Building the foundations of a lasting Skill-Based Organisation

TL;DR

Many organisations know they need to move beyond job-based structures and adopt a skill-based approach. But few have figured out how. This guide breaks down the key steps:

• Why skills data matters: High-quality workforce data is the foundation of skill-based decision-making.
• Common obstacles: Organisations struggle with change management, business buy-in, and data challenges.
• Getting started: Start small, define a clear use case, and use AI-driven skill inference to build an accurate skill framework.

TechWolf works with leading enterprises to simplify the transition. This guide shares insights from real-world success stories to help you build a sustainable skill-based organisation.

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Companies are under pressure to adapt to a changing workforce. According to the World Economic Forum:

• 85 million jobs will be displaced by automation by 2025.
• 97 million new jobs will emerge, requiring different skill sets.
• 73% of CEOs worry their workforce lacks the right skills to meet business goals.

Traditional job-based approaches make it hard to keep up. Organisations that still rely on outdated structures struggle with:

• Hiring inefficiencies: Relying on degrees and past job titles instead of real capabilities.
• Limited workforce visibility: Unclear insights into internal skills and gaps.
• Slow reskilling efforts: Difficulty adapting employees to new business needs.

Leading companies are moving towards skill-based models, but implementation remains a challenge. This guide outlines practical steps to build a solid foundation for a skill-based organisation.

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Skills data as the foundation

Before shifting to a skill-based model, companies need accurate, real-time skills data. Without it, any skill-based initiative will fail. Research shows that companies using skill-based insights are:

• 107% more likely to place talent effectively.
• 98% more likely to retain high performers.
• 52% more likely to drive innovation.

What Is a Skill Framework?

A skill framework structures how organisations collect, track, and apply skill data. A strong framework includes:

• Skill taxonomy: A hierarchical system categorising relevant skills.
• Job architecture: A structured representation of roles and their required skills.
• Job-to-skill mapping: Connecting skills to specific roles for better workforce planning.

With a well-defined framework, companies can make informed decisions about hiring, training, and workforce planning.

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Overcoming common obstacles

Many organisations face barriers when moving to a skill-based model. According to Deloitte, only 1 in 5 organisations feel ready. The main challenges include:

1. Change management resistance

• Traditional job structures make leaders hesitant to adopt skill-based models.
• Compensation frameworks aren’t designed to reward skill growth.
• Recruiters and hiring managers lack training on evaluating skills.

2. Business buy-in struggles

• Leaders see skills as an HR initiative rather than a strategic priority.
• Organisations struggle to show the direct ROI of skill-based approaches.
• Siloed skill projects fail to create a company-wide impact.

3. Data and technology challenges

• Inconsistent skill data across different HR and business systems.
• Outdated methods for tracking skills lead to unreliable workforce insights.
• No common skill taxonomy across departments.

These barriers can slow progress, but the right approach makes the transition easier.

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Three steps to getting started

1. Stop debating ownership and start where it matters

Many companies delay skill-based initiatives by debating who "owns" skills. Instead of waiting for a perfect structure, launch a pilot project in a department that will see immediate benefits.

Where to Start

• Talent & Workforce Planning: Align skills with business priorities.
• Learning & Development: Focus on upskilling and internal mobility.
• Recruiting: Shift hiring from experience-based to skills-first.

Real-World Example: Telenet

Telenet, a leading telecommunications company, needed to align its workforce with changing industry needs. Within eight weeks, TechWolf helped Telenet build a skill framework using AI-driven skill inference. This gave leaders real-time insights into workforce capabilities without relying on outdated self-reported data.

Key Takeaways

• Don’t wait for the perfect structure—pick a business area where skills matter most.
• Form a cross-functional team with HR, IT, and business stakeholders.
• Secure an executive sponsor to drive adoption.

2. Define a clear, measurable goal

Rather than overhauling the entire organisation, start with one specific business problem where skills data can create an immediate impact.

How to define a Skill-Based Project

• Identify a problem: Is attrition too high? Are employees lacking critical skills?
• Set success metrics: Define measurable goals, such as improving internal mobility or reducing hiring costs.
• Engage key stakeholders: Align with leaders who can drive the initiative forward.

Real-World Example: Global Tech Company

A leading tech company launched a skill-based career hub to reduce attrition. In the first six months, they:

• Reduced external hiring costs by 30%.
• Increased internal mobility by 45%.
• Improved retention in critical roles.

Key Takeaways

• Pick a clear business problem skills can solve.
• Use success metrics to measure and communicate progress.
• Prove early impact to secure long-term investment.

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3. Prioritise skill data first—integration can come later

Too many companies delay skill-based strategies while waiting for full system integration. Instead, start by applying existing skill data to a real business use case.

How to start with Skill Data

• Identify available skill data: Extract insights from HR platforms, learning tools, and project management systems.
• Validate the data: Check for accuracy, completeness, and relevance.
• Apply skills data to a small pilot before worrying about system-wide integration.

Real-World Example: Ericsson

Ericsson, a global telecommunications leader, needed to shift towards a more software-focused workforce. Within four weeks, TechWolf helped Ericsson build an AI-powered skills inventory, mapping skills for over 15,000 employees. This allowed for better workforce planning and reduced reliance on external hiring.

Key Takeaways

• Don’t wait for full integration—start using skill data now.
• Focus on immediate business needs rather than perfect systems.
• Use AI-driven skill inference to maintain real-time, unbiased skill data.

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The time to act is now

Companies that embrace skill-based models gain a competitive edge by improving talent mobility, increasing workforce agility, and reducing hiring costs. To begin:

1. Audit your skill data: Identify existing data sources and assess quality.
2. Define a clear use case: Focus on a high-impact business challenge.
3. Build trust in skill data: Ensure transparency in how skill data is collected and used.
4. Act on insights: Enable HR and business leaders to make skill-based decisions.

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