Consumer products companies were beset by changes on all sides during the 2010s. Customers were increasingly turning to ecommerce platforms rather than shopping in-store. Meanwhile, nimble, digitally-savvy competitors were gaining market share by capitalizing on the troves of newly available data on consumer preferences. Alibaba was the largest ecommerce platform company globally and operated in China, which was seen by many as an image of the future of western markets. In 2017, Alibaba launched the Tmall Innovation Center (TMIC) which provided a range of services to support innovation for companies selling through the Tmall business-to-consumer platform. These included robust consumer insights backed by data from across Alibaba and mechanisms to rapidly solicit customer feedback. Armed with the TMIC, consumer products companies were learning that effectively targeting consumer preferences required a new approach to product development which had far-reaching implications across roles, skills, and organizational structure.

This case touches on the topics of project-based work, agile methodology, and skill and talent management through Catalant's evolution as a company. Catalant's journey to becoming a software platform and talent marketplace provides context for students to explore new models of work and ways companies are mobilizing talent internal and external to their organization. This case urges students to contemplate how these considerations might shape the critical decisions business leaders will need to make about how to manage their organizations and use their resources to capitalize on their own vision for the future. This case also provides valuable lessons for technology entrepreneurship, introduces students to important gig economy concepts, and asks students to consider the unique challenges facing companies like Catalant that are selling a gig platform as part of their product or service.

Driven largely by advances in perception and situational awareness, robots in the 2010s were gaining functionality that allowed them to be applied to fundamentally new types of work. The expanding range of new types of tasks that could be completed by machines had significant implications for businesses, the workforce, and society. This primer grounds conversations about these implications by illustrating real-world examples of the new types of activities that robots were becoming able to perform. It describes the functionality limitations which dictated how earlier robots could be deployed and the advances in underpinning technology which have moved these boundaries and led to the current inflection point.

The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied in a wide range of new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product - a system which allowed robotic arms to "pick and place" individual items into boxes for shipment to consumers. Despite receiving significant attention in the robotics space, the problem of universal "picking" had not been solved. Osaro believed deep reinforcement learning would provide the solution by allowing robots to handle the complexity involved in perceiving and grasping a wide range of objects. The case discussion will focus on the decision to choose the warehouse automation market, provide an overview of key machine learning concepts, and introduce students to some of the contours of competing in the machine learning space during the late 2010s. The case also introduces key concepts for thinking about how the combination of artificial intelligence and robotics will change work in the future.

This primer provides an overview of the implications of automation technology based on state-of-the-art "Artificial Intelligence" (AI) in the late 2010s: including the trends leading to the surge in AI, applications of AI categorized by the type of work performed, limitations of the technology, discussion about AI adoption, and key concerns the technology brought about.

Autonomous vehicles have enormous implications for business and society. But, despite the headline-laden attention paid to the technology, there remain more questions than answers. Students will learn about the complex industry and have explicit discussions about the factors influencing development and commercialization and how various stakeholders might respond. They will also be put in the shoes of decision-makers looking for signals that might help them prepare for the disruptive change ahead.