20 ways to measure ROI from AI initiatives

Useful takeaways from this story.

Adoption of artificial intelligence seems to be happening at, well, the speed of AI.

OUTCOMES FOR SPECIFIC PROBLEMS AT A SUSTAINABLE COST Bigger frontier models don't automatically create bigger business value.

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.

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The useful part

Adoption of artificial intelligence seems to be happening at, well, the speed of AI. 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 investment?

How it works

We asked members of the Fast Company Impact Council how they measure ROI on AI initiatives.
For advanced programs, they have clear outcomes like higher throughput and workflow velocity and are scaling.
Mature efforts show clear business outcomes but are not yet reaching full potential even at their current scale.
Here, 20 of them share what matters when deciding whether AI is worth the buzz or has fallen flat.
OUTCOMES FOR SPECIFIC PROBLEMS AT A SUSTAINABLE COST Bigger frontier models don't automatically create bigger business value.

What to take from it

The real benchmark for AI is whether it delivers measurable outcomes for specific problems at a sustainable cost. One area where we really see this playing out for our customers is in internal applications and dashboards. Many Software-as-a-Service solutions are being replaced by custom AI-built tooling.

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