Introduction
Imagine a busy healthcare facility where doctors rely on AI to summarize patient visit notes. Almost every day, an AI-generated summary appears flawlessly—it organizes the information perfectly, and highlights key medical issues discussed. But there’s a problem: the AI hallucinates, which is that it provides incorrect information, information that is not actually there. It imagines a medical condition that the patient doesn’t have. The summary is so convincing that the doctor doesn’t immediately catch the mistake. This leads to confusion and now the doctor is spending more time identifying the imagined medical condition than treating the patient. In a profession where split-second decisions are required, a hallucination could result in life or death for a patient. This example perfectly illustrates how AI, when not grounded in reality, can create more problems than solutions.
In the previous article, we explained key AI terms and acronyms to help you understand AI’s role in business. Today, we’ll explore the good, bad, and ugly of using AI in life insurance, and give you a full picture of both the benefits and challenges.
The Good
- Efficiency: AI streamlines routine tasks like data entry and summarization, and policy management, allowing agents to focus on more important client-facing activities.
- Personalized Client Interactions: By analyzing client data, AI can recommend customized life insurance policies tailored to each client’s needs, which improves satisfaction and builds stronger relationships.
- Faster Decision-Making: AI processes information quickly, and helps agents make faster, more informed decisions that can accelerate the sales process, process the business, and close deals sooner.
- Cost Savings: Automating low value, repetitive administrative tasks reduces errors, NIGOs, and overhead costs, making operations more cost-effective and freeing up resources for growth and high touch customer relationships.
The Bad
- Data Dependency: AI relies heavily on large amounts of accurate data. If your data is incomplete or inconsistent, AI’s effectiveness can be compromised, leading to inaccurate predictions or recommendations.
- Learning Curve: Implementing AI requires a significant learning curve for teams. Agents and staff need to understand how to use the technology effectively, which can take time and training.
- High Initial Investment: While AI offers long-term benefits, the initial costs of implementing AI systems, including software, training, and maintenance, can be quite high, making it a significant upfront investment.
The Ugly
- Risk of Bias: AI models can unintentionally inherit biases, either from pre-built models that are used, or from the data that they’re trained on. This can lead to unfair or discriminatory outcomes in policy recommendations or client assessments.
- Hallucinations: As seen in our earlier story, AI can sometimes generate convincing but entirely false information, which can lead to confusion, mistakes, wasted efforts. Ultimately, this forms doubts in the usability and viability of implementing AI at scale inside of an organization.
- Over-Reliance on Automation: Relying too much on AI for decision-making can reduce human oversight, and laziness; potentially leading to critical mistakes when the AI fails to consider nuanced, individual needs, or a complex negotiation ensues.
- Compliance and Privacy Concerns: AI systems handle sensitive information like personal and financial data, which needs to be safeguarded. Mismanaging data security or compliance requirements can result in serious legal and ethical violations. Or worse, that an AI Hallucination exposes someone else’s private health or personal information.
Challenges of Leveraging Generative AI
Lack of Specialized Talent
Implementing generative AI at scale requires highly skilled professionals, and many businesses struggle with a lack of in-house expertise. Simply having access to an AI model isn’t enough—companies need talent to manage and maintain the computing infrastructure behind AI systems. Constant feature tuning, scaling, and more are needed to ensure the utmost accuracy and usability of these models. It is never, “set it and forget it”.
This is especially true as AI models evolve and new use cases emerge. AI systems need continuous monitoring and optimization to adapt to changing data patterns, market demands, and compliance requirements. Without ongoing adjustments and improvements, the performance of AI can decline over time, making it less effective in meeting business needs.
Additionally, development and implementation costs often make up the majority of AI spending, far exceeding the cost of the models themselves. Without the right talent, businesses may face difficulties in integrating AI effectively, leading many to rely on external services for custom AI solutions.
AI Technology Limitations: Hallucinations
As we’ve mentioned before, hallucinations are the biggest impediment for organizations looking to implement LLMs; we cannot understate this enough. Being one of the most notable limitations of generative AI, hallucinations can create serious issues if not properly managed, as AI might produce misleading or entirely false data.
This becomes especially problematic in industries that rely heavily on accuracy, such as healthcare or finance, where incorrect data can have significant consequences. Hallucinations not only diminish the reliability of AI systems but also add extra layers of complexity in verifying the outputs, requiring more human intervention than expected.
To prevent this, AI models need to be grounded in accurate and reliable information, and be tuned and updated frequently. Without proper oversight, hallucinations can lead to confusion and costly errors, undermining the trust in AI-driven solutions, leaving users feeling as if they are playing a persistent game of “whack-a-mole”.
How to Measure ROI When Evaluating AI Solutions?
Measuring the Return on Investment (ROI) of AI solutions can be challenging, as it depends on the specific goals of the business. Most companies are currently looking at increased productivity as a key indicator. Metrics like customer satisfaction and Net Promoter Score (NPS) are also used as proxies to measure the impact of AI on client relationships.
Another important aspect of measuring AI’s ROI is its impact on workflow optimization. AI’s ability to streamline operational processes, such as automating routine tasks or reducing human errors, allows businesses to focus on high-value tasks. This can significantly improve overall efficiency, which may not always be captured through traditional productivity metrics alone.
However, leaders are beginning to focus on more concrete measures like revenue growth, cost savings, and efficiency gains. These metrics provide a clearer picture of AI’s financial impact. Although many are still refining how they evaluate AI’s return, efficiency and accuracy improvements are common indicators.
It’s also essential to consider how AI contributes to risk management and compliance. Reducing the likelihood of mistakes in decision-making, while ensuring that operations remain compliant with industry regulations, can have a direct effect on a company’s bottom line. By minimizing risks, AI helps businesses avoid costly errors and maintain long-term sustainability.
In the case of selling life insurance with Xcela, one of the metrics we track is the “number of days to process a case”, which directly reflects how quickly our AI platform handles cases. We will explore this in detail in Part 4 of our series.
How to Make Sure You Are Using AI Responsibly and Ethically for Your Business?
Using AI responsibly and ethically is crucial for ensuring that it benefits your business while maintaining trust with clients. Research indicates that 53% of Americans believe AI is more harmful than beneficial when it comes to protecting personal information. That’s where a secure and encrypted data storage environment coupled with the RAFT framework comes into play.
Beyond compliance, responsible AI practices require businesses to take active steps in preventing issues like bias and data misuse. By prioritizing ethical AI development, companies can foster stronger client relationships and protect their reputations in an increasingly AI-driven world.
The RAFT framework—Responsible, Accountable, Fair, and Transparent—helps guide the development and deployment of AI systems in a way that prioritizes ethical considerations.
- Responsible: AI should be built with societal impact in mind, prioritizing the mitigation of risks like bias, discrimination, and privacy concerns. This means ensuring that AI systems are designed to reduce harm and make positive contributions to both users and society.
- Accountable: Clear accountability is essential for managing AI. Each stage of the AI development process should be well-documented, with specific individuals or teams responsible for its outcomes. This ensures that stakeholders are answerable for the AI’s actions and decisions, helping prevent issues before they arise.
- Fair: Fairness in AI means actively working to prevent bias and ensuring equitable treatment across different user groups. This involves using diverse, representative datasets and regularly evaluating the AI’s performance to ensure it avoids unfair outcomes, particularly for sensitive or marginalized groups.
- Transparent: Transparency is the source of truth, and key to building trust in AI implementation. AI systems cannot be a “black box” decision-making tool, they must be explainable, with clear and understandable decision-making processes. Users should be able to trace back all AI decisions to the source of data, which ensures that the entire process is open and verifiable.
Incorporating the RAFT framework into AI governance allows businesses to stay ahead of evolving regulations and societal standards. By ensuring that their AI systems can be easily updated and adjusted, companies can maintain ethical integrity and operational flexibility, even as industry demands and expectations change.
By applying the RAFT framework to your AI models, or making sure your vendor has implemented RAFT, you provide systems that are reliable, secure, and ethical. As AI plays an increasing role in life insurance, building ethical and responsible systems will not only protect your clients but also help you gain their trust.
With the ongoing advancements in AI, the importance of transparency and accountability cannot be overstated. Keeping your AI systems aligned with ethical standards not only reinforces client confidence but also ensures long-term business success.
In the next part of this series, we’ll dive deeper into how to measure ROI effectively and explore how AI can boost client satisfaction—from faster responses to more meaningful interactions.
By focusing on measurable outcomes, you’ll be able to fully understand AI’s impact on your business and ensure it continues to drive meaningful results.
Want to navigate AI responsibly while staying ahead in life insurance sales? Contact Xcela to explore how we can help you deploy RAFT-aligned AI solutions, so you can focus on what truly matters—your clients.