Czy AI zastąpi zawód: kierownik badania ankietowego?
Kierownik badania ankietowego faces a moderate AI disruption risk with a score of 53/100. While AI will automate significant portions of data tabulation, report writing, and analysis tasks, the role's core responsibilities—supervising field teams, conducting research interviews, and stakeholder communication—remain fundamentally human-centered. The occupation will transform rather than disappear, requiring workers to upskill in AI-assisted analytics.
Czym zajmuje się kierownik badania ankietowego?
Kierownik badania ankietowego organizes and supervises survey and research projects commissioned by sponsors. This role involves monitoring survey implementation to ensure compliance with requirements, directing teams of field interviewers, and overseeing data collection quality. Responsibilities include planning survey methodology, managing personnel, coordinating fieldwork schedules, and delivering findings to clients. The position requires both strategic project management and interpersonal skills to guide teams through complex research initiatives.
Jak AI wpływa na ten zawód?
The 53/100 disruption score reflects a nuanced future for this role. Vulnerable tasks—tabulating results, writing standardized reports, documenting interviews, and performing data analysis—represent approximately 69.35% of automation potential. Modern AI excels at these structured, rule-based activities. However, kierownik badania ankietowego retains significant resilience through irreplaceable human skills: stakeholder communication scores 71.23% on complementarity with AI, interview techniques and supervision remain deeply interpersonal, and the ability to capture attention during fieldwork is inherently human. Near-term (2–3 years), AI tools will handle data processing and preliminary report generation, but survey methodologists and team leaders will redirect effort toward strategy, client relations, and quality assurance. Long-term, this occupation evolves into a hybrid role where AI augments analysis while humans focus on stakeholder management and research innovation.
Najważniejsze wnioski
- Data handling tasks (tabulation, analysis, reporting) are highly vulnerable to automation, but represent only part of the role's total responsibility.
- Leadership, interview coordination, and stakeholder communication remain resilient due to their human-dependent nature.
- AI will serve as a complementary tool for this occupation rather than a replacement, enhancing analytical capacity while preserving supervisory and interpersonal functions.
- Upskilling in AI-assisted data interpretation and advanced survey design will be essential for career progression and role security.
Wynik zakłócenia AI NestorBot obliczany jest na podstawie 3-czynnikowego modelu wykorzystującego taksonomię umiejętności ESCO: podatność umiejętności na automatyzację, wskaźnik automatyzacji zadań oraz komplementarność z AI.
