Czy AI zastąpi zawód: asystent / asystentka ds. statystyki?

Asystenci ds. statystyki face very high AI disruption risk with a score of 85/100, primarily due to automation of routine data processing and clerical tasks. However, this role won't disappear—it will transform. AI will handle data collection, tabulation, and quality checks, while human assistants who develop statistical modeling skills and research support capabilities will become increasingly valuable to organizations.

Czym zajmuje się asystent / asystentka ds. statystyki?

Asystenci ds. statystyki collect and organize data for statistical research projects, applying statistical formulas and methodologies to support data analysis efforts. They prepare datasets, create visualizations through charts and diagrams, compile research reports, and manage the technical documentation required for statistical studies. This role bridges raw data collection and professional statistical analysis, requiring both organizational precision and foundational quantitative literacy.

Jak AI wpływa na ten zawód?

The 85/100 disruption score reflects a sharp divide in this role's future. Vulnerable tasks—process data (69.74 automation proxy), perform clerical duties, tabulate survey results, and data quality assessment—are precisely what modern AI systems excel at, automating routine workflows that consume 40-60% of typical assistant workload. Statistical financial record production faces similar pressure. Conversely, resilient skills including apply scientific methods, statistical modeling techniques, and research design remain distinctly human domains requiring critical judgment. The AI Complementarity score of 74.79 indicates significant potential for human-AI collaboration: assistants equipped with data science fundamentals and statistical analysis competencies can leverage AI tools for preliminary processing, freeing time for higher-value research support. Near-term (1-3 years), organizations will consolidate positions and redistribute responsibilities toward research-adjacent work. Long-term, the role evolves toward research coordinator positions for those who upskill, or faces workforce contraction for those remaining in pure data processing.

Najważniejsze wnioski

• Routine data processing and tabulation tasks face high automation risk, but research support and statistical methodology work remain human-centric.
• Upskilling in statistical modeling techniques and data science is critical to transition from vulnerable clerical duties to higher-value research assistance roles.
• AI will function as a collaborative tool rather than a replacement, amplifying assistants who develop complementary quantitative and research design skills.
• The role's future depends on organizational investment in research infrastructure—positions oriented toward pure data entry face consolidation within 3-5 years.

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.