Automation in technology; automate automation

Today, automation is typically done by individuals based on their beliefs and ideas about what would be the best way to mechanize and simplify a solution for many users. It's based on experience, on feedback from customers and users, on results from usability tests, and from results of various operational analyses.

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But here's the thing—all of those are finite in number and done at a specific time in a solution's life cycle. And then, a human interpretation of the data is turned into human-designed system policies and rules for how the solutions should react in defined situations.

 

Automate automation

What if the system had all the automation capabilities itself? What if it's data-centric in its design and can self-instrument, self-optimize, and self-automate its behavior and design? What if it can collect an infinite amount of usage and user behavior data and make use of unbiased deep learning and artificial intelligence to change the way it acts and its user interfaces? That would provide an end state with no end—in which every system continuously and automatically evolves and improves.

 

Listen to our Cloud Conversation podcast on automation

We are at "automate"—the seventh in the series of seven key principles that you have to understand to succeed in the digital era, explained by Jason Hoffman, Head of Technology, Business Area Digital Services at Ericsson.

 

Link to Podcast

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