Will AI replace Operations Research Analysts?

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

57/100

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 score

Will AI replace Operations Research Analysts?

Not entirely. Today's AI tools can take on about 57% of the typical work in this occupation, mostly: prepare management reports defining and evaluating problems and recommending solutions. The parts that stay human include: Define data requirements, and gather and validate information, applying judgment and statistical tests.; Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.; Collaborate with others in the organization to ensure successful implementation of chosen problem solutions..

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.

TaskAutomatable

Prepare management reports defining and evaluating problems and recommending solutions.AI already used 78%

Define data requirements, and gather and validate information, applying judgment and statistical tests. 68%

Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.AI already used 60%

Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary. 57%

Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes.AI already used 55%

Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources. 42%

+ 11 more tasks (40% of the week) in the full test.

Your human edge

Define data requirements, and gather and validate information, applying judgment and statistical tests.
Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.
Collaborate with others in the organization to ensure successful implementation of chosen problem solutions.

AI tools to try

Coding assistants (GitHub Copilot, Claude Code, Cursor)ChatGPT / Claude for design and debuggingAI test and documentation generators

Related roles

Data Scientists
Management Analysts
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 rebuild one past optimization or simulation model in a solver library, generating unit tests for constraints, and compare runtime and build hours to the original.

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 tool that calls an LLM API with a structured-output schema to turn model runs into a draft management report, with retrieval over your internal data dictionary; measure cost and latency per report.

Phase 3

Production-grade AI

Evals and regression tests for prompts.
Security: prompt injection, data leaks, permissions.
Monitoring and cost control.

Project

Add a 30-case eval suite and regression tests to that reporting tool, test it against prompt-injection in source documents, and log token spend per run with a weekly cost cap.

Take the free 11-question test