Wipro Demands Forensic Spend Tracking On Every AI Use Case. Averaging Is For Lazy Analysts.

Jayashree Arunkumar of Wipro argues that AI spend must be mapped with forensic precision to every individual use case, including frontier model iteration. She gives a concrete example: when evaluation improves and you switch from Gemini 2.5 Flash to Opus 4.7, pricing updates must occur from the exact switch point. Not averaged later. Not generalized across the portfolio. Not recognized only after production. AI projects should either increase revenue or decrease costs, ideally both, with revenue impact being clearest when the AI project results in a new product line or SKU.

This illustrates the principle of marginal cost attribution. The mechanism is straightforward: every unit of AI spend must be tied to a specific unit of business value at the exact moment of consumption. The mental model here is that averages obscure signal. If you switch models mid-quarter and only update pricing retroactively, you have corrupted your unit economics. Precision in measurement is not pedantry. It is the difference between knowing whether your AI investment generates value and merely hoping it does.

Jayashree Arunkumar, writing from her position at Wipro, lays out a framework for tech leaders to connect AI cost to measurable business value. She emphasizes that revenue impact is easiest to measure when the right tools are in place and when AI projects produce new product lines or SKUs, with marketing projects measured by metrics like lead increase.