This article summarizes the key points from a conversation between Huneety co-founder Simon Carvi and Dr. Dieter Feltzmann on the AIHR HR Dialogues podcast, on using a skills taxonomy for workforce and scenario planning. The practical points below are drawn directly from the discussion and from the client work referenced in it.
What is a skills taxonomy? A classification system that archives skills and competencies in a structure that makes sense for running the business. Skills are fine-grained and certifiable (Adobe Photoshop, a programming language). Competencies are combinations of skills, abilities, and knowledge used to solve a business problem (objection handling combines critical thinking, communication, and active listening). See the skills taxonomy term in our glossary for the short version.
Why skills are the currency of the future
The World Economic Forum estimates that 40% of workers will need reskilling that takes six months or longer. The pandemic made the timeline unforgiving. Huneety has worked with a manufacturer of rebar operating in 25 countries whose customer engineers could no longer travel to troubleshoot machines. Within weeks, the team had to learn how to build remote troubleshooting training content, design curricula, and edit video. The game reversed overnight, and the pace of required learning is now structurally higher than it was five years ago.
World Economic Forum estimate. Reskilling takes 6+ months per worker on average.
Huneety case study: remote-service pivot during the pandemic required new training, curricula, and content-creation skills.
Pre-built taxonomy across 12+ industries, used to seed client frameworks.
Delivery cadence with HR teams using AI-assisted taxonomy construction.
Skill vs competency: the distinction that matters
The terms are used interchangeably in HR conversations; the practical work requires that they aren't. For a deeper look at the competency layer specifically, see why core competencies matter. From the discussion:
- A skill is the capacity to perform a task. Skills are fine-grained, certifiable, and measurable. Adobe Photoshop is a skill. A specific programming language is a skill.
- A competency is a combination of skills, abilities, and knowledge applied to a business problem. Objection handling is a competency. It draws on critical thinking, communication, and active listening skills.
- A taxonomy is how both get organized into a structure the business can actually use: connected to learning and development, tied to KPIs, assigned to roles, owned by the people who work in those roles.
Skills should not be sleeping on an Excel sheet. They must be agile and easily deployable to people, connected to learning, to KPIs, and to the right positions across the company.
Start with strategy, not comprehensive coverage
One of the criticisms of skills taxonomies is that the big-bang organization-wide exercise produces a framework that's outdated within weeks. The practical rule from the podcast: start where the strategic priority is, not where the exhaustive list demands.
Four recurring triggers for a skills taxonomy project came out of the discussion:
- Digital transformation. A specific transformation program needs a skill baseline to measure against.
- A new business unit or expansion. Launching an adjacent line requires clarity on which capabilities exist internally and which need to be built or bought.
- An HRIS acquisition. A newly acquired HR system is an empty box that needs job descriptions, competency data, and skill definitions to be useful.
- A targeted population. High-potentials, high-performers, or another named talent segment is usually the first group to trigger a taxonomy project in mid-sized organizations.
The analysis behind the taxonomy
Skills gap analysis: the complete guide
The 5-step process, three data collection approaches, the priority rule, and the five mistakes that kill the analysis. Turn workforce planning from guesswork into a data-driven roadmap.
Read the guide
Three real-world use cases
The podcast walked through three client patterns Huneety has rolled out, each with a different starting point.
1. Ink manufacturer, Southeast Asia
Starting point: a core competency framework sitting on an Excel sheet, untouched for years, unknown to employees. The work started with a survey of all jobs and job families across the company, the job taxonomy has to be clear before the skills taxonomy can be built on top of it. From there, machine learning aggregated skills data across job boards for the industry, filling the existing competency framework with current skill requirements. Then assessments against the updated framework. Then full workforce analytics visible by job family. The gaps surfaced were substantial and immediately focused the learning and development spend.
2. Packaging manufacturer, Southeast Asia
Starting point: a highly industrial business where "competency" mapped naturally to a physical machine. Each machine was treated as a competency group, with its own skills cluster. The taxonomy revealed which countries had skill gaps on which machines, and which individuals held deep expertise. The output was a targeted knowledge-transfer program, experts paired with gap locations, rather than a generic L&D catalog.
3. Rebar manufacturer, global
Starting point: an existing taxonomy delivery that was in danger of staying HR-only. The fix was to integrate skillset reviews into the tool so the output became the input for a business-partner conversation with line managers. Skills data in HR alone has no force. Skills data in the conversation between HR and the business is where decisions change.
Why most skills taxonomies die
Two patterns kill skills programs after the initial rollout. Both came up in the podcast as practical warnings.
The Southeast Asia context: opinion vs data
One regional observation from the conversation: Southeast Asian HR culture is relationship-based and historically opinion-driven. HR is often positioned as "the friend" in the company, which makes it structurally harder to challenge the status quo with data. The mindset is shifting, but the shift is recent.
The practical move, in our experience running these projects, is to align the taxonomy effort with a specific strategic priority the CEO already cares about, a transformation, an acquisition, a targeted talent segment, and use that as the entry point for data-driven decisions. Starting with "we need a comprehensive skills framework" rarely gets past the first review. Starting with "we need to close the skill gaps on X transformation by Y date" does.
The ROI argument for the CEO
When the conversation turns to the C-suite, the return on a skills-taxonomy investment breaks into three concrete decisions the data enables.
- Focused L&D spend. Instead of spreading training budget across trendy topics, invest specifically where the skill-gap data says growth is needed.
- Build, buy, or outsource calls. A CEO with a skill-gap view can decide rationally whether to outsource a capability, recruit permanently for it, or invest in learning to build it. Most of the organizations that choose "build" do so because the capability is strategic enough to be a sustainable advantage.
- Career-path engagement. Employees who see how their current skills map to career paths, and what the specific gaps are, engage differently with development. The taxonomy is also a retention tool.
Built for HR teams
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Emerging skills named in the discussion
Skills that are rising in demand across the organizations Huneety works with, named in the podcast:
- Data literacy across all layers. Not just analyst roles. Every function now needs people who can gather, interpret, and act on data.
- Critical thinking, problem solving, and English (Thailand context). Sales professionals combining these three are hard to find and highly sought.
- Data scientists in Southeast Asia. API programming, programming languages, and communication skills together. Historically imported; regional strategies are shifting toward building local capability rather than borrowing it from Europe or the US.
Frequently asked questions
Summary based on the AIHR HR Dialogues podcast Episode 2 (AIHR), "Using Skills Taxonomies in Workforce & Scenario Planning." Huneety helps HR teams build skills taxonomies tied to strategy, mapped to roles with AI, and connected directly to individual development plans.