From Static to Dynamic: Reimagining Skills for the Future of Work

This article is Part 1: the why behind moving from static to dynamic skills.

Let me start with saying that this series sparked from a short post I shared on LinkedIn questioning whether our traditional ways of talking about skills truly capture what matters in today’s world of work. That post was less about answering that question and more about starting to reflect more and experiment.

Now, let’s expand and discuss why static categorizations of skills fall short, what research tells us about skills as dynamic and evolving, and how experimenting with ways to make this shift visible can help us evolve a more flexible skills framework.

Why Static Skills Fall Short

For decades, organizations have relied on static categorizations; long lists of competencies, rigid frameworks, and taxonomies that suggest skills can be fully captured, certified, and checked off.

But research shows otherwise:

• McClelland (1973) argued that test scores and fixed measures (like skills, or proxy for such) are poor predictors of success, calling instead for observation of competence-in-action.

• Zimmerman (2002) demonstrated that self-regulated learning, that is planning, monitoring, and adapting, predicts long-term achievement more than mastery of static categories.

• Feraco et al. (2023) showed that adaptability is what drives engagement and future performance, not rigid lists of past skills.

Static categorizations reward stability. But growth today demands flexibility, transferability, and reinvention, what I would label the hallmarks of dynamic skills.

Skills in the Future of Work

Core skills in 2030

Future-of-work research further reinforces this shift:

• World Economic Forum (2025): 40% of core skills will change by 2030; adaptability, systems thinking, and creativity are among the fastest rising.

• McKinsey (2024): Capability building is a top priority for organizations navigating transformation, with demand spiking for AI fluency and human leadership.

• Deloitte (2023–2024): Companies embracing skills-based models outperform peers in agility, talent mobility, and resilience.

• MIT Work of the Future (2020): Human–AI collaboration is shifting not only how tasks are done but what skills are valued, like judgment, creativity, and problem-solving.

I interpret this as that skills must be treated as dynamic, evolving capabilities, not static inventories. But that’s what most talent management or career pathing platforms do. Some add proficiency levels to simulate or provide a sense of dynamic adaptability, room to grow so to speak. It still leaves us guessing about the emergence of “new” skills before they show up.

My Experiment: Visualizing Dynamic Skills

This realization led me to experiment with mapping skills differently — through a skills wheel and spider graph and emerging horizon

• The skills wheel organizes broad capability clusters into a holistic view.

• The spider graph visualizes depth, balance, and growth potential across those clusters.

• The emerging horizon is speculative in nature in what way skills may morph.

My first model was the following skills wheel. But I realized quickly, static skills represent a snapshot of the past. I needed to show movement, potential, and future relevance. My focus shifted to dynamic skills. With that approach we can transform how we design talent strategies.

First model of clustered, color-coded and weighted skills (based on my profile)

And, by the way, I didn’t do this alone. I worked with AI as a collaborator:

• AI helped me analyze data to cluster skills and surface relationships I hadn’t considered.

• AI helped me generate visuals that revealed patterns in strengths and gaps.

• AI also accelerated the process, so reflection became more fluid and exploratory.

My role was cleaning data, researching concepts, defining my approach, application and then, tweaking prompts, interpreting outputs, and generally, meaning-making. AI’s role was augmentation — expanding my perspective and enabling a more dynamic map of skills.

With the advanced model , skills are reframed as living, evolving assets. This model was meant to highlight adaptability but also show strength and stretch areas.

Advanced model as skills spider graph, indicating strength/stretch areas

But I wasn’t yet happy. I was missing another layer or dimension. I wanted to not just visualize proficiency levels, but growth pathways to reveal core skills, emerging skills, and how they may intersect in practice.

By layering growth potential onto skills in form of an emerging horizon, organizations can stop asking “what can this person do now?” and start asking “what could they do next?” We then take agency in our development and in the direction our organization can take.

Added layer of emerging skills horizon overlaying the core strength and stretch areas.

Why This Matters

Reframing skills in this way creates new possibilities:

• For individuals: careers are seen as portfolios of evolving capabilities.

• For leaders: dynamic maps expose hidden strengths and stretch areas in teams.

• For organizations: strategy shifts from filling roles to building capabilities that adapt to change.

  
  

This isn’t just theory for me. As someone who has rebuilt identity and career across countries, I know that skills are the throughline that travels with you. Static categories don’t capture that story. Dynamic skills do.

Looking Ahead

This is just the beginning. It is a prototype model — but one I believe every organization will need in some form.

In Part 2, I’ll share the how: practical steps for creating your own skills wheel and spider graph, and ways to use them in career planning, leadership development, and organizational transformation.

Now, I’d like to invite your perspective:

👉 How is your organization moving beyond static skill frameworks?

👉 What role do you see AI playing in helping us map and grow skills dynamically?

Because the future of work won’t be shaped by static lists. It will be built by dynamic skills, continuously reimagined.

References

1. Deloitte. (2023). Skills-Based Organization: A New Operating Model for Work and the Workforce.

2. Feraco, T., et al. (2023). Adaptability, engagement, and academic achievement at university. European Journal of Psychology of Education, 38, 1375–1393.

3. McClelland, D. C. (1973). Testing for Competence Rather Than for Intelligence. American Psychologist, 28(1), 1–14.

4. McKinsey & Company. (2024). State of Organizations 2024.

5. MIT Work of the Future. (2020). The Work of the Future: Building Better Jobs in an Age of Intelligent Machines.

6. World Economic Forum. (2025). Future of Jobs Report 2025.

7. Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Practice, 41(2), 64–70.