Having a mentor or a coach is an invaluable approach to talent development. Unfortunately, the cost and scalability of this make it hard to apply business-wide — prompting a growing interest in AI-based coaching as a scalable alternative.
It’s no surprise, then, that many organizations are turning to artificial intelligence to help.
In this post, we’ll explore seven ways AI is transforming the coaching experience. Whether you’re ready to implement this technology or still on the fence, understanding these key benefits can help inform your decision-making process.
How AI-based Coaching is Transforming Talent Development: 7 Practical Ways
Before we explore the many ways AI is changing the employee coaching and mentoring scene, let’s set one thing straight.
AI isn’t out to replace human coaches. Instead, it’s there to act as a supplement to their efforts and improve their efficiency. By handling large parts of the process, AI frees up human coaches to focus their efforts on where they’re needed most, making the best use of their talents.
With that out of the way, here are seven ways AI is revolutionizing the coaching landscape and transforming how organizations approach talent development:
1. Personalized Learning and Development Paths with AI-based coaching
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Caption: AI-based coaching facilitates customized learning approaches.
Personalization is the name of the game in almost all aspects of modern business. Customers want content and products tailored to their needs; employees are no different.
But let’s be honest, how many employees can a human coach actually work with while maintaining that personalized touch?
The answer is not many, at least without trading off quality.
Enter AI-based coaching. This technology can analyze an employee’s skills, strengths, weaknesses, learning style, and career goals through various assessments, performance data, and feedback. It then creates a comprehensive profile for each individual.
Based on this, AI algorithms can curate personalized learning paths, recommending specific courses, training modules, articles, and videos. It can even recommend (human) mentors that align with individual needs and organizational objectives.
What’s more, AI-powered platforms can adjust the pace and content of learning in real-time based on employees’ progress and understanding. This ensures they’re neither bored nor overwhelmed. For instance, if employees demonstrate mastery in a certain area, the AI can skip redundant lessons. Whereas if they need extra help, they can refer back to the basics — or schedule a session with one of your coaching team.
2. 24/7 Availability for On-Demand Coaching
Traditional coaching typically starts by pairing an individual or group with a coach. Next, you’ll schedule a time and date that works for everyone. While modern technology means you can run these sessions virtually, they still take a lot of planning, time management, and everyone available simultaneously.
Thankfully, AI coaches are readily available. Employees can access support as they need it, without having to wait days or weeks to schedule a coaching session.
Plus, this on-demand nature of AI coaching respects different work patterns and learning preferences. Night owls can engage with development activities during evening hours. Early risers can start their day with coaching exercises. And global teams across time zones receive equal access to resources, regardless of their location—a win for everyone.
3. Data-Driven Insights for Talent Management
A human coach’s intuition and experiences can’t be underrated. However, data-driven decision-making adds an invaluable layer of objectivity and measurability to the coaching process.
AI coaches can analyze employee skills against current and future job requirements, highlighting areas where upskilling or reskilling is needed.
By analyzing performance and career trajectories, AI can anticipate future development needs and proactively suggest learning opportunities. Afterwards, AI can track employee progress, engagement with learning content, and performance improvement after training.
This, in turn, provides data-driven insights into the effectiveness of talent development programs. Basically, you can use concrete evidence to measure return on investment and make informed decisions about future initiatives.
4. Consistent Coaching Quality Across an Organization
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Caption: AI-powered coaching platforms deliver consistent, high-quality training across multiple teams.
Imagine this:
You hire a coach for your sales team. This coach pushes learners out of their comfort zone and asks tough questions. They really know their stuff, but they’re not always the easiest person to work with. Then, along the line, for some reason, the coach can’t continue working with your team. So, you hire a new one.
Now, this new coach takes a gentler approach. They go at a slower pace, and spend more time going back over the basics. They don’t have the advanced knowledge your first coach did, but they’re better at the interpersonal aspects of coaching.
This change in approach can affect all the progress your team has made and potentially disrupt their development journey. Some people who were excelling might fall behind, while others who were struggling might start to shine.
Neither approach is wrong, but the inconsistency is an issue.
AI-based coaching removes that risk. It ensures employees receive the same quality of coaching across all levels and departments. This standardization eliminates the variability, but still allows for personalization. Learners can go at their own pace, but receive the same quality of training regardless.
Similar to how Ongig’s Text Analyzer uses AI to make job descriptions consistent and compliant, organizations can implement AI coaching systems to maintain uniform coaching standards and methodologies.
5. Integration with Existing HR and Learning Systems
Most organizations hold off on implementing AI-based coaching because they are unsure of how it can work with their existing tech stack.
This is a valid concern. You don’t want to adopt a system that disrupts your current workflow and creates more problems than it solves!
Luckily, AI coaching platforms don’t exist in isolation. They come with integrations that connect seamlessly with the digital ecosystem that organizations already use to manage their workforce and learning activities. This creates a unified experience for employees and administrators alike.
Modern AI coaches pull data from HRIS platforms, performance management systems, and learning management systems. They can connect with team collaboration software, and integrate with your existing communication tools.
This means you can use existing information to provide content-aware development recommendations without requiring duplicate data entry. Employee profiles, goals, and historical learning activities automatically inform the coaching approach. Plus, tutoring can happen within your existing platforms, instead of requiring employees to access yet another piece of software.
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Caption: AI-based coaching integrates with your existing tech stack
6. Real-time Intervention and Course Correction
AI coaching systems monitor performance and provide on-the-spot feedback and improvement suggestions. This is unlike traditional coaching, where people typically get feedback days or weeks after the fact.
It may seem like a small detail, but instant feedback really makes a difference. It helps learners adjust their approach while the event is still fresh in their minds. As a result, they can make immediate corrections and reinforce the right behaviors.
Imagine your sales rep getting a discreet suggestion midway through a sales call to stop monologuing and ask open-ended questions. That’s a level of guidance you can’t get team-wide from a human coach, and it makes a world of difference. Combine this with other tools, such as an AI meeting recorder, and you unlock new ways to analyze interactions, providing deeper insights into communication patterns and areas for improvement.
7. Reduction of Human Bias in AI-based Coaching Interactions
Human coaches, despite their best intentions, may unconsciously bring personal biases into development conversations.
One common example is the response a female trainee might get when talking about her ambition to take on a leadership role. The coach, despite their best intentions, asks whether she has considered the work-life balance challenges. This is a question they might not pose to male candidates.
AI coaching platforms minimize — and, with the correct data, can eliminate — this issue. These systems deliver consistent feedback based on objective performance data and established best practices.
The algorithms can be specifically designed to support diversity, equity, and inclusion by using culturally sensitive and inclusive language. AI coaching systems can be programmed to recognize and flag potential bias in their own recommendations through continuous review processes.
The Takeaways
AI will continue impacting many aspects of everyday life and business, including coaching.
That said, AI coaches aren’t a replacement for human coaches. They are meant to complement human efforts. While these systems can handle several aspects of coaching, they can’t make up for the nuanced emotional intelligence that human coaches bring to the table.
What they can do is make the coaching and mentoring process more accessible. Businesses of all sizes, as well as low-level employees, now have access to personalized development, without worrying about cost or scalability.
So if you really want to revolutionize your talent development, it’s time to get ahead of the game and implement AI coaching to supplement your existing coaching programs.
