The first step to deciding if a process can be automated is to look at how often it is repeated. Frequent and redundant tasks are ideal candidates for automation as automation can save time and resources that would otherwise be spent on monotonous activities. Automation allows employees to focus on more complex and intellectually challenging tasks, making them a greater asset to the company.
The quantification of this redundance can be done through the evaluation of the real needs of every task. This involves understanding how often each task is performed, at what intervals and the resources required for its execution. If the frequency is high and consistent, then it's wise to consider automation.
Complexity is another factor to be considered when determining whether a process can be automated. Simple, straightforward tasks with clear inputs and outputs are typically easier to automate than tasks that require human judgement, creativity, or understanding of abstract concepts.
However, it's important to mention that complexity is relative to the capabilities of the automation tools and technologies at one's disposal. Advancements in Machine Learning and Artificial Intelligence have expanded the realm of automation capabilities, enabling automation to tackle complex tasks that could previously only be handled by humans.
Automation reduces human error. A process that is prone to manual mistakes can often be automated to ensure consistency and accuracy. In manual processes, a higher error rate can lead to significant losses in terms of both time and money. Hence, if a task involves handling crucial data or repetitive precision tasks, automation is a key player.
However, tasks with a low margin for errors must be automated judiciously. The automation system must be thoroughly tested and verified so that it does not introduce new errors or cause much bigger problems than it is supposed to solve.
In conclusion, there's no one-size-fits-all answer to "should this process be automated". Each case is unique and requires thorough investigation and trial. However, by focusing on repetitiveness, complexity, and error potential, you can start to identify excellent automation candidates in your workspace.