Is Machine Learning taking Cloud Productivity Suites to the next level?

Media coverage of productivity software from Microsoft and Google has been hammering home that artificial intelligence (AI) is here to make our lives easier. But with the likes of Hollywood and Stephen Hawking touting it as the harbinger of humanity’s demise, AI is often the recipient of heavy doses of skepticism. It’s time to set the record straight about this thing we call machine learning.

AI is a vision of a computer that can create new and original thought. Although we haven’t created a computer that could create a unique image unassisted, we have created computers that could recognise an image, and to a certain extent, it’s artistic quality. AI falls into the category of the former and machine learning the latter, so the debate between Hollywood and IT professionals is actually just one big misunderstanding.

What is machine learning?

Computers operate in a reality of 1s and 0s. So we can’t exactly ‘reward’ them for doing something correctly — especially if it’s a more nuanced task like responding to a question. So in order to create more helpful computing programs, we need to codify the good from the bad. While that has always been possible, processing power and storage capacity limited our ability to feed data analysis software enough information to reach a tipping point wherein reliable trends could be realised.

As computing power increases however, we’ve begun to create programs that autonomously convert new data into increasingly helpful suggestions. Probably the best example of this is By analysing trends regarding what people purchased after, say, a new CPU, it’s relatively easy for machine learning algorithms to discover that the most common purchase after that is a motherboard. Once you set the parameters and definitions of how to achieve that conclusion, the machine can be ‘taught’ to continue applying that logic every day, learning more about purchase habits and progressively revising the most appropriate product under a ‘suggested purchases’ section.


Before ever opening a new document, you first need to find a place to store it. Just a few short years ago, files were stored in one location, and if you had trouble finding them, you could always search by the file name. But for better or worse, the number of files an average employee shepherds, and their locations, are growing exponentially. Google and Microsoft are turning their machine learning algorithms loose on storage and search functions to make organisation easy.

In bygone years you were forced to cycle through a dozen different searches with variations on what you incorrectly remembered a filename to be. Drive, Gmail, Calendar, and several other G Suite applications allow you to search for concepts. Google achieves this by analysing countless searches and comparing the language of successful vs unsuccessful outcomes. For instance, if you don’t remember the name of a document, you can simply search ‘article written by me about two weeks ago.’

Although Microsoft has a number of similar features, its focus on data governance is where it really shines. With mountains of customer data reaching new heights every day, and government regulations requiring more and more safeguards to stay on top of everything, sifting through it all would be impossible without machine learning. Microsoft compares ever increasing data sources to reconcile records that contain the same data types written in different formats. That way, your Office 365 solution can recognise credit card numbers with or without dashes, cvv numbers, expiration dates, anything.


Fortunately for us, the magic of machine learning extends far beyond just file storage. Through various methods of data collection, Office 365 and G Suite have actually begun to recognise what is most visually appealing. Obviously, visual appeal is based entirely on subjective opinions, but with billions of clicks, likes, and shares per day, software providers have a good idea of what makes digital templates popular.

The best example of this is executed in almost the exact same fashion in our two cloud productivity suites, both of which have presentation design applications. In these apps, you have the option to dump text, images, and content onto the page like puzzle pieces from their box. But with Google or Microsoft at your disposal, you needn’t spend hours sussing out the edge pieces from the text pieces, and the click of a button arranges everything in a way that machines have ‘learned’ to be the most appealing.

The same principle applies to your text and data. Your word processing software may suggest links and sources based on the title of your document, your spreadsheet may automatically graph your geographic data in a map, or your email provider may suggest responses to an important email based on your habits and previous contacts. These all take the monotony out of your daily tasks so you have more time to get down to the truly unique parts of your workflow.

Teamwork/Time management

Even before the age of productivity software, collaboration was the fulcrum of content creation. Calendar appointments, conference calls, and project goals aren’t new concepts, but applying ‘AI’ to them definitely is. You may not notice that you always manage your calendar items in the morning and usually schedule your appointments after lunch, but productivity software employing machine learning does.

Beyond that, users can also analyse their entire workflow. Machine learning techniques in Microsoft’s MyAnalytics help users sniff out their most productive hours of the day, set project goals, and suggest project collaborators based on past successes. This all happens by monitoring as it is created.

…And everything else

Productivity software isn’t the only thing reaping these rewards. Every day you probably encounter machine learning without even realising it in the form of searches, website advertisements, and recommended media content. And the truth is, there’s nothing stopping you from creating your own high-end analytics. Both Google Cloud Platform and Microsoft Azure make these tools as easy as dragging and dropping data elements into a customised solution. All it takes is a little entrepreneurial ingenuity and a helping hand from one of our engineers.

If you’ve heard that machine learning doesn’t have staying power, you certainly heard wrong. Both Google and Microsoft have been steadily rolling out more and more of these features for years, and it’s time that you start taking advantage of them. For an end to tedious emails, lackluster presentations, and organisational entropy, schedule a free consultation with one of our cloud experts today.

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