Between 2020 and 2022, IT professionals were in high demand as lockdowns forced companies to digitize overnight: job offers for software developers, IT architects and data analysts surged. Since mid-2022, however, the tide has turned.
If unemployment is low, why are hospitals still short of caregivers and restaurants desperate for qualified staff? Read Chapter 2 of our white paper to understand why today’s historically low unemployment rates can backfire—deepening, not easing, the shortage of skilled workers.
For Hungary’s Prime Minister Viktor Orbán, nothing is more important than babies. But they must be “home-made.” In his words: “Migration for us is surrender.” Orbán, himself a father of five, has set a bold target: raising Hungary’s fertility rate to 2.1 by 2030, the level needed to maintain the population size.
Switzerland, like much of Europe, faces a growing labor crisis. Retail, logistics, hospitality and healthcare are in critically short supply. With fertility rates falling and young people choosing academic careers over vocational training, the talent gap is widening.
It’s not jobs we’re running out of; it’s alignment. Too often, the skills people bring to the market don’t match what employers demand and vice versa. But the debate about “AI replacing humans” is misleading.
A new Microsoft research paper has just dropped, and it’s one of the more ambitious attempts to map out how generative AI is actually being used in the workplace.
June 17, 2025—JANZZ, a global leader in AI-powered human resources technology, has been listed as a trusted Sample Vendor in Gartner’s prestigious Hype Cycle for AI in Human Resources, 2025. This recognition underscores the company’s continued innovation and market leadership in artificial intelligence applications for talent acquisition and workforce development.
The inclusion in Gartner’s Hype Cycle marks a tradition: JANZZ has been recognized by the world’s leading research and advisory company in 6 different publications. For the first time in 2020, JANZZ was chosen as a Sample Vendor for Skills Ontologies (listen to Podcast) in Gartner’s Hype Cycle for Human Capital Management Technology, highlighting the company’s semantic approach to HR technology solutions.
“Being repeatedly selected among the most trusted vendors by Gartner validates our ongoing investments in product development and innovation,” said JANZZ spokesperson Doris Hofer. “This recognition from the sector’s most respected authority confirms that our AI-driven solutions are meeting the evolving needs of modern governments and businesses.”
Gartner’s research indicates that by 2025, 60% of enterprises will adopt a responsible AI framework for their recruitment procedures. However, Jeff Freyermuth, Director Analyst in the Gartner HR practice, cautioned that “most of the innovations haven’t lived up to their overinflated hype,” emphasizing the importance of carefully evaluated technology adoption.
The Gartner Hype Cycle serves as a crucial decision-making tool for organizations planning to upgrade their recruitment systems. It provides an objective assessment of emerging technologies, helping companies understand the real risks and opportunities of innovation while avoiding premature adoption or delayed implementation. According to Gartner, innovations typically require 3–5 years to move through the full Hype Cycle, with some technologies falling off entirely during this maturation process.
What distinguishes JANZZ in the competitive landscape is its comprehensive multilingual skills ontology JANZZon!—the most extensive in the world. While many providers claim to offer ontologies suitable for AI applications in talent acquisition, JANZZ’s solution goes beyond simple skill libraries to establish contextual understanding when matching skills to job requirements.
This semantic capability addresses a critical gap in the market, where inadequate matching technologies can lead to missed top talent and suboptimal hiring decisions (watch on YouTube). The company’s approach ensures accurate skill-to-job matches across multiple languages and cultural contexts.
JANZZ’s technology platform demonstrates its commitment to global scalability and data security. The company’s ISO 27001-certified solutions are fully compliant with GDPR and CCPA regulations, while supporting over 60 languages. This extensive language support reflects JANZZ’s understanding of regional labor market characteristics and cultural nuances in different geographical areas.
JANZZ.technology utilizes advanced semantic technologies to parse, classify, and match various professional attributes, including occupations, job titles, qualifications, hard and soft skills, experiences, education, and training credentials. This comprehensive approach enables rapid matching of candidates with open positions across multiple languages and platforms.
JANZZ specializes in AI-driven recruitment solutions that address the unique characteristics of labor markets across different regions. The company’s secure, cloud-based solutions and white-label products serve businesses and governments worldwide, helping organizations identify top talent efficiently while supporting economic development and workforce optimization initiatives.
For more information about JANZZ’s AI-powered human resources solutions, contact JANZZ and book a call to explore your hiring needs.
In various fields, such as data science, biology, social networks, and labor markets, graphs play a crucial role in visually representing data and analyzing complex relationships and patterns. While automated graphs have their advantages, manually curated graphs stand out as more reliable and intelligent due to the human touch in their creation and maintenance. With regulations like the # EU AI Regulatory Act on the horizon, the explainability and interpretability of manually curated graphs are becoming indispensable for compliant use in areas such as labor market data, public employment services, recruiting, and human capital management.
Automated knowledge graphs, generated using algorithms and software, offer efficiency and speed in graph creation. They can handle large volumes of data and quickly produce visualizations, making them suitable for tasks that require rapid insights. Furthermore, automated graphs can be helpful for initial exploratory data analysis, providing a quick overview of the data distribution and trends.
However, automated graphs have inherent limitations. They cannot discern contextual nuances and may present misleading visualizations if not carefully monitored. The absence of human intervention in the curation process makes automated graphs prone to errors, especially in interpreting complex data relationships. Moreover, automated graphs may oversimplify or overlook crucial details, leading to inaccurate conclusions and decisions.
In contrast, manually curated graphs are crafted with human expertise, attention to detail, and DOMAIN KNOWLEDGE. The process involves thoughtful consideration of the data, its’ context, and the specific insights sought. As a result, manually curated graphs are more reliable in representing the true nature of the data, capturing subtle patterns, and avoiding misinterpretations.
The human touch in graph curation allows for the incorporation of domain-specific knowledge and expert judgment, ensuring that the visualizations accurately portray the underlying data relationships. Furthermore, manual curation enables the identification and correction of anomalies, outliers, and inaccuracies that automated processes usually overlook. This attention to detail enhances the reliability of manually curated graphs, making them indispensable in critical decision-making processes.
Beyond reliability, manually curated, multilingual graphs exhibit a level of intelligence that automated graphs cannot match. The curation process involves critical thinking, problem-solving, and the application of human intuition, leading to extracting meaningful insights from the data. Human curators can identify patterns that algorithms might miss, recognize outliers that require special attention, and contextualize the data within the broader domain knowledge.
Moreover, the iterative nature of manual curation allows for the refinement and improvement of graphs over time. As new data becomes available or insights are gained, human curators can update and enhance the visualizations, ensuring that the graphs remain relevant and insightful. This adaptability and continuous improvement reflect the intelligence embedded in manually curated graphs, making them valuable assets in dynamic and evolving domains.
The superiority of manually curated graphs stems from the irreplaceable role of human expertise in the curation process. Domain knowledge, experience, and intuition are indispensable in understanding the intricacies of the data and translating them into meaningful graph representations. Human curators can ask critical questions, explore alternative visualizations, and communicate insights effectively, enriching the understanding of the data for diverse stakeholders.
Furthermore, the interpretability of manually curated graphs is a significant advantage, especially in complex or interdisciplinary domains. Human curators can provide context, explanations, and narratives accompanying the visualizations, making the insights more accessible and actionable for decision-makers. This human-centered approach to graph curation fosters transparency, trust, and collaboration, enhancing the overall impact of the visualizations.
The reliability and intelligence of manually curated graphs have wide-ranging implications across various fields. In scientific research, manually curated graphs are crucial in presenting findings, supporting hypotheses, and conveying the richness of complex data relationships. In business and analytics, manually curated graphs empower decision-makers with trustworthy insights, guiding strategic planning and resource allocation. In healthcare and medicine, manually curated graphs aid in understanding patient data, treatment outcomes, and epidemiological trends, contributing to improved care and public health interventions.
Furthermore, the emphasis on manual curation highlights the value of human expertise in the era of data-driven decision-making. While automation and algorithms have their place, the irreplaceable role of human judgment, creativity, and intuition in graph curation cannot be overlooked. This realization underscores the need for investment in human-centric approaches to data visualization and analysis, ensuring that the full potential of data is harnessed for the betterment of society.
In conclusion, the differences between automated and manually curated graphs are profound, with the latter emerging as the epitome of reliability and intelligence. As the demand for precise, meaningful, and actionable insights from data and AI applications continues to grow, the importance of manually curated graphs is also increasing, especially in areas where explainability and interpretability are indispensable prerequisites. If you are looking for the largest, multilingual and unique hand-curated knowledge graph in the field of labor market data, let our experts show you what #JANZZon! can offer and how it can address potential challenges with new AI regulations. Keep an eye out for our next post, which will provide insightful comparisons of frequently used graphs in the market.
