--- title: Savings metrics description: LEAP tracks two types of savings metrics, projected and actual, across all automation outcome types including ServiceNow playbooks, Ansible playbooks, KB articles, and problem records. locale: en-US canonical_url: https://www.servicenow.com/docs/r/australia/it-operations-management/aiops-leap-learning-enhanced-automation-playbooks/understanding-savings-metrics.html release: australia product: AIOps LEAP \(Learning-Enhanced Automation Playbooks\) classification: aiops-leap-learning-enhanced-automation-playbooks topic_type: concept last_updated: "2026-08-13" reading_time_minutes: 5 keywords: [savings metrics, LEAP, automation, Ansible savings, savings by outcome type] breadcrumb: [Explore, Learning Enhanced Automation Platform \(LEAP\), ITOM Visibility, IT Operations Management] --- # Savings metrics LEAP tracks two types of savings metrics, projected and actual, across all automation outcome types including ServiceNow playbooks, Ansible playbooks, KB articles, and problem records. ## LEAP savings metrics overview When you use LEAP to identify and act on automation opportunities, the platform tracks savings at two stages of the workflow: - Projected savings — displayed on the LEAP home page *before* you automate, based on historical incident data. - Actual savings — recorded after automation executes, across all active outcome types including ServiceNow playbooks and Ansible playbook executions. Both metrics are important. Projected savings help you prioritize which opportunities to pursue. Actual savings help you measure the real return on your automation investment across all outcome types. ## Projected savings Projected savings represent the estimated value that could be realized if an automation opportunity is adopted. These figures appear on the LEAP home page and on individual automation opportunity cards. LEAP calculates projected savings using the following inputs from your historical incident data: - Historical incident volume \(number of incidents per month\) - Average manual resolution time \(mean time to resolve, or MTTR\) - Estimated automation rate - Cost per hour of IT labor The formula is: ``` Projected savings = incident volume × Avg. manual resolution time × Estimated automation rate × Cost per hour ``` Projected savings update dynamically as new incidents are ingested and clustered. Because they are based on estimates and historical patterns, they represent a potential value, not a guaranteed outcome. **Note:** The savings number displayed on the LEAP home page is a projected value. It indicates the potential savings if all identified automation opportunities were fully adopted. ## Actual savings Actual savings represent the realized value recorded when automation executes and resolves incidents. These figures reflect real, measured outcomes and accumulate over time. LEAP tracks actual savings separately for each outcome type. LEAP calculates actual savings per outcome type using the following inputs: - Number of incidents resolved by the playbook or automation - Time saved per automated resolution \(manual time minus automated time\) - Cost per hour of IT labor The formula is: ``` Actual savings = Sum of (Time saved per automated resolution × Cost per hour) across all completed executions for an outcome type ``` Actual savings are visible in the LEAP value dashboard. The **Overview** tab shows total savings from playbook executions in the **Savings by playbooks** section. The **Ansible playbooks** tab shows cost savings and agent-hours saved specifically from Ansible-driven resolutions. ## Projected and actual savings comparison The following table summarizes the key differences between the two savings metrics. | |Projected savings|Actual savings| |---|-----------------|--------------| |Definition|Estimated value based on historical incident data, displayed on the LEAP home page before automation executes.|Realized value recorded when automation executes and resolves incidents, tracked separately per outcome type \(ServiceNow playbooks, Ansible playbooks\).| |Data source|Historical incident volume, MTTR, and manual effort estimates.|Playbook and Ansible execution logs, time saved per automated resolution, and resolution outcomes per outcome type.| |Where displayed|LEAP home page and automation opportunity cards.|**Savings by playbooks** section on the value dashboard **Overview** tab; **Cost savings via Ansible** and **Total agent-hours saved via Ansible** on the **Ansible playbooks** tab.| |When calculated|Continuously, as new incidents are ingested and clustered.|Per resolution, upon successful execution.| |Formula|Incident volume × Avg. manual resolution time × Estimated automation rate × Cost per hour.|Sum of \(Time saved per automated resolution × Cost per hour\) across completed executions for an outcome type.| ## Use case: Password reset automation This walkthrough follows a realistic scenario to show how projected and actual savings are calculated at each stage of the LEAP workflow. **The scenario:** Your IT service desk receives approximately 1,200 password reset requests every month. Each request currently takes an agent about 15 minutes to resolve manually. Your organization values IT labor at $50 per hour. Stage 1: LEAP analyzes your incident history. LEAP ingests your historical incident data and identifies a cluster of 1,200 recurring password reset incidents per month. It recognizes this as an automation opportunity and displays it on the home page with a priority level based on the automation priority matrix. Stage 2: LEAP calculates projected savings. Using historical data, LEAP estimates how much time and money your team could save if this incident type were automated. The calculation: 1,200 incidents × 15 minutes each = 18,000 minutes = 300 hours per month. At $50 per hour, that equals **$15,000 per month** or **$180,000 per year** in projected savings. The LEAP home page displays *"Projected savings: $15,000/month"* on the automation opportunity card. This is an estimate based on historical patterns. Stage 3: You create and activate a playbook. Based on the automation opportunity, LEAP generates resolution steps for password resets. You review them, create a playbook, and activate it. From this point forward, incoming password reset incidents that match the cluster are handled by the playbook automatically. Stage 4: Playbook runs and LEAP records actual savings. Over the next month, the playbook executes against real incidents. LEAP measures the actual time saved for each automated resolution. - Coverage: The playbook successfully automates 1,040 of the 1,200 incidents \(87%\). The remaining 160 incidents require manual handling due to edge cases such as locked accounts, expired tokens, or multi-factor authentication issues. - Time per automated incident: Each automated resolution takes about 2 minutes \(versus 15 minutes manually\), saving 13 minutes per incident. - Actual calculation: 1,040 automated incidents × 13 minutes saved = 13,520 minutes = 225 hours. At $50 per hour = **$11,267 actual savings** for the month. Stage 5: Compare projected and actual savings. After one month of operation, you can compare the two numbers side by side to understand the real impact of automation. |Metric|Projected \(before automation\)|Actual \(after automation\)| |------|-------------------------------|---------------------------| |Time saved per incident|15 minutes \(estimated\)|13 minutes \(measured\)| |Monthly time savings|300 hours|225 hours| |Monthly cost savings|$15,000|$11,267| |Annual cost savings|$180,000|$135,204| ## Why projected and actual savings differ A gap between projected and actual savings is normal and expected. The most common reasons include: - Automation coverage: Not every incident matches the playbook. Edge cases, exceptions, and unusual incident variations still require manual handling. - Processing overhead: Automated resolutions are fast but not instant. The playbook takes some time per incident rather than zero. - Ramp-up period: Early in deployment, coverage is typically lower. As playbooks are refined and edge cases are addressed, actual savings trend closer to projected values over time. **Tip:** Over time, as playbooks are refined and coverage expands, actual savings typically trend closer to projected values. Regularly review your resolution reports to track this improvement. Projected savings tell you the potential value of an automation opportunity and help you prioritize which opportunities to pursue. Actual savings tell you the real value being delivered and help you measure return on investment. Both metrics are important, projected savings guide your decisions, and actual savings validate them.