Middle managers receive pressure from their leaders to spend more time developing and teaching their direct reports. The pressure also comes from below, as an employee expectations are high for real-time, personalized feedback, especially for those early in their careers or who have come a long way. The challenge is to satisfy these demands without getting even at a higher level middle manager burnout and stress. Based on our research and advisory work, we have seen new AI tools that make it easier for managers to provide high-quality coaching more efficiently.
Employees want more coaching, but managers don’t have the capacity
OC Tanner’s 2023 Global Culture Survey found that leaders whose management responsibilities – including training employees – increased in the past year showed a 21% higher rate of anxiety, increasing the likelihood of burnout by a staggering 520 %.
Despite the potential for overload, managers often want to coach their employees because they know it’s beneficial. In fact, managerial coaching has been shown to increase job satisfaction, knowledge sharingand display — all the results that organizations (and managers!) want.
The AI opportunity
Our field review shows that AI-based managerial coaching is not yet common, so we offer two case examples to show what it can do for your organization:
Eleos Health is a behavioral health technology company that Katherine studied in depth during her ethnography. RESEARCH REVEALS. The Eleos platform uses HIPAA-compliant software to record and analyze conversations between therapists and clients. In addition to saving time for therapists to take notes, the platform’s AI automatically identifies relevant evidence-based techniques and summarizes key moments in conversations between therapists and clients. client.
A therapist explained: “After each session, the Eleos platform analyzes the counseling techniques I used and provides a helpful summary. It gives me a good sense of where I left off with that patient and what interventions I should use next time to best help them. In this way, the AI platform enables therapists to make self-improvements in their work without having to rely heavily on supervisors for guidance.
In addition, the AI platform can facilitate more useful checkpoints between therapists and their supervisors when they need to meet. For example, it can be used to quickly synthesize or retrieve relevant data in a client session when discussing a complex case, reducing reliance on memory or notes. As one supervisor told Katherine, “New therapists always have questions … Eleos’ analysis allows me to zero in on key moments in their session visits, or on techniques that used by therapists, as well as the patient’s progress over time, so that our teaching time together can be used most productively.”
The second example comes from Gong, which is developing an AI platform for sales teams. Gong’s platform seamlessly captures live interactions between agents and prospective clients. As the conversation continues, the AI crunches data in the background, examining how variables such as pace, topic, tone, and turn-taking compare to that company’s best practices. This data is used to provide insights and advice to sales agents in real time.
In addition, Gong’s AI can automatically create a library of high-quality examples of recorded sales conversations to serve as a resource for independent learning. This has been found to be especially helpful for new hires, who can listen to good sales calls at their own pace, rather than relying on direct training from others.
Identifying important onboarding content and providing sales reps with real advice during sales calls saves managers hours of time spent listening to new hires’ calls to help them understand what works and what doesn’t in the sales process. The result is efficient knowledge transfer. As one Gong user told us, “The platform empowers me to improve my skills and share best practices with fellow sales reps. I find that AI-based knowledge-sharing through feedback and metrics lead to continuous improvement across the team.
Understanding the potential challenges
We are not endorsing any particular tool or company, but we encourage HR and people managers to keep an eye out for AI options that can improve the management of their organizations. Other companies in this space include Beamery, Hum, Culture Amp, BetterUp, Skillsoftand IBM. We expect new offerings to continue to emerge, including embeddings of generative AI capabilities.
As you shop, assess your workforce’s readiness for adopting an AI-assisted learning system. Remember that evaluating someone’s work and trying to improve it already emotional processes; adding technology to the mix increases anxiety.
A well-documented phenomenon is “avoiding the algorithm”: the reluctance or reluctance of people to trust algorithms over human judgment. This can be especially powerful when the algorithm judges an employee effectively.
In addition, introducing a new source of evaluation can create a social distance between managers and their employees, especially if there are concerns about bias, transparency, and privacy. . For example, a study shows that employees resent AI-based feedback, even if it is helpful, if they are not informed in advance that their manager is using it. This study discusses the importance of not only considering factors such as cost, accuracy, and timing when evaluating an AI-assisted teaching tool, but also the human factors that influence its success or failure. .
Five steps for successful AI-assisted coaching
To ensure that AI-assisted training initiatives achieve the best possible results for employees and managers, we recommend the following steps:
Build psychological safety and trust
Decades of research have shown that a trust-based relationship between parties is essential for effective feedback and coaching. This is even more important when dealing with the “black box” of AI-generated assessments. In a new study, for example, employees who feel their Managers have their best interests at heart are more willing to accept recommendations made by AI – even ones they don’t agree with at first. Managers can create psychological safety and trust in the tool by explaining the rationale for AI integration, providing assurances about how employee data will be used, and encouraging people to speak up about problems or questions.
Involve employees in design and implementation
For the best quality as well as the maximum purchase, this is important involve employees throughout the exploration and adoption process. Microsoft found a new one survey of 4,500 global executives that giving people a say in technological initiatives goes a long way toward keeping them satisfied and engaged with the tools. In fact, employees will be involved up to the level of determining performance metrics and type of teaching to be done by AI.
Give employees control over their data and participation
Giving employees agency in how they participate in AI-assisted learning systems can help alleviate scrutiny and privacy concerns. For example, the Eleos Health platform allows clinicians to opt out of using the platform for specific patient sessions. Another recommended method is to form an employee-participation AI governance board to ensure transparency of what data is collected, how it is used, and what safeguards are in place to protect the privacy of employees.
Streamline and customize the output
Generally, AI tools allow managers to customize the data displays and analyzes generated; we recommend organizations take advantage of these features. At the start of a rollout, for example, it may be helpful to focus output on just a few performance metrics until people feel more comfortable with the tool.
Managers can also adjust the quality of feedback to reflect specific employee needs – for example, by providing output more encouraging feedback during employee onboarding. In one study, the AI coaching tool was found to be most effective for moderately skilled employees, as less skilled employees sometimes found the feedback overwhelming (too many suggestions), while more skilled employees sometimes find it lacking in nuance. What employees want is personalized coaching, so it’s important to choose an AI tool that’s flexible enough to accommodate multiple people and situations in your organization.
Train the managers vigorously
Many managers can benefit from this basic training in effective teaching, including how to embed real empathy and compassion in the process. Integrating AI tools requires more improve the skills of managers. They must be trained not only in technical knowledge but how to interpret AI-generated data sensitively and competently to employees. AI tools can also prove useful for training new managers by providing practice cases and other forms of experiential learning.
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AI tools hold special promise for increasing the frequency, personalization, and accuracy of instruction, without increasing the burden on managers. These gains are not guaranteed, however. To be successful, it is important to integrate AI technology in a way that leads to better PEOPLE experience for managers and employees.