Tactic 4: AI-Powered Experiment Orchestration & Execution
Example Prompt:
What it is:
“We have 3 major experiments (details below)
Use AI to streamline the execution phase of experimentation –
but limited staff. Suggest an optimal rollout plan
from prioritizing which tests to run first, to automating some
(order and timing) to run all three as quickly as
development tasks, and even monitoring experiment status.
possible without quality issues. Include
This tactic treats the AI as a project manager or engineer that
reasoning.”
helps you run more tests with less manual effort.
How to use it:
If you have a backlog of experiment ideas, feed a summary of them to the AI and ask it to prioritize based on potential impact,
effort, likelihood of success and opportunity cost. For example, “Here are 5 test ideas with their goals and estimated impact; in
what order should we run them and why?” The AI can objectively assess and recommend an order (e.g. run the high-impact,
low-effort ones first). It can also identify if certain tests conflict (overlapping audience or page) and suggest a sequencing to
avoid interference.
For execution, you can leverage AI to automate development or content tasks. For instance, ask the AI to generate code snippets:
“Write a JavaScript snippet to change the ‘Add to Cart’ button color to blue on variant pages.” or “Provide HTML/CSS for a new
layout where the image is on the left of text.” Even if you’re not a developer, the AI’s output can jump-start implementation which
you then hand to engineering or use in your testing tool. Additionally, AI can help schedule or coordinate tests: you might instruct
it to create a calendar or plan (e.g. “Generate a timeline to run these 3 experiments this quarter without overlap”).
GenAI Quick-Win Playbook for Experimentation
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