- Envision the revenue, cost, productivity and other impacts of the AI projects you’re doing, plus new ones you believe will have a forceful impact.
- Gather data (internally, plus external research across companies) to benchmark today and measure the impact of your initiatives.
- Deliver initiatives, rinse and repeat.
For finances, you’ll want data on revenue growth linked to AI-augmented teams; sales per employee; reduction in labor, time, materials, etc.; customer acquisition/retention (including faster onboarding, less churn, etc.). For productivity, you’ll want task completion rates, error rates, process/project cycle times and throughput. For quality and innovation, you’ll want customer satisfaction, product/service quality and innovation rates, e.g., number of new ideas, new ideas implemented, features released, patents filed, etc.
For engagement, collaboration and skills/growth it’ll be engagement scores from Gallup Q12 surveys, Glint, etc.; net promoter score (for employees recommending your organization); job satisfaction; absenteeism; turnover; frequency and quality of human–AI interaction (self-reported and system logs); AI tool adoption and utilization rates; skills development using/alongside AI; and role changes/promotions linked to AI skill adoption.
Remember to collect and share qualitative data/stories that’ll soon become legends and part of your culture, as well as making the numbers feel “real” and personal.
