Implementing AI: realizing your business’s machine learning potential

in Advisory, 19.02.2019

It is estimated that businesses will use machines to carry out 80 percent of conversations with consumers by 2020[1]. In my view, this estimate is no exaggeration. The speed with which artificial intelligence (AI) is being adopted across industries in Switzerland is astounding. Yet many companies still view AI as something to be feared. They shouldn’t. AI can support your business in becoming both more efficient and more effective. Crucially, it can lead directly to higher levels of customer satisfaction. In my experience, achieving this requires a clear focus on four areas:

Unearth the value hidden in your data

Take a structured approach. It’s 80 percent again. This time, we are talking about the share of an organization’s data that is ‘unstructured’, according to several studies[2]. This is largely data that are held as text in disparate emails and documents. These data contain incredibly useful knowledge that is neither accessed nor used as it would depend on employees finding, sorting and interpreting it. Implementing AI can help you utilize what information you already hold across your organization, as well as improving the speed and effectiveness of compliance and risk management.

Plan beyond automation

Don’t restrict your potential benefits from AI by continuing to use humans and traditional analytics to process data. AI comes into its own when it is used with data that are difficult to process. For instance, the data sets may be extremely large or complex. The point of AI is that it is quicker and more effective than humans. Applying it properly can uncover insights that may not otherwise be identified, and do so faster in areas such as anti-money laundering, revenue leakage or customer segmentation, among many examples.

Help AI find its voice – example of an insurance company

Have a clear transformation vision and a culture that supports the shift from human to machine-driven interactions with customers. You do not need to implement everything on day one, though you do need a longer-term strategy in place.

Let me use the real-life example of one of our clients, an insurance company that had historically depended on humans to interact with customers but which decided to automate a large part of this work.

Machine learning technologies are being used on the front line for contact with customers. The insurer implemented Chatbots that can retrieve information and provide quotes to customers, whether the contact is online or via an app. Within the contact center, such new tools can increase speed and efficiency by giving the service agent a single user interface. All of the digital solutions work together to create an enhanced experience for the customer.

Furthermore, this client has already started to use emerging technologies to drive greater efficiency. Especially in the back office, where automation through robotics offers major operational improvements. Take the example of contract renewals, where there are big peaks and lows during the year. The heavy workload means that human error can increase, while staff overtime costs also make it an expensive process. Using machine learning tools such as RPA (Robotic Process Automation) can reduce errors, lower costs and increase speed.

Scale your AI strategy

Stop viewing AI as an isolated technology. Instead, align it to the wider business strategy and make sure it is built and developed to be able to scale up across the entire organization. It offers huge possibilities to gain deep insights into large volumes of unstructured data, automate and accelerate existing business analysis, and make customer interactions more streamlined and consistent. Approaching machine learning with isolated, bespoke technologies means that you may end up with pockets of machine learning that are incompatible when brought together, and which therefore cannot be easily scaled.

These four areas are where I believe AI can add the greatest value to the workplace. And how advanced analytics, machine learning and more can help you build a sustainable business that is fit for purpose in our digital age.



[1] Source: The Year of the Chatbot
[2] Source: The biggest data challenges that you might not even know you have


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