Introduction
Employee skills management is a crucial aspect of any organization, especially in today’s fast-changing and competitive world. Artificial intelligence and the large language models have made it much easier to handle company skills data.
Read this blog and find out how future skills management software Skilby will handle incoming skills data with AI.
Why it is difficult to gather skills data?
There are many reasons why organizations find it difficult to manage skills. One of the main challenges is to get employees to update their initial knowledge in the system and to update it frequently enough. Ideally, collecting the data should be fully automated. However, there are a number of challenges:
- Data on skills can be found in several different systems
- Skills data can be in different formats.
- Some of the skills are tacit knowledge.
- Skills are constantly evolving.
When employees’ skills are not known or up to date, an organization misses out on significant strategic potential:
- The skills gaps can grow significantly large, which can hinder business growth.
- Planning the future will be much harder.
- Achieving objectives will be harder.
- Motivation and well-being may be compromised.
- Managing changes becomes challenging.
Read more in our blog: Skills management strategy: All you need to know
How does artificial intelligence change the skill data input process?
Advances in generative AI in skills management
The rapid evolution of artificial intelligence (AI) technology has generated models that can produce text, summarize articles, and answer questions. These AI models apply well also to skills management data. We often hear terms such as “generative AI” or “large language models” in this context.
Let’s briefly explain these terms:
Generative AI broadly means AI models that use advanced algorithms to generate new content such as images or text. Potential use cases include creative writing or image synthesis.
Large Language models (LLMs) are special deep-learning algorithms that are trained with huge amounts of text. They are a subset of generative AI models. The massive data enables these algorithms to understand and generate text that is so precise and correct that it can be used in different applications. Moreover, the LLMs can be further fine-tuned to specific tasks in the given data.
Using Large language models for skills data
Especially the fine-tuning possibility of the LLMs is very useful in skills management tools and systems. It enables the models to be fine-tuned to specifically extract and categorize skills information from various texts. The text can be in different formats, and they can be written in natural language without specific formatting of skills-related and non-skills-related words.
Models can use any format that can be converted to a text file including image files, image-containing pdf files, database extracts, and much more. The data itself can be for example resumes, job descriptions, performance reviews data or project data. An AI-empowered skills management platform can easily import and structure all this data in a few seconds.
Skilby – a new tool to automatize the input of the skills
That’s why we have started to develop a new tool, Skilby, that simplifies and automates the process of skills management. Next, we will show you some of the features and functionalities of our tool, and how it can help you optimize your skills data and improve your team performance.
Upload skills management data from natural language with Skilby AI
One of the main features of our tool is the ability to upload skills data with AI from data that contain skills information.
This means that you can easily import your existing skills data from various sources, such as resumes, job descriptions, performance reviews, etc., and our tool will automatically extract and analyze the relevant skills information using natural language processing techniques.
This way, you can save time and effort in manually entering or updating your skills data in the beginning and ensure that your data is accurate and consistent.
Categorize skills automatically in a few seconds with Skilby
After you have several workforce members in the system, you can easily categorize all the skills just in a few seconds.
This means that you can organize your skills data into meaningful and logical categories, such as technical skills, soft skills, domain-specific skills, etc. This way, you can easily navigate and access your skills data, and gain insights into the strengths and weaknesses of your skills portfolio.
Overview of the skills data
A third feature of our tool is the ability to load multiple team members and show the overview of the data. This means that you can compare and contrast the skills data of different team members, and see how they complement or overlap each other.
You can also view the aggregate skills data of your entire team, and identify the gaps or opportunities for improvement. This way, you can optimize your team composition and allocation, and enhance your team collaboration and performance.
Skilby demo will be available early 2024 on the Skilby.ai.
Join the Skilby waitlist already now. By joining the waitlist, you will have the opportunity to be part of the pilot group, have information on Skilby’s progress first, and have an opportunity to influence the new features of Skilby.