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Why Insurers Can No Longer Ignore AI (Part 1)


What was once science fiction is fast becoming business reality. Here’s how insurers should be thinking about and piloting artificial intelligence.

First in our two-part series.

“The business plans of the next 10,000 startups are easy to forecast,” writes Kevin Kelly, founding executive editor for Wired. “Take X and add AI” (aka artificial intelligence).

Kelly’s prediction is consistent with many others. Be it autonomous cars or chatbots, iPhone’s Siri or Amazon’s Echo, artificial intelligence is already touching our lives in many ways. 

Industrial investment and implementation of AI has also risen exponentially. Companies such as IBM, Apple, Toyota and Fidelity have introduced AI platforms and solutions for customers, partners and employees. Toyota’s websites, for example, use AI to ensure manufacturing feasibility and inventory availability for the exact combination of options consumers choose to design a vehicle. 

The insurance industry hasn’t exactly stood still on AI. Vanguard and Charles Schwab use robo-advisors for financial management, and Chinese search giant Baidu is applying AI to reduce insurance risk. 

But AI comes with its own set of caveats that insurers must understand before jumping on the bandwagon — large upfront investments, slow adoption rates and reliability challenges chief among them. We’ll explore these challenges in greater detail in part two of this series. But first, let’s examine the state of AI, how AI can be applied to insurance, and how insurers can map their first steps, based on an assessment of their technical proficiency and AI readiness. 

Looking to the Future

At its essence, AI is about embedding intelligence into machines, thereby enabling them to “do, think, learn” and eventually “adapt” to solving problems without real-time or pre-programmed assistance from humans. (For more insights on the “do, think, learn” automation continuum, read our white paper “Intelligent Automation: Where We Stand — and Where We’re Going.”) Various AI-related technologies, such as natural language processing (NLP), computer vision, robotics and speech recognition, have coalesced in recent years into systems that form AI’s technological foundation. 

In fact, significant commercial activity is underway in every sector. Private investments in AI have grown an average of 62% annually since 2011. In 2014, total assets under management with robo-advisors in the U.S. stood at $19 billion and are estimated to reach $2 trillion by 2020. Driverless cars represents perhaps the biggest single investment, with Uber, Apple, Ford, Lyft, Tesla and others competing for the future. 

With so much activity around AI experimentation and implementation, insurers can no longer afford to overlook AI and its potential implications. Based on our research and analysis, we recommend a two-phased approach to embracing AI:

1. Use AI to assist human workers rather than displace them. 

This should be done in two key areas: 

  • Researching, aggregating and presenting required information to underwriters.

  • Using virtual assistants to improve advisory services through low-value activities, such as lead management, scheduling, planning and licensing. 

2. Evaluate AI pilot programs to turn underwriting claims into dynamic self-learning models.

In the next five to 10 years, virtual assistants, robo-advisors and chatbots could be capable of fully transforming the customer experience. 

How might these stages affect the future of insurance? In our view, the benefits are as significant as they are varied. With AI, insurers can gain continuous insight from structured and unstructured data. The result: Revenue will expand, as insurers explore new product lines and customer segments. Advice and guidance will improve, as robo-advisors eliminate human drawbacks and also assist advisors in honing their skills. Operations could be further simplified, turnaround time reduced, and overall costs lowered with greater productivity. 

Most importantly, organizations that embrace and understand AI ultimately gain a first-mover advantage and, potentially, a competitive lift.  

Three Stages of Maturity

To build an AI strategy, insurance companies must first take stock of their organizational maturity and appetite for AI-powered services. Such an assessment will reveal their AI adoption maturity level: 


Insurers at this stage rely heavily on traditional processes and legacy systems. Most have a limited online and social media presence and are slow to change. Conventional wisdom would dictate that these insurers concentrate on modernizing their legacy systems to improve scalability and service turnaround rather than investing in AI  We believe, however, that these insurers could benefit from easily configurable and implementable AI solutions, such as “smart” chatbots and rules-based personal financial trackers and advisors. This will ensure more efficient marketing and sales efforts at minimal cost to the company, while paving the way for greater use of AI. 


Insurers at this stage have embarked on the digital journey and improved engagement with distribution partners, customers and internal stakeholders. They have begun to implement Code HaloTM thinking and made significant investments in IT solutions that enable “pull” rather than “push” marketing approaches. In our view, these insurers are primed to build a strong foundation for AI takeoff. Examples of basic pilot solutions include the use of virtual sales assistants that manage basic routine work, such as e-mail, meetings and lead search, as well as automated algorithms for customer needs analysis that can then be deployed across all touchpoints, thus ushering in robo-advisors. 


Insurers at this stage are at technology’s forefront. These businesses have advanced point-of-sale capabilities, straight-through processing (STP) functionality and a single view of the customer across all channels. Insurers in this category are in a prime position to disrupt the industry. They are equipped to explore complex AI solutions, such as a dynamic underwriting model powered by machine learning to provide context relevancy for decision-making, claims transformation through intelligent prediction and adjudication, and contact center modernization through voice recognition and interaction mining.

Figure 1

In our second part of this series, we’ll enumerate early AI adopters, key challenges facing insurers and first steps to ensure success.

To learn more, please read our white paper “How Insurers Can Harness Artificial Intelligence” or visit the insurance section of our website.

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Why Insurers Can No Longer Ignore AI (Part 1)