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Council Post: Why Conversational AI Should Not Be An IT Project

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Artificial intelligence (AI) has woven itself into the processes of many industries, businesses and teams, but who should own the investment or implementation, and who does it best serve? Conversational AI, the use of automated messaging and voice assistants to create personalized interactions, can allow customers to sort out requests and problems, like inquiring about a package delivery or obtaining a mortgage quote. AI has taken transactional moments and pushed them beyond just messaging, transforming them into full-blown experiences with tone, sentiment and emoji comprehension, as well as the ability to include app-like features, such as carousels, maps, surveys, scheduling capabilities and much more. On paper, this seems like something every company would want. But despite companies having the ability to access and adopt technology with increasingly personalized messaging and a trove of accessible data, deployment of conversational AI often fails.


10 Ways AI Can Improve Voice Of The Customer Programs

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Customer's expectations are the guard rails that guide how their relationships progress with any business. The pandemic has made the predictable unpredictable, erasing marketing personas of the past and re-writing them in real-time. Old guard rails and expectations are changing fast. Having an accurate outside-in view from the customer's perspective is the value VoC programs deliver, with the best ones providing data to guide strategy. Pure e-commerce orders have grown 110% since January, and e-commerce revenue has increased by 96%.


Top Artificial Intelligence Trends that will Change the Decade Analytics Insight

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As we began the new decade, technology is changing by leaps and bounds. The initial predictions for 2020 point to a serious integration of AI and human experience to study how Intelligent Automation technologies can be used to augment an enterprise experience. In 2020, many factories of AI models and data will emerge helping AI technology and associated commercial solutions on a large-scale facilitating the enterprise. For instance, AI solutions in the customer service industry find its use cases in e-commerce, education, finance and related industries on a large scale. Digital IQ will rise in this decade.


The Impact of Artificial Intelligence in Retail

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As we've seen unfold in recent years, artificial intelligence (AI), machine learning (ML) and data analytics are rapidly changing the speed at which the retail industry operates. As these technologies become increasingly popular among leading retail companies, it's clear that early adopters of AI have seen a sizable financial advantage compared to retailers that haven't yet adopted the technology. Non-adopters will need to erode their margin to stay competitive on price, while adopters with sizable financial gain will be able to weather volatility on price inputs. AI is being used as a differentiating factor between smaller retailers as a way to get ahead and capture market share. The gap between adopters and non-adopters will continue to grow, meaning AI is no longer just a way to get ahead of competitors -- it's become a pivotal part of staying relevant in the industry and maintaining innovation.


An Essential Component In Any Insurtech Solution Tech-stack - Suyati Technologies

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The insurance industry is way past its time when timely response and a balanced price-quality relationship were enough to define customer experience. The advent of Artificial Intelligence, Machine Learning, and Advanced Analytics have disrupted the insurance industry and have reshaped the way it operates. Insurtech firms these days are using their AI and ML capabilities to drive high-quality customer experiences, increased loyalty, generate new revenue while simultaneously reducing the costs. The vision of the insurance firms today and for the future is where customers and customer experience comes first. The combination of AI and ML models built on top of the Customer Data Platform leads to improved customer experience through hyper-personalization.


Embracing AI in your quality journey

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We all know Artificial Intelligence and Machine Learning are transforming business. It's clear that many companies are rewiring their organisations and creating dedicated teams to capitalise on AI. Although this shift has been happening, up until this point it has been doing so on the fringes, or inconsistently. The development platforms, vast processing power and data storage that enable AI are becoming increasingly affordable and more "off the shelf." Companies are beginning to grasp how to cope with the inherit risks of AI, yet have only just begun to think about how AI can improve every aspect of their value chain.


Staying Ahead of the Game as AI Enters the Mainstream

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There is real potential for the technology to completely transform customer service experience and the modern enterprise but for various reasons full-scale adoption has taken time and uptake has been gradual across all industries. To understand why we're poised for widespread adoption, we need to explore what has held back AI implementations to date and how companies will eventually use the technology within their business. Gartner suggests that between 2018 and 2019, AI adoption rose from 4% to 14%. However, most AI projects are at the pilot stage (37%) with only 22% being fully implemented, according to Cognizant. As it stands, some businesses are unwilling to invest in technologies like AI and machine learning due to the expected cost.


AI and machine learning in finance: use cases in banking, insurance, investment, and CX - Fintech News

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Just 30 years ago, you would have to wait days for a bank to approve your credit. Or spend weeks bogged down by your insurance company's bureaucracy just to get a refund after a minor car accident. Today, these operations take less than a day as documents are submitted and processed online with little or no human interaction. In this article, we'll cover a set of technologies that promise to transform the whole idea of doing business in the finance world. Artificial Intelligence – is it simply a trendy phrase to put on your landing page or an innovation-ready use case?


Responding to Enterprise Challenges in Real-Time with AI and Machine Learning

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The ML market will be worth an estimated $96.7 billion by 2025, while the AI market is projected to reach $202.57 As your organization grows it has multiple solutions and processes that accumulate over time, all of which are impacted by changing business needs. These growth spurts inevitably lead organizations to experience challenges as they navigate new changes and opportunities for efficiency, innovation, and success. All of an organization's processes and solutions might be using workflows, documents, and integrations that today are typically handled by IT departments. Some of the processes are partially or manually automated, and front-end systems and processes might be more optimized than back-end processes, which is often the case in many organizations.


Ergomed to Collaborate with DataRobot, Automation Anywhere to Accelerate its Intelligent Automation Strategy

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Ergomed plc is pleased to announce PrimeVigilance's strategic collaboration with Automation Anywhere and DataRobot to accelerate the Company's Intelligent Automation strategy. PrimeVigilance, a division of Ergomed, is the leading global specialized provider of full pharmacovigilance (PV) services, and currently employs over 750 people. By empowering its clients and colleagues with the application of RPA and Machine Learning (ML), PrimeVigilance will enable clients to improve quality and consistency within safety databases, as well as productivity. A successful proof of concept has been completed, and PrimeVigilance will now implement a cloud software solution to automate specific pharmacovigilance processes. The productivity gains made possible by working with Automation Anywhere and DataRobot, are expected to deliver organic growth more efficiently, with the automation of manual, repetitive processes.