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Thought leadership, insights, and success stories

Driving AI Adoption in Life Insurance


In my last interview with Dr. Michael Wu, we talked about the different types of commercially available AI and what carriers need to do now to prepare for the future of AI. If you missed it, you can read it here.

Today I’d like to address a perennial challenge of insurtech implementations: the willingness (or lack thereof) to gear new technology towards who’s using it. Since AI applications get more than their fair share of pushback, I asked Dr. Wu for his input on how organizations can drive adoption while accelerating the pace of innovation.

There will always be people who drag their heels when it comes to change. How can companies address this issue so they can drive adoption to AI?

Dr. Wu: Even though AI will make our jobs and lives easier, some people will resist it. The AI adoption issue is probably even more pronounced than it is with other types of new technologies. It’s not misunderstanding the technology or finding it difficult to use. Rather, some people see AI the same way that 19th century textile workers in England saw the new textile machinery: as their replacement.

So, the first thing the organization must do is educate their employees about AI. Everyone, including senior management, has to shed those misconceptions they likely learned from sci-fi movies and understand what AI really is. In other words, they need to learn how it works, what it can do, and what it can’t do. Employees will then understand how it can help them do their jobs better and faster. Ultimately, AI will provide them with more time to perform value-adding functions of their role or perhaps even find a career path within the organization they hadn’t considered before.

Companies can further drive AI adoption by designing their AI deployment strategy in three phases:

1. Launch phase: Launch the AI solution quietly with a small pilot team who’ve been carefully selected to ensure design success. This launch phase will allow time to work out any kinks (and they will always arise) before the next step. 

2. Socialization phase: Publicly recognize and socialize the pilot team’s success and how AI helps them achieve performance levels that would otherwise be impossible.

3. Opt-in phase: Create an exclusive training program with limited participation that will equip employees with the skill and knowledge to utilize AI fully. Although everyone can opt-in, the exclusivity will build interest.

Part of my role at PROS is to provide the education and adoption strategies necessary for companies to drive AI adoption and realize AI’s full potential. I’ve shared additional details about this deployment strategy here.

Although it’s possible for AI to replace human decision-making in some scenarios, we’re far from entirely replacing customer-facing positions with AI tech. That shouldn’t be the goal of AI at this stage of the game.

That trust issue goes both ways, doesn’t it? We need our customers to trust AI as well.

Dr. Wu: That’s right. This is a chicken and egg scenario. For AI to work, businesses need data, and much of that data is collected from people. Around the world, however, data privacy is a growing concern, especially in Europe. With the GDPR, consumers have the right to demand that businesses delete any personal data they collect. That regulation presents a problem for organizations and governments. If people don’t allow companies to collect and retain data, how can they use AI to deliver better goods and services at a greater speed and at less cost? And if AI doesn’t work due to the lack of data, no one will adopt it because it won’t provide value to consumers. This lack of value will in turn exacerbate their reluctance to share data.

Of course, this hesitancy to share data varies by region. Again, Europeans are concerned with privacy; whereas, in China, people are open to sharing data. Consequently, AI technology can advance much faster there. Still, we have to ask ourselves where we draw the boundaries. And what are the tradeoffs between privacy and using AI to create better products and services, or for that matter, to grow an individual business or an entire economy?

We need to face all of these challenging problems as human beings. No doubt, it will depend on finding the right balance.

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How do we find that balance? 

Dr. Wu: It isn’t easy. The challenge is that the pace of change is so fast people have a hard time adjusting. And since an innovation such as AI builds on itself, the pace of change speeds up exponentially. Striking a balance means setting a pace for innovation within an organization or within the broader community that allows most people the time they need to adjust to change. While this approach may not be as fast as some of us would like, we have to be realistic. Nevertheless, striking a balance doesn’t mean it’s impossible for organizations to turbo charge their pace of innovation. 

We find that many insurance carriers want to innovate, but it isn’t in their DNA. What advice would you give them?

Dr. Wu: This is the classic Innovator’s Dilemma as described by Harvard Professor, Clayton Christensen. It’s difficult for an organization to innovate while keeping the rest of its business running. Insurance, as you’ve described it, is an excellent example. Most organizations follow a traditional business model, maybe one that they’ve used for decades or even centuries. That model wasn’t designed to promote innovation, but it works for the industry.

One way to drive innovation without upsetting what already works is the intrapreneur model. One can think of an intrapreneur as an entrepreneur whose startup is within an existing company. A common type of intrapreneurship involves creating an internal ”skunkworks” team that’s tasked to innovate. They aren’t kept under the current business constraints of the parent company, meaning they have considerable autonomy—just like a startup.

This model is common in Silicon Valley where innovation lives and breathes. Even the most innovative tech companies often have these internal skunkworks teams.

Who do these teams report to?          

Dr. Wu: That depends on the organization. They might report to the CTO, or they could directly report to the CEO as a separate organization. Intrapreneur programs differ from conventional startups because they can tap into a parent company’s resources and capital. In addition, the parent company can support an intrapreneur program by providing a wide depth of industry knowledge; opportunities to test, pilot, or validate its new ideas; and even access to technology and marketing resources. As a result, the success rate of intrapreneurs typically is higher than that of traditional entrepreneurs.

That sounds remarkably like the approach we’ve taken here at RGA with RGAX.  Thank you for your time, Michael.  

Facing a challenge driving innovation in your organization? RGAX offers Innovation-as-a-Service solutions for carriers and insurtech developers. Please contact us for more information. 

Putting AI to Work in Insurance - Download eBook

Written by: Leo Wong

Leo Wong is Managing Director, Asia for RGAX. Based in Shanghai and Hong Kong, Leo leads a team that invests in, incubates, and grows new insurance industry-focused ventures in machine learning, life sciences, insurtech, and data analytics.

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