Very few organizations provide consistently stellar customer service. Customers expect organizations to tend to their needs, and if the organization fails to do so, there is a chance that the customer will chose to instead spend money on a competing or substitute product. Some organizations have attempted to implement artificial intelligence in call centers as a way to reduce labor costs and streamline customer service processes. However, as many of us are far too familiar with, artificial intelligence in customer service hasn’t lived up to the hype. The first example that comes to my mind is Amtrak’s “Julie,” who fields all of Amtrak’s calls to get basic information before transferring customers over to an agent. There has not been a time where I haven’t had to read my six-character booking number to Julie at least five times before she finally understood all of the characters. I’m upset before the system even has the chance to transfer me to an agent. Amtrak’s Julie represents a traditionally reactive customer service model, where the customer reaches out to the company with an inquiry, and a support agent reacts to the inquiry. Salesforce & Cisco flipped the traditional customer service model upside-down, and recently partnered to help organizations use data to become more proactive when it comes to customer service. The two technology conglomerates bring together Salesforce’s Service Cloud and Cisco’s Contact Center to create a single agent desktop, where the agent is empowered to quickly handle every customer interaction with personalization and data-driven insights. All customer inquiries come through the Contact Center platform, where Service Cloud intelligence automatically classifies the case based on user history and trends. Contact Center then routes the call to the agent best equipped to tackle the case, along with recommended responses and rich details on the customer from all departments within the company. The more the agent uses the system, the smarter and more predictive it becomes, all benefiting the customer in the end of the day. By merging two major platforms in to a single desktop solution, Salesforce and Cisco bring new-market disruption to customer service. Has anybody experienced stellar customer service that somehow incorporated artificial intelligence? How else do you think artificial intelligence or tomorrow’s high tech could impact the future of customer service?
Rick Osterloh, Head of Google’s Hardware Division, said to The Telegraph that hi-tech glasses are “very interesting to [Google]… we’re trying to determine what the best future will look like for other kinds of ways to experience AR than just phones.” Google Glass was introduced to the public in 2014 in open beta, where “Glass Explorers” in the US could purchase a pair for $1,500. Google Glass is an optical head-mounted display developed with the mission of producing a ubiquitous computer. The announcement of Glass was met with high excitement, creating demand among non-consumers. In 2013, there was a lot of buzz going around about the potential of augmented reality, as AR was not easily accessible to consumers through a mainstream product. Google Glass was a hit in the healthcare industry, where it helped doctors, emergency medical technicians, and paramedics save lives, especially in the spaces of surgery and radiology. With all of the excitement about Google Glass, there were a number of safety, health, and privacy concerns, which caused Google to withdraw the device from the market in 2015. Consumers within the new market of eye-wearable AR have been eagerly awaiting Google’s next move in the space of AR. After successfully creating a market for AR, Google hasn’t released much information about their next moves in the space, but there is a good amount of speculation and excitement about what the future of AR has to bring. What can Google do to mitigate privacy and safety concerns about the Google Glass? How else can Google capture demand among non-consumers within the space of AR, potentially outside of Google Glass?
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Employing Pre-bureaucratic Structure With A Loose-Tight Governance Model in Local Government to Better Serve Communities
For many, the first word that comes to mind when we think of government is “bureaucracy,” and not just because of its literal place in government. Bureaucracy as we know it has a negative connotation, criticized for its inefficiency and inflexibility to individual situations. Within the context of organizational structure, a bureaucracy clearly defines roles and responsibilities within a hierarchy with respect for merit. In a typical bureaucratic structure, there are many levels of management. We can’t escape the endless hierarchy of government at-large. However, why does local government have to be inherently inefficient as a result of the bureaucratic structure it sits below? Anyone who has tried to work with the Philadelphia Mayor’s Office or City Council knows that it is as seemingly impossible to get anything done, as if we were attempting to pass a bill through congress. In society, we fantasize startup culture and its ability to “get stuff done.” Why can’t local government operate like a startup? To best serve its people within the constraints of democracy, local government should employ a pre-bureaucratic structure using loose-tight governance model. A pre-bureaucratic structure is completely centralized, where the leader (in this case the elected official) makes all key decisions through one-on-one conversations. In a loose-tight governance model, there is a balance between control and autonomy where the most importance processes are kept under central control (the elected official), but staff (subordinates, supporting government staff) are given huge amounts of freedom to think outside the box. This approach gives local government officials the freedom and agility to accomplish goals within the context of democracy and the larger structure of government, while empowering government employees to flex their creativity, all working towards serving their communities in a more flexible and efficient way. How else can we use course concepts, such as systems thinking, to improve local government? Can a similar structure and governance model support positive change elsewhere in society?