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Can AI solve information overload?

John Brandon | Jan. 4, 2016
In some ways, artificial intelligence – in the form of automated features within popular applications – is already helping us combat info-glut. Those small steps are leading inevitably to a future in which we’ll all rely on AI for daily assistance with mundane tasks.

Surprisingly, another good example of AI saving us time is Evernote. If you create a new note and start entering information, you can use a feature called Context. It automatically pulls in information from other notes you’ve added. If you have a shared notebook, you’ll see related notes from other users. And, the feature pulls in notes from Web sources and makes suggestions on sites that can help. 

These examples show how AI could evolve rapidly and even help us prioritize our day. According to the experts, this is the real goal of AI for productivity: To help us complete tasks and finish projects without having to focus so much on mundane activities. 

“Imagine when software can help you prioritize what you should do and, equally as important, should not be working on,” says Alan Lepofsky, a vice president and principal analyst at Constellation Research. “What if software could look at information across a variety of tools including collaboration apps, social media, news, weather, sales pipelines, customer support tickets and more, then help you prioritize your work?” 

Decision-making 

For IT leaders, AI can also help us make decisions better and faster. As Louis Rosenberg, the founder and CEO of Unanimous A.I. explained, there are already good examples of AI in the financial sector that can watch for patterns in the stock market and automatically initiate trades. In the medical field, software can look for patterns with symptoms and genetics and make a better diagnosis. Rosenberg says this could lead to a 50 percent savings in healthcare costs. 

Apps to help with digital overload

Several new apps help you cut through the digital noise by analyzing behaviors and reducing complexity. These are the most recent and best examples in the field:

  • IBM Verse
    IBM Verse is an experiment in machine learning, but it’s also a real product. The messaging app analyzes your conversations and automatically shows the most critical emails. It’s a good example of simplifying a complex process and presenting fewer options.
  • Salesforce IQ
    As with most AI programming that helps save time, Salesforce IQ automatically analyzes sales leads and highlights the ones that need your attention. The app does this by analyzing all of your activity including emails, your schedule and phone calls to determine where you should focus.
  • Gluru
    Gluru pulls together the files, emails and contacts for an upcoming appointment. It scans through services like Dropbox, Google, Evernote, and OneDrive. For example, if you need to re-read an email or have a file handy for the meeting, it will gather them in one place.

Of course, we are already seeing how this works with business intelligence dashboards like the one from Domo. It’s not technically AI, but the dashboards help us make better decisions because we can parse the data easier. We see a visual representation of quarterly sales or software uptime and can then react appropriately. Eventually, dashboards will include AI components that make automated decisions based on the collected data. 

 

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