4 Key Questions to Ask About Generative AI

GenAI represents game-changing possibilities but, like any new technology, comes with potential pitfalls that organizations must address. 

An artist’s illustration of artificial intelligence (AI)

It’s the rare organization these days that has not at least begun to contemplate using generative AI (GenAI) to unlock new opportunities and enhance workflows. There is good reason for this excitement; GenAI represents game-changing possibilities across industries, including insurance. But, like any new technology, it also comes with potential pitfalls that organizations are well-advised to address before proceeding.

Admittedly, it’s hard not to be enthusiastic about many of the potential benefits of GenAI, including its ability to elevate customer experiences, refine performance in certain areas and drive operational efficiencies. But wise executives will nonetheless proceed with caution when it comes to integrating these new tools into their technology ecosystem. 

If your insurance organization is considering GenAI integration, here are four key questions to ask yourself first.

Does this technology align with your organization’s ethical, security, data and client consent standards? 

Implementing trustworthy GenAI is paramount for business optimization, improved outcomes and reputation protection. Before adopting the technology for any business use case, it's essential to identify and address potential concerns tied to ethics, culture, human factors or change management. 

Once you have confirmed alignment with your organization’s standards, proper training and comprehensive risk assessment are crucial. To bolster trust and responsible AI deployment within your business, it's imperative that GenAI – like any new technology – undergoes thorough security and data privacy vetting to help ensure its ethical use and strategic incorporation. 

Implementing AI into core business functions also demands rigorous testing and validation. Take the challenge of bias in an image detection AI model. When AI is trained on skewed data, it can produce biased performance, which in turn can distort applications that end up relying excessively on these biased sources. To mitigate this problem, companies should diversify and balance their training data, adopting continuous monitoring and retraining practices and seeking diverse stakeholder input during regular audits. 

How will this tool lift up operational, customer and employee experiences?

Executives across the insurance industry are eager to see how GenAI tools can improve processes and increase efficiencies across enterprises. But as tempting as it may be to forge ahead with the technology, the integration of GenAI should always begin with the assessment of current processes. 

Insurance companies should consider GenAI as a tool to enhance processes, not replace them. While technology empowers the business, optimizing workflows is crucial – after all, existing flaws might be amplified by integrating technologies like GenAI. Identifying the pain points in existing workflows can help businesses understand where technology implementation is needed and what technologies can reduce, or in some cases remove, a problem.

See also: 3 Key Uses for Generative AI

In the backdrop of varied legal landscapes, are you adhering to regional laws? 

Given the evolving nature of data privacy laws and the high stakes surrounding client consent, organizations must approach GenAI with utmost caution. A misstep could result in significant legal and reputational repercussions.

Like all evolving technologies, the innovation that GenAI brings comes with its own set of concerns. Large language models produce human-like content but face challenges like misinformation, malicious use and opaque decisions. Unlike deterministic systems that predictably respond to set rules, GenAI operates probabilistically. As a result, it sometimes generates content that is disconnected from reality. In addition, its immense scale presents potential issues with interpretability, bias and control. 

A core concern is AI's potential for delivering results that, while appearing structurally sound, may not always be factually accurate. It's important to note that while AI systems can process vast amounts of information swiftly, their outputs require rigorous validation. Insurance companies should consider implementing a robust AI governance framework so they can harness AI's potential while ensuring that the trust and reliability that their clients expect remain uncompromised.

While deterministic AI provides consistent results and probabilistic AI embraces uncertainties, neither can fully capture the nuances of human understanding. This is where the "human in the loop" approach comes in. Humans bring empathy, ethics and contextual understanding to the assessment of AI outcomes that machines may overlook. This collaboration ensures that AI technology serves as a complementary tool, fostering decisions that are balanced, fair and contextually relevant.

See also: 5 Ways Generative AI Will Transform Claims

Is your GenAI investment calibrated for optimal ROI, and does it fit with your growth goals?

With a more complete understanding of when and how to use GenAI effectively and responsibly, your company will be in a strong position to enhance efficiency and effectiveness through the integration of this technology. Careful consideration of your organization’s goals and objectives and regular assessment to ensure tangible ROI will round out the successful planning and evaluation process for your GenAI investment. 

Remember, for effective AI integration, it's vital to test consistently, prioritize explainability and maintain robust performance to quickly address potential model or data discrepancies. It's also crucial to establish governance policies for assessment and strategy, culminating in a responsible AI framework solution.

With all of these pieces in place, your organization will be in a strong position to unleash the power of GenAI. 

Sam Krishnamurthy

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Sam Krishnamurthy

Sam Krishnamurthy is VP of corporate systems at Crawford, a leading global provider of claims management and outsourcing solutions to insurance companies and self-insured entities.

He is responsible for program and operational management of I.T. global corporate systems, including enterprise data science and analytics. 

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