Skills personalisation has been a long-standing ambition for learning and development but achieving it in practice remains a significant challenge. Competency, capability and skills frameworks promise a structured way to map an organisation, identifying the right development for the right employees. However, the gap between these frameworks and actual performance improvement is often a stumbling block for organisations.

Here, we focus on skills personalisation in practice and how to overcome the related challenges to drive meaningful performance improvement. 

Organisations often face discrepancies in how skills are assessed across different frameworks.

Skills frameworks and their limitations 

Competency models have been traditionally used to assess talent and identify development needs. As highlighted by the CIPD, competencies encompass both behavioural inputs and demonstrable performance outputs. However, these models have faced criticism due to the time and cost required for development, their often rigid nature, and their limited practical application. Moreover, the lack of alignment regarding definitions with and between organisations complicates their application, especially in the rapidly changing business landscape. 

Capability frameworks were introduced to solve the adaptability problem. These frameworks define the behaviours and skills in different scenarios, aligning more broadly with organisational objectives. However, confusion persists around the differences between skills, capabilities and competencies, leaving employees unsure of what is expected of them. 

Enter stage left, skills frameworks. Enabled by artificial intelligence and machine learning, they have the potential to facilitate skill development at scale.

The challenge lies in defining what constitutes a skill and how to reliably assess it. 

Organisations often face discrepancies in how skills are assessed across different frameworks, reducing the portability and clarity of skills between roles and contexts. To make skill personalisation effective, these challenges must be addressed. 

Skills personalisation in practice

Learning pathways have been hailed as a potential solution for skills personalisation. Skills can be tagged to learning content, allowing employees to follow tailored or AI-recommended pathways. However, without robust validation, these pathways are largely based on self-assessment or inferred skills from sources like the HR system and LinkedIn. This approach is fraught with limitations: self-assessments are subjective and often inaccurate, while inferred skills depend on the quality of the underlying data. 

The problem with relying solely on self-assessment is that it cannot consistently verify what an individual can do. For example, an employee may rate themselves as level 3 in data analysis on a proficiency scale of 1 to 8 and aim to progress to level 5. This creates an illusion of personalisation without genuinely addressing performance needs. 

Employees want to understand how skills relate to their work.

How to overcome challenges in skills personalisation

A focus on clarity, alignment and performance outcomes will help overcome the challenges associated with skills frameworks. The seven approaches outlined below will help to develop a foundation that enables skills personalisation to work in practice. 

1. Use case identification

Identify a specific use case where a skills-based approach can make a difference in performance.

For L&D teams, this might be aligning skill initiatives with a new product or market expansion where there are critical skill gaps. For Talent teams, it could involve using skills data to inform decisions about workforce mobility or succession planning in areas of high importance to your operations.

Starting with a use case allows for a manageable scope, making it easier to assess impact and refine the approach. 

2. Harvest existing data

Many organisations already have competency or capability frameworks in place, along with job profiles and recruitment data. Leverage what is available and assess what can be repurposed.

For instance, recruitment and assessment centre data can provide insights into skill gaps that prevent hires from meeting job requirements. 

3. Defining skills and proficiency

Provide clear skills definitions to ensure alignment with tasks and performance goals – both those that are unique to your organisation and more generic. Next, describe the levels of proficiency needed to meet strategic goals. This is vital to provide consistency in skills assessment (where needed) and alignment with performance. 

4. User-centred language

Employees want to understand how skills relate to their work. Gather insights from employees, managers, clients and other stakeholders to create user stories and descriptions that reflect their context and performance needs.

Observe workflows to help identify where critical skill gaps exist and what proficiency levels are required to bridge those gaps. This makes the skill framework accessible and relevant to employees. 

5. Low-tech, scalable solutions

Start with low-tech solutions to test and validate your skills framework. Avoid jumping into costly and complex technology implementations. Instead, use prototypes to experiment and learn.

Once you have tested your approach and gathered data, bring in technology to scale where it adds most value. 

6. Governance and data strategy

Skills-based approaches require robust governance to ensure data quality, consistency and transparency. This includes decisions about skill ownership, when and how skills are discussed, and how any skills data will be used.

Establish a clear governance structure to help build trust among employees and ensure skills data is used ethically and effectively. 

7. Performance-centred evaluation

To make skills personalisation effective, it is crucial to link skills development to performance outcomes. Use The Learning Transfer Evaluation Model (LTEM) alongside skills frameworks to measure whether skills development efforts translate into tangible performance improvements. This evaluation considers effectiveness and efficiency, ensuring that skills initiatives contribute meaningfully to business outcomes. 

Making skills personalisation work 

The goal of skills personalisation is not just to tag content to skills. It is to create meaningful development experiences that lead to real performance improvements, providing organisations and their clients with a supply of the right skills for the right tasks. For learning and development teams, this means moving beyond traditional learning needs analysis and adopting a data-driven approach that focuses on impact and results. 

Collaboration across the organisation is essential. L&D cannot achieve this alone; it requires input and alignment from HR, IT and leaders. A shared vision for skills development, supported by a system-wide approach, is crucial for ensuring that skills personalisation drives the intended performance outcomes. 

Ultimately, skills personalisation is about creating a unified framework that brings together existing content, skills data and performance needs, while remaining adaptable to change.

By starting small and focusing on specific use cases, organisations can gather evidence and gradually scale the approach in areas where it matters most.