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Artificial Intelligence (AI) is a valuable resource in today’s economy with the potential to reap great rewards in many avenues of business. However, as with any developing technology, it comes with new challenges. In a recent study by The Hartford, nearly half of the business leaders surveyed said they have risk concerns about using AI.

Whether it’s hesitation about software liability or integration concerns, companies want to understand and mitigate their risk as they build AI tools for their businesses and partnerships. After defining the business problem and assessing the AI project benefits, companies need to outline the possibilities of failure.

A key component of a company’s plan should be an emphasis on how to integrate the many silos or departments for optimal results.

AI and Risk Management

Looking at AI from a risk management perspective reveals a need for nuance. There are currently no global standards for developing AI, and best practices are still developing. Today, the field is self-policing with unbridled property, financial, and casualty risks. So, it’s increasingly important to focus on risk management techniques to improve data quality, testing, warnings, checks, and other processes that will help reduce or mitigate exposure to your business if something does go wrong.

Early Collaboration Enhances AI Quality

Today, most large businesses run on sophisticated technology and some type of AI model. According to the Stanford Human-Centered Artificial Intelligence (HAI) Institute, in 2024, the percentage of survey respondents reporting AI usage by their organizations jumped to 78% from 55% in 2023.

If that technology, including AI, fails to produce accurate and practical results, it can impact functions across the company. For instance, if project leaders are not aware of–and able to access–all data across departments, they cannot instruct AI technology to include it. That means the end user will think they are making decisions based on all available information, when they are not. Fostering an atmosphere of cooperation across the company can help protect the time and money invested in AI and pinpoint issues as it is implemented.

It is important to ensure that collaboration comes from across the entire company at the very beginning of the AI implementation journey. Otherwise, a company can run the risk of wasting valuable time and resources and may have to start projects over from the beginning.

Connecting the Data

Departments that operate in silos limit the capacity of AI to interpret, analyze, and share data. To address these new challenges, modern strategies value collaboration across company departments at the outset of a project, preventing the inefficiencies and breakdowns that can hinder progress and compromise the result.

Relying on siloed data when attempting to blend multiple sources of learning can create inefficiencies, miscommunications, and compromised data quality. The need for interconnectivity isn’t native to projects implemented with AI, but it is critical in this arena as any breakdowns from the beginning can render the full scope of a project ineffective. For all the effort placed on an AI project, changing the silo mentality is worth the time.

Connecting the People

To really use AI at its highest level, companies need to have buy-in from their employees and an agreement among departments to share and explore this new technology. Top-down messaging from leaders can jumpstart the process. Don’t just announce the rollout of AI in the workplace. Explain how it fits into the company’s goals and how it helps address issues and needs across departments. Make sure to loop everyone in along the way.

These tips can help with planning for AI collaboration across departments:

• Identify stakeholders: This should begin and end with risk managers but also include all business units and other departments, such as compliance and legal.
• Create subcommittees: Communities of employees with a vested interest in AI can help establish objectives and promote information sharing. Include a variety of stakeholders to create smoother implementation processes and set up these communities with reoccurring meetings to ensure plans and executions are hitting the necessary marks.
• Develop a plan of action: AI projects need outlines and clear objectives stating what falls within and what is out of scope, so that all parties know exactly how the project is expected to run. Just like AI learns as it goes, so should the team. Managers need to be prepared for all outcomes and pivot as needed.

Most companies understand the need to start testing AI in order to stay ahead of their competition. How to manage the risk associated with this technology is the challenge. Connection across departments and communities is a key piece of success.

It’s essential to be risk-conscious moving forward. And yet, with the increased speed of technology and AI changes, the bigger risk is doing nothing at all. Artificial intelligence is transforming the landscape of insurance and risk management. Explore additional perspectives and insights on business technology.

Topics InsurTech Data Driven Artificial Intelligence