articulate the full sequence of how change occurs. The work is effective, but not fully explicit.
This becomes a problem when AI is introduced. Without a clearly defined method, there is nothing precise to encode. The output looks fluent and confident, but lacks depth or consistency. In organizational settings, this can create a false sense of progress, while outcomes remain unchanged.
For learning and development leaders, the risk is subtle. AI does not simply replace weak coaching. It exposes where organizations have been paying for work that was never fully specified in the first place.
AI’s impact on different types of coaching
From a leadership development perspective, coaching can broadly be grouped into three levels. Each interacts with AI very differently.
1. Transactional and accountability-based coaching
This includes goal tracking, habit monitoring, structured check-ins, and standardized models such as Smart goals or Grow conversations. AI already performs these functions extremely well. It is consistent, tireless and inexpensive.For organizations, this means a straightforward decision. Paying humans to deliver transactional accountability is rarely an effective use of budget when AI can perform the same function reliably. This does not diminish the importance of accountability; it changes how it should
be delivered.
2. Method-based development work
At this level, coaching involves identifiable processes for shifting thinking patterns, decision-making habits and leadership behaviors. When these processes are clearly articulated, AI becomes a powerful support tool. AI can deliver frameworks consistently, surface patterns across large populations and support leaders between sessions. Used well, it reduces cognitive load on coaches and allows human time to be focused where it adds the most value.
The key is methodological clarity. Without it, AI produces polished language that lacks substance. With it, AI becomes an extension of a well-designed system.
3. Presence-based leadership work
Some aspects of leadership development do not scale and should not be automated. This includes real-time human presence, relational attunement, and the ability to regulate and respond to complexity in the moment.
AI has no nervous system. It cannot sense hesitation, emotional undercurrents or shifts in group dynamics. It cannot calibrate itself relationally under pressure. These capacities remain uniquely human, and they are often where the most meaningful leadership growth occurs.
From an organizational perspective, this is where human coaching should be protected and prioritized.
Practical implications
For those responsible for designing and funding coaching programs, four key implications emerge. First, clarity matters more than ever. Leaders need to know what kind of coaching they are commissioning. If a program primarily delivers accountability and structure, AI should play a central role. If it relies on human presence and judgment, that value should be preserved rather than diluted.
Second, methodology should be explicit. Organizations should expect coaches and providers to articulate their process clearly. What patterns are being diagnosed? What interventions are used in response? How does change unfold over time?
Third, AI tools should be narrow and well-designed. Systems that attempt to do everything tend to do very little well. Effective AI supports specific functions, acknowledges its limitations, and leaves final judgment with human decision-makers.
Finally, AI should be used to amplify strengths, not to compensate for unclear practice. When the underlying work is well designed, technology enhances it. When it is not, technology merely hides the problem.
Integrating AI without losing what matters
The more conscious an organization is about how leadership development actually works, the less threatening AI becomes. Used intelligently, it removes inefficiencies and sharpens focus. Used indiscriminately, it risks hollowing out the very capacities that organizations are trying to build.
When I translated my own transformation frameworks into AI tools, the goal was not to replace human connection. It was to ensure that human attention was available at the moments where it mattered most. That same principle applies at scale. AI can handle structure, pattern recognition and consistency. Human coaches remain essential for judgment, presence and integration.
Integrating AI into coaching without losing human connection requires a clear understanding of both. It asks leaders to systematize what can be systematized, and to protect what cannot.
Dr Lisa Turner is an award-winning coach and founder of CETfreedom
