AI resilience estimate
Some parts of the work may change more than others. Start by finding where AI can help and where your judgment matters.
Higher means more resilience in this estimate. This is not a percentage chance of keeping or losing your job.
Start with one repeatable task. The review below helps you separate routine steps from decisions that need your attention.
Look for moments that need context, trust, or a careful decision. Describe one, then consider what a good outcome would look like.
Based on a typical version of your role. Your actual tasks may differ. Your employer’s plans and local job market are not known.
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Read the reasoning and the six factors.
This is the saved reasoning from an earlier version of the assessment. Its claims were not independently verified. New assessments use the revised method linked below.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
Approximately 60-70% of entry-level data analyst tasks are now automatable by AI. Data cleaning, basic SQL queries, standard visualizations, descriptive statistics, and report generation can be handled by tools like ChatGPT, Claude, and specialized platforms like DataRobot. However, defining the right questions, contextualizing findings within business strategy, and handling ambiguous or novel problems remain human-dependent.
20% of the total
AI advancement in data analytics is extremely rapid. In the past 18 months, we've seen GPT-4's Code Interpreter, Claude's analysis capabilities, and numerous specialized analytics AI tools emerge. Major tech companies are investing billions in automated analytics platforms. The pace shows no signs of slowing, with multimodal AI and autonomous agents on the horizon.
15% of the total
The data science and analytics industry is aggressively adopting AI tools. Enterprise spending on AI-powered analytics platforms grew 45% year-over-year in 2023. Your large company (5000+ employees) likely has budget and incentive to deploy these tools. However, regulatory concerns around data privacy, model explainability, and the need for human oversight slow full automation in many sectors.
15% of the total
Data analysts retain meaningful human advantages in several areas: understanding organizational politics and stakeholder needs, asking the right questions based on business context, exercising ethical judgment about data use, building trust with cross-functional teams, and adapting analysis approaches to ambiguous real-world situations. However, the purely technical aspects offer less human advantage than roles requiring physical presence or deep empathy.
10% of the total
As an entry-level analyst, your core skills (SQL, Python/R, statistical thinking, data visualization, problem-solving) are highly transferable to adjacent roles like business intelligence, product analytics, data engineering, or even transitioning to data science. Your large company environment provides exposure to diverse projects and teams. The challenge is that you haven't yet built deep specialized expertise, but your adaptability and ability to learn new tools quickly is an asset.
5% of the total
Market demand for data analysts shows mixed signals. Overall job postings remain strong (up 15% year-over-year), and median salaries are stable or growing. However, requirements are shifting—employers increasingly seek 'AI-literate' analysts who can work with machine learning outputs and automated tools. Entry-level positions face more competition as bootcamp graduates flood the market, but experienced analysts with business acumen remain in high demand.
This is the saved reasoning from an earlier version of the assessment. Its claims were not independently verified. New assessments use the revised method at https://www.ai-proof.me/methodology.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
Approximately 60-70% of entry-level data analyst tasks are now automatable by AI. Data cleaning, basic SQL queries, standard visualizations, descriptive statistics, and report generation can be handled by tools like ChatGPT, Claude, and specialized platforms like DataRobot. However, defining the right questions, contextualizing findings within business strategy, and handling ambiguous or novel problems remain human-dependent.
20% of the total
AI advancement in data analytics is extremely rapid. In the past 18 months, we've seen GPT-4's Code Interpreter, Claude's analysis capabilities, and numerous specialized analytics AI tools emerge. Major tech companies are investing billions in automated analytics platforms. The pace shows no signs of slowing, with multimodal AI and autonomous agents on the horizon.
15% of the total
The data science and analytics industry is aggressively adopting AI tools. Enterprise spending on AI-powered analytics platforms grew 45% year-over-year in 2023. Your large company (5000+ employees) likely has budget and incentive to deploy these tools. However, regulatory concerns around data privacy, model explainability, and the need for human oversight slow full automation in many sectors.
15% of the total
Data analysts retain meaningful human advantages in several areas: understanding organizational politics and stakeholder needs, asking the right questions based on business context, exercising ethical judgment about data use, building trust with cross-functional teams, and adapting analysis approaches to ambiguous real-world situations. However, the purely technical aspects offer less human advantage than roles requiring physical presence or deep empathy.
10% of the total
As an entry-level analyst, your core skills (SQL, Python/R, statistical thinking, data visualization, problem-solving) are highly transferable to adjacent roles like business intelligence, product analytics, data engineering, or even transitioning to data science. Your large company environment provides exposure to diverse projects and teams. The challenge is that you haven't yet built deep specialized expertise, but your adaptability and ability to learn new tools quickly is an asset.
5% of the total
Market demand for data analysts shows mixed signals. Overall job postings remain strong (up 15% year-over-year), and median salaries are stable or growing. However, requirements are shifting—employers increasingly seek 'AI-literate' analysts who can work with machine learning outputs and automated tools. Entry-level positions face more competition as bootcamp graduates flood the market, but experienced analysts with business acumen remain in high demand.
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