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AI exposure report
Will AI replace Web Administrators?
Many of your tasks are within reach of AI. The role will change, so it pays to act now.
Task exposure to today's AI
High exposure
This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.
Get your personal scoreWill AI replace Web Administrators?
Not entirely. Today's AI tools can take on about 56% of the typical work in this occupation, mostly: review or update web page content or links in a timely manner, using appropriate tools. The parts that stay human include: Determine sources of Web page or server problems, and take action to correct such problems.; Collaborate with development teams to discuss, analyze, or resolve usability issues.; Collaborate with Web developers to create and operate internal and external Web sites, or to manage projects, such as e-marketing campaigns..
People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.
Which tasks AI can take on
The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.
+ 29 more tasks (67% of the week) in the full test.
Your human edge
- Determine sources of Web page or server problems, and take action to correct such problems.
- Collaborate with development teams to discuss, analyze, or resolve usability issues.
- Collaborate with Web developers to create and operate internal and external Web sites, or to manage projects, such as e-marketing campaigns.
AI tools to try
Coding assistants (GitHub Copilot, Claude Code, Cursor)ChatGPT / Claude for design and debuggingAI test and documentation generatorsRelated roles
- Web and Digital Interface Designers
- Web Developers
- Software Developers
A 3-phase AI learning plan
Phase 1
Pair programming with AI
- Use a coding assistant for boilerplate, tests and refactoring.
- Give it context: project rules, examples, small steps.
- Review AI code like a colleague's pull request.
Project
Use a coding assistant to write a server log-parsing script plus unit tests that flags the top 10 recurring 404s and 500s, reviewing every line before merging.Phase 2
AI in your systems
- Call an LLM API with structured output.
- Retrieval over your own documents or data.
- Evaluate quality, cost and latency before shipping.
Project
Build a retrieval tool over your site's runbooks and change logs that returns structured JSON incident diagnoses, and benchmark its accuracy, cost and latency on 20 past outages.Phase 3
Production-grade AI
- Evals and regression tests for prompts.
- Security: prompt injection, data leaks, permissions.
- Monitoring and cost control.
Project
Ship the incident assistant with a 30-case regression eval, prompt-injection tests on scraped page content, least-privilege API keys, and a monthly token spend alert.