Annie Tsai

This week, we’re focused on one of the most overlooked but urgent challenges of AI transformation: talent development. As hybrid and distributed work continues to fragment traditional learning and development models and as AI tools automate early-career tasks, companies are having to rethink how they onboard, develop, and retain talent. The traditional apprenticeship model – which was built on shadowing, mentorship, and active face-to-face engagement – no longer works in a world where distributed and automated workforces are more the norm than not. What’s replacing it? Structured learning, embedded AI assistants, adaptive content, and deliberate mentorship design. From internal rollouts to cross-industry surveys, we’re seeing the early blueprint for next-gen workforce enablement start to take shape.

“On the job” learning is upended by AI, hybrid, and distributed work

JPMorgan’s Jamie Dimon warns that hybrid work and AI (and distributed workforces, to be honest) are undercutting apprenticeships. Big firms like PwC and Citigroup are responding with simulations, structured mentoring,. and peer-based learning. The takeaway? Passive learning is gone – it’s time to focus on intentionality. Read More →

How AI integration and smart agents are redefining global mobility

For corporations with a global footprint, supporting multi-geo mobility can be a key benefit driving talent retention and growth. AI and smart agents are transforming distributed work logistics, from visas to housing to onboarding. AI agents are now handling everything from visa compliance to onboarding workflows, allowing distributed teams to scale without sacrificing personalization. The insight: growing global teams no longer means growing overhead. AI makes scalable, high-touch onboarding across borders possible. Read More →

AI delivering scaled apprenticeship models

What if your next mentor is embedded in the software you already use everyday? As traditional mentorship becomes harder to scale in today’s workforce, companies are embedding AI into their training systems. AI can codify expert workflows and now deliver real-time feedback, turning everyday tasks into teachable moments. For your business: consider what the 20-25% of a role’s work is that can be codified and mentored through micro-learning and AI supported delivery. Read More →

The rise of reverse mentoring

GenZ-led reverse mentoring programs are becoming a strategic channel for cultural insight, AI adoption, and breaking down hierarchical blind spots. Senior leaders at companies like British Airways, Estée Lauder, PwC, Procter & Gamble, and law firms are being mentored by younger staff on topics ranging from AI and social media to remote work and employee experience. BA’s scheme grew from just 11 executive/junior pairs to 80, providing real-time feedback on digital trends and internal culture. What to implement: establish reverse mentoring circles specifically focused on AI. Pair junior AI-native employees with senior leaders, framing the program around real business objectives like streamlining specific processes with GenAI, train both parties, and ensure psychological safety and executive sponsorship are built in. Read More →

Case Study: Carlyle’s AI rollout offers a playbook for enterprise adoption

With 90% of its workforce using generative AI tools, Carlyle’s adoption success came from embedding AI into onboarding, building internal councils, and tying usage to measurable outcomes like time saved. Human oversight stayed central, reinforcing trust. For your business: treat adoption as a systems change rather than a traditional technology rollout. Think onboarding, champions, outcomes, and feedback loops. Read More →

Case Study: AI-powered learning at distributed scale – LAZ Parking

LAZ Parking deployed an AI-enhanced learning system across 4,000+ locations. This enabled 200+ localized, micro-learning modules ranging from safety to leadership which were built from standard operating procedures. Instead of replacing trainers, LAZ Parking used AI to amplify them, allowing HR to rapidly tailor learning content and reduce compliance risk. What to implement: Consider where people-led topics should be prioritized and where AI is the right tool to drive effective learning and development. Read More →

Your Takeaway This Week: AI may be the forcing function to rearchitecting how talent gets developed moving forward, but it also opens the door to creative, scalable solutions.

AI is driving a structural shift in how companies onboard, train, and develop their people. Shadowing and passive absorption no longer scale. What’s emerging instead are modular and highly personalized learning systems that themselves can learn: role-based onboarding, embedded AI coaching, micro-learning, and reverse mentoring tied to business outcomes to name a few. The companies moving fastest are they’re running focused tests, measuring outcomes, and building adaptive systems that connect enablement with performance. If your L&D strategy still requires fixed systems and physical proximity, it’s time to rethink the foundation.