Earlier version · Where enterprise AI value actually lands
Where does enterprise AI value go?
A successful automated case can be cheap. The business case still depends on the share of cases using the system, how often exceptions occur, and how much work those exceptions create.
Before historical examples and clearer titles — preserved text. Read the current version.
The cheap answer is only part of the cost
I tend to ask who will do the work an AI system leaves behind. A wrong field, an ambiguous document or an unresolved exception still needs somebody’s time. A small inference bill can sit beside a large review queue.
Brynjolfsson, Li and Raymond’s 2023 NBER study examined 5,179 customer-support agents and found uneven productivity gains across workers. It is a useful warning against carrying one uplift across an organisation. The process and the people using the tool matter.
Try the operating model
All values are illustrative. Currency is USD; change the costs to fit your own model.
| Fully manual baseline | $20,000 |
|---|---|
| Unadopted cases: labour | $8,000 |
| Exception review: labour | $4,500 |
| AI usage | $600 |
| Fixed operating cost | $2,000 |
| Total with AI | $15,100 |
The same model can make or lose money
Consider 1,000 cases a month, 20 manual minutes per case and labour at $60 an hour. The manual baseline is $20,000. With 60% adoption, 25% of adopted cases needing 30 minutes of review, $1 AI usage per adopted case and $2,000 fixed monthly cost, the model gives $4,900 net benefit and 125 hours of released capacity.
Hold the volume and costs steady. At 85% adoption, 10% review and 20 review minutes, the benefit rises to $12,450. At 75% adoption, 60% review and 50 review minutes, it becomes a $10,250 loss and adds 125 hours of work. The difference comes from the work around the answer.
Fewer exceptions can still cost more
At $60 an hour, a 25% chance of a 30-minute review costs $7.50 per adopted case. In the starting scenario, that is more than the $1 AI usage cost. The useful question is how much attention each exception consumes.
Five percent needing two hours costs $6 per adopted case; twenty percent needing fifteen minutes costs $3. Automating the easy work can leave a smaller queue of harder cases. I would measure the spread of resolution times as well as the average.
More adoption scales the economics already present. If the value per adopted case is positive, adoption helps spread fixed costs. If it is negative, adoption grows the loss. A review bottleneck can also lengthen turnaround even when the average cost looks attractive.
Capacity needs somewhere useful to go
The 125 hours in the starting case are an operating choice. They could support more throughput, shorter turnaround or less overtime. Turning them into cash savings needs a staffing or demand decision.
For a stable, repetitive process with little review, a simple cost-per-case comparison may be enough. Before carrying that result elsewhere, I would check eligible-work adoption, intervention frequency and the time needed to resolve an exception. Those three observations tell me more than the price of a successful demonstration.
How the calculation works
Manual hours = monthly cases × manual minutes ÷ 60. Review hours = adopted cases × review rate × review minutes ÷ 60. Net benefit subtracts remaining manual labour, review labour, AI usage and fixed cost from the manual baseline.
Break-even adoption is fixed cost divided by monthly cases and the value per adopted case. With zero fixed cost and negative per-case value, only zero adoption breaks even; with zero fixed cost and zero per-case value, every adoption level breaks even. “Not reachable” means the model finds no break-even level between zero and 100%.
The calculation assumes constant volume, average labour rates and linear costs. Adopted cases without exceptions need no further handling in this model. Routine human handling across all adopted cases needs a separate cost estimate; review minutes apply only to cases counted as exceptions. Implementation, transition costs, undetected quality failures and changing demand need a separate estimate.
Sources and attribution
This is an original, self-published piece by Vihang Patel. The sources below support the referenced frameworks; fictional examples and personal judgments are identified in the text.