Lightcast Launches 7th Generation Global Occupation Taxonomy, Setting the Universal Standard for Classification of Labor Data

Latest Taxonomy Helps Workforce Experts Map New Occupations & Align on Dynamic Labor Market Shifts with Real-time Jobs Data

Lightcast, the trusted provider of global labor market data, analytics, and expert guidance, launched its latest Lightcast Occupation Taxonomy (LOT), the only global occupation taxonomy that sets the universal standard for labor market occupation and classification data.

In its 7th generation, the LOT provides a granular, contextual framework derived from real-world job postings, employee profiles, and official government statistics that brings consistency and structure to workforce data. This global standardization across 1,800+ specialized occupations, 800+ core occupations, 180+ occupation groups, and 28 career areas ensures employers, researchers and policymakers ‘speak the same language’ internally, across systems, divisions, and ecosystem partners. This reduces ambiguity and streamlines workforce planning by ensuring consistency in occupations, job roles and careers regardless of variations in geography or language.

Real-time Data for a Fast-changing Market

With the labor market changing rapidly, organizations, government agencies and educational institutions need timely data on the evolving roles, titles and skills to keep pace with hiring, training, workforce planning and career development. The World Economic Forum predicted that as many as 85 million jobs could be displaced by labor shifts, and Lightcasts’ own data shows that one-third of the skills required for the average job have changed over the last three years, driven by new fields like Generative AI, green technology and cybersecurity.

Catch more HRTech Insights: Why Employers That Prioritize Learning Come Out Ahead

“Having the Lightcast Occupation Taxonomy translated across countries is essential for our work on the annual Stanford AI Index Report, including our recently released 8th edition,” said Nestor Maslej, research manager at Stanford’s Institute for Human-Centered Artificial Intelligence. “We are proud to partner with Lightcast to deliver an in-depth, country-level comparison and longitudinal analysis of AI-related jobs, which is possible because of the granularity and up-to-date data Lightcast is known for.”

While other taxonomies are geography-specific and generally updated only every five to 10 years, the LOT uses real-time data from current job postings worldwide to reflect the latest occupations and emerging specializations, including 25 new specialized and niche roles. This, alongside Lightcast’s decades of labor market data and proprietary models, classifiers and AI provides curated categorization across both demand and supply data.

The Universal Standard for Labor Data

Already trusted by thousands of global enterprises, educational institutions, government agencies and industry partners, the LOT provides more depth than other taxonomies that Lightcast competitors rely solely on SOC, ISCO, O*NET and NOC across key metrics:

• Global reach: LOT is the only global taxonomy enabling direct comparisons across regions and industries. This allows better cross-border talent planning and analysis, particularly for multinational organizations and those hiring remote workers.
• Granular detail: LOT is at least 2x more detailed than the U.S. SOC and 4x more detailed than ISCO, capturing nuances in emerging roles. For example, it precisely maps occupations such as cardiology registered nurse or emergency room nurse rather than lumping them into catch-all titles like “healthcare workers.”
• Consistency: Unlike scattered job titles, LOT offers a unified structure for interpreting diverse occupations. For example, it distinguishes between a theatre assistant in the UK as an operating room role in the medical field and a theater assistant in the U.S. as a performing arts role, and it understands that a barrister and lawyer are the same categorized occupation.
• Timeliness: The LOT is regularly revised to keep pace with rapid labor market changes, ensuring relevance and accuracy. The latest version includes emerging roles like neurolinguistics programmer, GenAI engineer, drone pilot, machine learning engineer and specializations in data science, blockchain and hybrid roles that won’t appear in government taxonomies for at least two or more years.

Read More on Hrtech : AI’s Fables: Lessons in Impactful AI Implementation

[To share your insights with us, please write to psen@itechseries.com ]