Automation process enhances the productivity. If the results show it is not, then serious consideration should be given. Evaluate if the system is really in line with the automation objectives Has there been a lapse in the implementation process. Are the system handlers have clear idea of the automation process. Is there has been a breech of security in the system. Whatever is the cause, it should be addressed immediately and thoroughly.

Identify and optimize any inefficient processes within the automation. This might involve reconfiguring workflows or updating automation scripts.

If productivity metrics have plummeted after automation, it is important to address the issue quickly to get the system back on track. Talk to team members to understand their experiences with automation. Ask what problems they encountered, if there were difficulties in adapting or if they lack specific skills. Check whether staff have received adequate training on the use of new automated systems. If necessary, organize additional training sessions to improve skills and confidence in using new technologies. Addressing plummeting productivity after automation requires a systematic approach and open communication. With the right analysis and intervention, you can identify the causes and take effective measures to restore productivity.

Usually, the most common cause of slump in productivity post-deployment is attributed to defective change management. Either the users haven't been adequately trained to use the enhanced process, or there might be a resistance to the adoption. The latter is more difficult to address and fix but usually a phased approach may see some improvement in metrics.

Process mining is an effective technique in identifying the root causes of issues following automation. Process mining use data from event logs to visualize and analyze the actual workflow, to see how processes are operating in real time. By comparing the expected automated workflow with the actual one, process mining helps uncover bottlenecks and deviations that may not be visible otherwise. For e.g. if an automated approval process is causing delays, process mining can reveal where tasks are getting stuck or taking longer than anticipated.