The first article in this three-part series on artificial intelligence (AI) use cases in learning and development (L&D) explored using AI for content development. The second article discussed using AI to assess learners’ skills before, during and after training. Here, we’ll consider how AI can help deliver personalized coaching and feedback at scale.

Businesses across industries have long looked to coaching as a way to deliver individual (or small group) development opportunities, specifically to high-potential (HiPo) employees and leaders. In a typical coaching engagement, a professional coach or external consultant works with the coachee to develop their skills and competencies in alignment with business objectives. Coaching can also support employee engagement and retention by delivering personalized feedback that helps employees reach their career goals.

While the benefits of coaching and feedback are vast, delivering it effectively and at scale isn’t easy. Some common challenges include:

  • High Costs: Traditionally, human coaches are asked to facilitate sessions with leaders or HiPos as part of a business’s leadership training efforts — if they had the budget, says JJ Xu, founder and CEO of TalkMeUp, an artificial intelligence (AI)-powered coaching tool. AI-driven coaching tools (which are typically less costly than working with human coaches) can help businesses with smaller learning and development (L&D) budgets still deliver quality coaching and feedback to their employees.
  • Scalability: From a logistical standpoint, human-facilitated coaching is difficult to deliver at scale and may not be readily available when an employee encounters a challenge in the flow of work.
  • Human Bias: All humans have unconscious biases that unintentionally impact our views, actions and ability to make objective decisions. If coaches don’t actively work to recognize and mitigate their unconscious biases, they can impede their ability to provide objective feedback to coachees.

Let’s consider how AI can help solve the above challenges and more to help deliver personalized coaching and feedback at scale.

Uncovering Personalized, Data-Driven Insights With AI

Coaching tools with AI capabilities can help uncover data-driven insights into an individual’s skill set and, as a result, offer personalized suggestions and areas for improvement.

Training Industry’s e-book, “The AI Advantage in L&D: A Strategic Guide,” explains that AI-driven coaching tools “enable the seamless integration of video-based evaluation models, allowing trainers to simply drop in a video and generate data on various attributes such as engagement, persuasive arguments, information sharing and eye contact” to offer learners immediate, targeted feedback.

For example, TalkMeUp analyzes video meetings by identifying various elements within the session and assessing their impact on overall communication. It uses a persuasive communication training methodology (that human coaches have used for decades) to analyze users’ performance based on their target audience and decision-making criteria.

The tool breaks down their performance into a set of metrics within three distinct components:

  1. A visual component: How the user comes across visually (i.e., body language and gestures, eye contact, facial expressions, etc.).
  2. An audio component: How the user sounds to the listener (i.e., tone of voice and delivery). The tool measures this by processing audio data.
  3. A content component: How effective the user’s message is, which is assessed by tracking elements like persuasion, empathy, logical structure and the use of filler words.

In total, TalkMeUp uses over 10 metrics to measure communication effectiveness and achieve its ultimate goal, which Xu says is to “provide each individual user with a personalized learning experience.”

There’s also AI-driven coaching tools available that are designed for specific industries. For instance, ELB Learning’s Rehearsal is uniquely designed for sales coaching. Rehearsal allows users to upload videos of sales interactions, and the algorithm will then analyze the content and provide insights related to communication effectiveness.

Scott Provence, senior learning strategist at ELB Learning, says, “Companies I know will use Rehearsal for things like product demonstrations [and] sales pitches.” The tool will analyze the uploaded videos using keyword analysis to evaluate elements like a “call to action” at the end of a call, Provence says. On the back-end, trainers can indicate whether they want — or don’t want — sales reps to use specific words or phrases during product demonstrations or pitches, and the tool will specifically track them moving forward.

Provence says that AI can “be a great partner” in scanning through much larger amounts of data than managers can reasonably be expected to. This automated process helps “put less reliance on individual leaders” to spot mistakes and areas for improvement, which can help scale personalized feedback.

Role-Playing With AI Avatars

Some AI-enabled coaching tools, such as Lepaya and Synthesia, use AI-generated, lifelike avatars to help learners practice things like having difficult conversations. With Lepaya, users can log on to a video call with an avatar and practice having tough conversations or breaking bad news in a simulated-yet-realistic environment, says Dr. Clemens Lechner, Lepaya’s research lead.

While the avatars seem realistic, they’re supported by a large language model that has been trained according to “a very clear playbook” around communication best practices and steps for having effective conversations. Using this framework, the platform provides personalized feedback to users.

Role-playing different scenarios and conversations is a form of “active realistic practice,” Lechner says, which is essentially the opposite of passive learning.

Active realistic practice is effective in driving behavior change for three key reasons, he says:

  1. It requires learners to do something, rather than passively consume learning content.
  2. It’s realistic and simulates real-world scenarios that learners may face on the job.
  3. It requires learners to practice applying what they’ve learned.

Without practice and application-based activities, learning often fails to drive behavior change. Human coaches are not always available to help learners practice having difficult conversations or to role-play different scenarios. AI-powered coaching and feedback tools offer learners the opportunity to engage in active realistic practice whenever and wherever is most convenient for them.

Final Thoughts

The more personalized we can make coaching, the better the results will be, Xu says. “I think AI can make that happen,” as it can adapt the learning experience according to learners’ strengths and skills gaps, learning paths and more.

Ultimately, AI-enabled coaching and feedback tools can help deliver personalized learning experiences that lead to real behavior change and improved business outcomes as a result.