Just recently I read that 2017 is the year of Artificial Intelligence. The number of AI start-ups is growing rapidly, there are more and more conferences dedicated to the topic and we hear a lot about the big tech companies investing huge amounts of money in AI related developments. Somehow this doesn’t come as a surprise, as we’ve had a lot of buzz about machine learning, deep learning and analytics over the last couple of years. All alluding to the simple fact that data alone, although the “new oil” as it’s been praised, is not doing the trick. It takes analytics to really translate data into a competitive advantage – and that’s just one necessary ingredient.
Leveraging machine learning, deep learning or even artificial intelligence to drive business strategy and shareholder value is a serious ambition. It requires highly skilled, dedicated experts and significant resources. The current hype around these topics creates a blurry picture calling for clarification. Complex data landscapes can be explored to understand what information they provide. However, data will only translate into significant business potential if related to the right use case. Similarly, the use case determines the appropriate analytical approach and degree of automation. Just labelling a solution AI won’t make it relevant nor successful.
What does artificial intelligence mean anyways? Is it just a synonym for extremely powerful algorithms and neural networks or is there more to it? In this series I’ll shed some light on the different types of Artificial Intelligence and discuss what it takes to use it to tackle actual business challenges.