In 2022, 25% of insurers will report widespread adoption of artificial intelligence. Although most of the top 50 insurers have AI implementations, there are still other industries where adoption is more mature. A few examples:
- Research from Google shows that 76% of automotive OEMs (original equipment manufacturers) use AI in day-to-day operations.
- A study from KPMG shows that over 50% of retail executives say AI is at least moderately functional within their organizations.
- Lastly, 90% of large pharmaceutical companies initiated AI projects in 2020, likely spurred by the critical role that AI played in developing the Covid vaccine.
All these are examples where outside industries may be advancing more quickly along the AI maturity curve than insurers as a whole. The ways that they’re investing in AI can potentially guide insurers towards making better decisions about deployment and usage. Here are three lessons that insurers can apply to their own adoption plans.
Automotive – finding better ways to integrate the human and AI workforce
When you picture the state of AI in auto manufacturing, you’re likely to envision metal welding arms equipped with welders and rivet guns, safely screened off from the human workforce. While the robot arms haven’t gone anywhere, more recent auto manufacturing investments in AI are a lot more human-friendly.
“Cobots” – a combination of “coworking” and “robots” are perhaps the biggest expression of this change. These will usually take the form of small carts with padded sides that are programmed to follow behind a human warehouse picker.
As the picker finds parts that are needed for the assembly line, they place them into the cobot. Once the cobots are full of parts, they plot their own path to the assembly line, unload themselves, and return. The robots always choose the most efficient routes, which means that pickers experience much less downtime.
The lesson for insurers is that this successful application of AI doesn’t replace human workers, nor does it silo humans and AI apart from one another. Instead, this solution is simple, safe, and intuitive for humans to use. It leverages human dexterity and robotic pathfinding to make both solutions more productive and efficient.
Retail – using AI to improve the customer experience
Traditional brick and mortar retailers have recently stepped up AI. Here, investments have skewed towards improving the customer experience without scaling the workforce.
There is a cross-industry worker shortage that has been affecting retailers. For these companies, improvements to the customer experience must take place without adding workloads to human employees.
As such, retailers have been adding AI solutions such as chatbots and virtual call centers. These can analyze natural language and then route customer inquiries through appropriate channels, while escalating more complex queries to human workers.
Although you might think that chatting with robots might degrade the customer experience, it turns out that customers are starting to like them. As of 2022, 40% of users prefer chatting with virtual agents as opposed to human customer service representatives.
For insurers, this proves that it’s possible to invest in AI without losing the human touch. As long as the solution can understand what policyholders are saying and answer their questions effectively, they’ll enjoy – and sometimes even prefer – their experience. Meanwhile, human claims adjusters will appreciate that they can spend more time working on more complex cases.
Pharma – Using AI to streamline mountains of data
Clinical trials generate data which includes dosage information, administration methods, health metrics from the participants, and more. This can be a lot to sort through, and some of the data may need clarification. For example, Participant X got sick, but was it because of the trial medication or because of unrelated food poisoning? The former is an important data point, but the latter is a false positive and investigating it will waste cycle time.
Insurance claims also have this issue. There’s a lot of information – the make and model of two vehicles involved in an accident, the name of the responding officer, the age of the drivers – but it’s only relevant some of the time. To speed claim resolution, they need to flag important information and discard the rest.
In the pharmaceutical industry, any delay in interpreting the results of a drug trial will directly impact time to market. This can hurt drug makers competitively, while also impacting patient wellbeing. Meanwhile in the insurance industry, any delay in claims resolution might cause customers to churn, or potentially impact their health and livelihoods.
Pfizer knew that time-to-market was critical in the aftermath of its Covid-19 vaccine trials. Instead of relying on manual efforts to sort, clean, and optimize their data sets, they turned to a purpose-built machine learning algorithm. This choice saved an entire month of development time, allowing the manufacturer to start saving lives that much sooner.
Insurers also have a lot of data to process, but only some of it is instrumental. When insurers improve their speed in terms of getting useful data from claims documents, they can speed the claims process itself. This improves the customer experience and helps policyholders recover.
AI lessons for insurers: the final takeaways
Outside industries have found several positive outcomes from AI investment. These benefits all represent improvements that insurers can replicate.
- Auto manufacturers are pioneering innovations when it comes to improving the relationships between human workers and AI.
- Retailers are using AI to improve the customer experience – without adding to human workloads. Customers don’t seem to mind the increased AI interactions.
- Pharmaceutical companies use AI to reduce the time it takes to help patients by cleaning data from drug trials.
By considering these investments, insurers can create more seamless interactions between humans and machines by investing in more autonomous AI. They can improve the customer experience by augmenting their workforce with AI that uses natural language processing. Lastly, they can speed up the claims process by using machines to clean up incoming data.
Shift Technology thinks about these topics every day as part of the way that we help our customers. Our AI-powered solutions are designed specifically to solve these challenges for the insurance market, which means that our customers can:
- Use AI-based intelligent decisioning to support claims handlers
- Improve policyholder interactions
- Speed claims resolution
If you’d like more information, we just released a new white paper that goes into more detail on the benefit that insurers can expect from AI investment.