Start with a real baseline, not an assumption
The most common failure in AI ROI measurement is not having a clean "before" number to compare against. Before any AI deployment, capture average handle time, escalation/transfer rate, first-contact resolution rate, and fully loaded cost per contact for the specific queue or workflow being changed — not a company-wide average that will dilute the signal. Without this, any post-deployment number is unverifiable, no matter who is reporting it.
Handle time: measure the whole interaction, not just the bot portion
A deployment that shortens the automated portion of a conversation but increases the length of a subsequent human escalation has not necessarily improved handle time overall. Measure end-to-end interaction time — automated portion plus any escalation — against the pre-AI baseline for the same workflow, not just the piece the AI tool touched.
Escalation rate: track it alongside resolution, never alone
A falling escalation rate is only good news if resolution rate holds steady or improves alongside it (see the companion piece on containment vs. resolution). Track escalation rate and resolution/satisfaction metrics together; a falling escalation rate paired with flat or falling resolution usually means customers are getting stuck, not helped.
Cost per contact: include the full cost stack
A defensible cost-per-contact calculation includes platform/licensing cost, implementation and integration effort, ongoing maintenance and content upkeep (someone has to keep a rule-based bot’s decision trees or an embeddings knowledge base current), and any consumption-based AI usage charges — not just the headline per-seat or per-resolution price. Several vendors bundle "free" AI features that are metered or capacity-gated once usage grows past an entry tier; that consumption cost belongs in the calculation from day one, not discovered later.
Apply the framework to any vendor, including Voz360
This framework is deliberately vendor-neutral so it can be applied to any platform under evaluation. If evaluating Voz360 specifically: measure Answer Engine’s effect on handle time and resolution rate for the specific query types it is scoped to handle, and measure Context Retrieval’s effect on agent time-to-answer and suggestion-acceptance rate for the knowledge base it searches — using your own baseline, not a published benchmark, since none is claimed here.
Report a range, not a single number, until the baseline period is long enough
Early-deployment metrics are noisy — a single strong or weak week is not a trend. Report a range with a defined measurement window and sample size, and treat any single-number ROI claim (from a vendor or from an internal team) with the same skepticism you would apply to an unlabeled statistic anywhere else.
Can the vendor tell you — in one sentence — which of their AI capabilities are rule-based, which are generative, and which are still roadmap?