AI coaching for managers: what it is and how to use it well

AI coaching tools are appearing everywhere. New platforms are launching, HR and L&D teams are being asked to evaluate them, and managers are starting to experiment with them for themselves. With so many tools and claims about what AI coaching can do, it's worth getting clear on what AI coaching is, how it works and where it can help.

The opportunity presented through AI coaching is easy to see. Managers don't always need support during a scheduled coaching session or training course. They often need it in the middle of a working day, when a difficult conversation is approaching, a decision needs making or something with their team isn't going to plan.

AI coaching can offer support in those moments. But there's a difference between getting an AI-generated answer and being coached, and not every tool calling itself an AI coach offers the same experience...

So what does good AI coaching look like, what makes the tools different and how can organisations choose one managers will use and benefit from in the long run?

Let's start with the definition.

What is AI coaching for managers?

An AI coach is software that uses artificial intelligence to support learning and development through conversation. Rather than simply providing answers, a coaching-focused tool can ask questions, challenge assumptions, offer different perspectives and help managers decide what to do next.

For example, a manager preparing for a difficult feedback conversation might be asked:

• What have you observed?
• What assumptions might you be making?
• How might the other person see the situation?
• What outcome are you hoping for?

The aim is to help managers develop their own judgement, not make decisions for them. AI coaching can also support rehearsal, goal-setting and reflection. However, the experience varies significantly between tools and not every AI coach follows an established coaching methodology.

Where can AI coaching help managers?

Much of the practical value lies in preparation, practice and reflection around everyday management challenges.

1. Before a difficult conversation. Managers can clarify what they want to say, explore different perspectives and practice how to respond to disagreement.
2. When delegation isn't working. AI coaching can help managers explore why they're holding on to work, whether expectations are clear and what they might change.
3. After a difficult meeting or decision. Reflective questions can help managers examine assumptions, consider what went well and identify what to do differently.
4. When developing management habits. Managers can practice behaviours such as listening, asking better questions and giving clearer feedback, then reflect on how they applied them.

While these are all potential use cases it's worth noting that the quality of support depends on the tool, the context and how managers use it.

So, if AI coaching platforms have a role to play in supporting managers, the next step is understanding how they differ and how to assess which is right for your organisation.

What makes AI coaching tools different?

Not all AI coaching tools are created with the same purpose in mind. Some help managers navigate challenges in the moment. Others take a more structured approach to developing management capability, improving team performance or supporting lasting behaviour change.

The differences aren't always obvious at first glance, but they matter when deciding which platform will deliver the most value for your managers and organisation.

Here are six things you can look for when assessing AI coaching platforms.

1. Is the coaching grounded in research? Some AI coaches are built around established coaching approaches, behavioural science or researched models of effective management. Others rely more heavily on general-purpose AI. A clear behavioural framework can give coaching greater structure and consistency. It may also help connect everyday challenges to specific management behaviours. However, there's a difference between a tool using a research-informed model and having evidence that the tool itself improves management performance. Organisations should ask about both.
2. Whose strengths does it focus on? Some tools focus on developing the manager's own strengths, behaviours and working style. Others help managers understand their employees' strengths, motivations and preferences, so they can adapt how they lead, communicate and delegate. Some combine both approaches. The distinction is important because developing a manager's capabilities isn't quite the same as helping them get the best from their team.
3. Is it connected to goals and performance? Some AI coaching platforms link conversations to development goals, performance objectives or specific outcomes. They may help managers identify obstacles, track progress and follow through on commitments. Others focus on everyday leadership behaviours without requiring formal goals. Both approaches have a place. Coaching linked to performance goals can provide direction, but it shouldn't be confused with using AI to make formal employment or performance management decisions.
4. Is it designed for development or individual coaching moments? This is one of the more important differences. Some tools are primarily designed for immediate situations: preparing for a conversation, solving a problem or thinking through a decision. Others provide a more continuous development experience. They may remember previous conversations, identify recurring challenges, encourage practice and revisit progress over time. A one-off coaching conversation can be useful. But it's different from a structured approach that helps managers recognise patterns and work on changing their behaviour.
5. How personalised is the coaching? Some tools use assessments, development goals, feedback or previous interactions to tailor their coaching. Others rely mainly on the information a manager provides in each conversation. The more relevant context a tool can use appropriately, the more specific its support may become. But personalisation should be balanced with privacy. Organisations need to understand what information is collected, how it's used and who can access it.
6. Does it support practice and follow-through? Good coaching isn't only about having a useful conversation. It's also about what happens afterwards. Look for tools that help managers rehearse situations, choose actions, reflect on what happened and return to development priorities. Some offer role-play, reminders, learning resources or connections to human coaching. These features create opportunities for practice, although their presence alone doesn't demonstrate improved performance.

What makes managers want to use an AI coach?

This may be the most important practical question when choosing an AI coaching tool.

Managers already have competing demands on their time. An AI coach might offer assessments, development plans, goal tracking, learning libraries and detailed reporting, but each additional step can create friction.

More functionality doesn't automatically mean more value. A platform that requires lengthy onboarding, multiple logins or regular administrative effort may struggle to become part of a manager's working day.

When evaluating a tool, ask:

• Is it easy to start? Can managers get useful support without lengthy setup?
• Does it fit existing workflows? Is coaching available in tools managers already use?
• Is the value clear? Does the first interaction help with something real?
• How much effort does it require? Does the tool simplify development or create another task?
• Will managers return? Is there a useful reason to come back beyond reminders and notifications?

There's a balance to strike. More structured development may require more engagement and that can be worthwhile. The question is whether the additional effort produces value for managers... The most sophisticated tool isn't necessarily the one that gets used.

And adoption is only the starting point. Regular use matters, but organisations also need evidence that managers are applying what they learn.

How to choose an AI coach: the ART framework

With so many differences between tools, a useful starting point is to assess whether coaching is Accessible, Relevant and Timely (ART).

Accessible

Can managers get support easily, without unnecessary steps? Accessibility isn't simply about having a licence or being available 24/7. It's about how naturally coaching fits into everyday work.

A tool that managers can access quickly through familiar systems may face fewer barriers to use than one requiring a separate process.

Relevant

Does the coaching reflect the manager's actual needs? Relevance might come from a researched behavioural model, an understanding of the manager's strengths, their development goals or the context of their team.

Different tools achieve this in different ways. The important question is whether the coaching goes beyond generic advice and helps with the challenges managers are facing.

Timely

Is coaching available when it can make a difference?

That might be ten minutes before a difficult conversation, immediately after a challenging meeting or during a period of focused development.

Some tools respond when managers seek help. Others offer prompts or check-ins linked to goals and activities.

Timely support should be useful rather than intrusive.

ART is a practical way to assess how well a tool fits managers' needs and working routines. It isn't, on its own, a validated measure of coaching effectiveness.

AI coaching for managers offers a way to make development support more available during everyday work. But the tools vary considerably in their approach, depth and purpose.

Finding the right AI coach starts with your managers

The right choice for your organisation depends on what you want your managers to get better at, how the tool supports that development and whether your managers will use it.

My advice? Start with the management behaviours you want to develop. Then look for coaching that's accessible, relevant and timely, with evidence that it helps people put learning into practice.

Sources

• Terblanche, N. et al. (2022). Comparing artificial intelligence and human coaching goal attainment efficacy. PLOS ONE. https://doi.org/10.1371/journal.pone.0270255
• International Coaching Federation (2024). Artificial Intelligence (AI) Coaching Framework and Standards.