AI-augmented customer support
Absorbing growth without hiring, and shortening ramp-up
A 60-agent service centre. Real-time drafting assistance grounded in the internal knowledge base. The gain reads as recovered capacity, not headcount removed.
- Modelled yearly gain
- €335k – €503k
- Payback
- 2.1 to 3.2 months
- Over 3 years
- €825k to €1.33M net over 3 years
yearly capacity recovered
Sixty agents handle customer requests, at roughly 1,500 productive hours per person per year and an average loaded cost of €45/h. This is the best-documented use case of all: a study published in the Quarterly Journal of Economics tracked nearly 5,000 agents at a Fortune 500 software company through the staged rollout of a generative assistant, with a control group. That is a measurement, not a vendor estimate.
- Real-time response suggestions grounded in product documentation and resolved-ticket history
- Automatic conversation summaries and pre-filled interaction records
- Semantic search across the knowledge base, replacing keyword search
- Detection of recurring requests to feed the knowledge base and self-service
Everything on the table
- Service centre headcount
- 60 agents
- Productive hours per agent
- 1,500 h/year → 90,000 h of capacity
- Loaded hourly cost
- €45/h
- Productivity gain applied
- +13.8% (the value measured in the source study)
- Effective adoption rate (haircut)
- 60% / 80% / 90% by scenario
- Year 1 investment (integration, knowledge base, guardrails, training)
- €90,000
- Annual running cost in later years
- €45,000/year
Three scenarios, not one number
| Parameter | Conservative | Central | High |
|---|---|---|---|
| Adoption rate applied | 60% | 80% | 90% |
| Capacity recovered | 7,452 h | 9,936 h | 11,178 h |
| Full-time equivalent | 5.0 FTE | 6.6 FTE | 7.5 FTE |
| Yearly value | €335k | €447k | €503k |
| Payback period | 3.2 months | 2.4 months | 2.1 months |
| Net cumulative gain over 3 years | €825k | €1,161k | €1,329k |
Where the ranges come from
- Recovered capacity is only an economic gain if it is redeployed: absorbing volume growth, bringing an outsourced flow back in house, opening a channel or extending service hours. Decide that up front or the gain evaporates.
- The source study covers English-language software support with rich documentation. On highly specialised technical support, or in a regulated domain where every answer commits the firm, the expected gain is materially lower.
- Answer quality depends entirely on the knowledge base. Outdated documentation produces wrong answers faster than before.
- The gain concentrates on junior profiles. On a very experienced, stable team the measured effect is close to zero.
- An up-to-date knowledge base with a named owner responsible for maintaining it.
- An explicit decision, taken before rollout, on what the freed capacity is used for.
- Guardrails on sensitive topics: contractual commitments, goodwill gestures, personal data — the assistant proposes, the agent decides.
- Before/after measurement against a control group. Without one, the observed effect cannot be separated from seasonality.
What these scenarios are, and what they are not
These are models, built for illustration on public sector benchmarks. They are not results observed at Cardan-AI clients, and they constitute no commitment as to outcome. The improvement rates come from the studies cited; the choice of assumptions, the scope haircuts and the arithmetic are Cardan-AI's, and are shown in full so they can be challenged. Transposed to your organisation, these orders of magnitude can vary widely — in both directions.
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