Workera has developed the industry’s most granular skill ontology, spanning thousands of skills and their relationships to one another. Those granular skills come together to form what Workera refers to as “domains” — broader skill categories like Machine Learning and Data Analysis. Workera maintains the domains — and the skills and assessments within — and keeps them up to date and relevant as technology advances.
Workera has nearly 100 Signature Domains: expert-designed, industry-validated frameworks covering the skill categories that are most important to today’s technology organizations. Signature Domains help employees acquire and improve the essential skills they need for their roles.
Signature Domains are useful building blocks for specific roles or projects and help companies achieve an AI-ready workforce. But why should an organizational leader trust that Workera’s Generative AI domain is the best option for upskilling on that subject? How did Workera choose the 48 granular skills that make up the Generative AI domain?
This article will explore the way Workera builds Signature Domains, as well as the industry benchmarks that help companies understand how to become best-in-class in critical areas.
Let’s dive in.
Building domains with AI and subject matter experts
Workera’s decision to build a specific Signature Domain is driven by a combination of client and industry demand. When a subject area is highly requested — or it’s clear that the topic is of increasing importance — Workera’s internal team of assessment developers takes on the process of building the domain.
Building a Signature Domain is like building a product at any other company. When an electronics manufacturer wants to develop a new printer, they assign the task to a product owner who oversees the process. Assessment developers who are subject matter experts (SMEs) in the given category take on the role of product owner, later working with other SMEs to validate the content of the domain.
The assessment developers will be the first to research and define the concepts and skills that should be included in the Signature Domain — the essential knowledge that adds up to proficiency in a category like “Machine Learning.” Creating an initial version of the domain previously could take up to six months, but advances in AI have accelerated the process. Today, assessment developers can write a prompt to create a domain, specifying the topic, audience, level of difficulty (technical, non-technical, etc.), and several topics to be included.
An additional SME is then pulled in to review, validate, and edit the results. Workera employs the “human in the loop” approach to artificial intelligence, always using experts to validate and refine the material created by AI. The AI algorithms themselves are constantly optimized by Workera’s team of data scientists and machine learning engineers.
A Step-By-Step Guide to Signature Domains:
- Research. Workera’s assessment developers — each bringing relevant experience in data science, computer science and AI — research the concepts that should be part of the new domain.
- Creation. Using focused prompts from the developers, AI creates the domain based on the subject matter, target audience and level of difficulty.
- Validation. Workera’s psychometricians and subject matter experts review and verify the materials, ensuring skill data will be relevant to the job and engaging for employees. These experts also copy edit each question to ensure technical correctness and accuracy.
Establishing trustworthy benchmarks
A Signature Domain provides Workera customers with the right content to rapidly upskill in a specific area. But these domains also require context in order to set reasonable, productive goals and expectations for employees.
Within each Signature Domain, Workera establishes benchmarks to help users understand how they compare to their average and best-in-class peers across the industry. The most popular benchmark is Enterprise, which provides an anonymous aggregated average of all employee scores over the previous two years.
How does Workera set these benchmarks? The platform requires a specific number of people to create a statistically reliable result. Once 20 people have assessed themselves using the platform, Workera’s data analysts will identify the average result and use that to set the benchmark. Over time, the benchmark will become more precise as more people complete assessments, but at least 20 assessments are shown to deliver a reliable reference.
These benchmarks make it easy for companies to see where they’re falling short in specific domains. If they’re well below the benchmark in Machine Learning, they can prioritize upskilling in that domain through various incentives, from competition and leaderboards to cash compensation.
Benchmarks also set the threshold for scores considered “best-in-class,” or the top 25% of users. Companies and individual employees will know the standard they need to reach to be considered industry leaders in that skill category.
Signature domains vs. customer-created domains
Signature Domains support topics that are applicable to a broad range of organizations, but every leader typically has company-specific subjects that they need to train employees on, as well.
Customers can use the same tools as Workera’s assessment developers to build domains that are applicable to their specific company. While Workera can’t always provide subject matter experts to validate the content in these custom domains, the company should be able to verify it themselves. Workera also recently launched Signature Domain templates that companies can duplicate and then customize to meet team-specific needs when creating an employee learning pathway.
This combination allows companies to establish a well-rounded upskilling program: signature domains for the building blocks, complete with customer-created domains that cover the more detailed edges unique to each company and their workforce.
Read more about our Signature Domain templates and new domains, including Snowflake and Speech Recognition, here.
