The idea of artificial intelligence (AI) has been around for thousands of years, dating back to some of the earliest Greek myths. Those early stories show our infatuation with the concept that we could imbue machines with that most human of qualities: independent thought.
Like many of humanity's greatest technical achievements, the quest to create AI is fundamentally about making our lives easier.
We are now entering an era where the reality of AI is beginning to catch up with the myths and science fiction stories of our youth. Advances in the area of machine learning (ML) and natural language processing (NLP) have resulted in devices and applications that we interact with daily.
Almost every smartphone today contains a digital assistant that understands spoken language. These digital assistants are able to understand our likes and dislikes and provide useful information that simplifies our daily lives and gives us instant access to a huge library of knowledge.
AI has much broader utility than just simplifying how we check the weather forecast or receiving reminders of our tasks for the day. AI is becoming a critical business tool, incorporated into an ever-increasing number of business applications.
Since the dawn of the computer age, businesses have generated increasingly larger volumes of data. One of the biggest business challenges today is how to understand and make sense of all the data that an organization collects.
Historically, companies used analytics software to compress large volumes of data into easily understood charts and graphs. Unfortunately, these charts and graphs only provide a picture of past activities. They don’t show what activity causes sales to increase or customers to leave.
Machine learning algorithms are adept at sorting through large data sets to find correlations and provide valuable insights. These insights might show you where your organization is operating efficiently or where you‘re having problems, so you can focus your efforts on the right solution.
Example: Gainsight Supports Proactive Customer Relations
Gainsight's customer success product provides a good example of the power of machine learning at work. Using sophisticated machine learning algorithms, Gainsight’s software is able to tell the best time to upsell or cross-sell a customer. It can also predict when a customer is likely to leave, so your customer success team can speak to the customer before they decide to depart.
These insights allow organizations to be proactive in dealing with customers and can improve both sales and retention.
Example: BrightFunnel Points Marketers to What's Working
BrightFunnel focuses on leveraging predictive analytics to help marketing organizations understand how their activities are directly impacting sales. Its tools let marketers easily determine which campaigns and channels are most effective and can predict how changes to your marketing efforts will affect future sales.
In the past, organizations have struggled to understand the ROI for various marketing activities. Tools like BrightFunnel use AI to help identify correlations between marketing activities and sales results.
Example: Gigster Streamlines Development
Some companies are also leveraging artificial intelligence to provide insights into their own internal business processes. Gigster provides software development services to its customers. Customers can write up a plain language description of what software it needs and Gigster puts together a team of freelance developers and project managers to get the job done for a guaranteed flat rate.
Behind the scenes, Gigster uses an AI platform to understand the customer request and assemble a team with the necessary expertise to accomplish the task. The Gigster platform also can determine where parts of previously completed projects may be reused to help streamline development and eliminate uncertainty. Gigster continues to learn from each new job, so that future jobs become even more predictable.
Companies are also tapping AI to help improve employee efficiency. Our lives are filled with repetitive tasks — automating simple processes frees up more time for us to think up creative ideas. Office workers spend time every day sifting through email and scheduling meetings. Machine learning algorithms can offload some of these tasks and make our work lives more manageable.
The average office worker spends over a quarter of their workday dealing with email. With all the spam and miscellaneous marketing emails hitting our inboxes daily, it is becoming harder to identify legitimate email that requires our attention. This problem is only getting worse.
Example: Anti-Spam Tools and Email Clients
One of the earliest applications of machine learning in the workplace was the introduction of anti-spam tools. While the original tools lacked sophistication, they incorporated machine learning algorithms by the late 90s.
Today, almost every email application includes some sort of spam filter that relies on machine learning. These filters learn how to identify new spam threats almost as soon as they appear.
Beyond anti-spam tools, machine learning is being applied to automatically classify email. Gmail introduced the use of tabs to automatically sort email to prevent social and promotional email from mixing with emails received from individuals. Outlook analyzes your network of contacts to identify what email you should focus on and what email is just clutter.
Examples: X.ai and TripIt - Managing the Mundane
Mundane tasks fill our work lives. Whether it is coordinating schedules for a meeting, or putting together an itinerary for an upcoming trip, machine learning is used to dramatically simplify common business tasks.
X.ai provides a digital assistant to handle all the coordination needed to set up a meeting. By CCing firstname.lastname@example.org when coordinating times with one or more people, she will handle the back and forth with each individual to find the best time for getting together.
Frequent travelers know the tedium of tracking multiple travel reservations. TripIt understood this problem and made it easy to build a personalized travel itinerary.
Forward any reservation email from a hotel, airline or car rental company and TripIt’s machine learning software pulls out the relevant details to automatically build an itinerary, including local weather reports, travel maps and driving directions to the hotel. TripIt even alerts travelers when they're eligible for a refund due to ticket price changes.
AI is Making a Difference
Artificial Intelligence is significantly impacting our work lives. Machine learning manages and simplifies everyday tasks. Businesses are gaining new insights into how to better serve their customers and increase sales.
And these are just the tip of the iceberg. With the advent of sophisticated machine learning development platforms from Microsoft, IBM, Amazon and Google, AI will continue to expand into the workplace. It doesn’t take a sophisticated machine learning algorithm to make that prediction.
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