Opening Note from Sandy
In more than two decades of leading learning and development (L&D) functions in global organizations, I’ve seen many shifts in how companies build capability. Few, however, rival the transformative potential of artificial intelligence (AI). AI is no longer a distant or speculative promise; it’s actively reshaping how talent is developed, how learning experiences are designed, and how capability building aligns with business strategy. The scale of change and opportunity for learning leaders is unlike anything I’ve seen before.
Over the last two weeks, I’ve spent significant time speaking with learning leaders across North America about AI’s role in L&D. While experimentation is widespread, no one has all the answers yet. What is emerging, however, are consistent themes that signal how AI is beginning to redefine learning.
AI Enables Personalization at Scale
At its core, AI introduces a new learning paradigm, one that moves beyond static content, course catalogs, and annual training plans toward learning that is personalized, adaptive, and embedded in the flow of work. AI responds directly to our employees’ most common need: “I just need help right now.”
I think of my former colleague, Tom, an executive preparing for a promotion. He sought targeted guidance to develop a few skills he knew would challenge him in his new role. Drawing on years of experience, I suggested specific actions tailored to his needs and timeline. He didn’t need a full leadership program; he needed focused, relevant support at the right moment. That worked well for Tom, but what about the many “Toms” who don’t have access to a coach or don’t feel comfortable asking for help? AI-enabled coaching platforms have the potential to be a gamechanger for them.
Traditional L&D models built around course catalogs and outdated platforms are becoming obsolete in an environment where learning must be timely, contextual, and precisely matched to individual needs. This requires a fundamental shift from designing content to enabling real-time, performance-centric learning. It pains me to remember a business leader reviewing a beautifully designed curriculum and saying, “This is impressive Sandy, but my team doesn’t have time for any of it.” AI helps close that gap between learning intent and workplace reality.
Long before AI entered the conversation, L&D teams attempted to personalize learning manually, and it was exhausting. AI now makes personalization at scale possible. By analyzing role requirements, performance data, and learner behavior, AI can tailor learning pathways that deliver the right skills at the right time. This increases engagement, improves retention, and accelerates proficiency – outcomes that traditional programs often struggle to achieve.
In practice, this can be relatively simple: a “test-out” capability in an LMS that allows a salesperson to complete only the training they actually need, or a leadership assessment that directs learners to focus on challenges linked to their leadership style. These light-touch AI applications are likely to gain rapid acceptance among busy employees while still delivering meaningful value.
Employees Are Often Ahead of Their Organizations
One of my most eye-opening moments as an L&D leader came when an employee, whom I had praised for her exceptional data synthesis and presentation skills asked whether it was “okay” to keep using an AI tool she had already embedded into her daily workflow.
Research from McKinsey and others reinforces this experience: employees are often adopting generative AI faster than organizations are integrating it into formal learning strategies. While AI tools are proliferating, their strategic deployment across talent systems remains uneven. This creates situations where individual productivity advances faster than organizational capability.
Closing this gap requires intentional investment in AI fluency. This means not just technical skills, but the human skills required to apply AI effectively and responsibly. L&D leaders play a critical role in helping organizations move from fragmented experimentation to deliberate, enterprise-wide capability building.
AI Can Dramatically Reduce Administrative Burden
Beyond personalization, AI is reshaping the operational side of L&D. At one point in my career, I realized that my own senior learning leaders were spending more time managing learning than designing it. Weeks were consumed by enrollment processes, reporting cycles, and content administration, often producing outputs that did little to demonstrate business impact.
AI can automate many of these administrative tasks, from enrollment and reporting to content tagging and curation. This frees learning professionals to focus on what matters most: strategic design, business partnership, and performance measurement. Rather than replacing human expertise, AI acts as a multiplier enabling L&D teams to shift from transactional work to true consultation. In my experience, this is exactly how most learning professionals want to spend their time.
L&D’s Role in AI Governance
Responsible adoption of AI in learning requires thoughtful governance. Speed has to be balanced with ethics, ensuring AI recommendations are fair, transparent, and aligned with organizational values. In many pilots I’ve seen, hallucinations (incorrect or misleading outputs) remain a real risk. Human judgment is essential to validate, contextualize, and guide AI-generated insights.
When I began my career, success in L&D meant delivering great programs. Today, it means building systems that support continuous learning—often without anyone ever “attending” training. As AI accelerates the pace of change, learning leaders are no longer just program designers. We are architects of capability ecosystems that integrate technology, culture, and performance. To succeed, we must develop our own AI fluency, build cross-functional partnerships, and ensure our organizations are prepared not just for AI’s technical capabilities, but for the future of work it is creating.
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