Chapter 6:
Challenges with Implementing Artificial Intelligence
Despite AI's indisputable benefits, legitimate obstacles prevent carriers and contractors from integrating intelligent solutions into their digital ecosystems.
Furthermore, various scenarios can arise that threaten AI's long-term success for organizations in this sector.
Below, we detail the most common roadblocks to getting started with AI, as well as the critical factors that prevent organizations from maximizing AI's value after implementation.
Changing weather patterns requires updated regulations to protect against damage from catastrophes.
Complex Insurance Regulations
The insurance industry is one of the most heavily regulated in the world. Insurers and their partners must follow complex and stringent legal requirements to maintain compliance.
In the United States, each state has its own set of regulations and compliance requirements for insurance carriers. This also governs the types of technology insurers can use. There are regulations for how insurers can build rating systems and pricing models, how they should handle the private data of policyholders, and so on.
These regulations impact how insurers use hazard models to set pricing and establish reinsurance purchasing procedures. Before allowing the use of certain models, state regulators look for disparate impacts, scrutinizing complex algorithms and predictive models to ensure they do not result in unfair discrimination on the basis of a protected characteristic (like race, age, gender, income, religion, national origin, etc.)
Most states require carriers to submit rate filings that comply with a multitude of standards to ensure that the models are appropriate for the intended use. Specifically, models must support rates that are:
- Not excessive
- Not inadequate
- Not unfairly discriminatory.
State regulators face a dual challenge: they must enforce complex rate filing requirements while constantly updating rules to address the increasing probability and severity of catastrophic weather events. This makes for an overwhelmingly complex and burdensome regulatory atmosphere.
To remain compliant amid these moving parts, carriers and their partners must establish data governance processes that address how to handle all the complex and varying rules in every state where they have policies in force.
Only after establishing the proper data governance structure—an invariably complicated process—can an organization determine how Artificial Intelligence can operate in their digital workflows.
Even with clearly defined data governance policies, finding a place for AI often involves significant scrutiny; most state regulations still have yet to detail specific AI technology-related requirements for Insurtech.
As a result, insurance carriers and other stakeholders in the property insurance ecosphere require guidance on how to proceed with AI.
Given that AI systems ingest vast datasets—including sensitive, personal information of policyholders—the stakes couldn't be higher for compliance and data security
Compliance requirements that vary between states have created an overwhelmingly complex, and burdensome regulatory atmosphere.
Data Silos and Low Data Quality
The most effective way to operate in this sphere is to execute all insurance processes as one unified workflow. For instance, instead of underwriting functions operating independently from other parts, they should feed directly into policy servicing, claims, and other critical engagement points. Data collected for one function is often relevant to others.
A key step toward providing the best, most efficient experiences for everyone is to dissolve departmental silos. This approach requires all teams to be anchored by a single source of up-to-date, accurate data that maps the entire policy journey.
Without a centralized source of truth for the entire insurance operation, information gaps are inevitable, especially for organizations early in their digital transformation journey. This fragmentation leads directly to one severe consequence: the creation of data silos.
A data silo is a collection of information controlled by one function or group and inaccessible to other stakeholders or business units. These systemic walls inevitably lead to data inaccuracies and critical workflow discontinuities, creating bottlenecks that stall progress across the entire insurance lifecycle.
For example, underwriting teams often use one tool to store data, while claims teams use another platform that’s incapable of syncing with outside sources.
As a result of disjointed systems for finding and storing data, multiple professionals working on the same claim can easily find themselves working with entirely different, contradictory information.
Remember the saying “garbage in, garbage out?”
Data silos are a massive culprit of garbage data, which poses a significant roadblock to AI implementation. Efficient AI relies on large, comprehensive sets of consistent and high-quality data.
Data silos are common, for one, because of legacy technologies that businesses are hesitant to replace. Also, insurance companies do not traditionally function as one collaborative, holistic ecosystem; the industry hasn't always embraced the idea of cross-functional collaboration.
Until stakeholders across the property insurance ecosphere view their functions and individual datasets as part of a continuous, unified lifecycle, they will severely diminish the power and return on investment (ROI) of any AI-driven system.
To realize the transformational benefits of AI, insurance companies and their business partners must confront these gaps head-on.
It is imperative to invest in data management and architecture that enable the secure sharing of accurate, up-to-date information. There is no room for silos when the most effective insurance lifecycles are powered by seamless, comprehensive intelligence.[18]
[18] https://connect.comptia.org/content/research/emerging-business-opportunities-in-ai
Workers need to understand the value of AI and how it will complement—and not displace—them.
Computational Expenses
It quickly gets expensive to replace legacy technology with any new system. AI is no exception.
Any business that considers such an investment should first carry out a careful evaluation. This review must weigh the known, immediate capital expenditure against the core challenge: while a sustained increase in natural disasters and claims is inevitable, the precise degree of that future financial risk remains highly uncertain.
Depending on a company’s specific business objectives, growth goals, and the nature of its customer base (including its geographic location), certain investments may not make sense.
That being said, there are also risks associated with delaying investment in AI.
Are you prepared to evaluate the cost of forfeiting competitive advantage by either failing to adopt AI, or not implementing it at a high enough level?
Sometimes you have to spend money to make money.
Company-Wide Buy-in
Achieving long-term Return on Investment (ROI) with any technology involves more than just identifying a sound solution. Continued success depends on widespread usage and understanding of the system's benefits. This is especially true for AI; it can only be optimized when the entire organization is aligned on how to leverage the new system effectively.
Still, getting people willing, let alone excited, to use a new platform is an inevitable challenge.
The property insurance and restoration sectors, in general, are slow to digitalize, thus increasing the possibility of worker hesitation posing a significant challenge to AI adoption. Also challenging is the fear that people will lose their jobs to technology that can perform duties and responsibilities more quickly, accurately, and efficiently.
The key is finding a way to get people to understand how AI will complement—and not displace—them.
For AI to transform any organization, leaders must take ownership of the narrative and invest proper time and effort in organization-wide AI literacy. Every employee must feel confident in their value, understanding that AI will free up more opportunities for them to innovate and shine in high-value, complex activities.
The ultimate success with AI depends on teams not just accepting it, but embracing it.
The Ultimate AI Challenge: Achieving the Right Balance Between AI and Humans
Too much of anything is never a good thing—even efficiency-driving artificial intelligence
As more of the property insurance ecosphere adopts AI, organizations must inevitably draw a distinct line where technology stops and human oversight retains ultimate authority.
There will always be instances when technology, although faster, cannot process data as a human would. And regardless of how rare these occurrences are, in the insurance world, a single decision at any point in underwriting, claims, or restoration processes can have a meaningful impact on the lives of real people.
At the foundation of every insurance-related business, there must be a hybrid of innovative technology-backed workflows and human authority. Striking the right balance is a critical challenge that every company must achieve in order to ensure that they consistently leverage AI optimally.