Feature
AI, automation and data analytics are reshaping work faster than most organizations can adapt, making skills management business-critical.
The 2025 World Economic Forum Future of Jobs report found 85% of employers are prioritizing training the workforce, with 63% identifying skills gaps as a “major barrier” for the next five years. The report also observed an increase in the proportion of workers “training, reskilling or upskilling,” up from 41% in 2023 to 50% in 2025.
But skills management is complex. It involves recording, validating, updating and analyzing skills, and then aligning the results with learning, knowledge management and operations. However, fragmented initiatives and poor funding prevent skills management efforts from being effective at scale.
Table of Contents
- Decentralized Approaches to Skills Management
- Skills Are Dynamic. Profiles Are Static
- Challenges Around Skills Verification
- Skills for the Future
- Mapping Learning to Skills
- How AI Solutions Help
- What to Look For in AI-Powered Skills Management Software
- Major HR and LMS Providers Muscle in to Skills Management
- Skills and Expertise Location
- Changing Perceptions of Skills
- An Emerging Skills Landscape
Decentralized Approaches to Skills Management
Decentralized approaches to skills management raises challenges. Within a large enterprise, Learning & Development may design and plan learning to support skills development, but the wider HR team may also be involved in analyzing skills gaps. Knowledge managers may play a part in recording and facilitating access to knowledge and expertise, as well as related applications that also support skills management.
Additionally, specialist functions throughout the enterprise may manage their own skills program. Sales, IT, specialist lines of business and technical teams may all have individual approaches to managing skills focused on their own areas of domain knowledge.
This ad hoc approach means no single system of record to establish skills may exist. "Employee skills are often fragmented across HR applications, external platforms like LinkedIn and departmental spreadsheets, making it challenging to maintain a single source of truth," said Saju Sadasivan, a knowledge management leader at a global pharmaceutical company.
There may also be no corresponding enterprise-wide approach to describing skills. Without a standardized skills taxonomy, skills could overlap or be recorded inconsistently, which overwhelms employees and hinders effective skill matching, Sadasivan said.
Skills Are Dynamic. Profiles Are Static
The dynamic nature of skills, as employees learn and gain new experiences, is also a challenge for skills management. When skills management systems of record don’t reflect these changes, they lose their value. For example, skills on a people profile on an intranet may hardly ever be updated.
“It is always best to capture information such as skills and expertise in the flow of work,” said Krista Kennedy Groenwoldt, a knowledge and digital leader at a U.S.-based research organization. “When employees need to enter it manually, it always only reflects a point in time.” People rarely go back to make manual updates to a profile because they are busy and focused on the work they need to complete, she said.
Manual updates lead to issues, Sadasivan agreed. “Skills data is usually incomplete or outdated because updates are manual and typically occur only when employees apply for new roles,” he said.
Challenges Around Skills Verification
Another challenge is around validating skills. How do we know when someone really has a skill? Who validates it? Skills validation is a can of worms, with no consensus on what constitutes a verified skill, and if an individual has that skill. When people self-validate their skills, declarations of expertise may be taken less seriously, or even regarded as an area of risk.
Logistics, cost and effort also make skills validation tricky to implement at scale. For example, skills required or gained by individuals in specialist areas may require verification from busy subject matter experts who do not have the time to give their input, resulting in a series of approval bottlenecks. These logistical hurdles mean skills verification doesn't happen, or if it does, is difficult to maintain.
Skills for the Future
The ever-changing skills landscape poses yet another obstacle. The GrECo Group is a European risk consulting and specialist insurance group headquartered in Austria, with more than 1,300 employees spread among 21 countries. Managing skills and knowledge is important, particularly in a specialized area with evolving regulations.
To help manage knowledge and learning in this area, GrECo identified a number of knowledge sectors each with recognized subtopics. Relevant learning in the company’s learning management system (LMS) is tagged to each knowledge sector, while learning also covers "soft skills" as well as technical know-how. GrECo is also addressing needs around future digital skills in areas such as AI.
Additionally, GrECo takes some innovative approaches in developing and managing knowledge and skills, including managing its own internal academy and working in partnership with a leading Austrian university offering internships, helping to develop future knowledge and identifying talent.
However, there are some specific challenges when it comes to managing skills.
One of these is about considering skills in the future. “We consider ourselves the risk thought leader,” said Gabriele Andratschke, head of human resources. “We want to anticipate future risks for companies, and hand in hand with future risks go future skills. So, what skills will we need in three, five, 10 years or even longer?”
Mapping Learning to Skills
A second challenge at GrECo is defining the learning required to develop a skill, and in turn, mapping the skills required by different roles. "We have to define what the skill is and what you’re able to achieve after three or four relevant pieces of learning,” Andratschke said. “And, for example, if you want to be an account manager for us, what are the skills you need?”
The group is working to define this mapping by talking to subject matter experts throughout the business, Andratschke said. Everything is then recorded in a master spreadsheet that links learning, skills and roles. “It is a lot of work, but this will strengthen us in our position, because when we work with clients, we need very specialized knowledge,” she said.
How AI Solutions Help
The challenges around skills management faced by organizations like GrECo Services are considerable and resource intensive. To overcome many of these challenges, organizations ideally need to take an approach that is dynamic rather than static, and replace manual approaches by:
- Taking into account a wider set of systems and sources
- Building dynamic profiles based on interactions that sidestep some of the issues around self-validated skills
- Building skills taxonomies that also potentially reference existing frameworks
Sadasivan sees a role for AI-based solutions. "AI can transform skills management from a manual, episodic task into a continuous, evidence-based process that operates in the background of daily work,” he said.
Groenwoldt agreed that AI could deliver what traditional skills management approaches haven’t. “Given that the process of gathering such data has been manual up to now, it has been a heavy lift to gather and integrate data in this way,” she said. “That’s where AI can help. AI is great for aggregating data and identifying patterns.”
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Using AI helps organizations standardize, with an end result that is likely to be more consistent, Groenwoldt added.
AI could play a role in skills management at GrECo, Andratschke said, although she is wary about some of the risks. “AI can potentially help us to identify, revise and review the learning and skills we have, and propose a structure,” she said. “But everything will need to be carefully reviewed. I’m a big fan of AI, but you have to be super careful with what comes out.”
And while AI hallucinates, there’s less risk if users are then responsible for validating any suggested skills tags in their profiles, Groenwoldt said.
What to Look For in AI-Powered Skills Management Software
The skills management software space is mature, but a range of newer AI-powered solutions help organizations manage skills and overcome many of the associated challenges.
“Organizations should implement an integrated skills management platform that consolidates data from multiple sources, automates skill capture using AI insights from daily work and establishes a governed taxonomy to standardize skill definitions,” said Sadasivan.
Typically, skills management solutions involve some of the following features:
- An intelligent skills data “layer” or graph fed by data and interactions that powers skills-related processes.
- An in-built taxonomy or underlying ontology that describes or categorizes skills, sometimes informed by external data or proprietary skills taxonomies.
- Analytics, measurement and reporting to oversee and plan approaches to skills, including catering for gaps.
- Personalized skills profiles for individuals that are drawn dynamically from multiple sources.
- Automation brought to more challenging aspects of skills management such as skills validation and dynamic profile building.
- Suggestions for courses or for roles that drive career paths and underpin growth and development, supporting both individuals and managers.
- Integration with talent management, learning and knowledge management systems to provide a unified approach to skills management across the tech stack.
Workera is an AI-native skills assessment platform that aims to provide a “verified skills data layer” that works with recruitment processes, skills profiles and more by powering the talent management stack, including LMSs, application tracking systems or core HR systems of record.
Interest in Workera is growing, building on a strategic investment from Accenture in early 2025. It also recently announced a partnership with Udemy, to add AI-powered skills verification into the platform, as well as recommended targeted learning for users.
Some solutions are narrower in scope and focus on managing skills within particular processes. For example, Spotted Zebra focuses on recruitment with skills assessment tests and “interview intelligence” that measures and verifies the skills of candidates during the recruitment process.
Major HR and LMS Providers Muscle in to Skills Management
Understandably, existing HR and LMS tech providers are also incorporating AI-powered skills management capabilities into their platforms for a unified approach to skills across their modules.
For example, Workday’s Skills Cloud seeks to provide an intelligent layer that tracks skills across the workforce and then integrates back into Workday applications to enable skills profiles, role and learning suggestions and reporting. Correspondingly, Cornerstone has its skills engine, a knowledge graph that also incorporates data about the global labor market, and provides skills profiles, suggestions for learning and reporting.
Other tech providers are also keen to get a foot in the door in the skills management space. Microsoft introduced its AI-powered service People Skills to provide a skills “data layer,” a taxonomy informed by LinkedIn data and personalized skills profiles across Copilot for Microsoft 365 and the Microsoft Viva suite of tools.
Skills and Expertise Location
Other AI-powered solutions focus more on knowledge management but have some overlapping features with skills management software. These include Starmind, a mature solution that has been around since 2010, that maps company knowledge based on everyday interactions and then helps connect employees to experts, as well as building a knowledge base of verified answers.
Sadasivan views skills management platforms as a way to connect employees to experts, which has challenged KM professionals for decades.
“AI-powered knowledge graphs map relationships among skills, experience, project history, location, team dynamics and availability to enable intelligent expert matching,” Sadasivan said. “When expertise is needed, AI interprets the context, understands specific requirements and recommends individuals who are both qualified and best positioned to assist based on workload, collaboration history and domain experience.”
Ultimately, this shifts expert discovery from basic keyword searches by users to context-aware recommendations, Sadasivan said. He sees a potential role for intelligent agents to automate profile updates for experts by collecting evidence from daily contributions and flagging experts to validate these, ultimately keeping profiles current and relevant.
Changing Perceptions of Skills
AI seems not only set to change how we record, manage and validate skills and competencies, but also to influence our perceptions of skills. This is not only in the skills areas we need to focus on, such as prompt engineering, that help us get the best out of AI, but possibly even how we define a skill.
Some tech vendors, including Microsoft, are positioning skills as a particular characteristic or ability of an AI agent. This terminology is part of a wider use of language that equates agents with human roles and abilities, although academic research from Carnegie Mellon University suggests that the current failure rate of agents for basic tasks is high.
There has already been considerable debate about how AI may change our perception of the value of expertise. Over time, could the use of the word “skill” as an AI facet, rather than a celebrated human characteristic, affect the value we put on human skills?
An Emerging Skills Landscape
Skills management is complex, costly and often an ad hoc, manual process. AI-driven solutions are now emerging to bring structure, scale and insight.
With the arrival of AI agents, new AI products and features and likely twists and turns to related AI-enabled processes such as recruitment, the collective approach to skills management looks likely to be in a state of flux for some time to come.
