December 7, 2017
Organizations across the industry spectrum are being reshaped and redrawn from technological disruption. Some argue that this is perhaps most notable in banking/financial services, retail, transportation and media/publishing. In some cases we are seeing immediate market disruption that is forcing through new customer facing businesses.
“I’ve heard this phenomenon referred to as ‘The Uber moments’ - meaning that almost overnight an entire industry can experience significant disruption based on a single company’s innovation.” Gary Oliver is the Chairman and CEO at Razorthink. He points to the prediction that by 2020, 40% of market leaders will be out of the top ten.
“AI-driven customer insights have surfaced as the requirement since we have entered the age of Digital Disruption”. Oliver believes that the future industry leaders will be the experts that can predict customer behaviour. For him AI is uniquely suited to make superior predictions about customer behaviour. Today, Razorthink have created the first AI Data Scientist, Big Brain for global businesses. Big Brain is a deep learning platform that empowers enterprises to solve complex business challenges across an adaptable and multi-purpose framework. Razorthink say that the platform can dramatically improve customer insights beyond the capabilities of human data scientists.
Disruption across Industry
“In financial services, traditional services are being commoditized as Fintech start-ups and tech giants like have begun offering non-traditional financial services. These are eroding the stickiness between consumers and banks. Fintechs are in a position to offer consumers greater convenience through innovative digital services. Tech giants already know the behavioral patterns of consumers, which can be leveraged to entice consumers to use new financial products. Some leading banks now realize they need to transform from ‘product-centric’ to ‘customer-centric’ organizations.”
Disruption is pushing fast through other industry sectors. “Telecom Providers are now realizing the need to expand from heavy revenue reliance on minutes/lines/data to leveraging their customers’ behaviour to offer new services. Similarly, retail has traditionally been a seasonal business, but the future of the sector is also about becoming customer-centric.”Oliver believes these realities are forcing organizations to rethink their business strategies and find new ways to strengthen their competitive advantage.
There are many challenges for deriving customer insights at scale and these revolve around the sources and resources for managing data. “Predicting customer behaviour at scale is not trivial. This is especially true for businesses who want to take immediate action to capitalize on predictive insights. And with 90% of the data in the world today created in the last two years, organizations are now flooded.”
“Added to this is the fact that data is now coming from more sources with increased complexity. Companies have structured and unstructured data, data from multiple channels such as online, mobile, point-of-sale, and possibly data from IoT devices. Furthermore, Data experts are in short supply. By 2018 the U.S. alone will experience a shortage of 190,000 skilled data scientists!”
The good news is that AI has become a practical solution for monetizing the explosion of data that companies have collected over the past few years
“Data is the fuel for AI. And the more data you have, the more accurate machine-learning algorithms become. The exponential increase in computing power with the use of GPUs rather than CPUs has been a key driver in enabling AI. There has also been recent improvements in AI algorithms that enable a hybrid of supervised and unsupervised learning models. The combination of these advancements has created an opportunity for companies to finally take AI out of the lab and into the core business to improve business outcomes.”
Key Priorities for Implementing AI
It is vital for an organization to consider the strategy that works for their organization and what the implications of AI could mean. In this way adaptability should be the absolute priority when considering implementation.
“For a company to have an AI-driven business, they need an AI platform that can solve multiple business challenges by bringing components together quickly and easily. This will enable the business to create intelligent solutions previously impossible using traditional analytics or data science technologies. Ultimately the multi-solution platform approach accelerates time-to-market for new AI system creation and encourages companies to lead with sustainable advantage.”
Oliver believes Deep Learning is therefore a must-have capability. "Deep Learning as fundamental feature of the AI platform because of its adaptability over time and its ability to identify nonlinear patterns not predetermined by humans."
Innovating with AI
With emerging technology offering companies a huge window of opportunities for competitive advantage, Oliver believes we are now at a unique point in time and AI is informing business actions in a number of key areas:
? Predicting customer behaviour using micro-segmentation and deliver highly accurate recommendations for products and services in time to impact outcomes.
? Delivering AI-informed customer service and enhanced customer experiences through delivering real-time intelligence to bots and/or human service reps.
? Gaining superior fraud detection by identifying patterns not predetermined by humans and ‘know what you don't know.
? Anticipating and proactively determine credit risk in real-time to deliver digital micro-loans.
? Automate manual processes based on AI decision-making models combined with embedded domain knowledge and human input.
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