Can AI help? Let’s find out.

Why do organizations need taxonomies?

For most companies, employees are the biggest expense (and asset). But how companies invest in those employees can quickly become disjointed. If the job description, interviewing process, onboarding, performance management, and career pathing aren’t in sync, you create a mess. It reminds me of coaching my daughter’s soccer team: I yell for kids to stay in their positions and the parents yell “Go get the ball!” The result is chaos and confusion. 

Taxonomies create a shared understanding of what’s important. Companies use taxonomies to organize resources (discovery), connect people to opportunities (matching), and align activity to insights (reporting). Taxonomies can align each part of the talent lifecycle. As HR expert Dave Ulrich says: “Every discipline, to become a sustainable science, uses systematics (classification of separate items into common groups) to create taxonomies (types of things that go together) to make progress.” 

Taxonomies make the ambiguous actionable. Has a business leader told employees to “improve your business acumen” and left them wondering what that even means? Are there roles your people are interested in but they don’t know the steps needed to get those roles? Taxonomies can help. Taxonomies break complex ideas or challenges into smaller, more actionable steps.

Experiment No. 1: Skills Taxonomy for a Generic Role

First, let’s create a skill taxonomy for a fairly generic role using AI. This taxonomy could serve as the backbone for writing job descriptions, guiding interview questions, building training programs, and helping form the criteria for performance management conversations. We’ll do this one as a video so you can see the process.

The taxonomy came together fast and the results look reasonable. I could easily add or remove extra layers of details (even getting into tasks within a skill). Being able to drill down let me get as granular as necessary. 

This would be a great place to start a taxonomy effort.

The biggest downside is allowing the AI to make its own judgments (within the parameters of our prompt) to come up with its own skills. It doesn’t use a predefined list of skills which limits the ability of the taxonomy to create a common language across tools and applications.

The real benefit will come when the dynamic capabilities of the AI are combined with a company’s internal skills language. This will require some extra implementation, but it is definitely doable and something you should watch out for from the Degreed product team in the future.

Experiment No. 2: Skills Taxonomy for a Hyper-specific Role

Every company has unique sets of job titles. Let’s see how well the AI does when we select a very specific role. Using the same process for our previous experiment, here’s the result:

The more detailed skills are cut off in the screenshot, but even without any edits, the results look impressive. This could be a great way to build out skill taxonomies, especially for roles that wouldn’t typically have good skill suggestions from your HR and L&D tools.

Experiment No. 3: Highlighting Skills That Are Out of Date

Keeping taxonomies up to date is such a manual, time-consuming task. Let’s see if AI can do the heavy lifting and identify which skills in a taxonomy may be out of date (e.g. it lists an old technology).

To do this we aren’t creating a new taxonomy. Instead, we’re evaluating our existing taxonomy with customizable criteria. First I’ll create a rubric with values for “Current” and “Out of Date” and then ask the AI to classify each skill according to the rubric.

Here’s a video to show the steps:

You can see that one skill—React 16—was flagged as being out of date. React is a technology library that is on version 18, so React 16 was rightfully flagged. Having a system like this would make it easy to identify when and where you would need to make updates.

Experiment No. 4: Highlighting Increasing or Decreasing Demand for Skills

To have our taxonomies guide talent decisions, it would be helpful if we knew which skills were becoming increasingly important. Let’s create another custom rubric and see if AI can help us identify which skills are increasing, or decreasing, in demand.

Here are the results:

One thing to remember in this type of analysis is that the training data for LLMs can be more than a year old (e.g. April 2023 for GPT-4 Turbo). There are techniques available to provide LLMs with information from search engines to give them up-to-date information. This would be especially useful when trying to make future projections.

This analysis could also be made more powerful if combined with some of your own internal data from your hiring or learning systems.

Experiment No. 5: Highlighting the Impact of AI on Skills

Upskilling takes time, which means it’s helpful to have projections on how skills will change. In this case, we want to see which skills or tasks are more or less likely to be impacted by AI. This would help us plan appropriate skilling initiatives.

After creating another custom rubric, here are the results:

Again, the results look fairly reasonable. For this type of in-depth analysis, we could likely improve the results by using different prompting techniques. Techniques such as chain-of-thought reasoning (which makes the LLM approach a problem a step at a time), multi-shot prompting (where you provide examples of good results in your prompt), or using multiple agents (having multiple LLMs work together to do the analysis) would give us even better results.

Experiment No. 6: Showing User Progress Against Skills

So far we’ve used taxonomies at the administrative level—for planning and organizing talent across the company. Let’s now explore the other benefit of taxonomies—their ability to deconstruct goals into more actionable items. This could be useful at the employee level as a way to guide learning and measure progress.

Here we’re visualizing a taxonomy that reflects an employee’s mastery level of each step.

I love this example because it highlights the power of combining AI capabilities with employee data to create something personalized that you would not get from a generic tool like ChatGPT.

Experiment No. 7: Using Taxonomies to Guide Personal Learning

As we saw in the last experiment, taxonomies can be useful at the individual level. I’m going to use a personal use case for this next one. I’ve been trying to learn photography, but it’s hard to know where to start.

Here I’ve asked AI to create a personalized learning plan for me based on my specific camera:

This feels like a really nice outline. I would want to remove some things and go deeper in some other areas. But, now that I have this outline, I can imagine adding actions such as “Show me an example” or “Give me ideas on how to practice this.” I could even ask the system to analyze some of my photos and highlight the skills for me to improve. I could also use this outline to create an assessment that could then inform a baseline for each skill. 

I think there’s a lot of potential here. This could really get us closer to personalized, dynamic skill-building.

What do you think? 

Do you see potential upsides or too many challenging downsides to AI-generated taxonomies? If you have thoughts, or feedback, or would like to build your own taxonomies, please email me at tblake@degreed.com. To keep up with updates, follow me on LinkedIn.

Ready to learn more about taxonomies?

The Degreed Professional Services team offers free consultations. Its core focus is acting as a partner to you, not offering transactional services. Let’s work together to explore your learning strategy, technology goals, and questions about taxonomies.

Book a private consultation with the Degreed Professional Services team.

Thank you for experimenting with us.

We’ll see you at the next one.

This is the second post in a regular series. You can also read the first post Degreed Experiments with Emerging Technologies.

The post AI Taxonomies for Skills: Actionable Steps for Career Goals appeared first on Degreed.

Just like Mike, everyday workers need to build skills with practice. Skills are needed to perform well and can help lay out a path forward to the next role. Skills empower workers to achieve results and build experience. 

Skills are today’s currency of work. Your people and their skills are your organization’s biggest assets. Assessing, growing, measuring and cultivating skills enables your talent and your company to succeed.

A skills taxonomy can help you make sense of what your people can offer as you work toward achieving business goals. 

A skills taxonomy is:

A hierarchical system of classification that can categorize and organize skills in groups or “skill clusters.” A skill taxonomy is very structured and will usually include the skills that are most important to business goals, sometimes with the skills’ definitions as well.

A skills taxonomy gives much needed structure to a company’s abilities to assess, grow, measure and cultivate key skills that translate to business results.

Skills vs. Competencies 

When you’re creating a skills taxonomy, it’s important to distinguish between skills and competencies.

A competency is “knowledge, behaviors, attitudes and even skills that lead to the ability to do something successfully or efficiently.”

A skill, on the other hand, is “learned and applied abilities that use one’s knowledge effectively in execution or performance.”

Skills are the components that go into building a competency. For example, the ability to communicate effectively is a competency. Skills that help you communicate effectively include writing concisely, speaking confidently and crafting informative, easy-to-read documents.

Going back to our basketball analogy, a player needs to know how to make three-point shots, layups, free throws, jumpers, fades and step backs. Each type of shot requires different skills. These skill sets are developed through practice, and the team must know where each player’s strengths are in order to make plays and score. One team member might have a knack for setting up a pick and roll, another might be especially good at getting fouls called to help rack up points. Knowing these skills and competencies helps develop crucial team strategies and helps players and coaches make decisions. A team’s ability to insert shooting competencies into the right structure and format contributes to scoring enough points to win.

Skill Taxonomies vs. Competency Models

Skill gaps are widening across industries, and traditional talent models like competency models, aren’t sufficient to help plug those gaps.

Those competency models are too complex and static, and they’re never used the way L&D leaders hoped, because they become quickly outdated. Contrast that with skill taxonomies that focus more on what people can actually do for a particular job. They’re dynamic and constantly updated as new skills emerge and others fade.

When your company builds and uses a skills taxonomy that reverberates with workforce strengths, it can upskill or reskill talent in key areas that need improvement. Aim. Shoot. Score!

Supporting Your People

A taxonomy-based skills strategy can help you differentiate your company when employees are evaluating the next steps of their careers. In today’s competitive job market, retaining talent can cut hiring costs and build a culture where employees see how they can grow and develop internally. A skills taxonomy — in a cohesive way — shows employees how they can demonstrate skills and competencies.

If employees have a clear direction, they can build skills and competencies to perform better in their current roles and obtain future roles. While the line from skill building to promotion isn’t always crystal clear, people certainly benefit from having a rubric for what to work on.

Having a good understanding of skills taxonomies can equip you to help develop upskilling and reskilling programs for your employees. 

Advancing L&D

By partnering with business units to evaluate key skills needed in each function, L&D can play a pivotal role in targeting relevant learning opportunities that help employees build skills and competencies. 

Depending on the companies’ tech stack, some HRIS (Human Resources information systems) feature skills taxonomies and can track and measure skills across the organization. 

Having a system and the data to map out where skills gaps occur, developing programs to close them, and building on current skills and competencies is a competitive advantage. 

A skills-based approach can future-proof your company, even if every one of your employees isn’t an A-Team all star. A skills taxonomy can provide your employees with the structure, clarity and paths needed for them to grow at your organization.

The post What Is a Skills Taxonomy? And Why Is Your Competency Model Obsolete? appeared first on Degreed.

This blog will expand beyond the basics into the different methods of structuring your organization’s skill data. There are many overlapping (or even contradictory) ideas in the market about what it means to have a skill strategy and how that relates to a skill taxonomy or ontology. 

These ideas aren’t inherently simple, but they don’t need to be overly complicated. That’s why we’re breaking them all down for you below.

Organizing Skill Data

Skill Strategy

Definition: A strategy for talent development that prioritizes skills as a way to measure the ability of your people. This measurement is then aligned with the work that your organization needs to get done and the career opportunities that exist internally. Skill strategies can vary greatly between companies and can use any combination of upskilling technology, skill taxonomies, skill ontologies, skill clouds, or none of those.

Why it matters: Using a skill strategy as opposed to a competency model (or in tandem with one) can help make your workforce more agile and enable opportunities for internal mobility and career growth.

Skill Taxonomy

Definition: A hierarchical system of classification that can categorize and organize skills in groups or “skill clusters.” A skill taxonomy is very structured and will usually include the skills that are most important to business goals, sometimes with the skills’ definitions as well.

Why it matters: This can help workers understand which skills they have from the taxonomy, how they relate to organizational needs, and what they should learn next. It can also help show what skills are included in various categories of skills. The purpose of the framework is not to capture every skill, but rather to capture information about the most essential skills that are relevant to your business strategy.

Skill Ontology

Definition: A set of skills and their relationships between one another.

Why it matters: A skill ontology allows organizations to define and measure relationships between skills (and even jobs and people). It helps create a common language and understanding of skills across a variety of different dimensions or platforms. For example, across individuals, teams, and companies, the definition and terminology used to describe a UX designer will vary. A skill ontology is able to aggregate all of that data and recognize that different systems are talking about the same entities and build relationships between them. Another way of looking at an ontology is that it is a “smart system” that helps maintain, aggregate, and simplify the skill data within a taxonomy.

Skill Graph

Definition: A skill graph shows the relationships between other skills and determines how skills map to roles, content, and other skill-related features. It’s often simply a visual representation of a skills ontology. 

Why it matters: Understanding how different skills are related to one another (and how closely related) can inform how artificial intelligence and models offer upskilling and mobility opportunities. For more on skill models, check out Part 1 of our dictionary.

Skill Cloud (Also called a Skill Inventory or Skill Registry)

Definition: An inventory of skills across organizations that includes all known skill terms. It is the data set that is used to evaluate skills to include in organizational skill lists, ontologies or taxonomies. It is basically a single source of truth for any skill, but it does not order or categorize skills like a taxonomy does.

Why it matters: A skill cloud helps organize and standardize skills across an organization, but a skill cloud alone does not make these skills actionable. They simply sit in the cloud.

Skills I/O

Definition: Degreed’s Skills I/O (which stands for input/output) is able to manage skills, skill data, and the structures mentioned above. You can use the Skills I/O to build taxonomies, manage multiple skill sources, integrate different taxonomies, and edit the skills in your organization. 

Why it matters: Whereas taxonomies, ontologies, and graphs help us understand skills in relation to our business objectives, but our Skills I/O is able to put those concepts into practice together.      

For more skill data definitions, keep an eye out for our final installment of the Skill Data Dictionary and download our Ultimate Skill Data Handbook today!

The post Skill Data Dictionary Part 2: Organization appeared first on Degreed.