Shift AI Learning Platform

Your AI Workflow Audit

A five-minute, 25-question diagnostic across 8 departments — Operations, Sales, Marketing, Customer Service, Finance, HR & People, Product & Engineering, and Leadership & Strategy. Every "5" requires a named artifact (DPA, eval scorecard, AI risk register, measured weekly active use), so this is an honest read on where your company actually sits — and exactly which department to harden first.

Department 1 of 8

~5 min total

Operations

Day-to-day workflows, data flow, and reporting

0 of 25 questions answered

How much of your company's AI-assisted work is captured and shared as team workflows, rather than staying inside one person's private AI habits?

1 = A few people are getting faster with private AI workflows, but their prompts, processes, and learnings are not visible or reusable by the rest of the team, 5 = High-value AI workflows are documented, shared, improved, and embedded into team systems so everyone benefits, not just the person who discovered them

What share of repetitive operational tasks (data entry, copy-paste, status updates, report generation) is AI-assisted or fully automated, and how is it measured?

1 = No measurement; we assume less than 10% is automated, 5 = Per-role weekly time-saved is tracked; >40% of repetitive work is AI-assisted with documented hours-saved per FTE

How well do your operational systems (CRM, ERP, ticketing, BI) share data automatically, in a form your AI tools can act on?

1 = Most data lives in silos, spreadsheets, or shared docs, 5 = A documented data layer (warehouse, lakehouse, or pipeline) exposes structured data to AI by API with named owners and refresh SLAs

How long does it take from "we need a new operational report" to a leader being able to see and act on it?

1 = Weeks; engineering or analyst bottleneck, 5 = Hours; self-serve or AI-assisted analytics is in production with eval coverage on AI-generated queries