For many organizations, employee development remains constrained by a simple problem: traditional coaching does not scale easily. One-on-one coaching can be valuable, but it is often limited to a relatively small group of employees because of time, cost and access.
That challenge was at the center of a recent webinar featuring Nick Stauffer, director of organizational development at MacLean-Fogg, and Kirsten Moorefield, head of research at Cloverleaf. The discussion explored how MacLean-Fogg has embedded AI-enabled coaching into its talent lifecycle, from onboarding and leadership development to mentorship, employee communication and everyday work.
MacLean-Fogg, an industrial and automotive component manufacturer, has around 2,400 employees and roughly 20 manufacturing facilities. About a quarter of its workforce is frontline, creating an additional challenge for a development strategy built around digital tools. Many of these employees are deskless and may not have regular access to a computer or corporate systems.
The challenge extends well beyond manufacturing. According to Boston Consulting Group (BCG), deskless workers represent between 70% and 80% of the world’s labor force, spanning industries such as manufacturing, healthcare, retail, hospitality and logistics.
For Stauffer, the starting point was not introducing another assessment. MacLean-Fogg was already using tools such as DISC, but the organization found that employees would often complete an assessment, receive a report and then move on without incorporating the information into their day-to-day work.
“The power of being able to take the value of assessments and connect it with something that’s in the flow of work” was what changed the equation, Stauffer explained.
Moving AI Coaching Into the Flow of Work
MacLean-Fogg’s approach has been to make coaching less dependent on formal training sessions and more connected to the moments in which employees actually need support.
The company began with a small early-career pilot before expanding its use of Cloverleaf across different employee groups. Daily tips, Microsoft Teams and calendar integrations helped introduce coaching into existing workflows rather than requiring employees to seek out a separate development experience.
That distinction is important. Stauffer emphasized that simply giving employees access to a platform does not mean they have adopted it.
“Just because someone has access to the tool does not mean that they’re using it effectively or to its fullest extent,” he said.
MacLean-Fogg therefore focused heavily on communication and accessibility. The company created a short internal video explaining the platform, used automated onboarding emails and adjusted the language around assessments. Rather than presenting the process as another formal “assessment,” the company began referring to it as a “questionnaire” to make the experience less intimidating.
The organization also makes clear that the platform is voluntary and developmental. Employees are told that their results are not used to rate, rank or evaluate them. That distinction has been particularly important in a manufacturing environment where employees could otherwise interpret assessments as part of performance management.
The need to rethink how managers support employees is also highlighted by Deloitte’s 2025 Global Human Capital Trends research. Deloitte found that managers spend nearly 40% of their time solving immediate problems or handling administrative work, while only 13% of their time is spent developing the people they manage.
AI coaching does not eliminate the manager’s role in this model. Instead, MacLean-Fogg is using technology to provide managers with additional context and practical support before conversations, meetings and development activities.
One example is the connection between Cloverleaf and large language models through the Model Context Protocol, or MCP. Stauffer described how an HR leader can use an LLM together with employee profile information to prepare for a difficult conversation.
Instead of asking an AI system for a generic guide to handling a difficult employee conversation, the manager can provide the specific context and use the employee’s profile to make the guidance more relevant.
“It goes from being a very generic ‘this is how you should have a difficult conversation with an employee’ to this is how you have a really, really hyperpersonalized conversation,” Stauffer said.
The same principle can apply to much simpler tasks, such as drafting an email. Rather than asking an AI assistant to write a generic message, Stauffer described using information about how a colleague works to shape the communication around the recipient.
The objective is not simply to generate more content. It is to use AI to make existing development practices more contextual.
From Mentorship to Everyday Development
MacLean-Fogg has also incorporated AI coaching into its mentorship programs. The company uses DISC information as one element of a broader matching process that also considers development goals, availability and preferred mentoring styles.
Stauffer stressed that assessment results do not automatically determine whether two people should be paired. Instead, they provide another source of information that can help the organization make more informed matches.
Once the pairing is established, Cloverleaf is used to help structure the conversations themselves. Mentors and mentees receive prompts and guidance ahead of meetings, giving them a starting point for deeper conversations.
The approach is designed to make limited development time more productive, particularly for programs such as internships, where participants may only spend a few months inside the organization.
Stauffer recalled hearing an intern tell MacLean-Fogg’s CEO that the company genuinely demonstrated that it cared about employees because of how personalized the development experience felt.
“They truly say they care and they mean it,” the intern told the CEO, according to Stauffer.
The company has taken a similar approach to meetings through what it calls a “Cloverleaf moment.” Inspired by the safety moments commonly used in manufacturing meetings, these short activities introduce a development question at the beginning of a meeting.
The questions can focus on strengths, working styles or areas employees want to develop. The objective is deliberately modest: create a two-minute conversation that can make development part of the team’s normal rhythm.
“It takes two minutes. It’s super easy,” Stauffer said, explaining why the format has gained traction.
The strategy reflects a broader shift in how organizations are approaching AI and employee development. Rather than treating AI coaching as a standalone platform or a replacement for human coaches, MacLean-Fogg is embedding it into programs and processes that already exist.
That includes onboarding, leadership development, mentorship, engagement surveys, HR conversations and meetings. Stauffer’s team has also worked with IT and HR leaders to reduce the friction involved in accessing the technology, including through single sign-on and managed browser bookmarks.
For Stauffer, the lesson is less about finding a perfect AI coaching strategy from the beginning and more about starting with a practical use case and improving it.
“Just try something,” he said. The first version of MacLean-Fogg’s videos and emails was not perfect, and the company adjusted them based on what it learned.
Moorefield similarly highlighted the incremental nature of the company’s approach. MacLean-Fogg began with individual programs and gradually expanded AI coaching into onboarding, leadership development, HR support and other parts of the employee experience.
The result is a model in which AI coaching becomes less of a separate destination and more of an infrastructure for continuous development. The technology provides personalized guidance, while managers, HR teams, mentors and employees remain responsible for the human conversations that turn that guidance into action.
As organizations look to make development available to larger portions of their workforce, particularly employees who have historically had less access to digital learning and coaching, MacLean-Fogg’s experience suggests that scalability may depend not only on the capabilities of the AI itself, but on how deeply it can be embedded into the everyday systems and moments where work already happens.
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