004THE MEASURED WIN
The AI Wins You Can Measure Are Table Stakes
A study of 5,172 support agents estimated roughly 15% more issues resolved per hour. That operating result leaves costs, competitive response and retained value to be tested.
THE FIELD RESULT
One firm resolved more issues per hour
- 5,172 customer-support agents at one firm
- Roughly 15% higher issues resolved per hour
- Staggered rollout; access was not randomly assigned
Brynjolfsson, Li and Raymond's final 2025 study estimates the effect using difference-in-differences. Agents could edit or ignore suggestions. The study supplies no estimate of rival adoption, price pass-through or durable competitive advantage.
A shared gain can still be worth buying
Faster support might reduce a queue, lower a price or raise the service customers expect. Those are possible responses, not findings of the study. A profitable gain need not be exclusive. But throughput alone does not justify deployment: quality, tool fees, integration, checking and upkeep belong in the operating case. The retention test asks who benefits over the chosen horizon.
THE SEAM
Better prediction changes which judgment pays
In a May 2018 working paper model, Agrawal, Gans and Goldfarb make better prediction a complement to judgment about hidden costs and a substitute for judgment about hidden opportunities, under specified payoff and information assumptions. It does not make every form of judgment more valuable. In the support workflow, predicting a likely resolution leaves the cost of a mistaken commitment to be assessed. The prediction–decision distinction identifies that question; it supplies no proof of scarce expertise.
TWO CORNERS
Ownership is only the start of a scarcity claim
Suppose rivals acquire the same support assistant. One firm uses a resolution history that competitors can reproduce; another claims exclusive access to a useful history. The second is a candidate advantage only if the input improves results, stays hard to replace and lets the firm retain a benefit after costs. These are hypothetical cases. Owning the history proves none of those conditions.
Rare and consequential is not the same as defensible
Kahneman and Klein (2009) identify two conditions for skilled intuition: an environment with learnable regularities and an adequate opportunity to learn them through practice and feedback (pp. 520, 524–525). Confidence is an unreliable test. Before treating an escalation team's judgment as an advantage, examine the decisions it has learned from and the outcomes it actually sees.
RARE FOR YOU
A retrieved precedent still needs a comparison
An unfamiliar support escalation may have precedents elsewhere. Traceable cases can supply candidates for comparison; model familiarity establishes no frequency. Check inclusion, outcomes, observation periods and relevant differences. A usable reference class may be missing. Rare for you, common for the corpus keeps that boundary explicit. Private context and responsibility for the decision establish neither expertise nor scarcity.
TWO MAPS
Gross value and retained value are different maps
Keep the support productivity result fixed. To estimate retained value, add operating costs and assumptions about competitors, customer terms and the horizon. These inputs require evidence; they are not percentages to subtract from throughput. The maps need not invert. Differentiated value develops the accounting; selection must carry its uncertainty into the funding decision.
POINT IT THERE
Make the proposal answer four questions
- Metric: what moves, for whom, and over what horizon?
- Diffusion: how soon can rivals match the capability?
- Complement: what do we control, and what evidence shows it remains scarce?
- Capture: who receives the benefit after competition, verification, and upkeep?
Mark each answer as measured, assumed or unknown. A sound operating case can justify a shared capability; an exclusive advantage needs further evidence. Test the cost of verifying the work before scaling.