Will AI Take the Risk Out of Insurance, Leaving Nothing But Pure, Unadulterated Coverage? Artificial intelligence (AI) is a rapidly growing field that has the potential to revolutionize the insurance industry. As a result, 74% of insurance executives are planning to increase their investments in AI. AI can significantly improve the efficiency and effectiveness of group insurance. McKinsey estimates that across functions and use cases, AI investments can drive up to a whopping $1.1 trillion in potential annual value for the insurance industry.

Fonixa uses artificial intelligence, deep learning and voice-emotion technology to transcribe and analyse up to 5,000 hours of customer calls and scans them for emotional patterns to identify happy, sad and angry customers, get insights and improve their customer's experience. This solution can help you eliminate the time and money needed to screen calls manually, and fully automate the process with usable data insights Ubility uses predictive analytics, artificial intelligence and machine learning to help customer support agents quickly find the knowledge base articles and methods they need to support a customer reducing resolution times from minute/hours to seconds, what's more, is it even suggest how you should be talking to each customer in a way that relates to them Dynamics 365 Field Service uses the power of predictive analytics and machine learning to Automate and improve scheduling to dispatch the right technician and get the most value out of your resources Dynamics 365 Field Service uses predictive analytics and machine learning to help empower technicians with a 360-degree view of your customers and real-time guidance to improve resolution time and earn customer trust. Fonixa uses artificial intelligence, deep learning and voice-emotion technology to transcribe and analyse up to 5,000 hours of customer calls and scans them for emotional patterns to identify happy, sad and angry customers, get insights and improve their customer's experience. Ubility uses predictive analytics, artificial intelligence and machine learning to help customer support agents quickly find the knowledge base articles and methods they need to support a customer reducing resolution times from minute/hours to seconds, what's more, is it even suggest how you should be talking to each customer in a way that relates to them Dynamics 365 Field Service uses predictive analytics and machine learning to help empower technicians with a 360-degree view of your customers and real-time guidance to improve resolution time and earn customer trust.

Enterprise organizations recognize the key functions that artificial intelligence and machine learning will play in their businesses in the future, and, depending on what survey you have read in the last year, they are investing to make that future a reality. But what does it take to build a successful AI and machine learning practice? What does it take to go beyond building that practice and actually integrate AI and machine learning tools into the business itself where professionals across the organization touch it every day? How can we make these technologies which promise to provide a competitive edge into the lifeblood of the enterprise. A couple of new Forrester Wave reports, which evaluate predictive analytics and machine learning tools, provide a blueprint for the successful AI-driven business of the future.

The Introduction gives an overview of artificial intelligence and its use in marketing, explains key terms, and sets the scene for following chapters. Here, we will bring you up to speed on what you need to know moving forward, whether you're new to the topic or an experienced digital marketer. How Does Marketing Software Use AI? This chapter provides an overview of how currently available AI systems can be deployed by purchasing commercial solutions. We look at what types of products are available and what they can do for your business.

Almost half of HR leaders say predictive analytics and machine learning would improve their HR function, according to a Censuswide survey commissioned by MHR Analytics. Only one in ten (11%) of respondents currently use advanced analytics techniques, but the research indicates a clear desire among HR professionals to implement predictive analytics to upgrade and enhance almost all aspects of their role. Laura Timms, MHR Analytics' Product Strategy Manager said: "The results mirror the conversations we are having with our 750 customers – there is a real step-change in how HR professionals see their roles evolving and becoming more sophisticated. "While most organizations still use spreadsheet-based data, they are aware that harnessing predictive analytics and machine learning tools will enable them to spend much less time on admin and manual tasks and become valuable and efficient business strategists." "As we approach the next decade, it's clear that organizations in general, as well as the HR experts employed within them, are thinking about how they can climb the data maturity scale to remain competitive, using HR analytics, workforce planning systems, and even AI."

In today's hyper-fast cloud computing era, machine learning solutions drive exponential progress in improving systems. Machine learning's ability to leverage Big Data analytics and identify patterns offers critical competitive advantage to today's businesses. Often used in combination with artificial intelligence and deep learning, machine learning uses sophisticated statistical modeling. These complex systems may reside in private cloud or public cloud. In any case, the passage of time boosts machine learning: as more data is added to a task and analyzed over time, ML produces more accurate the results.

Enterprise organizations recognize the key functions that artificial intelligence and machine learning will play in their businesses in the future, and, depending on what survey you have read in the last year, they are investing to make that future a reality. But what does it take to build a successful AI and machine learning practice? What does it take to go beyond building that practice and actually integrate AI and machine learning tools into the business itself where professionals across the organization touch it every day? How can we make these technologies which promise to provide a competitive edge into the lifeblood of the enterprise. A couple of new Forrester Wave reports, which evaluate predictive analytics and machine learning tools, provide a blueprint for the successful AI-driven business of the future.

Forrester Research has identified 14 leading tools for predictive analytics and machine learning. Vendors included in the research firm's assessment included: Alpine Data, Angoss, Dataiku, Domino Data Lab, FICO, H2O.ai, IBM, KNIME, Microsoft, RapidMiner, Salford Systems, SAP, SAS, and Statistica. Each of these vendors has a comprehensive, differentiated PAML solution; a standalone PAML solution; install base and revenue history; and motivated client inquiries. Seven predictive analytics and machine learning products were selected as keaders among the 14 evaluated. Angoss KnowledgeSTUDIO "Angoss KnowledgeSEEKER is a must-have for data science teams that wish to use beautiful and comprehensive visual tools to build decision and strategy trees," according to Forrester.

Morgan Stanley is on a mission to combine the best of machine learning, predictive analytics and workflow technology with the human touch provided by its 16,000 financial advisers. Despite the popularity of robo-advisers, clients still want a human being to help them navigate complex life questions, according to Naureen Hassan, chief digital officer for wealth management at Morgan Stanley. Recent studies show that 82% of millennials who work with a financial adviser want more time with that adviser, not less, Hassan said. At the same time, clients also want a simple mobile app to keep track of everything when these decisions have been made. "I don't want to be forced into in-person meetings or shipping and signing a bunch of paperwork," Hassan said.

The frustration of trying to find a place to park could soon be over, thanks to a new app that promises to predict where spaces are available. Find and Pay uses big data, predictive analytics and machine learning to reduce the time it takes to find parking in congested urban areas. Easy Park, the Stockholm-based firm behind its creation, claims it could slash your search time for a spare spot in half. The smart software could be coming to a city near you in the near future. The frustration of trying to find a place to park could soon be over, thanks to a new app that promises to predict where spaces are available.