The panel discussion highlighted that workforce readiness is now the critical factor in determining whether AI adoption drives growth, anxiety, or inequality within organizations. While access to AI technology is no longer the main barrier, employees often lack the skills, confidence, and guidance to use tools effectively, and executive nervousness can slow adoption. Skilling must keep pace with innovation, aligning learning with job competencies and embedding it into real work, rather than relying on generic or optional programs. Without deliberate design, there is a risk of creating a two-tier workforce—AI-enabled and AI-not-enabled—where inequality grows despite opportunity.
Leaders play a central role in this transformation, with success dependent on leadership training, unlearning outdated approaches, and fostering learning agility. Structured pathways with clear competency baselines, time-bound learning, coaching, and accountability are essential, particularly in multigenerational workplaces. Access also involves aspiration: educators and learners need exposure to real workplaces and career pathways to guide credible skill development. Vocational roles, often overlooked, are expected to be AI-enabled and in high demand, highlighting the need for inclusive pathways that reach learners without prior social capital.
Scaling workforce readiness requires ecosystem collaboration between employers, educators, and governments, supported by career navigation systems and co-designed learning programs. Learning and development should be treated as a core business function to ensure enterprise resilience, rather than a peripheral activity. Ultimately, deliberate effort is needed to prevent AI’s benefits from clustering among a few and to ensure all workers can adapt, thrive, and contribute in an AI-driven economy.
