20 ways to measure ROI from AI initiatives
Adoption of artificial intelligence seems to be happening at, well, the speed of AI. Instant everything doesn’t always translate to instant return on investment. Jumping on the AI bandwagon without knowing what outcomes you want and how you plan to measure them makes for much noise without any real traction. So, how can you tell if all the new AI tools you’ve added or want to amount to sound…
Measuring the return on investment (ROI) from artificial intelligence (AI) initiatives can be challenging, but it is crucial for ensuring that AI investments are worthwhile. Here are 20 ways to measure ROI from AI initiatives, as shared by members of the Fast Company Impact Council:
1. Focus on specific problems with sustainable costs. The real benchmark for AI is whether it delivers measurable outcomes for specific problems at a reasonable cost. Internal AI applications and dashboards can be cheaper, faster, and more adaptable than SaaS solutions.
2. Break initiatives into four stages: advanced, maturing, emerging, and nascent. Advanced programs have clear outcomes and are scaling, while mature efforts show business outcomes but are not at full potential. Emerging initiatives have a clear outcome but no measurable impact yet, and nascent efforts are mostly individual experiments.
3. Focus on standard business outcomes rather than AI usage. Measure high-impact use cases that augment teams, speed up innovation, and tighten security.
4. Look at quality and reuse potential rather than just usage volume. Ensure that work meets quality standards and can be reused by the next team to achieve better results faster.
5. Consider safety and privacy impacts, especially when AI involves children. Weigh every AI use against safety and privacy concerns, not just reach.
6. Measure engagement and time saved in real time. Focus on outcomes that matter most to your business, such as engagement and time saved. The real value comes from what people do with that time.
7. Examine the relationship between revenue and operating costs. If revenue grows faster than operating expenses, it indicates that AI is working. AI should handle repetitive tasks so people can focus on judgment-requiring work.
8. Don't rely on AI tokens. Instead, focus on the productivity gained and how much more "good" work can be completed in the same time frame.
9. Measure ROI by agreeing on the business outcome you're trying to improve and the metrics you'll use to evaluate success. Track those results over time to focus on what's working and where adjustments are needed.
10. Consider the relative improvement of outcomes rather than absolute dollar value. The relative improvement matters more than the absolute value in determining whether AI creates value for the business.
11. Assess how consumers interact with AI-powered experiences designed to personalize them.
Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
