Next-Gen Education System


In gig-economy there is a tremendous opportunity to leverage the full potential of digital disruption including AI, Gamification, and Automation paving path to next-gen educational methods and job reorientation. Firstly, finding ways on how AI technologies could aid education methods, augment human skills in professional jobs and there by the challenges posed by AI. We commonly hear news like – an artificially intelligent computer system built by Google has just beaten the world’s best human, Lee Sedol of South Korea, at an ancient strategy game called Go. The Google program Alpha Go, actually learned the game without much human help. It started by studying a database of about 100,000 human matches, and then continued by playing against itself millions of times. As it evolved, it reprogrammed itself and improved. This self-learning program is based on a neural network, and theories of how the human brain works. Another classic example is Pearson – the world’s leading Education Company tapping IBM’s Watson as a virtual tutor for college students. With continued impact of AI on education and gig-economy, analysts are estimating a net reductions in jobs/workforce between 4% and 7% across various industries. It is simultaneously creating demand for high skilled digital workforce. Likewise AI and advance machine learning is paving new paths to education methods and future focus areas to complement and supersede machines to take full advantage of AI.

Second focus area is Gamification that has become the frontier of training, capitalizing on a new generation born into a computerized world. The idea behind the concept is to take elements of game design and logic and apply it to a work situation. One of the biggest companies to utilize gamification is McDonald’s, which introduced a new till system using a simulation game. Employees were asked to engage customers and use the till while under time restraints. Air Cargo Netherlands also used gamification when they needed to train employees on a specific utility. They created a game version of a new logistic system called Smartgate. They used the game to develop employees’ “chain thinking” and help them realize the consequences of their decisions in a risk-free environment.

Lastly, driving the automation agenda leveraging advances in robotics, artificial intelligence, and machine learning as machines match or outperform human performance in a range of work activities, including ones requiring cognitive capabilities. Examples include guiding customer service representatives to more quickly resolve customer problems and anticipate future purchases, quickly and securely reconciling mass overnight transactions for financial institutions, or giving time back to HR professionals by managing the time consuming on-boarding processes for new hires. Technical, economic, and social factors will determine the pace and extent of automation. Continued technical progress, for example in areas such as natural language processing, is a key factor. Beyond technical feasibility, the cost of technology, competition with labor including skills and supply and demand dynamics, performance benefits including and beyond labor cost savings, and social and regulatory acceptance will affect the pace and scope of automation. Hence the next-gen education should focus on learning futuristic competencies with an aim to complement realizing full potential of automation.

Is Artificial Intelligence (AI) Making Inroads to Outsourced Customer Support?

A robot sitting at a desk

Rethinking overseas outsourcing is on the cards for a while. For years, Westerns have had their service calls on credit card bills and broken cell phones handled by customer service agents in the low cost locations. A decade ago, it made a lot of sense to outsource customer support overseas. But that’s changing. Increasingly, companies that want to outsource their customer service are happy with domestic arrangements.

As we are aware of there is still a cost advantage of handing customer support by a Filipino vs an American, but the differential is shrinking and also few companies along the learning curve are experiencing the adverse impact on customer loyalty and retention rates. While cost arbitrage vis-a-vis customer loyalty is a food for thought, the good news is that, as the global outsourcing industry matures, more and more companies are turning towards leveraging advanced technologies for providing customer support services. While there is still a need for a human intervention to answer a customer support call, the actual point of human contact is constantly being pushed further and further down the chain of engagement.

Historically, the automated call distribution (ACD) system would route a call to the first available agent (first in, first out or FIFO). That approach was augmented by skills-based routing (SBR), in which calls would flow based on organizational logic such as subject matter or issue type. These traditional approaches rely on relatively straightforward, tangible and objective decision criteria, and have only marginally been updated in the past two decades. More recently, advances in artificial intelligence (AI) and big data have enabled routing based on more complex bases. Chief among these is personality, or behavior influenced by following sets of attributes.

  • Customer’s behavior, modeled on demographic data from public sources as permitted by local regulation
  • Agent’s behavior, based on the same modeling and enhanced by optional survey responses
  • Company’s customer support center charter and objectives, including but not limited to maximizing customer satisfaction or retention, minimizing call handling time, or making sales

Given the diversity of industries in which companies operate customer support centers (ex: banking and financial services, insurance, consumer products, telecom) as well as the types of centers (collections, service, telesales etc.), the organization through which the customer and agent connect heavily influences the outcome.

Experts are evangelizing the following specific predictions for how intelligent self-service will continue to transform customer support in the coming years.

  • Conversational IVR will become the new standard for automated phone experiences
  • Reactive virtual assistants will become proactive virtual advisors
  • Self-service and assisted service will converge

For example, virtual assistants have received quite a bit of attention lately with the introduction of Siri, the virtual assistant on iPhone 4S. By enabling natural language and voice as input to a customer support center, the virtual assistant makes it easier and faster for the user to interact with the customer support program and benefit from its databases and knowledge. Instead of experiencing long waiting times or punching ill-fitting selections with dial-pad numbers, customers can immediately begin a digital but verbal interaction with the virtual assistant, which can answer questions, identify potential sales and marketing opportunities, solve moderate-to-sophisticated problems, and determine when a live assistant is best warranted.

In the evolution of personality/behavior driven customer support, AI’led software increasingly paving path to handling the earlier levels of engagement between the user and support desk. Highly sophisticated system/software using artificial intelligence and extremely elaborate decision logic trees are improving overall customer service results. Moreover, this reduces the overall cost footprint of outsourcing companies. This means companies have an alternative to people intensive customer support and rather hire few high-end resources with higher wages in exchange for higher skill levels, subject matter expertise and work quality.

Does this mean AI to replace the human being in customer support? Absolutely not. The good news is that core sales/service cannot be outsourced. There will always be a need for a flesh and blood human being who can read the signals that the prospect is sending out over the phone to increase the chances of a sale/success of touch service. That is never going to go away. While artificial intelligence has been used to simulate human response, its real power lies in optimizing the interactions between two humans, and enterprise behavior matching is one such application that is already achieving results in contact centers today. AI can elevate outsourcing to new heights pushing enterprise’s profit margins to the next level. Don’t be surprised if artificial intelligence continues to make further inroads in the world of outsourced customer support.

Service Desk 2.0 : A Perspective

“Transform or you will be transformed!” – Robert Stroud

Both end users and customers evolved with their personal experience of IT being transformed. The real shift here is in customer power. This note highlights the opportunities that exists in service desk operations for both service providers and customers and thereby put an emphasis on capability building in transforming to next generation service desk – SD 2.0.

Service desk has to elevate from “entry point” where the “Timed” user receive help and guidance on IT service issues, problems and requests to “Mobile” user in the realm of Apple Genius Bar. While Social Mobile Analytics Cloud is changing the user experience in many ways, let us have a close look at the key focus areas for building next generation service desk (SD 2.0) capabilities.

  • SDaaS (Service Desk –as-a-Service): Mangers facing on-going challenges to reduce operation costs are weighing dedicated vis-à-vis on demand mode of service desk function. SD 2.0 should focus on building shared service desk for multiple clients covering multiple languages offering efficient and effective service.
  • Moving from Average Handling Time to instant and continuous resolution: Customers are demanding faster or spontaneous response to service calls. Equally important is meeting the expectation to be able to engage support through the channel of customer choice. This include the move away from desktops/laptops to smart pads, meeting expectation of NextGen where social media taught to IM and to expect instant and continuous feedback and analytics-led pattern analysis where exceeding customer expectation will be norm of the support operation
  • BYOD integration to workplace: Forward thinning organizations are embracing Bring Your Own Device (BYOD) in workplace. Service desk has to prepare for the future to support wide variety of devices offering the same levels of service.
  • Artificial Intelligence (AI) enabled self-healing and self-awareness capabilities: The younger generation expects the convenience of an end-user facing searchable knowledge base. Adoption of AI platforms is taking momentum for self-service /Google/SocMed facilitating peer support before coming to the SD – filtering out all the simple, typical first time fix interactions. This will lead to higher support automation and next level of SD standardization.
  • IoT facilitated SP2P (Smarter peer-to-peer) support: In view of increasing impact of Internet of Things, businesses are becoming more social in the way they work and interact, both externally and internally. Enabling IT users to help each other’s resolve IT issues leveraging social IT support as a channel. Hence service desk has to harness the latent of IT knowledge its customers have and alleviate the complexities of endless varieties of support request that BYOD user will generate for successfully enabling social IT support channel.

Industry is debating on journey of service desk into future. Service desk has come a long way and has earned its name service desk as opposed to helpdesk. Customer skills definitely continues to be in very high demand, but embracing the above technology-enabled capabilities is becoming absolutely crucial for SD 2.0 in delivering the standard of service that customers expect.