Our mission is to build entertainment for millions of people around the world and connect people through high-quality games. Since we were founded, we've drawn 500 of the world's most talented people into our ranks. Our team has worked on international AAA titles like Transformers, Star Wars: The Old Republic, Real Steel, Rio, Mech Conquest, and Dueling Blades. Our designers have worked on some of Hollywood's biggest hits including the movie Avatar. Junglee is not just a gaming business - it is a blend of data science, innovation, cutting-edge technology and, most importantly, a values-driven culture that is creating the next set of conscious leaders.

Here's a fun game: Name one person you know who enjoys sitting on hold, waiting for a customer service agent to resolve an issue or answer a question. AI can transform your customer service experience--if you implement it with care. But what if I were to say that AI can enable a world where you'll never again be held hostage on hold, wincing every time the smooth jazz version of Duran Duran's "Hungry Like the Wolf" repeats ad nauseum? It's true--customer service and support is a prime candidate for using AI to improve the customer experience and enable the team to scale. As the person responsible for operationalizing AI within ServiceNow's customer support experience, I seek out stages in the customer journey where AI can automate processes.

While AI deployments are picking up steam, challenges to success, both technical and cultural, abound. One of the biggest challenges is the lack of alignment among the three pillars of enterprise AI success: business users who are closest to the data, data engineers charged with keeping the data pipes open, and data scientists who make AI work. This lack of alignment typically means that data science teams find themselves "boiling the ocean" without a clear scope, while data engineers don't know what data sets to focus on – which can lead to very disappointing results for AI projects. Only by aligning these groups around a standard data science methodology can consistent AI success be achieved. Business leaders who came out of school 15 or more years ago were never trained to take advantage of big data.

We have all heard of "automated" support features, but as artificial intelligence (AI) has continued to advance, the support landscape is shifting to focus on the possibility of "autonomous" support. What exactly is the difference? Here, Jens Trotzky, head of Artificial Intelligence Technology for SAP Support, discusses the role of AI in the development of autonomous support, and how SAP innovation is helping to make it a reality. A: Most customer support resources available today – even some of the most sophisticated – are considered automated in some capacity. In a nutshell, automated support is predictive technology that is pre-scripted by support engineers based on a fixed set of standards and defining factors.