You adopt AI to improve ROI, time, conversions. But without measurement, it’s guesswork. Here’s how to track AI’s real effect on your operations.

AI Isn’t Magic Unless It Moves Metrics

Too many businesses try AI experiments but never measure whether they matter. To get real ROI, you need metrics before, during, and after deployment to see what’s working.

1. Baseline Before You Launch

Record your current metrics: conversion rates, content creation time, ad ROI, support response times, churn, upsell rates, etc. These baselines give you comparison later.

  • Content & Copy: drafts per hour, clicks, open rates, conversion lift
  • Video / Sora Use: view counts, engagement, lead gen, share rates
  • Ad Optimization: CPA, ROAS, clicks per variant
  • Agent Workflows: time saved, tasks completed, errors vs manual effort
  • Recommendations / Upsell Engine: uplift in average order value, attach rates

Run the AI version vs control “no AI” for a test period. See lift. Don’t just flip everything on at once—test, measure, roll out.

Let your AI tools generate weekly reports: “Content drafts increased 3×, conversion improved by 12%, support responses halved.” Use that feedback to refine prompts, strategies, or abandon failures.

Use performance data to tune prompts or decision logic. Let your AI “learn” what styles / formats / workflows drive results—and bias its output accordingly.

Ultimately, your POS is where revenue happens. Map AI-driven changes (e.g. new video, ad creative) to actual sales uplift in your POS. M&M POS acts as your source of truth so you know what AI moves really matter.

AI is only as good as its impact. Don’t adopt for novelty—adopt to move metrics. Measure, iterate, and let performance drive your AI strategy.