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Robotic process automation (RPA) can fundamentally change the way companies operate. While many financial services firms have experimented with the technology, universal best practices have yet to surface, leaving many to learn as they go. Deutsche Bank made significant investments in RPA over the past year, integrating it in trade, finance, cash and loan operations, taxes, and employee training functions. Deutsche Bank achieved 30-70% automation in each area. Dean Mazboudi, former head of Deutsche Bank’s innovation arm says, “I don’t think robots will ever replace humans. But robots will make humans more efficient and smarter.” He also notes automating monotonous tasks can increase employee satisfaction. RPA is poised to be embraced by more companies and industries, and leadership must recognize that RPA can deliver significant benefits if done properly. However, it should not be considered a panacea. To unlock RPA’s potential, start with controlled pilots that produce clear ROI, rethink all processes, and consider the broader firm’s technology architecture and organizational alignment.
While digital technology has disrupted most industries, insurance has been notoriously slow to adapt. Insurance startup Lemonade recently made waves thanks to its user-friendly interface, low prices, and fast payments. By expediting routine claims through algorithms instead of underwriters and chatbots instead of brokers, Lemonade can shift employees to personally handle only the most complex claims. AI and machine learning enable Lemonade to operate below the industry’s 30% expense ratio and attract new market segments: 80% are first-time home and renters insurance buyers. Digital technologies that change and scale with organizations create process efficiencies, but before making the shift to digital-only, analyze existing processes for potential efficiencies and bottlenecks. Inefficient processes carried through to a digital operating model negate a fluid model’s benefits.
Despite numerous structural and cultural barriers that often hinder innovation, the healthcare industry has made great strides towards digital transformation. The entry of technology companies, including Apple, Google, and Amazon, into the sector challenges traditional players to leverage emerging technologies to transform how to deliver care and achieve cost competitiveness. Several healthcare companies experimented with innovations such as facial scans instead of blood tests and tapping wearables’ data to predict health issues. Yet healthcare companies must not chase technology without understanding how it will integrate, complement, and scale with existing operations or enable broader operating model transformation (e.g. fee-based to value based reimbursement). Healthcare must look to other industries for adaptation examples. GE’s shift from selling a jet engine ‘product’ to selling a ‘guaranteed solution’ of zero unscheduled jet engine maintenance illustrates how to create more customer value by integrating new technologies and data with existing capabilities. Healthcare faces inherently unique challenges, but other industries that have undergone technology-related disruption offer important proof points. Healthcare’s new landscape ensures disruption, regardless of political decisions. Organizations that apply technology and data to transform the customer/patient value proposition will flourish.
Many call data the new oil, and companies are racing to build data refineries. The Economist notes that data is “extracted, refined, valued, bought, and sold in different ways.” Companies–including Alphabet (Google’s parent company), Amazon, and Microsoft–invested nearly $32 billion in data centers in 2016, up 22% from 2015. In these centers, companies refine and analyze data in real-time to better understand operations and generate new revenue sources. Data is now many companies’ most valuable resource. In 2015, Caesars Entertainment’s most valuable asset was its 45 million customer database, valued at $1 billion. Challenges arise when companies cannot effectively and strategically manage their data and when they lack a clear vision of what they want to achieve with it. A data strategy is an enterprise-level strategic investment that must cascade from the top business leaders, not IT.
AI and machine learning have moved from hype to reality. They continue to become commonplace in companies of all sizes and stand to become increasingly democratized. Progressively available amounts of standardized and usable public data, exponential growth of processing power of software and hardware, and new tools dramatically reduce costs and remove barriers to entry for companies without billion-dollar market caps and an army of data scientists. Most organizations get lift by simply training their business analysts to think and use data strategically. As tools and skills continue to evolve, forward thinking companies can create more applications in AI and “power the next generation of consumer and business tools.” SSA & Company recently partnered with General Assembly to help train business analysts and teams in data analyst skills.
Advanced analytics and machine learning have enabled many companies to customize consumer experiences in real-time, helping improve retention, reduce churn, and enhance top-line growth. Faced with the highest churn rate in the industry, Sprint deployed an analytics engine to create personalized offers that target customers at risk of switching carriers. The initiative reduced churn by 10%, increased Net Promoter Score by 40%, and increased new line activation and device purchases. Netflix, Amazon, Apple, and others use personalized analytics to recommend items based on buying and browsing behavior. Here, targeting becomes a value-added touchpoint, fostering deeper engagement. By aligning consumer action data with customer experience decisions, companies can better learn about their customers, more accurately predict cost and growth, and enhance their brand and bottom-line in real-time.