Generative AI can be expensive at scale. VP and C-level leaders need a clear view of cost drivers and a framework for ROI to make sound investment and prioritization decisions. Without that, you'll either underinvest and miss the upside or overinvest in use cases that don't pay back. Getting the economics right is a core part of technical leadership in the gen AI era.
Cost drivers
Model API or inference cost, engineering and platform build, data and tooling, and risk/compliance. Break down by use case: cost per query, per user, per business outcome. Track trends as models and efficiency improve. As model prices drop and you add caching and optimization, your unit economics should improve; if they don't, dig into why (e.g., prompt growth, model choice, or inefficient usage patterns).
Don't forget hidden costs: engineering time to build and maintain integrations, evaluation and safety pipelines, and compliance and legal review. Allocate these in your cost model so that the full picture is visible. Share cost dashboards with product and eng so that teams see the impact of their choices and can optimize.
ROI frameworks
Map use cases to revenue (e.g., conversion, retention), cost savings (e.g., support, ops), or strategic value (e.g., differentiation, time to market). Require hypotheses and measurement plans before major investment; kill or pivot when data doesn't support. For revenue and cost savings, define the metric you'll move (e.g., support tickets deflected, conversion rate) and the baseline; run controlled experiments where possible so you can attribute impact to the AI intervention.
Strategic value is harder to quantify but still needs discipline. Define what "strategic" means: market position, capability that unlocks future revenue, or talent and brand. Set a time horizon and review criteria so you can reassess; "strategic" shouldn't mean "we'll figure out the ROI later" forever. Require at least a narrative and a review date for every significant investment.
When to double down or pull back
Double down where unit economics improve with scale, user value is clear, and strategic importance is high. Pull back where costs grow faster than value, quality is inconsistent, or the use case is undifferentiated. Make the criteria explicit so that the team knows what "good" looks like and when to pivot. Run regular business reviews where you look at cost, usage, and outcomes together; use that forum to decide what to scale, what to optimize, and what to sunset.
Be willing to reallocate. The gen AI landscape is moving fast; a use case that looked promising six months ago may be commoditized or superseded. Keep a portion of your budget and capacity for experimentation, but don't let "we've always done it this way" keep you in a losing bet. The best leaders course-correct quickly when the data tells them to.
Treat gen AI like any major platform bet: define success, measure relentlessly, and be willing to reallocate when the numbers tell the story.