{"id":676068,"date":"2025-11-23T00:00:00","date_gmt":"2025-11-23T00:00:00","guid":{"rendered":"https:\/\/enterpriseam.com\/egypt\/2025\/11\/23\/rethinking-job-titles\/"},"modified":"2025-11-23T00:00:00","modified_gmt":"2025-11-23T00:00:00","slug":"rethinking-job-titles","status":"publish","type":"post","link":"https:\/\/enterpriseam.com\/egypt\/2025\/11\/23\/rethinking-job-titles\/","title":{"rendered":"Rethinking job titles"},"content":{"rendered":"

? In<\/strong> the world of corporate reporting, we\u2019ve perfected the art of financial <\/strong>disclosure<\/strong>, creating a standardized language that allows investors to compare apples to apples across industries and markets. But when it comes to human capital \u2014 often a company\u2019s largest and most valuable asset \u2014 there\u2019s an obvious<\/a> language barrier.<\/p>\n

Human capital at the forefront:<\/strong> Since 2020, the US Securities and Exchange Commission (SEC) has required<\/a> publicly traded companies to disclose information about their human capital resources, including measures and objectives related to workforce management. This marked a significant shift from previous requirements that only mandated basic headcount reporting, reflecting a growing recognition that human capital is increasingly viewed as a critical factor in a company\u2019s success, with investors and stakeholders demanding greater transparency about how businesses manage their workforce.<\/p>\n

But transparency without standardization is like each company publishing financial <\/strong>statements using different accounting principles.<\/strong> Because there is no single template for disclosure, each company tailors their reporting to what\u2019s material for their specific business, creating what Gibson Dunn called<\/a> a lack of comparability across companies after their four-year analysis of S&P 100 human capital disclosures.<\/p>\n

Job titles are among the most inconsistent attributes found in people datasets<\/strong>, according to workforce analytics firm Visier<\/a>. Classifications tend to be extremely general and fall out of date quickly in a labor marketplace that\u2019s transforming more rapidly than ever before. This lack of standardization creates barriers for cross-sector recognition of skills, and makes it difficult for employers, educators, and policymakers to align workforce development efforts. This fragmentation creates what researchers call a \u201cTower of Babel\u201d situation where each company\u2019s workforce data speaks a different language.<\/p>\n

What makes an economist?<\/strong> The title is seemingly straightforward, but Amazon\u2019s 150 PhD economists \u2014 according to their own career pages<\/a> \u2014 solve applied economics questions in market design, pricing, forecasting, program evaluation, and online advertising. Some help build risk models for lending to third-party sellers, and some advise on product design and engagement tracking for devices like Alexa. But at IBM, economists work on macro-level reports to analyze how geopolitical situations might affect business operations \u2014 essentially functioning as industry analysts or specialized reporters. At other companies, economists might serve as hybrid data science consultants and client success managers who help customers understand product data and produce marketing content about industry trends.<\/p>\n

These positions share the same title and educational requirements, but comparing <\/strong>workforce data across these companies would lead to completely wrong conclusions<\/strong> about what each company is actually doing. An investor analyzing a company\u2019s economist headcount could mistakenly conclude the company is over-investing in research when those employees are actually driving client retention and marketing initiatives.<\/p>\n

Meta is a case study of why raw numbers without context fail investors.<\/strong> The tech giant reports that 75% of its workforce consists of engineers, but what does that actually tell us? Without understanding what types of engineering work these employees perform \u2014 frontend, backend, infrastructure, product development, AI research, or application engineering \u2014 the number is effectively meaningless for comparative analysis, and the business implications vary enormously. Without taxonomic clarity, investors can\u2019t determine whether a company\u2019s engineering investment aligns with its stated strategic priorities.<\/p>\n

The solution lies in moving beyond arbitrary job titles to activity-based taxonomies.<\/strong> Rather than accepting whatever label a company chooses, workforce taxonomies build from the ground up by analyzing the actual activities that comprise work. In an analysis<\/a> of standardized taxonomies, TalentGuard offered that a consistent framework to define and categorize job roles, skills, and competencies across departments would provide a common language that helps align not just investors and companies, but leadership and employees as well.<\/p>\n

In tomorrow\u2019s For Your Commute, we\u2019ll dive deeper into what activity-based <\/strong>classification is, why it matters, how it helps investors, and how it enables an effective workplace.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

But when it comes to human capital \u2014 often a company\u2019s largest and most valuable asset \u2014 there\u2019s an obvious language 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