Define the metric before you build the feature. The most common mistake is deploying AI and then trying to figure out what success looks like. For cost-saving AI (automation, process optimization), measure time saved multiplied by labor cost, minus the AI operating cost.
For revenue-generating AI (personalization, recommendations), measure incremental revenue attributable to the AI feature using A/B tests. For quality-improving AI (fraud detection, content moderation), measure error rate reduction and its downstream financial impact. The key is being honest about attribution — AI rarely works in isolation, so measure the system improvement, not just the model's accuracy score.
This question reflects common advisory themes. It is editorially curated, not sourced from individual conversations.
Related questions
What is the hidden cost of AI that most enterprises miss?
The model inference cost gets all the attention, but it's often the smallest line item. The real costs hide in the supporting infrastructure: data pip…
How should we budget for generative AI when costs are so unpredictable?
Stop budgeting AI like infrastructure and start budgeting it like R&D. Traditional cloud costs are relatively predictable — you provision capacity and…
What is Jevons Paradox and why does it matter for AI costs?
Jevons Paradox observes that when a resource becomes more efficient, total consumption often increases rather than decreases. In AI, this plays out cl…
Should we build or buy our AI capabilities?
It depends on whether AI is your product or your tool. If AI is core to your competitive advantage — your recommendation engine, your fraud detection,…
Spending more than you should?
Let's find where your cloud and AI spend can work harder.
Get Started