How to Measure Business Performance Before and After Automation

How to measure the business before automation

Before implementing automation, it’s important to capture the current state of your processes and define the baseline. This reflects how the business actually operates before any changes and serves as a reference point for comparing results later.

To gather this information, you can use existing reports, historical system data, process logs, and interviews with responsible team members. Common mistakes include incomplete data, inconsistent approaches to measuring metrics, and reliance on subjective assessments. The baseline should cover the key processes and metrics the company aims to improve through automation.

Key metrics to evaluate the impact of automation

Operational metrics:

• Process cycle time — the time it takes to complete a task from start to finish (cycle time) and from a client request to the delivered result (lead time).

• Number of manual steps — how many steps in a process are performed manually, highlighting opportunities to reduce routine work and minimize errors.

• SLA / response time — how quickly a process is completed according to established service standards.

• Process throughput — the number of tasks the system can handle within a given time period.

Financial metrics:

• Process cost — the total cost of completing a process or individual task, including resources, time, and materials.

• OPEX before and after automation — the company’s operational expenses.

• ROI / payback period — the ratio of net profit from an investment to its cost and the time it takes for the investment to pay off.

• Opportunity cost — an estimate of potential revenue or benefits the company missed due to inefficient or manual processes.

Quality and errors:

• Number of errors — the proportion of tasks that require correction or rework.

• Failure frequency — a measure of critical stoppages that disrupt process continuity.

• Deviation from standards — the number of violations of established procedures and process rules.

• Data quality — a measure of the completeness, accuracy, and uniqueness of information.

Human factor: 

• Employee workload — a measure of the number of tasks assigned to a single employee over a given period.

• Time on routine vs. value-added tasks — a measure of how work hours are divided between repetitive operations and tasks that create additional value.

• Dependence on key roles — a risk indicator showing how much processes rely on the expertise of individual employees.

• Onboarding speed — the time required for a new employee to become fully integrated into company processes.

Customer / partner experience:

• Request processing time — the duration from the moment a request is received to its completion. 

• CSAT / NPS — metrics for measuring customer or partner satisfaction and loyalty.

• Process transparency — a measure of how accessible information is regarding the status of requests, tasks, or processes in the system.

The first assessment should be conducted after implementation to capture the initial impact of automation, with a follow-up 3–6 months later once processes have stabilized. The observed results should reflect not only direct savings but also indirect benefits, such as reduced operational risks, increased scalability, and the ability to respond more quickly to changes. Ultimately, automation should be supported by data that demonstrate its long-term value to the business.

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