AI is now embedded across planning, sales, automation, and knowledge systems. As adoption increases, leadership expectations are becoming more specific. It is no longer sufficient to demonstrate activity or incremental efficiency. Organizations need a disciplined way to evaluate how these systems influence performance inside real operations.
Developed in collaboration with Optimizely, The AI Experimentation Series provides a structured framework for evaluating AI systems within live operating environments.
This guide outlines four controlled experiments designed to examine how AI agents affect decision quality, workflow efficiency, and measurable business outcomes over time. Each experiment introduces AI into an existing operating model in stages, allowing performance to be observed as intelligence is layered into the system.
Content planning and creation
Sales and lead prioritization
Digital workflow automation
Knowledge and support systems
For each domain, the guide defines a baseline, incorporates AI into the workflow, and identifies the operational indicators that signal progression. It also outlines how results can inform decisions about scaling, refining, or adjusting deployment.
Rather than treating AI as a standalone capability, the series frames it as part of a broader operating system that must demonstrate reliability, adaptation, and measurable contribution.
Performance deserves proof.
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