Part 2: From Reflection to Visualization: Creating Your Skills Wheel
- Anke Sanders
- Sep 7, 2025
- 5 min read
In Part 1, I explored why static achievements and skills profiles are poor proxies for growth. They show what we have done but fail to reveal what we can do next. Skills are dynamic, fluid, and future-facing. We need new ways to see them in motion.
This brings us to Part 2: the how.
Before I dive into steps and application considerations, let me share how this practice came alive for me.
From Learning Professional to AI Enablement Enthusiast
For much of my career, I identified as a learning professional. My focus was designing and implementing programs that support others’ and an organization’s growth. But as AI began to transform the world of work, I realized my role had to evolve. I wasn’t just asking how people learn, but also how we learn alongside machines, responsibly and creatively. And what skills we’d have to develop or how we’d have to shift our skills to stay not only competitive but being able to create meaningful work.
I was lucky in that I’ve had some great thought partners over the years who’d push my thinking and help me explore not in isolation. With this dynamic skills experiment, I have to acknowledge a colleague who played a crucial role in shaping the approach. Thank you, Niv Iyer. When I shared my early versions of the skills wheel with her, just to get feedback and bounce ideas, she challenged my thinking and ultimately advanced it into the spider graph dimension which has since become a central feature of the dynamic skills model. And when it comes to dynamic skills, I began to see the research supporting it. For example, Deloitte (2022) research reinforces: skills aren’t static lists but networks in motion, systems with balance, gaps, and growth trajectories.
More than an Experiment
Through experimentation, I learned that dynamic skills mapping is not only a technical exercise. It’s a practice in:
Clarity: Seeing my current strengths and blind spots.
Direction: Revealing horizons for future growth.
Conversation: Using the visual as a springboard for dialogue with leaders and peers.
Strategy: Aligning individual growth with the organization’s evolving needs.
Why This Exercise Matters
Creating a skills wheel and spider graph isn’t just about producing a visual artifact. The process itself is transformative.
Research in educational psychology shows that metacognition — thinking about your own thinking — strengthens self-awareness and adaptability (Flavell, 1979; Zimmerman, 2002). Further, McKinsey (2023) places self-awareness and adaptability among the most critical skills for the future of work.
Newer studies echo this. A 2024 Microsoft/UCL paper argues that generative AI tools actually increase the metacognitive demands on users, requiring us to continuously decide how to prompt, evaluate, and rely on outputs (Tankelevitch et al., 2024). In other words, AI doesn’t reduce the need for self-awareness but raises the stakes.
And the need is urgent: I’ve already mentioned in part 1 that the World Economic Forum’s Future of Jobs Report 2025 predicts that 40% of core skills will change within just five years. Static inventories just won’t keep pace.
Dynamic skills exercises like the wheel and spider graph provide an antidote so to speak. They make skills visible, contextual, and future-facing.
Step by Step
Step 0: Ethics & Responsible Use First
While this should go without saying, we are not there yet, so I am saying it:
Confidentiality: Don’t upload proprietary or sensitive data. Period.
Bias Awareness: AI often overvalues technical skills while overlooking relational or contextual ones.
Ownership: You control your skills story. AI is your helper — your Socrates — not the author.
Metacognitive reflection: How might my eagerness to use AI bias me toward accepting its outputs without questioning?
Step 1: Gather Your Inputs
Think of this as building your personal dataset. Use multiple lenses:
Resume & portfolio (static, but useful)
Feedback (peers, managers, 360 reviews)
Work samples (abstracted from deliverables)
Learning signals (certifications, projects, informal learning)
Even chat history — the questions you ask can reveal what you value and where you’re headed.
Bias check: Am I privileging formal credentials at the expense of informal or emerging skills?
Step 2: Define Your Buckets
Choose 4–6 categories that reflect both today’s demands and tomorrow’s horizons:
Human Skills
Leadership Skills
AI & Tech Skills
Strategic Skills
Enabling Skills
Metacognitive reflection: Do my categories reflect only my current role, or also where I want to go?
Step 3: Build Your Wheel
Draw a circle, divide into buckets, and place skills within. Mark levels (Awareness / Proficient / Advanced) and map more granular subskills that fit the buckets. You can make this your own:
Use color coding (one color per bucket and gradients within)
Apply a weighted system to rank and organize skills in the buckets
Map evidence as applicable
Bias check: Which categories look empty? Is that a gap, or am I undervaluing them?
Step 4: Augment with AI (+ Metacognition)
AI can accelerate clustering, suggest emerging skills, and draft visuals. And why not let it help us and add another perspective? But:
Check assumptions: What is AI amplifying or omitting?
Trust judgment: Accept what resonates, reject distortions.
Stay self-aware: Notice your reactions when AI “defines” you.
Research confirms this is not trivial. Generative AI places new metacognitive demands on users, requiring high levels of monitoring and control (Tankelevitch et al., 2024). This is why I believe skills wheels are so powerful! They double as metacognitive tools, training you to evaluate not just skills, but how you think about skills when AI is in the loop.
Now, step 4 also includes review, refinement and revisiting everything. Maybe it should come after step 5 or both, is woven into steps 3 and 5 as we build or refine the wheel and spider graph.
Metacognitive reflection: Am I letting AI tell my story, or using it to sharpen my own perspective?
Step 5: Visualize with a Spider Graph
Each axis = one bucket. Shade your levels. I like color-coding 😅
Reflection: Do I want my graph to stay this shape? Which areas matter most for my future?
Step 6: From Visualization to Action
The real power is in application:
Clarity: Sharper self-awareness
Direction: Horizons for future growth (I do this using AI to overlay that emerging horizon space taking into account research and reflections)
Conversation: Coaching, mentoring, talent discussions
Strategy: Connecting individual growth to team and organizational goals
Metacognitive reflection: How has this exercise shifted how I see myself? How might it shift how others see me when I share it?
Final Outcome
By creating your wheel, you don’t just have a visual. Ideally you’ve built an ethically grounded, bias-aware, AI-augmented, human-led compass for growth. And all that while strengthening the metacognitive muscle of thinking about your own thinking (I hope a skill AI can never replace…).
I’m now exploring how this practice can scale to teams and organizations, where skills wheels evolve into collective dashboards that guide workforce strategy and shape culture.
💭 I’d love to hear from you: What do you think? Where do you see the greatest value in a skills wheel for yourself, your team, or your organization?
References
Bersin, J. (2025). AI is rewiring how we learn, and it’s a game-changer for L&D. Josh Bersin Insights, July 2025.
Deloitte (2022). Building the Skills-Based Organization: The New Model for Work and the Workforce.
Flavell, J.H. (1979). “Metacognition and Cognitive Monitoring: A New Area of Cognitive–Developmental Inquiry.” American Psychologist, 34(10), 906–911.
McKinsey & Company (2023). The State of Organizations 2023.
MIT Sloan Management Review (2023). “The Risks of AI in Talent Management.” MIT SMR, Summer 2023.
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A.E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. arXiv preprint.
World Economic Forum (2025). Future of Jobs Report 2025.
Zimmerman, B.J. (2002). “Becoming a Self-Regulated Learner: An Overview.” Theory Into Practice, 41(2), 64–70.
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