Changing AI Perceptions To Drive Widespread Adoption In Traditional Industries

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Changing AI Perceptions To Drive Widespread Adoption In Traditional Industries

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Changing AI Perceptions To Drive Widespread Adoption In Traditional Industries

Changing AI Perceptions To Drive Widespread Adoption In Traditional Industries

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They say that every gray cloud has a silver lining, so we can assume that the grayest cloud in recent memory — the pandemic — has a silver lining of its own. Artificial intelligence (AI) and data analytics, for example, were two areas that flourished during the lockdown and will continue to grow.

A P&C Industry Case Study

During the pandemic, one of the biggest challenges for many businesses was the limited amount of time that could be spent in person with clients. While few industries remain that still rely on manual processes, property insurance is certainly one of them. When lockdowns forced property and casualty (P&C) insurance professionals to assess properties and fulfill claims remotely, firms that were previously bogged down by paper-based and in-person tasks needed to adopt more digital and remote workflows to continue serving their customers.

For the P&C industry, the past year has also presented other challenges. The U.S. was hit by 22 weather and climate disasters that each created more than $1 billion in damages. People and their properties affected by these weather events needed help quickly from insurers, forcing industry professionals to process claims faster than ever.

Of course, the insurance industry was not unique. Enormous challenges brought on by remote working affected every sector and how they responded distinguished the innovative and customer-focused companies from the rest. One year later, the early adopters are reaping the benefits of devising entirely new ways to solve these business problems virtually that have put them ahead of their competitors.

For example, in P&C insurance, standard procedures for underwriting and claims at times require a professional to go to a home or commercial building in person to perform an inspection and collect a significant amount of property information. They make a broad range of estimates about the property — building height, roof condition, tree coverage — that can be very difficult to capture accurately from the ground.

In-person insurance inspections were not preferred by homeowners or insurers prior to the pandemic either. They were time-consuming, costly and prone to human error. Adding to that, there was the potential to contract or spread Covid-19. Aerial imagery analyzed using AI, something my company offers, became one way to underwrite and process property claims. It allowed property insurance assessments to be completed fully virtually and freed up time for humans to focus on more important tasks.

Making AI More Approachable

AI, machine learning and big data are still unfamiliar and sometimes intimidating topics for many business leaders, but — as was exemplified in the insurance space — the pandemic amplified their practical applications. In the technology world, AI is used widely, but it remains a mystery in many other industries.

If barriers to adopting AI include the costs and risks of hiring a team of AI experts to build a proprietary solution, a compelling workaround would be for companies to partner with industry-specialized providers. When presented in the specific context of their industry, AI can quickly become a far easier concept to grasp, implement and scale. Specialized AI tools motivated even the most traditional companies to start investing in them, and they have now gained a real competitive advantage over industry laggards.

For AI companies looking to break into specific industries, it’s crucial to be able to communicate the unique value propositions the technology has for each individual vertical. AI is viewed as a very complex topic, but building the solution and benefiting from its power are two completely separate business ventures.

In order to break down the business barriers that typically curb AI adoption, providers need to have highly adaptive business models and messaging frameworks, as well as substantial use case examples in each industry that they serve. It’s important to focus your time and resources — especially in the startup phase — on the things you do better than anyone else. If your AI company is too generalized, it will be more difficult to instill confidence in customers that service niche industries, especially those that are highly regulated.

An AI Revolution

When we all return to the office (in whatever form that takes), AI will continue to demonstrate its value. Implementing AI initially is the biggest hurdle, but we’ve reached a pivotal point where there are experts in nearly every industry who are solving the most critical pain points using AI. We're also seeing company leaders who were historically resistant to change start to understand and invest in this technology as a means to stay ahead of their competition and keep up with the demand for services.

As data literacy increases with more widespread adoption of AI, businesses will start to identify new practical uses for the information they’re collecting and how machine learning can aid in that process. Armed with arsenals of data, businesses can more effectively keep up with customer demands and ultimately demonstrate that — even in the most traditional industries and the most dynamic markets — AI and other high technology are the keys to the future.

Arturo will play a big role in identifying and reducing fraudulent claims. The ability to identify pre-existing issues will save money on claims in the future.

Arturo Customer 4

Arturo allows us to identity customer safety issues. We can see what a home is made of and identify potential risks that the customers may be exposed to.

Arturo Customer 3

Previously, we'd spend half an hour searching for properties. This time is now spent on value adding activities.

Arturo Customer 2

Changing AI Perceptions To Drive Widespread Adoption In Traditional Industries