Penetration of artificial intelligence into the software development life cycle: An empirical labor market analysis

Olga Vladimirovna  Stoyanova; Ivan Sergeevich  Okuskov

Olga Vladimirovna Stoyanova HSE University, St. Petersburg, Russia https://orcid.org/0000-0002-7281-2708
Ivan Sergeevich Okuskov HSE University, St. Petersburg, Russia https://orcid.org/0009-0004-8701-847X

Keywords: artificial intelligence, software development life cycle, competencies, job posting analysis, labor market, generative AI

Abstract

The integration of artificial intelligence (AI) into information technologies is reshaping competency requirements for IT specialists. However, AI competency penetration into the early phases of the Software Development Life Cycle (SDLC) – requirements analysis and planning – remains underexplored. Existing studies either rely on static competency models lacking empirical validation or analyze the labor market at an aggregated occupational level without differentiation across SDLC functional clusters. The research objective is to empirically test the hypothesis of AI competency penetration into the skill sets associated with early SDLC phases. The scientific novelty lies in the methodological transition from static competency models to a process-oriented analysis. Roles are cross-referenced with SDLC phases through weighted clustering with normative alignment to SWEBOK v3.0, BABOK v3.0, and ISO/IEC/IEEE 12207:2017. The empirical basis comprises 182,447 unique IT job postings from the hh.ru platform for the period March–December 2025, covering 19 SDLC-relevant roles. A reference list of 307 AI competencies was compiled via LLM-based preliminary screening followed by expert verification. Eight functional clusters are identified. Weighted demand S(s) is distributed as follows: analytics – 16.0%, architecture – 16.0%, development – 13.6%, management – 12.2%, documentation – 12.2%, testing – 12.0%, support – 9.1%, design – 8.9%. Weighted penetration P(s) ranges from support (0.34%) to design (1.70%). The hypothesis is confirmed: penetration in the Analytics cluster (0.69%) corresponds to the market average, while in the Management cluster (0.76%) it exceeds it, though early SDLC phases lag behind the Design, Development, and Architecture clusters in absolute terms. The findings are applicable to corporate early-warning strategies, educational curriculum revision, and updating professional standards in the context of the proliferation of generative AI.

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