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From support function to growth engine: The future of AI and customer service

MIT Technology Review

When it comes to imagining the future, customer service often gets painted in a dystopian light. Take the 2002 sci-fi film Minority Report. Tom Cruise's John Anderton walks into the Gap, an identity recognition system scans him, and a hologram asks about a recent purchase. There's something unsettling in this vignette--an unsolicited non-human seems to know everything about you (or, as in the movie, mistakes you for someone else). But the truth is, customers today expect this kind of sleek, personalized service.


Supercharge Knowledge Management With Help From AI

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In his 1999 book Management Challenges for the 21 Century, Austrian-born American management consultant, professor, and author Peter Drucker wrote of the importance of "the coordination and exploitation of organizations' knowledge resources, in order to create benefit and competitive advantage." Today, businesses have embraced his point, demonstrating how maintaining and growing an organization's information to assist its employees and customers offer those benefits and advantage. As a practice, this collecting and sharing of information is referred to as knowledge management. Even prior to Drucker's observation, the Consortium for Service Innovation had already begun its work in 1992 on Knowledge-Centered Service (previously known as Knowledge-Centered Support) or KCS *. KCS is a method that focuses on organizational knowledge as a key asset that can benefit, among other things, customer service delivery.


The RPA Noise: The Long and Short of It - ReadWrite

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The topic of robots and automation was initially met with hysteria not so long ago. We had reports of robots stealing human jobs and other misinformation that was making the rounds. Cut to the present day, some of the fear remains -- but the technology is here -- in different forms or avatars (spelled bots, RPA, chatbots). Here is the long and short of the Robotic Process Automation (RPA) noise. RPA has not created a meteoric crater yet, but what has it really been up to? Much is being said about bots and all the things it can impact. There is a fundamental difference between actual bots and robotic process automation.


Is Artificial Intelligence the Right Investment For Your Business?

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Artificial Intelligence is quickly becoming a reality for businesses of all shapes and sizes. With the massive amounts of data, your business needs smart machines that derive valuable insights. Artificial Intelligence enables you to make sense of that data, understand the patterns and use it to your advantage. A recent report shows that the worldwide data will grow by 61% to 175 zettabytes by 2025. It would be a result of businesses collectively generating tons of customer data.


The State of Chatbots: Pandemic Edition - InformationWeek

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When it comes to technologies that got a big boost from the response to the pandemic, probably the first thing that comes to mind is video conferencing, followed closely by other collaboration tools. Zoom announced August 31 that its revenue for the second quarter was up 355% year over year. Maybe that's why everyone is talking about Zoom fatigue. But video conferencing isn't the only technology that's gotten a boost as companies try to navigate the challenges of a pandemic. Chatbot use and deployment is also on the rise.


The GPT-3 Model: What Does It Mean for Chatbots and Customer Service?

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In February 2019, the artificial intelligence research lab OpenAI sent shockwaves through the world of computing by releasing the GPT-2 language model. Short for "Generative Pretrained Transformer 2," GPT-2 is able to generate several paragraphs of natural language text -- often impressively realistic and internally coherent -- based on a short prompt. Scarcely a year later, OpenAI has already outdone itself with GPT-3, a new generative language model that is bigger than GPT-2 by orders of magnitude. The largest version of the GPT-3 model has 175 billion parameters, more than 100 times the 1.5 billion parameters of GPT-2. Just like its predecessor GPT-2, GPT-3 was trained on a simple task: given the previous words in a text, predict the next word. This required the model to consume very large datasets of Internet text, such as Common Crawl and Wikipedia, totalling 499 billion tokens (i.e.


How Will Machine Learning Be Able To Change The Future Of The Insurance Industry?

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Artificial Intelligence within the Insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of Artificial Intelligence in Insurance is already being used in many ways. With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around its implementation are now fading quickly as it begins to trust in the caliber and numerous opportunities brought forward by Artificial Intelligence and Machine Learning. The only question that remains is -- how far can we push its capabilities? In 2017, Artificial Intelligence has shown its substance in various business verticals by rapidly creating controlled, digitally enhanced automated environments for maximum productivity.


Glia Integrates Boost.ai to Offer AI-Powered Self-Learning Virtual Agents

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Glia Customers Can Use Boost.ai's Boost.ai, a global leader in artificial intelligence for Fortune 1000 companies, has announced a partnership with Glia, a leading provider of Digital Customer Service, to integrate Boost.ai's The integration means Glia customers can build AI-powered self-learning virtual agents using Boost.ai's "Self-learning AI from Boost.ai makes it possible for Glia's customers to create specially developed and finely tuned virtual agents that are even more valuable when coordinated by the Glia platform throughout the course of a customer engagement," said Henry Iversen, co-founder and CCO at Boost.ai. "This might involve filling out a loan application or opening a new bank account, where seamless transition between channels including social, SMS, webchat, and voice is assistive to both customers and agents alike."


Big Data and the Future of Retail

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The retail experience was undergoing a transformation even before the current pandemic forced massive changes in shopping behavior. A 2019 survey revealed that 92 percent of 1,400 retail leaders identified "reinventing the customer experience" as their top business priority. However, with the new reality we've all been forced into, the stakes for retailers are higher now than ever. To understand the changing needs of consumers and respond to those needs, retailers must leverage big data strategies based on artificial intelligence (AI) and machine learning (ML) technologies. In a crowded marketplace, retailers must find ways to differentiate their brands through increased personalization, better customer service, and improved demand forecasting.


How AI And Data Analytics Are Shaping The Future Of Fintech - The largest technology publication on emerging trends

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Historically, the fintech industry has been among the earliest Artificial Intelligence adopters. As of today, AI is becoming the main driver of digital transformation in traditional finance and the golden standard for fintech services. In fact, according to a recently published report authorized by the World Economic Forum and conducted in partnership with the Cambridge Centre for Alternative Finance, by 2022, we can expect mass adoption of AI in the financial industry on a global scale. In other ways, legacy financial services will become obsolete in as little as two years. AI and Data analytics go hand in hand, and nascent technologies like Machine Learning, Neural Networks, and Natural Language Processing, continue to improve data-crunching capabilities for financial industry players.