There’s a pressure on us to make our organisations more skills-based - but why? What does it mean to be “skills-based”? And what practical steps can we take in our roles to make it happen?

In my mental model, a skills-based organisation is defined in contrast to the alternative, which is to divide up work according to jobs.  A traditional organisation is the sum of its jobs or roles.  A skills-based organisation is the sum of its capabilities. 85% of HR executives are considering a transition to a skills-first model in the near future. But what is driving the trend?

Why become skills-based?

Role-first organisations made sense when roles, and careers within them, lasted 30 years.  But when an organisation needs to respond more rapidly to the changing world, the model breaks.

Roles are no longer a good building block for capability

The skills required in a given role (or to achieve a given objective) change much more rapidly than they used to.  The half-life of technology skills, always short, is falling - for specific technologies to as low as 1 or 2 years.  With transformative technologies such as generative AI, the impact on skills is much wider than just the technology itself, as the economy adapts around them. The World Economic Forum forecast that 40% of the workforce will need to reskill in the next three years. In this rapidly changing environment, ‘role’ is no longer a useful shorthand for capabilities - it’s necessary instead to think directly about the skills inside the role.

We have more skills than are engaged in our current role

The skills we employ in our current roles or projects are the visible tip of an iceberg.  Role-first thinking ignores a sub-surface reserve of skills that could be available to our organisations. And while the shelf-life of hard skills is short, the softer skills that allow growth, adaptation, realisation of value from technology, sound decision making… these are long-lived and built through a career and with experience outside work.

Skills-first thinking suggests more than one solution

Defining requirements in terms of skills encourages more flexible thinking on how those requirements might be met.  Thinking of requirements (objectives or tasks) in terms of the roles needed to deliver them naturally leads to an answer of hiring.  Thinking in terms of skills suggests the alternatives of reallocating resources or skills development.

It’s happening anyway

To some extent, transitioning to a skills-based organisation is just leadership thinking catching up with the reality on the ground. According to Deloitte Insights, “… 71% of workers already perform some work outside of the scope of their job descriptions, and only 24% report they do the same work as others in their organization with the same exact job title and level”.

What does a skills based organisation look like?

A skills based organisation thinks in terms of goals and the skills needed to accomplish them, rather than roles and the people needed to fill them.  More tangibly, we see that skills-first organisations often have:

• a current expression of the skills that are mission critical (rather than a rigid framework which has fallen out of date);
• initiatives to allow mobility within the organisation, so people are able to move where there skills are valuable (rather than treading a fixed career track);
• initiatives to improve data on what people can do - the skills they have (rather than defining people with their role);
• a partnership between learning & development and talent acquisition (rather than a power disparity or competition for resources).

What does L&D need to do?

Right Skills

We need to specify skills (capabilities) required with the same degree of care, urgency and focus as would specify a role for recruitment.  We wouldn’t update a job description we were hiring for only once every two years.  We shouldn’t produce static skill frameworks with a shelf-life of years - we need our focus skills to refresh and evolve.

Generative AI tools can get us quickly to the right focus skills because they allow us to combine the base knowledge of the large language model (on the skills economy and labour market trends) with contextual information concerning the particular “DNA” of our own organisation (business objectives, strategic differentiators, sector characteristics, priority initiatives and existing thinking on skills priorities).

We recently worked with a customer to generate a skills framework that reflected business priorities and supported curation and content selection efforts.  We took two approaches:

• a traditional data analysis, synthesising diverse data sources on the skills economy and internal demand for skills;
• using our genAI tools primed with the business context.

Whereas producing the manual framework occupied the team for a two-month project, the AI-built framework was generated in hours, and optimised & finalised in less than two weeks. Our analysis of the similarity of the resulting frameworks (based on semantic similarity of the two sets of skills) suggested over 85% alignment, but the genAI framework incorporated higher quality skill definitions (more consistently written, more effective for identifying relevant learning content).

This dramatic foreshortening of the time it takes to produce a serviceable skills framework means we have the tools to keep pace with disruptive change, and adopt a more agile and iterative approach to aligning our learning resources with business needs.

Right content

Having established focus skills, we need a way to connect them with learning resources that support their development, so that the best learning assets reach the people who need them.  That means being able to identify the most relevant content to a given skill, audience and learning opportunity.

We recently launched Filtered Analytics, which means our service analysing the skills relevance of content sources is available in real time.  As soon as a new skill is identified, it can be added to Filtered and insights on how content sources support the skill are available instantly.

Again, technology is having a transformative impact on the time to value.  It allows us to deliver our service of content value analysis in hours and days rather than weeks and months. The cycle time between business questions and evidence-based answers is short, leading to iterative questioning and more robust decision making on “right content”.

Right results

It’s natural to think of the platform or environment for learning first, and add content later.  But this is backwards-thinking - our skills strategy is more important than the individual technological choices that will help deliver it.  The same technologies that are disrupting skills are disrupting the way in which learners learn, and we need to adapt to new channels and platforms for learning.  We need to be able to deploy the right content, flexibly and with control, to where it’s needed.  Whether that’s an LxP, LMS, email channel or AI assistant.  We’ve recently developed a way of combining AI chat with an existing learning library, so that learners can engage with an AI coach to build skills but receive validated content recommendations from their organisations existing content portfolio. Or as one learning technologies leader described it, “being able to ‘talk’ to their content library, and get recommended relevant learning opportunities”.