• Identify and activate critical skills across markets to keep pace with digital, AI and business transformation. 
• Use AI and skills intelligence to shape workforce structures, internal mobility and strategic workforce planning. 
• Embed learning in the flow of work to support continuous upskilling, resilience and long-term adaptability. 

• Shift from jobs and grades to skills, capabilities and internal opportunity marketplaces. 
• Enable internal mobility for both professional and frontline roles through transparent skills frameworks and gig/project assignments. 
• Use workforce data and real-time insight to guide redeployment, career paths and organisation design. 

• How to design non-linear, personalised development journeys supported by AI coaching and nudges. 
• Blend human mentoring, digital platforms and data to target the right learning at the right moment. 
• Empower employees to own their careers while aligning growth to future skill needs. 

• Ways to build an internal marketplace that matches skills to projects, roles and cross-functional moves. 
• Redesign career architecture and policies to unlock movement for mid-level and frontline talent. 
• Track impact on time-to-fill, retention, diversity and critical-role risk. 

• Apply skills and potential-based assessment to widen talent pools in a constrained market. 
• Use AI-driven screening and assessment tools ethically, with clear governance and bias controls. 
• Integrate hiring data with internal talent and learning data to improve quality of hire and progression. 

• Blend pay, wellbeing, flexibility and learning into an EVP that feels credible for multi-generational and frontline workforces. 
• Position development opportunities, internal mobility and coaching as core EVP pillars. 
• Strengthen trust through transparent communication, fair AI use and visible leadership behaviours. 

• Build dashboards that connect skills, learning activity and mobility data to business outcomes. 
• Move beyond “training hours” to indicators such as role readiness, internal fill rates and capability risk. 
• Use evidence-led measurement to steer investment in learning, technology and workforce transformation. 

• Develop leaders who can balance digital fluency, ethical AI decisions and human connection. 
• Equip managers to coach, give feedback and support careers despite time pressure and dispersed teams. 
• Build leadership habits that sustain credibility, inclusion and performance in uncertain markets. 

[Case Study]

• Identify the cultural, structural and leadership blockers that stall transformation. 
• Apply practical tactics for fixing misalignment, low trust, skills gaps and slow adoption of new tools. 
• Use data and workforce insight to guide change decisions and keep transformation on track. 

• Prioritise adaptability, problem-solving and digital skills in operations, logistics and support functions. 
• Design reskilling pathways for roles impacted by robotics, AI and new planning technologies. 
• Position workforce reinvention as a driver of innovation, retention and business performance. 

• Decode how expectations around careers, progression, recognition and flexibility are shifting across generations. 
• Explore what employees now expect from leaders, learning and technology, and what turns them off. 
• Translate workforce insights into practical actions for EVP, L&D strategy and manager behaviour.