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9 IT projects primed for machine learning

9 IT projects primed for machine learningMachine learning is fast becoming a reality for forward-thinking organizations. But for most businesses, the best way to take advantage of the capabilities of machine learning technologies remains something of a mystery. Still, the drumbeat to experiment keeps getting louder.

And the truth is, your competitors may already be laying the groundwork. IDC forecasts revenues for AI systems worldwide will almost double to US$12.5 billion this year, and keep growing at a similar rate until they hit US$46 billion in 2020. Some of that spending will go on hardware to run machine learning systems, but even if you don’t have the budget and the data scientists to build systems from scratch there are still plenty of tools and services that will let you use machine learning in practical ways that help your business.

Here are nine IT projects that almost any organization will find useful in getting started experimenting with machine learning technologies.

1. A customer service chatbot

If you have a list of frequently asked questions for customers to look up, you can turn that into a chatbot that can answer support questions using the Microsoft QnA Maker. It doesn’t have to be customer support, of course; you could create a bot to answer questions from new employees about HR benefits or how to contact the help desk.

Feed in the URL of your FAQ or upload spreadsheets and documents that have questions and answers and QnA Maker creates pairs of those questions and answers that you can review and train, and then call as an API. If you want to have a more interesting interface than just text replies, you can use the .NET SDK and the Microsoft Bot Framework to create a bot that shows pictures and rich content.

If you prefer the serverless approach, QnA Maker is one of the templates in the Azure Bot Service, so you can create a bot that works on email, GroupMe, Facebook Messenger, Kik, Skype, Slack, Microsoft Teams, Telegram, text/SMS and Twilio.

In the longer term, chatbots will evolve into intelligent agents more like Amazon Alexa and Microsoft Cortana. But rather than just answer individual questions, agents create a “goal-directed” conversation that works through the customer’s problem to help them solve it, which is what you need for ticket sales or diagnosing why a projector can’t connect. Microsoft has just added a customer care solution to Dynamics 365, in which a virtual agent suggests solutions, passes the customer on to human support, along with conversation details and the suggestions it made, if it can’t resolve the issue and learns what to do next time. HP, Macy’s and Microsoft’s own support service are already using this agent for online support.

2. Marketing automation and analytics

Marketing is often the first department to experiment with new technology, which is why marketing services like Adobe Marketing Cloud, Dynamics 365 and Salesforce are starting to offer machine learning predictions for everything from recommending related products for customers, to showing personalized search results, to classifying sales leads, to warning you when a deal is going cold, to finding alternate contacts at a potential customer company, even suggesting how and when to reach out to them. After all, predictive models for customer churn can help you with forecasting and planning.

If your marketing team isn’t already looking at these tools, this is a good way to apply machine learning directly to your bottom line. If they are, find out what’s working and look for other departments that could benefit from similar analytics. AXA is using a TensorFlow deep machine learning model with 70 variables to predict which customers are likely to have accidents that will cost the insurer more than $10,000, so it can optimize policy prices. Older models weren’t accurate enough to be useful, but with prediction accuracy improving from 40% to 78%, it might be good enough to consider when targeting potential customers.



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