This post was written by a guest contributor.
For decades, corporate learning and development has largely operated under a predictable playbook: Create more courses. Deliver more training. Expand access to knowledge.
Yet, when we examine how professionals develop true expertise in reality (the kind that navigates ambiguity, handles high-stakes client conflicts, and makes sound decisions under pressure), we find that these types of formal training often fall short of impacting performance readiness among employees.
This is because the most valuable workplace capabilities, such as intuition, contextual decision-making, and adaptability, are primarily developed socially. These critical skills are built through experience, feedback, and most importantly, interacting with others.
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The tacit knowledge imperative
Historically, tacit knowledge was passed down through professional proximity: sitting next to a seasoned colleague, listening to their stories, and receiving tips and feedback. Over time, this gave employees the pattern recognition, intuition, and situational awareness to make the nuanced decisions that distinguish competent professionals from exceptional ones.
This type of knowledge transfer becomes even more important in today’s workplace. Hybrid environments, distributed teams, rapid technological shifts, and multigenerational workforces have fundamentally changed how employees learn from one another. Organizations can no longer rely on informal “learning by proximity.”
As the workplace continues to evolve, one-to-one mentoring will become even more important for developing the professional expertise, leadership capability, and adaptive performance required in the modern business landscape.
The challenge of modern mentorship
Mentorship has long been one of the most effective, yet underutilized, talent development strategies. Many companies still treat mentoring as an informal activity that develops organically, an approach that frequently reinforces existing inequities. Employees with stronger internal networks gain access to informal mentorship, while others remain excluded from development opportunities.
Mentorship programs are also notoriously difficult to scale. Many organizations still try to run global programs using manual tracking, unorganized files, and inconsistent feedback-collection methods. Even structured mentoring programs fizzle out because leaders focus on simple participation metrics rather than analyzing career progression, retention rates, or improvements in core competencies.
Without specific development goals, conversations rapidly devolve into aimless chat or repetitive problem-solving sessions that yield no measurable growth. To leverage relational learning as the powerful tool it is, organizations need modern development systems that are personalized, adaptive, measurable, and continuous—capable of supporting employees not only during formal programs but also in the flow of work itself.
How agentic AI redefines the equation
Early enterprise AI applications focused primarily on administrative efficiency or content generation. Learning teams used AI to generate course outlines, summarize materials, or automate repetitive tasks. Today’s AI systems are far more sophisticated.
Leading enterprise talent development platforms now feature agentic AI systems capable of orchestrating entire development workflows. These innovative solutions can now help identify skill gaps, recommend personalized learning pathways, prompt reflection exercises, track developmental progress, and reveal insights that can unlock untapped dimensions of workforce potential.
When used to accelerate relational learning initiatives, AI can remove many of the friction points that prevented traditional programs from succeeding. For example, AI-assisted matching can analyze professional goals, cognitive styles, and development needs to pair mentors and mentees based on actual compatibility rather than surface-level availability.
Once paired, generative AI can act as an on-demand coach. Rather than relying exclusively on pre-scheduled workshops or static frameworks, employees can receive contextual prompts, coaching suggestions, reflection questions, and conversation guidance exactly when challenges arise. By combining knowledge transfer, active application, and targeted expert intervention into a cohesive journey, organizations can translate development initiatives into actual business outcomes.
The emerging role of AI-supported learning
One of the most promising developments in modern L&D is the rise of AI-supported roleplay and experiential practice. Historically, employees would complete courses but only get limited opportunities to safely practice difficult conversations, negotiations, leadership scenarios, or customer interactions before facing them in real-world situations.
With AI-supported practice environments, employees can rehearse high-stakes interactions in psychologically safe settings while receiving immediate feedback on communication style, decision-making, and adaptability. Unlike static branching scenarios, AI can adapt to a learner's tone, sentiment, and specific wording, providing a high-fidelity experience that reinforces learning.
By engaging with text, voice audio, or video avatars grounded in an organization's proprietary content and methodologies, learners can bridge the gap between theory and action. This active roleplay boosts long-term retention and builds confidence in a safe space, providing employees with critical practice reps without any real consequences.
When implemented thoughtfully and paired with timely interventions from internal subject matter experts, these solutions can enhance developmental readiness, making mentoring conversations deeper, more strategic, and ultimately, more impactful.
Protecting human development in an automated workplace
As AI capabilities continue to expand, organizations must confront an emerging developmental risk.
Traditionally, routine assignments such as drafting basic reports, organizing data, or scheduling workflows served as the "training wheels" of the corporate world. These mundane tasks gave junior professionals the time and space to observe organizational dynamics, absorb company knowledge, and cultivate operational understanding.
As automation or agentic AI absorbs more and more of these entry-level responsibilities, organizations risk severing organic pathways towards developing performance readiness. Without those routine tasks and the foundational learning experiences they bring, how do early-career professionals learn the ropes?
This creates new responsibilities for L&D leaders. Organizations must design development pathways that intentionally preserve experiential learning, even as automation increases. AI-supported practice, relational learning, peer collaboration, and guided reflection will become essential mechanisms for helping employees build foundational business judgment.
Human-centered, AI-enabled mentorship
As AI becomes embedded more into everyday work, one-to-one mentorship will become even more valuable as a mechanism for transferring judgment, context, and lived experience across organizations. At the same time, AI will increasingly support relational learning ecosystems by removing administrative friction, expanding access, enabling personalized practice, and helping employees develop skills in real time.
This human-centered and AI-enabled balance represents the next evolution of talent development. Organizations that embrace this model will be better positioned to build adaptable leaders, accelerate readiness, and foster cultures where learning is continuous, collaborative, and deeply connected to performance.
The companies that succeed in the next era of work will not simply train employees faster. They’ll develop people more effectively, using intelligent systems and internal subject matter experts to amplify human growth.