Artificial Intelligence has the potential to be become as embedded into everything that we do, just like the Internet. It is scaling rapidly and solving many problems and in future will change the very way we lead lives or conduct business. Most executives consider AI as a disruptive technology which will make or break their business, employees think of it as a job destroyer, consultants position it as a solution to everything and media delivers AI as the hype of the millennium. While there is an element of truth and myth in each, the observed reality is that productization of AI on the ground is extremely hard, rudimentary use cases have been addressed and barriers to go mainstream are several. Outside the Silicon Valley, even the most aggressive use cases of AI i.e., retail, banking, telecom etc. are still in their early stages.

"Uber is making decisions in real time at global scale, while needing to take into account local nuances of the marketplaces," explained Franziska Bell, Senior Data Science Manager on the Platform Team at Uber. "And, of course, we also want to incorporate the user preferences on the product." As a result, Uber has invested heavily in data science, and Bell outlined some of Uber's data science strategy last month at the AI World Conference & Expo in Boston. Uber employs hundreds of data scientists working across the company, and Bell reports constant efforts to, "increase the innovation and speed with which these data scientists move." To speed up the rate of data science at Uber, the company has taken a dual approach: first to maximize each step of the existing data science project life cycle, and second to commoditize data science by creating platforms applicable to multiple use cases that are transferable and reusable. Data science projects at Uber fall into four life cycle stages, Bell explained: data exploration, iterative prototyping, productization, and finally monitoring.

The jury is still out on who the biggest AI winners in the enterprise space will be. So far, applying AI to enterprise has not made as much impact as people have expected. Cloud computing, for instance, has had far greater impact in the enterprise space than AI has. There's a huge opportunity to help other enterprises unlock the full potential of AI. Databricks, a company whose roots came from helping enterprises with big data processing, recently took a $140M round to make "Artificial Intelligence (AI) achievable for enterprise organizations with its Unified Analytics Platform." Databricks has a unique advantage in their recent move to become an AI platform company.