Building a Resilient Workforce: Implementing a Unique Upskilling and Reskilling Program

In today’s rapidly evolving job market, organizations must prepare their workforce to meet both current demands and the emerging roles shaped by new technologies, especially artificial intelligence (AI). To remain competitive and resilient, companies need to equip their teams with both core and emerging skills. An effective upskilling and reskilling program must result in several key outcomes: enhanced skill retention and application by enabling employees to apply new skills in real-time, bridging the gap between learning and doing. It should also lead to increased employee engagement and ownership over career progression, empowering employees to take charge of their paths, which builds a motivated, adaptable workforce. Finally, it should promote a stronger alignment with the company’s vision, reinforcing a values-driven culture that strengthens morale.

With workforce adaptability and cross-functional knowledge now essential, traditional skill-building methods often fall short. Companies must adopt a transdisciplinary approach that integrates insights and practices from multiple fields to solve complex challenges—many of which involve AI.

The SEAM framework (Snapshot, Envision, Act, Measure) provides a structured roadmap to develop a resilient workforce that thrives through ongoing transformation. SEAM emphasizes continuous learning, AI skill integration, and transdisciplinary growth to prepare employees for a dynamic, technology-driven future. This article explores how companies can leverage SEAM to build an adaptable, purpose-driven upskilling and reskilling program aligned with organizational goals, creating a workforce that’s ready to excel in AI-integrated roles.

Snapshot – Conduct a Transdisciplinary Skills Audit with AI Integration

The first step in implementing a robust upskilling and reskilling program is to conduct a transdisciplinary skills audit that incorporates AI competencies. Transdisciplinary approaches go beyond integrating knowledge from multiple fields; they combine insights and practices across disciplines to solve complex challenges. By incorporating AI into the skills audit, organizations can identify existing core skills, cross-functional capabilities, and emerging AI-related competencies essential for both current and evolving roles. This integrated assessment enables the company to understand workforce strengths and gaps, particularly regarding AI’s role in reshaping functions across departments.

Actions:

• Perform a “transdisciplinary skills audit” to capture employees’ core, cross-functional, and AI-related skills.
• Analyze industry trends and organizational needs to identify future-ready skills, with a focus on areas where AI is expected to influence job requirements.
• Host team workshops to allow employees to explore interests and potential in AI applications beyond their immediate roles, fostering a proactive approach to AI integration.

The outputs of this phase include a comprehensive skills inventory mapped to current and future AI-related roles, as well as a report on skill gaps and AI competency needs aligned with strategic goals. This foundational step ensures a purpose-driven upskilling program that aligns individual aspirations, AI competencies, and organizational objectives, preparing the workforce for the future of work.

Envision – Co-Create Personalized, Purpose-Driven Learning Paths with AI Integration

The Envision phase focuses on creating a vision that aligns personal growth with the company’s mission, incorporating AI competencies and familiarizing employees with how AI is being applied within the organization. By co-designing learning paths that include AI-related skills relevant to the company’s goals, employees can better understand how their development supports both individual objectives and the company’s readiness for an AI-driven future. This phase introduces a transdisciplinary approach through mentoring groups and peer-led groups, where employees learn from varied perspectives across departments, including insights into AI’s role within the organization. This group-based mentoring structure promotes collaboration across functions and builds an adaptable, innovative, AI-aware workforce.

Actions: 

• Conduct “Envision Sessions” to help employees outline career goals and identify areas where they can contribute to company objectives, including opportunities for applying AI knowledge.
• Develop individualized learning paths that integrate technical, soft, and AI-related skills aligned with business needs and the ways AI is currently being implemented within the company.
• Integrate company values and purpose into each learning path, with a focus on how AI can enhance the organization’s mission.
• Establish mentoring groups where small teams meet with two or three leaders from different departments, rotating leaders and topics—including discussions on current AI initiatives within the company—to gain transdisciplinary insights and a practical understanding of AI’s organizational impact.
• Implement peer-led groups where employees share knowledge, discuss challenges, and explore AI applications in their respective fields, creating a collaborative environment that promotes skill-building and AI understanding across the organization.

The outputs of this phase include personalized development plans, a roadmap with specific goals, and a structured schedule of mentoring and peer-led group sessions that incorporate discussions on AI competencies and company-specific AI applications.

Act – Embed Learning in Daily Work with Transdisciplinary Micro-Experiences and AI Integration

The Act phase focuses on implementing the upskilling and reskilling program by embedding learning directly into daily workflows. This phase leverages transdisciplinary micro-experiences and AI-related applications to ensure employees can apply new skills across different fields and functions, enhancing both adaptability and AI competency. By incorporating real-time, relevant tasks into daily activities, employees can immediately practice and reinforce new skills, particularly those involving AI tools and collaborative problem-solving that span multiple disciplines.

Actions: 

• Introduce “micro-experiences” where employees apply new transdisciplinary and AI-related skills to real tasks in their daily work (e.g., a team member uses AI for data analysis in routine reporting or integrates cross-functional skills for a project).
• Launch “learning labs” led by employees with advanced skills in specific areas, including AI, to promote peer-to-peer learning, cross-functional knowledge sharing, and a deeper understanding of AI’s practical applications within the organization.
• Recognize milestones and achievements in transdisciplinary skill-building and AI competencies to keep employees motivated, engaged, and aligned with company goals.

The outputs of this phase include regularly scheduled “micro-experiences” that integrate learning with daily work and a series of peer-led “learning labs” that foster collaborative growth in AI and cross-functional capabilities.

Measure – Evaluate Program Effectiveness on Engagement, Alignment, and Skill Application with Transdisciplinary and AI Components

The Measure phase assesses the success of the upskilling and reskilling program in achieving three core outcomes: enhanced skill retention and application, increased employee engagement and ownership over career progression, and stronger alignment with the company’s vision. By focusing on these outcomes and integrating transdisciplinary metrics and AI-related components, this phase provides a comprehensive understanding of how new skills impact individual development, workforce engagement, and organizational alignment.

Actions: 

• Track KPIs: Set metrics to measure skill acquisition and confidence in applying new skills, especially in transdisciplinary and AI competencies. Monitor changes over time to gauge program effectiveness, assessing gains in productivity and efficiency to link skill development with organizational performance.
• Conduct Skill Retention Assessments: Use assessments or practical tests to evaluate skill retention and application in real-world tasks, ensuring skills bridge the gap between learning and doing.
• Collect Feedback: Gather regular qualitative feedback from employees on their use of new skills in cross-functional and AI-driven projects. This provides insights into how the program supports engagement, career ownership, and ongoing learning.
• Measure Alignment with Company Vision: Use surveys and feedback sessions to gauge employees’ perceptions of how their development aligns with the company’s mission and values, assessing how effectively the program reinforces a purpose-driven culture.
• Recognize Achievements: Implement a recognition program to celebrate milestone achievements in skill application, particularly in transdisciplinary and AI projects, to boost morale and reinforce alignment with the company’s mission.

This comprehensive measurement approach offers a well-rounded view of the program’s impact on engagement, alignment, and skill retention, fostering a workforce that is adaptable, innovative, and equipped for future challenges in an AI-driven world.

Written by Dr. Madeleine F. Wallace.
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