# Measuring ERP AI ROI After Go-Live > Source: https://netray.co/erp-help/cio-measuring-erp-ai-roi-after-go-live > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: ERP Help ## Short answer Measure ROI with three metric families - time saved on named tasks, error or rework reduction, and adoption depth - tracked against a pre-go-live baseline you captured before the tool launched. Most CIOs skip the baseline and then cannot prove anything six months later, so capture it in week one even if the AI tool is not fully ready. ## Kind cio ## System cio ## Erp ERP Strategy (any platform) ## Module IT Finance / Performance Measurement ## Query How do we measure ROI on our ERP AI project after go-live ## Alternate Queries - ERP AI ROI measurement - how to prove AI on ERP is working - post go-live metrics for ERP AI copilot - AI ERP project success metrics ## Applies To Any ERP AI copilot, chatbot or agent deployment, 30-180 days post go-live ## Steps ### How to build a measurement plan **Items:** - Capture a baseline before go-live: time to answer 10 representative questions the old way, error rates on the target task, and current adoption of any existing tools. - Pick 3-5 named tasks the AI tool is meant to speed up (e.g. 'find a customer's open order status', 'draft a PO from a requisition') rather than measuring 'usage' in the abstract. - Instrument query logs from day one so time-to-answer and query volume are measurable without manual surveys. - Track adoption depth, not just login counts: percentage of the target user group using the tool weekly, and how many of their queries are ERP-grounded versus generic. - Set a 30/60/90-day review cadence with the governance board, comparing against baseline, not against an arbitrary target picked before launch. - Separate hard savings (headcount hours redeployed, error costs avoided) from soft gains (faster onboarding, fewer escalations) and report both, labeled clearly. - Re-baseline once after 90 days - early numbers are inflated by novelty and depressed by unfamiliarity in roughly equal measure, so a 90-day number is more honest than a 30-day one. ## Sections ### Why the baseline is the step everyone skips **Body:** Most ERP AI ROI stories fail not because the tool underperformed but because nobody measured the 'before' state. Without knowing that a customer service rep spent 6 minutes finding order status in the ERP before the AI tool existed, a report that says '2 minutes now' has no weight in a budget conversation. Capture the baseline in the first week of the pilot, even informally with a stopwatch on ten real queries, before the tool goes live for real users. If the project is already past go-live with no baseline captured, the next-best option is a matched comparison: measure the same task performed by users who have not yet adopted the tool against those who have, over the same week. ### The three metric families that actually matter **Body:** Time saved on named tasks is the most defensible metric because it maps directly to labor cost. Pick tasks that are frequent and well-defined - order status lookups, PO drafting, report generation - not vague categories like 'general productivity'. Error or rework reduction matters more in finance and operations than time savings alone: a grounded AI tool that cuts wrong-customer-address shipments or miscoded AP invoices by even 10-15% often produces larger dollar savings than time saved, because rework costs compound across departments. Adoption depth is the leading indicator for both. A tool with high time-savings-per-query but only 8% weekly active usage among the target group has an adoption problem, not a value problem, and the fix is training or access, not more features. ### What to report to the board, and what to leave out **Body:** Report hard and soft numbers separately, and never blend a projected annualized saving into a 90-day actuals report - that conflation is the single fastest way to lose credibility with a finance-literate board. State the measured period clearly: '190 hours saved across 40 users in Q1, extrapolating to roughly 760 hours annualized if adoption holds' is honest; '760 hours saved annually' after one quarter is not. Include a short section on what did not work: use cases that were tried and dropped, queries the tool answered wrong, access requests that were denied. A ROI report with zero negative findings after 90 days of real usage reads as incomplete, not successful. ### Common measurement traps **Body:** Counting logins or 'queries run' as a success metric inflates results without proving value - a user asking the same question five times because the first four answers were unhelpful still counts as five queries in a naive dashboard. Track query-to-resolution instead: did the user get a usable answer without escalating to a human or the ERP itself. Another trap is measuring only the power users. If three enthusiastic early adopters generate glowing numbers while the other 27 licensed users never log in, the real adoption rate is 10%, not the aggregate time-saved figure the three power users produced. ## Pitfalls - Skipping the pre-go-live baseline and trying to reconstruct it from memory six months later. - Reporting annualized projections as if they were measured 90-day actuals. - Using login counts or query volume as the headline success metric instead of task completion and time saved. - Measuring only enthusiastic early adopters and extrapolating their results to the whole license pool. - Never reporting what did not work, which makes the eventual first real failure look like a surprise rather than a known risk. - Changing the measured task list every review cycle, which makes trend comparison impossible. ## Ai Angle Netray's ERPray ships with query logging by default, so time-to-answer, query-to-resolution rate and weekly active usage per team are available from day one rather than requiring a separate analytics build. That gives a CIO the raw data for an honest ROI report without instrumenting anything extra after go-live. ## Faq ### What is a realistic ROI timeline for an ERP AI copilot? **Answer:** Most measurable time savings appear within 30-60 days for well-scoped use cases like order status lookups or report generation. Error-reduction and rework savings typically take 90 days or more to show a clean trend, since they need enough transaction volume to separate signal from noise. ### What if we never captured a pre-go-live baseline? **Answer:** Use a matched comparison instead: measure the same task performed by adopters versus non-adopters in the current user base over the same period. It is less clean than a true before/after baseline but still defensible. ### Should ROI reporting include the cost of the AI project itself? **Answer:** Yes, always net out implementation, licensing and ongoing hosting or support costs against the measured savings. A gross savings number without cost context will not survive scrutiny from finance. ### How do we measure ROI on a tool with no direct time-saving use case, like an anomaly-detection agent? **Answer:** Measure avoided cost instead: count and value the incidents it caught (duplicate payments, pricing errors, stockouts) against a baseline rate from before the tool existed, using the same detection criteria a human review would have used. ### What adoption rate should worry a CIO? **Answer:** Weekly active usage under 20% of the licensed or target group after 90 days usually signals an access, training or trust problem worth investigating before renewing or expanding the deployment. ## Search terms erp ai roi, measure ai roi after go-live, erp ai success metrics, erp copilot roi measurement, post go-live ai metrics, ai erp project baseline, erp ai adoption metrics, proving ai value on erp, erp ai business case results, ai roi report ERP --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json