AI has been capturing people's imaginations since sci-fi movies in the 80s–at least. Some folks say the even Ancient Greek dreamt of AIs, I mean what else is the Oracle of Delphi if not a personified Big Data prediction machine…
Jokes aside, Artificial Intelligence has been the promising technology that makes our life easier through technologies like Machine Learning or Artificial Intelligence (read their definition here). Every smartphone today has a voice-activated personal assistant feature that makes restaurant reservations, books movie tickets, or replies to your emails. Of course there is a big platform battle going on between Siri, Google Now and even Amazon’s Alexa; but that’s a topic for another time.
AI is experiencing a level of maturity beyond weather forecasts, which makes it of interest to the smarter business that might see it is an increasingly critical business tool. These are the main drivers of AI in the workplace:
a) Better insights into data
For many businesses making sense of the data they collect is one of the biggest challenges. Traditionally, dedicated analytics software was able to condense large amounts of data into indicators and charts which are easy to interpret and compare. Only problem is: The data is only historic.
Machine Learning however takes it upon itself to let algorithms find undiscovered correlations and obtain new insights into data. This can help surface opportunities or areas of concern. Other use cases include determining the best times to cross-sell or upsell a customer or when a customer may be at risk of leaving you, so you can intervene proactively and keep them happy. AI can also help judge the effectiveness of individual marketing channels, where previous methods were about as indicative as reading tea leaves.
b) Productivity and efficiency
A good chunk of our daily work is repetitive, i.e. they’re chores. Going through your inbox, checking lists, administering meetings etc. takes up a lot of time, that if assisted by an AI would free up time we could spend on the more creative aspects of our jobs. Machine Learning again can help by taking some of these chores off us.
One really cool example of this type of AI is scheduling meetings. X.ai is the name of a meeting schedule assistant you just add to your mail in CC and it negotiates suitable dates between you (and your calendar) and your business partners. All while appearing to be completely human through the use of Natural Language Processing (NLP).
Another example is email decluttering and spam detection. Right around the time Gmail became popular, spam started to spread like a wildfire. But with machine learning enabled spam filters they’ve become sophisticated enough to weed out most of the mails that can be classified as spam. Pre-sorting of emails has also become popular:
Gmail sorts all email into tabs for social updates, promotional mails, personal, etc. using again machine learning in the background. Outlook offers a “clutter” folder that learns which emails you frequently don’t read despite not being straight-forward spam and then moves them out of your inbox the next time.
With Microsoft, Amazon, Google, Facebook and IBM all having sizeable teams working on ever more sophisticated machine learning developments, it is safe to say that this type of AI is going to find more and more useful ways to impact our workday.
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