AI and machine learning are best-suited to specific types of tasks and these vary in complexity. For this reason, it is important to determine what you’re looking to accomplish so that you can intelligently configure and aggregate your data for the AI process to produce favorable results.
As more businesses are incorporating AI and ML into their strategy to reduce costs and create better products, they are gaining a competitive advantage over those holding on to traditional means of operation. Employed effectively, AI and ML will provide tide-turning returns for companies. If you’re looking to introduce AI into your business strategy, how do you choose the most effective business cases? How do you measure the empirical value?
As AI is increasingly adopted globally, it is vital to consider how AI can bolster your business. Gartner predicts that by 2020, 85% of customer interactions will be managed without a human. While AI is no silver bullet, when applied effectively, it can enable businesses to tune their customer interaction, marketing, and product design to competitive levels.
Summarizing the key learnings from Northwestern’s Kellogg School of Management’s Design Thinking workshop and pulling together various resources as well as articles on the subject. After graduating with an MBA from Kellogg in 2005, I have been involved in many aspects of user experience and this workshop brought it all together for me.
Design Thinking consists of applying the (design) process of designing physical products to business decisions, services and even process improvement initiatives.