AI Coaching: What’s new, how organizations are experimenting today, and what’s next.
Over the past 18-24 months, the interest, inquiry, and exploration of AI coaching in enterprise environments has taken off. This is especially true in HR, where learning and talent leaders, tasked with ensuring there is a workforce equipped with the skills they need to perform, have realized the potential possibilities that AI coaching can play in common objectives for talent and learning teams.
At The Edge of Work, we and our clients have been interested in AI coaching with a keen emphasis on how organizations can use it to unlock the performance and development of every employee. Over the past few months, we’ve had dozens of conversations with vendors, researchers, experts, as well as organizations that have implemented AI coaching. In this article, you’ll find some of our latest thinking on this topic.
But first, what is AI coaching?
In all of our interviews, this was one of the most basic but fascinating questions. There wasn’t a clear consensus, and a number of individuals we spoke with actually preferred to use other terms. Some of this speaks to the newness of the category and some to the fact that coaching also doesn’t have one singular definition. As leadership advisor Rebecca Rutschmann points out, AI coaching shines a light on the fragmentation that has always existed in the coaching field. There is no single, universal definition of coaching, only overlapping and sometimes conflicting expectations.
For the purposes of this piece, we’re going to define AI coaching as the use of artificial intelligence technologies, specifically large language models (LLMs), to facilitate growth-oriented coaching conversations and developmental experiences.
Human Coaching and AI Coaching
A common question that comes up is: how does AI coaching connect with human coaching? A second question is whether it replaces the need for human coaching altogether. According to Brian O’Hagan, VP of Product at BetterUp, the relationship is not about replacement but reinforcement. He describes “with our offering, Grow, the AI coach serves as the daily partner,” supporting individuals across a wide range of challenges on an ongoing basis, whether that’s preparing for a performance review, engaging in role play, or building skills through consistent daily practice. Human coaching, by contrast, typically happens bi-weekly and is irreplaceable for deep emotional intelligence or handling complex challenges ; as he puts it, “Humans unlock breakthroughs.”
In this collaborative model, AI builds capabilities day by day, while human coaches bring judgment, and lived experience to help individuals step into new leadership opportunities.
Why Does AI Coaching Matter Right Now?
Zooming out for a second, a better question to ask might be, why is AI coaching becoming a topic of interest and usage at this moment right now?
Transformation and Change – Many organizations have experienced wave after wave of change initiatives and large-scale transformations, and the pace shows no signs of slowing. These shifts can take many forms: mergers and acquisitions, cultural resets, new performance models, technology rollouts, AI adoption, and more. Successfully navigating this landscape requires more than new strategies; it demands new mindsets, behaviors, and the right tools and resources to help employees adapt and perform.
As Nicole Helmer, Chief Product Officer at Degreed, shared, many organizations are turning to them, and specifically Degreed’s AI experience product Maestro, for support with these challenges. She pointed to several customers undergoing significant transformations that require meaningful behavioral shifts at scale. “Getting thousands of leaders to think and behave differently is a massive organizational challenge,” Helmer noted. “ We believe we can support them in achieving this, while also accelerating their time to delivery.”
Supporting Employees in Their Workflows – As an industry, we’ve been talking about “learning in the flow of work” for nearly three decades. The aspiration has always been clear: development shouldn’t be confined to formal programs or workshops, and learning happens in or close to the work. Managers need support, feedback, knowledge, and practice in the moments that actually matter. These shifts happen when leaders have tools that are accessible and embedded close to their day-to-day work. “You increase your chances of development happening when it becomes repeatable and accessible,” James Cross, Co-Founder at Tenor, an AI leadership development platform explained. “If a manager has to step outside their workflow to get support, they won’t do it consistently. But if coaching is proximate to the work itself, it becomes part of how they go about their workday and operate,” Cross shared.
Going Beyond The Limits of Traditional Leadership Development – While there is still significant emphasis placed on formal leadership development programs and experiences, especially as organizations revisit and reset their leadership pipelines, there is a growing acknowledgement that there needs to be more accessible ways to develop leaders outside of formal programs to provide them with the leadership and management skills to perform their jobs effectively. An additional benefit of this is being able to provide a specific experience for leaders that is both precise and personalized. For example, Helmer, shared that numerous Degreed customers are leveraging Maestro, their AI coaching experience, to deliver high-impact development experiences at scale. What makes this approach compelling is the ability to deploy a common scenario or capability focus across the organization while still personalizing the experience to the individual leader engaging with it. As Helmer noted, “The capability to provide that experience inclusive of knowledge, practice, and feedback provides a critical space for development.”
Use Cases for AI Coaching
While definitions are still being worked out, a better proxy for understanding AI coaching is to look at what it’s actually being used for. After conversations with over a dozen companies that have implemented some form of AI coaching, we’ve identified several use cases and problem spaces where organizations are trying to use AI coaching to drive specific business goals.
Leadership Development – One of the most common areas where AI coaching shows up is within leadership development. And even here, there are a few different opportunity areas.The first is embedding coaching, practice, feedback, and role-play through AI into an existing leadership development experience, such as a part of a formal leadership program, and weaving in moments of practice, repetition, and skill building. The second is using an AI coach as an extended development opportunity, expanding access to coaching to more leaders beyond those who might traditionally receive it.
Organizational Change – Supporting enterprise change and transformation initiatives can require leaders to show up differently and lead in different ways. These tools can help throughout the transformation to ensure that leaders are not just aware of what they need to do differently but that they have the tools to practice, improve, get feedback, and build capability throughout the change.
“Moments That Matter” – In every organization, there are a handful of critical “moments that matter” across the talent and employee lifecycle where additional support can assist in achieving desired outcomes. AI coaching can be particularly helpful in these moments. This is especially true in many moments for managers that are hard and difficult. Multiple vendors we spoke to shared that the top topic in AI coach conversations was “difficult conversations. We find that leaders use it as a readily-accessible safe space for grappling with challenging moments, the in-the-moment decisions that really move teams and the business forward,” O’Hagan shared. This might include helping managers prepare for performance conversations, supporting employees as they think through career discussions, or reinforcing newly introduced leadership behaviors. In these interactions, AI coaching can provide just-in-time preparation, reflection, and feedback.
Supporting Development Closer to the Work – With growing emphasis on integrating development into everyday workflows, some organizations are turning to AI coaching to provide feedback, practice, and reflection directly in the flow of work. This could look like practicing before a big presentation, debriefing after a long project, or having a quick development conversation tied to a real business challenge.
Market Landscape
There is a growing number of providers in the market offering AI coaching and coaching-like experiences. While the category is still evolving, several distinct types of vendors are emerging, each shaped by their origins, primary customers, and strategic intent. Below are a few examples of how different types of vendors are approaching AI coaching in this space.
- AI-Native Companies – AI-native companies are built from the ground up to deliver AI-powered coaching. Artificial intelligence isn’t a feature layered onto an existing product; it is the core of the offering. These providers typically begin as focused point solutions centered on scalable, growth-oriented coaching conversations powered by large language models. Over time, some expand into broader talent or analytics capabilities, but their foundation is AI-first coaching delivery.
- Digital Coaching Platforms – Digital coaching platforms are established providers that historically delivered development through human coaches. In response to advances in AI, many have introduced AI coaching tools to complement and extend their human coaching offerings. In these models, AI often supports reflection between sessions, expands access to coaching across broader employee populations, or serves as a lower-cost entry point, all within a primarily human-centered coaching ecosystem.
- Learning Platforms – Learning platforms are enterprise providers already embedded in organizations through learning management systems, learning experience platforms, and content ecosystems. For these companies, AI coaching is an extension of their broader learning infrastructure. Rather than positioning as standalone coaching providers, they integrate AI coaching features to reinforce learning, support skill practice, and provide in-the-moment reflection directly within existing development workflows.
- Sales Enablement Solutions – Sales enablement solutions are platforms designed to improve revenue performance by supporting sales teams with training, call analysis, and performance feedback. Many have incorporated AI coaching capabilities that are tightly integrated with sales data and workflows. In this context, AI coaching is often skill-specific and performance-driven, offering real-time feedback and practice opportunities directly connected to measurable sales outcomes.
Examples of AI Coaching in Action
Coaching to Support Performance Management
A major global hospitality company adopted AI coaching to strengthen its high-performance culture, specifically to help managers deliver tough feedback and create meaningful performance goals. Partnering with its AI coach, the company replaced static training materials with an interactive, conversational tool embedded directly into its Microsoft Teams.
The rollout followed a phased approach. It began with a 500-person pilot to test confidentiality and ethics, then expanded to 1,000 employees across operational functions before scaling to thousands of employees enterprise-wide. Seamless integration into existing workflows eliminated the need for separate guides or manual uploads.
The impact was notable. Managers, particularly in technical roles, reported reduced friction in drafting performance documentation, with the AI helping translate rough thoughts into clear, company-aligned feedback. The tool quickly became one of the highest-rated HR technologies in recent years. Beyond adoption metrics, the organization also gained deeper insight into how managers think through performance challenges, offering a more nuanced understanding of managerial capability and decision-making.
Making Development Accessible Through AI Coaching
A global financial services organization deployed AI coaching to modernize and democratize access to development, replacing what had long been viewed as an exclusive, and often biased, coaching selection process. Instead of reserving coaching for a small group of high-potential employees, the company made the tool available to its entire U.S. workforce and tens of thousands of employees globally, shifting from elite access to broad inclusion.
The rollout began with a 4,000-person pilot that required careful navigation of legal and data privacy considerations. While leaders initially expected people managers to be the primary users, early-career employees quickly emerged as the most active adopters, using the AI coach to ask “novice” questions they felt uncomfortable raising with supervisors. The organization also embedded its internal leadership frameworks into the system to ensure alignment with company language and expectations.
Within months, strong executive endorsement accelerated global expansion. The result was a scalable, inclusive development engine that transformed static frameworks into an interactive platform for talent growth.
Considerations
Here are some things to keep in mind if you are considering AI coaching for your organization.
Tech, Legal, and Security – Vendors and customers agreed that one of the biggest areas of time and energy in the buying process is around IT, security, and legal. This is especially true for enterprise customers who have stringent IT, cybersecurity, and data privacy requirements. If you are considering AI coaching, it is best to get your IT and InfoSec team involved early in the process. In addition to traditional IT and InfoSec requirements, vendors and buyers highlighted several other key areas to probe with your vendors:
- Transparency and Fair Use:Clarity on how the provider’s AI system works, what it is designed (and not designed) to do, and how outputs should be appropriately used within the organization.
- Data Privacy: Protection of user data through compliant data collection and retention practices, including adherence to regulations like GDPR. Additionally, getting insight into the data that comes from the specific interactions or conversations. Multiple vendors we spoke to shared that 100% of the conversations are private and encrypted, so they cannot be seen or accessed.
- Bias Testing Protocols: Structured processes to identify, measure, and mitigate unfair or discriminatory outcomes in AI-generated responses. For example, Helmer shared that Degreed’s AI coaching experiences include at least 20 different bias tests.
Measurement/Impact – Like any important business initiative, making sure you have clarity around success objectives and how to measure them along the way is critical. Right now, most vendors and buyers are still leaning heavily on engagement data and NPS. In conversations with multiple organizations, there have been multiple people who shared that their AI coaching pilots often generated strong enthusiasm.
Several leaders told me it was one of the highest-rated experiences they’ve rolled out in years. But high satisfaction and strong usage aren’t the same as impact. For example, one L&D leader who implemented AI coaching in their sales organization described using structured role plays and feedback rubrics to measure capability progression, but did sellers improve their pitch? Did they demonstrate stronger messaging or execution over time?
Customized to Organizational Context – While most AI coaching platforms technically “work” out of the box, Cross encourages leaders to think intentionally about tailoring the experience to your organization’s context. “If it just feels like another generic LLM, adoption drops off.” That’s why he and his team work closely with customers to embed critical organizational levers into the system, leadership behaviors, company values, performance standards, operating principles, and even specific language managers are expected to use.
What’s Ahead
There is no shortage of ways to apply AI coaching today. But the technology is evolving quickly. In conversations with providers, several themes consistently surfaced about where this category is headed next.
Capability Building at Scale – Today, most measurement conversations center on engagement data and NPS. But both vendors and buyers acknowledge that those metrics are only the starting point. The next phase is about demonstrating capability progression, not just usage. That means building more robust ways to measure skill development, behavioral shifts, and readiness to act. As one vendor shared, organizations need support in becoming more agile, building new skills, and transforming at scale. AI coaching, if designed well, can help meet that moment, but it will require a clearer linkage between coaching activity and capability growth.
Suite of Experiences, Not Just a Single Tool – Several talent leaders expressed interest in moving beyond a standalone AI coach toward a broader set of AI-enabled experiences. The aspiration is less about “one chatbot” and more about a cohesive suite that supports skill validation, performance support, career development, and leadership growth. Interestingly, vendors echoed this direction, not necessarily as a bundled product today, but as an evolving ecosystem of use cases and experiences that span different moments in the employee lifecycle.
People Behavior Data as a Strategic Asset – One interesting long-term value proposition of AI coaching and AI talent and learning software broadly lies in the data it generates. Coaching conversations, when aggregated and anonymized responsibly, can provide insight into how managers are thinking, where they struggle, and what themes are emerging across the organization. As O’Hagan shared, aggregated and anonymized AI coaching could become “the heat map of the organization for the HR team.” That said, this is also where the highest stakes exist. Data privacy, transparency, and ethical guardrails are essential to ensure trust with the organization and employees. The future value of AI coaching will depend on balancing insight generation with employee trust.
Extending Integration into the Flow of Work – Leaders don’t want another standalone system that requires a separate login or workflow. Vendors are increasingly focused on embedding AI coaching into existing environments, collaboration platforms, HRIS systems, learning platforms, and performance tools. This includes integrations, contextual nudges, and support during specific high-stakes moments. The goal is not to create another destination, but to become part of how work already happens.
Conclusion
Organizations today are operating in an environment defined by ambiguity, accelerating change, and rising performance expectations. Meeting that moment requires a workforce that is adaptive, agile, and continuously building new capabilities. AI coaching, when positioned as part of a broader, systemic approach to development, represents a meaningful investment in accelerating capability building at scale. It offers a way to create the conditions for development and growth, embedded in the flow of work and aligned to real business priorities.
Done thoughtfully, AI coaching is not just another HR tool, it is a lever for building a more adaptive and performance-ready organization.
Have you implemented AI coaching in your organization? We’d love to hear from you. Send us a note at hello@theedgeofwork.com