AI Employee ROI Benchmark 2026.
Measured outcomes from DigiAI installations, with the method used to produce each one. Every figure traces to a published case study. No modelled or projected numbers appear on this page.
Citation: DigiAI.pro, “AI Employee ROI Benchmark 2026”. https://digiai.pro/ai-employee-roi-benchmark — free to cite with attribution and a link.
Compliance triage installation: classification, risk scoring and routing of inbound regulatory correspondence.
Source: Compliance triage →Same installation. Measured as median time from item received to item routed to the responsible officer.
Source: Compliance triage →Share of items routed correctly on first pass, sampled against reviewer decisions.
Source: Compliance triage →Proposal pipeline installation drawing on 12 years of indexed engagement history, with 100% citation coverage.
Source: Proposal pipeline →Proposals produced per period after installation, at unchanged headcount.
Source: Proposal pipeline →Conversational intake installation: share of started intakes finished without staff intervention.
Source: Conversational intake →Same installation, after intake-aware reminders and rescheduling were handled by the AI Employee.
Source: Conversational intake →Conversational intake installation, alongside a +39 point shift in net promoter score.
Source: Conversational intake →How these are calculated
Baseline before install
Every engagement starts with two weeks of measurement on the target process: volume, median handling time, response time and completion rate. Nothing is claimed against a remembered baseline.
Same measure after install
Post-install figures use the identical definition and data source as the baseline. Where a definition had to change, the metric is excluded rather than restated.
Recovered hours, not replaced people
Hours are counted as time no longer spent on the process by any person, at the volumes observed. They are not converted into redundancies, and we do not model a headcount saving the client has not made.
Accuracy is sampled, not asserted
Routing and classification accuracy is measured by re-scoring a sample of agent decisions against a human reviewer on the same items.
What is excluded
No projected or modelled figures appear on this page. Every number comes from a named installation with a published case study. Client identities are withheld under engagement terms.
Apply the same method to your business with the revenue leak calculator, grade your systems on the AI Maturity Index, or read the full case studies.