Integrating AI and Human Collaboration to Reduce Business Risks

Published Date : 21/10/2025 

Discover how combining human expertise with AI can help businesses effectively manage risks and ensure successful AI integration. 

Artificial Intelligence (AI) is a valuable resource in today’s economy, with the potential to reap great rewards in various business sectors. However, like any emerging technology, it comes with its own set of challenges. According to a recent study by The Hartford, nearly half of the business leaders surveyed expressed risk concerns about using AI.

Whether it’s hesitation about software liability or integration concerns, companies are eager to understand and mitigate their risks as they develop AI tools for their businesses and partnerships. After defining the business problem and assessing the AI project benefits, companies need to outline the potential for failure and how to address it.

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

AI and Risk Management

From a risk management perspective, AI requires a nuanced approach. Currently, there are no global standards for developing AI, and best practices are still evolving. The field is self-regulated, which can lead to unbridled property, financial, and casualty risks. Therefore, it is increasingly important to focus on risk management techniques to improve data quality, testing, warnings, checks, and other processes that can help reduce or mitigate exposure to your business if something goes wrong.

Early Collaboration Enhances AI Quality

Today, most large businesses rely on sophisticated technology and some form 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 this 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. This 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 crucial to ensure that collaboration comes from across the entire company at the very beginning of the AI implementation journey. Otherwise, a company can waste 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 from the outset of a project. This prevents 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 inherent to projects implemented with AI, but it is critical in this arena. 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 fully leverage AI, companies need 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. Ensure everyone is looped in along the way.

Here are some tips to 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 recurring meetings to ensure plans and executions are hitting the necessary marks.

- Develop a plan of action: AI projects need clear outlines and 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 recognize the need to start testing AI to stay ahead of the competition. The challenge lies in managing the risks associated with this technology. Connection across departments and communities is a key piece of success. It’s essential to be risk-conscious moving forward. However, with the rapid pace of technological and AI advancements, 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. 

Frequently Asked Questions (FAQS): 

Q: What are the main risk concerns companies have about using AI?

A: Companies are concerned about software liability, integration issues, and the lack of global standards for AI development. They also worry about the potential for data quality issues and the impact on business operations if AI fails to produce accurate results.

Q: Why is early collaboration important in AI implementation?

A: Early collaboration ensures that all departments are aligned and that the AI project considers all available data. This helps in making more informed decisions and prevents the need to start projects over from the beginning, saving valuable time and resources.

Q: How can siloed data affect AI projects?

A: Siloed data can lead to inefficiencies, miscommunications, and compromised data quality. It limits the AI's ability to interpret and analyze data effectively, which can render the project ineffective.

Q: What are some strategies for fostering collaboration across departments for AI projects?

A: Strategies include identifying stakeholders, creating subcommittees, and developing a clear plan of action. Top-down messaging from leaders and regular meetings to ensure alignment and progress are also crucial.

Q: Why is it important to be risk-conscious when implementing AI?

A: Being risk-conscious helps in identifying and mitigating potential issues that can arise from AI implementation. It ensures that the project is well-managed and that the company is prepared for any outcomes, thus protecting the investment in AI. 

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