November 7, 2017
5 Challenges When Innovating With AI
Early adopters have the potential to reap greater rewards by improving efficiency and customer engagement but face challenges.
Artificial intelligence is booming in insurance. In a recent report, Celent identified AI use cases around the globe and across the insurance value chain.
Uses include customer engagement (USAA’s Nina); product optimization (Celina Insurance Group, Protektr); marketing and sales (Usecover, Insurify, Optimal Global Health, Ping An); underwriting (ZestFinance, SynerScope, Intellect SEEC, Swiss Re); claims (Tractable, Ant Financial, Gaffey Healthcare); fraud detection (Ant Financial, USAA); risk management (Achemea); and business operations (Ping An Direct, Union Life).
Insurers are wise to innovate with AI technologies. Early adopters will face challenges but will also have the potential to reap greater rewards by improving efficiency and customer engagement.
Here are five challenges for carriers to consider when innovating with AI:
1. What technology to use when. When embarking on a digital transformation, there may be a number of solutions available for a given problem, one of which could be AI. But while AI may resolve an issue, it is important to examine all the potential solutions and decide which one is the best fit. Perhaps robotic process automation (RPA), application programming interface (API) or another automated solution is best suited. Can an existing technology be leveraged?
Deciding what solution to apply when requires you to look at the whole organization and all the issues upfront. This allows CIOs and CEOs to examine each problem, decide on the right technology solution and make sure it complements the overall strategy and budget.
2. Big data + AI = big strategy. A second challenge surrounds the management of big data obtained from customers, core systems, brokers/agents and insurance exchanges. Add to that the varied types of data that AI is managing, analyzing, communicating and learning from and things get a little more complicated. Here’s a list of the different data types AI may be working with:
- Structured, semi-structured and unstructured data
- Sensors (IoT)
- Augmented/virtual reality
Data is also classified as real-time, historical or third-party — yet another dimension to consider. Make sure your strategy takes the necessary data variables into account: where data will come from, where it will flow to and how it will be handled at various points in the customer journey.
3. Managing customers across swim lanes. This leads us to challenge No. 3: the ability of AI to engage with customer data at key touchpoints during the customer lifecycle. For example, if Lucy has group benefits as well as voluntary products, car and house insurance, how will her data be managed and optimized across swim lanes?
What will be the touch points for AI? When will other insurtech solutions be present? When is human intervention required? And how will this data be used to inform future risk decisions?
4. Harnessing AI’s multidisciplinary capabilities. AI encompasses machine learning, deep learning, natural-language processing, robotics and cognitive computing, to name a few. You can read my blog post here to learn more. Deciding what technical abilities will be required to solve your problem could present challenges as the lines between disciplines blur.
Additionally, the next wave of AI could come from entirely different industries, such as aerospace, environmental science or health — but it will still have applications for insurance. The best way to overcome this is to examine your AI needs across solutions and select vendors with the right capabilities to execute them.
See also: The Insurer of the Future – Part 3
5. Communicating past tech speak. As AI becomes mainstream, the challenge of helping non-technical business professionals understand these complex applications is real. AI systems can require a level of technical expertise beyond the everyday scope of business.
True digital transformation, regardless of technical complexity, affects everyone in the organization. Ensuring the vision is shared will matter as day to-day operations, tasks and activities change. Find someone who can break down the benefits of these new solutions into bite-sized pieces that everyone understands to ensure buy-in and ultimate success.
The question of whether AI will indeed disrupt the industry or simply enable its full digitization is still not known. It will not be the solution to every problem. However, if implemented strategically, it may hold the capacity to create an entirely new way of insuring — and delighting — customers in a rapidly changing landscape.