Nowa strona 1 » How to Leverage AI-Powered Skills Intelligence Tools

Enhancing talent management with data-driven insights and automation.

Introduction: Embracing the Future of Skills Management

In an era where business landscapes are evolving at breakneck speed, skills have become the currency of success. Organizations need to build teams capable of executing current tasks while preparing for future demands. This challenge is amplified in a hybrid work environment, where tracking, mapping, and aligning employee skills can be cumbersome without the right tools.

AI-powered skills intelligence tools offer HR leaders the ability to automate the identification, mapping, and analysis of workforce skills at scale. These tools not only improve efficiency but also allow organizations to make smarter talent decisions — from personalizing development programs to identifying redeployment opportunities.

However, like all technology, these tools need to be used wisely. This guide will walk you through the leading AI-powered tools in the market, how to leverage them effectively for skills management, and the ethical considerations that must be addressed when implementing these solutions.

Step 1: Overview of Leading AI-Powered Skills Intelligence Tools

The first step in leveraging AI for skills intelligence is to choose the right platform. Several leading tools are gaining traction for their ability to analyze and synthesize large volumes of employee data, allowing for smarter workforce decisions.

A. Gloat

Gloat is an AI-powered talent marketplace that uses machine learning to match employee skills with development opportunities. It automatically suggests potential career growth paths and internal mobility opportunities based on employee skills, aspirations, and organizational needs.

Key Features: Skills matching, internal talent marketplace, career growth recommendations.
Use Case: Ideal for organizations looking to leverage internal mobility and build personalized employee development paths.

B. SkyHive

SkyHive utilizes quantum labor analysis to create a comprehensive view of current and future workforce skill demands. The platform uses AI to identify skills adjacencies and map the transferability of skills, enabling organizations to optimize talent utilization and drive reskilling initiatives.

Key Features: Skills mapping, labor market trends, skills adjacencies.
Use Case: Organizations focused on creating a skills-first strategy and workforce transformation.

C. Eightfold

Eightfold employs deep learning algorithms to match skills with business needs, helping HR teams understand the skills gap and identify future skill needs. Eightfold’s platform aggregates data from employee resumes, career paths, and performance reviews to create dynamic skills profiles for each employee.

Key Features: Talent acquisition, career development, skills inference.
Use Case: Companies that need to enhance talent acquisition, succession planning, and skills management.

D. Workday Skills Cloud

Workday Skills Cloud combines machine learning with human insights to automatically classify skills, track skill development, and align workforce capabilities with business goals. It also provides skills heatmaps to visualize organizational skill gaps and enable targeted reskilling programs.

Key Features: Skills tracking, development path mapping, skills gap analysis.
Use Case: Ideal for organizations seeking an integrated HR solution that links performance, learning, and skills.

Step 2: Automating Skill Inference from Roles, Resumes, and Tasks

One of the most valuable aspects of AI-powered tools is their ability to automatically infer skills from various data sources. Traditionally, organizations relied on manual updates from employees, managers, or HR teams to track skills, which could lead to incomplete or outdated information. AI automates this process, making it faster and more reliable.

A. Extracting Skills from Roles and Job Descriptions

AI-powered tools analyze job descriptions, employee roles, and resumes to automatically infer the skills required for a particular position. This automation eliminates biases and helps identify critical, yet often overlooked, skills.

For example, when an employee changes roles, AI can automatically analyze the new job description and cross-reference it with the employee’s historical data, suggesting new skills they might need to develop for the role.

Practical Example: A global software company uses Eightfold to analyze their existing job descriptions and infer required technical skills (e.g., cloud architecture, AI/ML) and soft skills (e.g., leadership, collaboration). This automated process allows the HR team to quickly understand the skill gaps in their workforce.

B. Leveraging Resumes for Skill Inference

AI tools scan resumes, extracting explicit and implicit skills. Explicit skills are directly stated (e.g., “Java programming”), while implicit skills are inferred based on experience (e.g., “collaborating in cross-functional teams”).

Explicit skills are easier to identify (e.g., specific software tools or certifications).
Implicit skills require deeper analysis, where AI looks for patterns in an individual’s career trajectory, task achievements, and behavioral insights to infer their hidden skillset.

C. Task-Based Skill Inference

AI can even go beyond resumes to infer skills based on actual job tasks. For example, analyzing data from collaboration tools (e.g., Slack, Microsoft Teams), performance management systems, or project management tools can reveal skill competencies. AI-powered platforms can aggregate these inputs and use advanced analytics to generate a robust skills profile.

Example: SkyHive uses task-based analysis to assess employees’ proficiency in various tasks, such as customer service, technical troubleshooting, and project management. By monitoring activity across different tools, the platform offers a more dynamic and holistic view of employee capabilities.

Step 3: Visualizing Skills Proximity and Adjacencies for Redeployment

One of the primary advantages of AI in skills management is its ability to help visualize skills proximity — or how closely different skills are related. This can be particularly valuable when redeploying talent across teams, departments, or geographies.

AI tools can identify skills adjacencies — areas where employees might already have a foundation and can quickly develop new, complementary skills. By identifying these adjacencies, HR leaders can optimize talent redeployment, ensuring that employees are matched to roles that align with both their capabilities and aspirations.

A. Skills Proximity Mapping

Many AI tools come equipped with skills adjacency graphs that illustrate the proximity of different skills. These graphs can highlight clusters of related skills, enabling HR to visualize opportunities for employees to move into adjacent roles or projects with minimal additional training.

For instance, an employee with project management skills might easily transition into roles requiring agile project management or scrum master expertise.

B. Skills Heatmaps

Tools like Workday Skills Cloud provide skills heatmaps, which visually display areas of strength and gaps in the organization. This can help HR teams make data-driven decisions when considering internal mobility and redeployment strategies.

Step 4: Risks and Ethical Considerations of AI-Driven Skill Inference

While AI offers immense potential, its application in HR is not without challenges. The ethical implications of using AI for skill inference must be carefully managed to ensure fairness, transparency, and data privacy.

A. Bias in AI Algorithms

AI algorithms are only as good as the data they’re trained on. If historical data reflects biases (e.g., gender, racial, or age-based), these biases can be perpetuated by AI models. It is critical that AI-driven tools are regularly audited to ensure they are free from bias and provide equitable outcomes for all employees.

Mitigation: Regularly test algorithms for fairness and ensure a diverse range of data inputs.

B. Data Privacy Concerns

AI tools rely on a vast amount of personal and performance-related data. Organizations must ensure that they comply with data protection regulations (e.g., GDPR) and be transparent about how employee data is used, stored, and shared.

Mitigation: Implement strong data governance frameworks and ensure consent from employees before collecting or processing their data.

C. Transparency in Decision-Making

AI tools can sometimes function as “black boxes,” where HR leaders and employees don’t fully understand how decisions are being made. It is important to ensure that the decision-making process is transparent and that employees are able to challenge or understand the rationale behind skill evaluations or role recommendations.

Mitigation: Use AI tools that provide explainability — allowing HR to trace how decisions were made and to ensure that employees have access to their own data and the reasoning behind skill assessments.

Conclusion: Navigating the Future with AI-Driven Skills Intelligence

AI-powered skills intelligence tools offer HR leaders the ability to automate, scale, and optimize skills management processes. From automating skill inference to visualizing skills adjacencies for redeployment, these tools provide invaluable insights that can transform how organizations develop their people.

However, their success depends on selecting the right platform, implementing it effectively, and ensuring that it aligns with ethical standards and organizational goals. By using AI wisely, HR leaders can create a future-proof workforce that is equipped to handle whatever challenges lie ahead.