“The system knows you're going to miss your forecast next week; it also knows that one of the things that might help you hit the forecast is pulling forward the marketing promotion that's being sent out. There's no reason why Cortana wouldn't proactively reach out to you and say ‘hey George, it looks like you're going to miss your forecast for sales next week and you've got these promotions lined up. Would you like to pull them forward, or talk to the team that owns those promotions to get them to pull them forward? It's a more proactive way for the system to interact with you and a more natural way to do it.”
Whether it’s IoT, big data or analytics, companies have a lot more data to base their decisions on, and data-driven decision making sounds obvious. And the next step beyond data-driven decisions is decision support systems and even automation. Are we ready for intelligent assistants with business advice?
While a recent study of 50,000 American manufacturing organizations found that the use of data-driven decisions had almost tripled between 2005 and 2010, that was still only 30 percent of plants. And when telecom provider Colt surveyed senior IT leaders in Europe in 2015, 71 percent of them said intuition and personal experience works better for making decisions than using data (even though 76 percent of them say their intuition doesn’t always match the data they get).
More positively, Avanade’s new study of smart technologies says business leaders globally expect to be using digital assistants and automated intelligence for problem solving, analysing data, collaborating and making decisions – and they also expect them to increase revenues by more than a third. With those kinds of expectations, attitudes are also more positive; 54 percent said they’d be happy working with those systems.
Certainly, early adopters that Accenture has spoken to who are using machine learning to improve the way they manage customer service, financial resources and risk and compliance, in sales and marketing and in developing new areas of business found “significant, even exponential, business gains” in costs, revenue and customer performance, by using a mix of what Oberoi calls “perceptual intelligence” using natural language and voice biometrics, advanced analytics and business decision support .
Those gains included cutting costs up to 70 percent, increasing revenue up to 20 times by tracking buyer behaviour more quickly and getting happier customers by handling call routing more quickly and accurately, and those results might help overcome reluctance.
Getting business users involved in building these systems should also increase adoption. The demand for data scientists is larger than supply, which means companies without their own deep expertise will look to cloud services and marketplaces for analytical solutions and algorithms like the Cortana Analytics gallery, which includes tutorials and experiments as well as APIs and templates you can use to get started and then customize, plus pre-configured services for recommendations and forecasting.
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