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Five Steps to help your organisation implement Machine Learning technologies

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In fact five hundred CIOs were recently polled for the annual Global CIO Point of View Survey and nearly 90% are using machine learning in some capacity, with most developing strategies or piloting the technology. But, despite investing in machine learning, the new survey indicates that most CIOs do not have the skilled talent, data quality and budgets to fully leverage the technology. For most CIOs, many decisions still require human input. Only 8% of respondents say their use of machine learning is substantially or highly developed, as opposed to 35% for the Internet of things or 65% for analytics. According to a McKinsey study, the three main challenges companies have related to machine learning are designing an organisational structure to support data and analytics, having an effective technology infrastructure, and ensuring senior management are involved.


How can artificial intelligence assist the financial services industry?

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There is huge amount of noise currently about the use of artificial intelligence (AI) in the financial services sector. Every fintech application or new piece of banking software must be accompanied by bold claims about its use of AI, even though in many cases they are simply upgraded algorithms. It would certainly be beneficial if we had an AI application that could cut through the spin and present us with only what is relevant to the specific requirements of our job or organisation. In truth, such advanced filtering applications are already available. They are just one of the AI-driven applications that are going to transform the way customers view financial organisations, especially in the retail banking and lending sector.


Customer Experience: It's Not Rocket Science. It's More Complicated. - eTouchPoint

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It is apparent in almost all markets around the world now that growth comes from keeping and growing current customers. This brings the importance of CX to the forefront across organisations. Customer experience is not rocket science. So what has made it this way? A combination of classical silo-based organisation structures, misaligned KPIs, disengaged and unempowered employees, lack of an all-encompassing customer-culture, escalating consumer expectations in the digital age–and most importantly, power in the hands of customer through social media–has made customer experience difficult for the best of brands.


Workforce of the future: The Red World in 2030

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My skills tend to be important right at the start of a project – I'm the innovative design end – so it's normal for me to move on to the next job after a few months or a year (or days, sometimes, if an idea doesn't work). There are a few people I meet on jobs regularly but so many people are competing to get into 3D work, more often than not I'm working with new faces. This project has taken space at the 3D Warehouse in Edinburgh. I love the fact that it's always packed full with people sparking ideas off each other, usually in several different languages at once (thank goodness for instant translate)! VR lets us get the right people together easily, but there's no substitute for being in the room when people are getting excited about an idea.


Why the Organisation of the Tomorrow is a Data Organisation

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The fast-changing, uncertain and ambiguous environments that organisations operate in today, requires organisations to re-think all their internal business processes and customer touch points. In addition, due to the availability of emerging (information) technologies such as big data, blockchain and artificial intelligence, it has become easier for startups to compete with existing organisations. Often these startups are more flexible and agile than Fortune 1000 companies and they can become a significant threat if not paid attention to. Therefore, focusing purely on the day-to-day operation is simply not enough and organisations have to become innovative and adaptive to change if they wish to remain competitive.