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Automation Decisions- Automation is great — when it is used correctly. Properly applied, automation can produce faster results, increase staff productivity and reduce the noise stemming from a growing number of large data sets.
Driven by chains of simple rules and advanced machine learning technology, tasks such as employee onboarding, reporting, business intelligence and cloud migration are being automated. And, with greater adoption of machine learning and automation, organizations move closer to the point at which they will be ready to embrace future advances in artificial intelligence that will help guide critical business functions. Think of this like a child crawling, then walking and finally running. Businesses today are crawling — but with growing confidence!
However, neither rule-based or machine-learning powered automation is an “implement and forget” solution; rules must be updated and refined, and machine learning algorithms have to be constantly fed data and individually coded to fit the bespoke needs of an organization and use case. This reality represents a serious risk to any organization that rushes in: embracing automation without fully considering its implications and follow-on effects. It’s important to think about the conditions under which a set of rules or a given algorithm will act appropriately.
Before Automating: Know Your Business
The first and most important step of implementing automation into any business is understanding what functions should never be automated. The goal of automation is to decrease time-consuming work for humans. Painful as it is to admit, automation is not about kicking back and relaxing while the machine works unsupervised. In fact, this is how organizations open themselves up to potentially crippling mishaps and attacks. Automation requires substantial maturity and is ineffective when pursued in lieu of developing and actioning a limited number of well-defined manual processes first.
One rule of thumb is that automated tasks should never be those that employees are unfamiliar with or cannot complete themselves. It goes without saying that if the humans do not understand the task at hand, there is no way for them to ensure the task is being completed correctly by the machine, or that the results are expected or predicted.