Data Literacy for Measuring AI & Analytics ROI Playbook
- Practitioner
- Intermediate
- Template Included
A framework for building data literacy specifically around measuring AI and analytics initiative ROI — establishing genuine counterfactual baselines, attributing value correctly amid multiple concurrent changes, and avoiding common ROI measurement pitfalls that overstate or understate an initiative's actual impact.
Isn't ROI measurement for AI and analytics initiatives just a
straightforward before-and-after comparison? A naive before-and-after comparison is a common but flawed approach — it fails to account for what would have happened anyway without the initiative (the counterfactual), and for other concurrent changes that might explain some or all of an observed improvement.
What's the most rigorous way to establish a genuine counterfactual
baseline for AI initiative ROI? Where feasible, a controlled comparison — a similar team, region, or time period without the AI initiative — provides a much stronger counterfactual than simply comparing performance before and after rollout across the board.
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