Nearly nine in ten organizations now use AI in at least one business function, yet only 37% can attribute any EBIT impact to it, a share that has barely moved in a year.

In most cases the models perform as specified. But too often the specification itself fails: teams build against a working summary of the process, and the exceptions nobody described surface after go-live, at production cost.

Leaders realizing meaningful outcomes from AI do the design work first. Nearly three quarters of them report redesigning workflows around AI, against roughly a quarter of their peers. That redesign depends on a validated map of how your work runs today: where decisions fork, where people apply judgment, and where exceptions route.

This guide sets out how to decide where AI belongs in your operation, starting from how to effectively map how work gets done.

Inside you’ll learn how to:

• Test the robustness of your documentation and process flows
• Surface the decision points, business rules, and exceptions that discovery tools and planning sessions miss
• Allocate each step to automation, augmentation, people, or removal before you open a vendor conversation
• Avoid the design failures behind an AI project failure rate that RAND puts above 80%

Get the Guide

Download The Map Before the Machine guide to see how leading organizations put AI where it wins and people where it matters.