There’s been a lot of talk about what a skills-based organization is and the benefits. Still, skills-based transformation can feel overwhelming, slow, and unpredictable, especially if you go at it alone.
Yes, I’m looking at you HR 👀
This isn’t an article to link to endless stats like AI and automation impacting nearly four in ten jobs globally, but to provide some practical insights and considerations for your journey – while hopefully making things a bit easier.
We believe that there are three core principles to successful skills transformation:
- Process – Which is what this article is about,
- People – Who needs to be involved, and communicated to (coming soon) , and
- Technology – Building a skills stack that scales with your needs (coming soon)
All are just as important as each other, aiming to achieve quick, tangible ROI for your organization.
For anyone just starting, there is no silver-bullet solution (despite what some will tell you). Failing is good, but learn from mistakes, iterate, and improve.
Let’s get into it.
The Process Element
You’ll see scope creep, project delays and burnout if you go too big, too soon. No person or team can navigate an organization-wide skills-transformation project, so an agile and iterative approach is key.
How to frame your transformation problem
The issue many HR leaders face when first starting is that they try to boil the ocean. The notion of “I want to become skills-based” is an aspiration goal, but it’s not achievable and usually not measurable… At least in the context of business decisions, reporting cadences and project viability.
Instead, the best way to frame this is to include a few variables in your problem statement(s).
- What quantifiable metric are you trying to influence? ( productivity, training costs, employee satisfaction, employee retention etc.)
- Who is the primary beneficiary/persona? (Managers, Executives, HR, employees, contractors etc)
- What is the use case? (resource allocation, training optimization, career mobility etc)
- What is the specific task they are trying to accomplish?
- Who is the secondary beneficiary/persona? ( (Managers, Executives, HR, employees, contractors etc)
- What is the specific outcome of the task?
With these variables in mind, you can refine what you’re trying to accomplish. Let’s look at a few examples.
I want to improve {productivity}, by helping our {Engineering Managers} better {allocate} the {right employees with the right skills and certifications} to {resource upcoming RFP bids & projects}.
I want to improve {workforce resilience}, by helping our {executive team} better {analyze} the {current and future skill profile of the organization} to be able to {align workforce capability to business strategy and investment decisions}
I want to improve {Employee Retention}, by helping our {Employees} clearly understand {internal career mobility} opportunities {that they have an interest in pursuing within the organization}, and {provide tangible training recommendations to get them there}.
By adding context to your high-level problem statements, you can begin to articulate what needs to be measured, who needs to be involved, and where to start on your journey.
Now that we’ve looked contextually at the problem statement, let’s break down each variable further.
How to untangle the monolith
The first thing you want to remember – relative to your organization – is where you can demonstrate and communicate the most value, sooner. This is where maturity comes into play.
Role-based or function-based Maturity.
Tracking and measuring skills aren’t a new phenomenon. You will often find that in some capacity, highly technical teams and roles will already be applying skills data to workforce decisions. For example, in manufacturing, “If I don’t have the right skilled and certified person on this piece of machinery, it could cost the organization $xxxx if we were to be audited.”
Starting with those already working in this way means that you will have a more engaged, grass-roots uptake allowing you to get primary data sooner.
Use Case Maturity
Not all use cases are the same, and usually, operational or tactical use cases will deliver value sooner. Usually, this is because your organization manages and controls the data involved, and the supply and demand metrics are known. For example, “I know what skills and capacity we need for this project; who do we have to fill in?”
When you begin integrating 3rd party data sources like external pay benchmarks and labor market demand & supply metrics, you tend to lose control, and complexity increases due to data normalization efforts. (I’m not saying don’t do this, but if you want to show value sooner, start with what you can control, prove the process, and then work on the external data sources).
Demographic Controls for Timeline and Budget
To further manage the scope and budget of your initial project, you can further break out the different groups and cohorts to start with. This is more useful for larger organizations.
Instead of looking at the entire Engineering team of 10,000, you can consider setting geographic boundaries. For example, starting with 2000 North American Engineers, expanding to 3000 EU Engineers, and after bringing on the remaining 5000. Your problem statement is still valid, but you’ve just segmented it a bit further.
Ultimately, by focusing your transformation efforts and goals, you’re working to do three main things;
- Manage how the process is communicated
- Measurement based on primary data and tangible ROI
- Control over priorities and timelines
We’re already a fair way in and guess what, we haven’t even talked about skills!
Instead, we’ve framed the problem areas that we want to look at solving, now we can use that to influence what work has to be done to get started.
Defining the skills to be tracked
Let’s stop and think about the initial statement “I want to become skills-based”. Without breaking down the problem, where do you start?. Maybe a bit of googling, Chatgpt, or dusting off the old competency framework.
Before you know it, you’re a few hours in with a list of skills, but really, are they aligned to your actual organization? Are they helpful to workforce decisions? Ultimately, very little progress is made.
Instead, let’s take another look at this example from before.
I want to improve {productivity}, by helping our {Engineering Managers} better {allocate} the {right employees with the right skills and certifications} to {resource upcoming RFP bids & projects}.
With that, we know that we need to talk to the engineering manager. We know what tasks we’re trying to help them with and the outcome. This gives us context to start a conversation around skills.
- “Engineering Manager”, are you currently tracking skills for your team?
- What skills are required on different projects?
- What is your current technology stack?
- What certifications does your team hold?
Engaging the key persona or stakeholder sooner, means that you can get buy-in to the project, and ultimately ensure an alignment with their goals and objectives.
As a way to also manage expectations around controlling your data structure, there are four key principles to keep top of mind:
- Just Granular Enough. Don’t be too broad, and don’t be too specific.
- Tailored. Your organization’s skills profile is a competitive advantage, and different from others.
- Structured. Skills don’t have job boundaries, so they should be grouped by specialization to avoid duplications.
- Curated. Data structure and control is everything for a skills-based organization. It’s crucial to set the parameters and ensure it’s done in a way that supports reporting needs, and that are a priority for the organization.
It’s very easy to get stuck at this point and spend months building out your skills taxonomy. The goal should be to get your initial teams on board and begin your assessment process.
In assessment, you add context to your data, moving from a list of mapped skills to verifiable skill gaps, strengths, and interest levels.
Deciding on the Assessment Approach
Measuring and validating skills effectively is just as crucial as identifying which skills to track. The assessment approach you choose should align with your organizational goals and provide actionable insights.
For optimal results, your skills assessment process should be:
- Fast and efficient to complete
- Easy to administer and maintain
- Accurate and reliable in capturing real capabilities
- Consistent across departments and teams
- Scalable as your initiative grows
Traditional annual assessments are insufficient for a truly skills-based organization. When skills data drives operational and strategic decisions, you need fresher, more current information. Consider implementing a continuous assessment model where:
- Core skills are assessed quarterly.
- Project-specific skills are validated at project completion
- Technical certifications are updated as they’re earned
- Self-assessments are balanced with manager validation
The key is finding the right balance between assessment effort and data utility. Too frequent assessments create fatigue, while infrequent ones lead to outdated information. Start with a pilot group to test your cadence before scaling across the organization.
Building Your Skills-based Foundation
The process element of skills-based transformation provides the essential framework for your journey. By framing your transformation with specific, measurable goals, untangling the monolithic challenge into manageable pieces, carefully defining relevant skills, and implementing an effective assessment approach, you create a solid foundation for success.
However, process alone isn’t enough. To complete your skills-based transformation, you’ll need to address the equally important people and technology elements:
- Coming Soon: The People Element – Discover how to engage employees as data providers, managers as validators, HR as orchestrators, and executives as vision-setters in your skills transformation journey.
- Coming Soon: The Technology Element – Learn about building an effective skills technology stack, ensuring seamless integration with existing systems, implementing proper security and governance, and leveraging AI opportunities while maintaining realistic expectations.
Start with the process blueprint outlined here, then watch for our upcoming articles on the people and technology elements to complete your skills-based transformation roadmap. Remember, there’s no one-size-fits-all approach—the key is to start small, learn quickly, and scale systematically.
