"Artificial intelligence analyzes data to detect patterns and determine potential responses to different stimuli," Otto says. "By applying AI to employee benefits, we can detect the different ways members should be using or accessing their benefits and then respond as needed to help guide them to the right resources.

"For example, AI not only identifies those members who have chronic conditions, but then helps lead them to programs that can help them take steps toward better health. Similarly, for those not adherent to medications, AI can send alerts and connect members with a coach or other benefit to get them on track."

Degner compares AI applications in the benefits industry to what it already does for consumers. "At first, I was happy just to ask my Alexa to play music and set timers, but now it's the hub for my home. Alexa shops for my groceries, provides news stories, organizes my day and much more."

What could Alexa and AI do for health care? Here are a few examples:

• "Alexa, what's left on my health plan deductible?"
• "Alexa, find a provider in my network within five miles."
• "Alexa, search for Protonix pricing at my local pharmacies."
• "Alexa, find a five-star rated health insurance broker within 20 miles."

AI can ultimately improve customer experience by delivering a better product at a better price and with greater convenience.

"AI is coupling claims data with online behavior within pricing transparency tools, engagement platforms and more, to drive people to high-value care that can help close existing gaps in care," Otto says. "Because AI looks at vast amounts and types of data, it can be applied to both health plans and other benefits to maximize value and drive engagement.

"For example, these tools can help determine whether members are enrolled in the best plan for them and using their benefits appropriately, and then help direct them to the resources that will be most impactful."

Benefits applications

The variety of ways in which AI can be applied to the benefits industry is limited only by the human imagination, says Dan Shields, vice president, product specialist of data-driven solutions for Health Advocate.

"By utilizing and analyzing massive amounts of data, we can apply artificial intelligence to build and run multiple models to anticipate what will happen next and respond accordingly," he says. "In the employee benefits landscape, AI allows us to anticipate needs, such as predicting high-cost claimants in advance, so we can proactively address potential future expenses.

"From identifying who will be high-cost claimants in the future, to plan selection tools, to resources to coach people toward better care, existing AI tools and capabilities are already enabling both brokers and HR pros to get ahead of the curve."

One of the biggest challenges that brokers face is evaluating the array of new AI products to determine which are best suited for their business and client base.

"Brokers should ask themselves, 'Does the technology actually lead the member to make a better decision or take action to improve their health?'" Otto says. "AI has so much potential, but we need to be smart in how we apply it. The most effective programs provide multiple vehicles or channels to meet each person where they are and connect and communicate in their preferred channel—which is more effective than a one-size-fits-all approach."

Degner advises brokers to beware of what she calls MVP: the minimum viable product. "It can be tempting to adopt or release new technology with speed in mind. There's definitely a benefit to moving fast, but it's far better to be sure a new technology is reliable and helpful to your team and clients than to focus on a specific timeline. A product rolled out before it's completely polished could come with glitches that make it unreliable and frustrating for users."

Also important to consider is how the new product fits in with the company values in areas of service, sales and marketing.

Finally, Degner recommends ensuring you keep the focus on the customer by asking these three questions:

• Is it easy and intuitive to use?
• Does it create more work or frustration for the client?
• Are there newer trends that could add value?

The human component

One of the greatest concerns with any new technology is that it might one day take the place of humans. This is especially true in what has traditionally been a highly personal business.

"As great as AI is and has the potential to become, it is important to remember that most people still want someone to talk to about their health and benefits," Shields says. "While AI, machine learning and similar approaches provide actions for members to consider, as well as the tools to do it, people may still need encouragement and support from a live expert in order to take the next step. When evaluating new tools, it is important to find a program that integrates high-tech and high-touch support in order to maximize success and help users navigate and put the AI recommendations into action."

Degner agrees, noting, "If used correctly, it can only enhance the relationship between brokers and clients. Brokers are going to be seen as advisors and advocates who are coming to the table with a deeper understanding of the metrics and a more-personalized experience. There will not be a machine substitute to the advisor role that brokers fill."

However, brokers must be more vigilant than ever about cybersecurity and data protection. "Data is what makes machine learning and AI work," Degner says. "Without data, there's nothing to mine, and that means there's no information from which to learn. Due to the sheer amount of data that machine learning technology collects and consumes, privacy will be more important than ever.

"Also, there's potential for a lot more personally identifiable data to be collected. It will be imperative that companies pay attention to masking that data so the specific user is not revealed. These steps are critical to ensure you and your customer are protected as laws continue to catch up with this emerging technology."

This data must also be managed efficiently to maximize results.

"You're already collecting a lot of data on your clients, of which you may not be taking full advantage," Degner says. "As more data are collected, it's important to keep it organized so that you can use it effectively to gain insights and help your clients. Data architecture is a set of models, policies, rules or standards that govern which data is collected and how it is stored, arranged, integrated and put to use in data systems and in organizations. If you're not already thinking about this, it might be a good idea to consult with a developer to get help."

The bottom line is that AI can be a powerful tool for brokers who are prepared to take advantage of it. "Brokers need to educate themselves about the latest developments, stay on top of it and be open to new ideas," Shields says.

Brokers have already demonstrated the ability to be flexible and learn new skills as they arise, Degner says. "They should do the same with technology—educate themselves through articles and websites about technology. Pay close attention to how other industries are taking advantage of them and stay vigilant. Remember, shiny and new is great, but longevity is found in focusing on the right outcomes for your company and customer."

Read more: 

• How will AI affect your business?
• How technology can help doctors recommend cheaper alternative drugs
• Machine learning ready to rock the health care industry