#custom-quote-block_f5a24c3df35ce6edf9d5275ca7f6688e { /* Add styles that use ACF values here */ } #custom-quote-block_0a8cf89f6f73a110c60bdfb13f6389ae { /* Add styles that use ACF values here */ } If AI can build a marketing campaign from scratch, what’s stopping it from designing your next training program? Enterprise learning leaders are under pressure to keep pace with AI while still protecting quality, compliance, and culture. This Future Focus episode of Performance Matters asks a simple but urgent question: if AI can run complex work tasks end‑to‑end, what does that mean for how L&D designs, delivers, and measures learning? Theodora Michaelidou, Innovation Learning Consultant from Cyprus and Paul Andrews, Learning Experience Consultant from Shrewsbury, England join the show to explore whether enterprise L&D is truly ready for AI browsers and agentic tools, and how leaders can turn them into safe, adaptive learning and performance support at scale. Why AI Browsers Matter for Learning and Work Traditional browsers like Chrome helped people find information; AI browsers and agentic tools are now helping them complete work. In the episode, Paul Andrews explains that newer “agentic” browsers can plan, click, and navigate on a user’s behalf—more like a digital co‑pilot than a search bar. For L&D and HR, this shift changes how employees seek support and solve problems on the job. Instead of searching a learning portal, they can delegate tasks to an AI that reads policies, compares options, and drafts outputs in real time. If learning teams do not design for this reality, employees will still use these tools, just without appropriate guardrails. From Static Courses to Dynamic AI‑Powered Pathways Much of today’s digital learning is still built around predefined paths: pick a role, follow a linear journey, complete a course. Paul describes how AI can now remix content on the fly, adjusting the experience based on what a learner gets right or wrong and what they need next. Instead of a single branching scenario, an AI‑enabled system can draw from a library of approved assets and assemble a bespoke path in the moment. The result looks more like a modern video game that adapts to how you play than a rigid eLearning module that looks the same for everyone. For global organizations, this also opens the door to GEO‑sensitive experiences that reflect local regulations, markets, and examples while still aligning to a common global framework. An AI layer can pull region‑specific scenarios, terminology, and compliance nuances without forcing L&D teams to rebuild every course from scratch. AI as a Just‑In‑Time Subject Matter Expert One of the most common complaints from business stakeholders is that they invested heavily in training content, but their people still cannot get quick answers when they need them. Theo highlights how AI tools can turn existing content libraries into conversational, just‑in‑time support. By feeding policies, procedures, playbooks, and learning assets into an AI browser or chatbot, organizations can let employees ask questions in natural language and receive targeted guidance grounded in their own content. Crucially, Theo stresses that this must be a human‑plus‑AI model: experts still validate outputs, monitor risk, and refine prompts and guardrails. For regionally distributed workforces, this just‑in‑time approach can also be localized without fragmenting content. A single global knowledge base can be tuned with GEO‑specific rules so that, for example, a retail manager in North America and one in Europe each receive guidance that reflects their local environment. NPCs, Simulations, and More Human Practice The episode also explores how ideas from gaming—especially non‑player characters (NPCs)—are shaping the next generation of learning simulations. Paul explains that NPCs in games already respond dynamically to a player’s behavior, and AI can bring similar responsiveness to learning. Instead of static role‑plays where the “customer” or “leader” always reacts the same way, AI‑driven characters can respond differently based on what the learner says or does. This enables more realistic practice for leadership, coaching, customer experience, and sales, tuned to regional norms and customer expectations. For L&D and HR, this means practice environments that can scale globally while still feeling local—for example, simulating conversations with customers in specific countries or coaching scenarios shaped by local labor practices. Practical Steps for L&D and HR Leaders to Implement AI L&D and HR leaders do not need to rebuild their entire ecosystem to benefit from AI; they need targeted, high‑value use cases. The conversation suggests several practical starting points: Offerings such as GP Strategies’ Learning Experience Design & Innovation services can help organizations experiment safely, define governance, and align AI use with measurable performance outcomes. Key Questions to Ask About AI Readiness The episode ultimately reframes “Is L&D ready for AI?” into more actionable questions. Leaders can use these as a simple readiness checklist: Where are employees already using AI browsers or tools outside official channels? Which content is safe and valuable to expose through AI, and what must remain tightly controlled? How will human experts monitor, validate, and continuously improve AI‑generated outputs? What GEO‑specific considerations—regulation, culture, language—must shape AI use across regions? By treating AI as an extension of learning and performance strategy, not a separate experiment, L&D and HR can move from anxiety to action and ensure AI is rearranging the furniture in ways that actually support capability, culture, and business results. Enjoyed this article? Subscribe to Performance Matters on Apple Podcasts, Spotify, or your favorite platform for more Future Focus insights. For more on AI in L&D
