Let's have a Chat(GPT) about the promise and problems of AI
Along the ever-evolving landscape of technology, artificial intelligence has become the focal point of attention, captivating audiences across various platforms. From podcasts to morning news, people are tuning in to explore perspectives ranging from a dystopian doomsday scenario to utopia where AI is a force for the benefit of humanity, contributing to prosperity and resolving global challenges.
Whether you envision a future where AI revolutionizes industries for the better or harbor concerns about potential risks, the reality is that broad experimentation with large language models (LLMs) like ChatGPT are propelling AI into our everyday lives, personally and professionally. Individuals are tapping into AI’s potential, from generating creative ideas for newsletters to streamlining email responses. Even teenagers, often tight-lipped about their AI exploits for school, can’t hide the satisfaction that lights up their faces when the topic arises. On the business front, teams embrace AI tools to digest information, analyze data and even revolutionize the editing of video content.
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The journey can be exciting and complex for those navigating the AI landscape in the employee benefits arena. Demystifying the AI realm becomes essential, especially when considering its potential applications, promises and challenges. But first let’s delve into key terms and definitions to set the stage for exploration into both the promises and potential problems that our highly regulated industry faces. We will then outline a strategic approach for organizations looking to embrace AI without putting themselves at risk.
AI refers to advanced computer systems designed to mimic human-like intelligence and perform tasks that typically require human cognitive abilities. These systems are capable of learning from data, recognizing patterns, making decisions and solving problems. AI encompasses various approaches, including machine learning and deep learning, enabling machines to adapt and improve their performance over time. AI applications range from speech recognition (like Amazon’s Alexa or Apple’s Siri) and image analysis (Google Lens) to autonomous vehicles (Tesla) and natural language processing, showcasing its versatility in tackling complex tasks across diverse domains.
LLMs, exemplified by OpenAI’s ChatGPT, Meta’s LLaMA and Google’s Bard, are advanced AI models designed for natural language understanding and generation. Trained on extensive text and code datasets, these models excel at generating diverse content, including human-like text, language translation, creative writing and informative responses to questions. Their sophistication lies in learning intricate patterns from data, making them versatile tools for a variety of language tasks.
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Generative AI is a subset of AI focused on creating new content, like text, images or videos based on learned patterns from extensive datasets. The key characteristic of generative AI is its ability to produce novel and realistic outputs based on patterns learned from training data. Applications include image synthesis, text generation and creative content creation wherein the AI system can autonomously generate content that wasn’t explicitly present in its training dataset. Some examples include DALL ·E 2, Midjourney and Canva’s Magic Media.
Chatbots are AI-powered programs engaging in conversation with users, often providing information or assistance (i.e., Intercom, Hubspot). Predictive analytics use data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The primary goal of predictive analytics is to forecast trends, behaviors and events, enabling organizations to make informed decisions and take proactive actions (i.e., SAP, IBM Watson).
Now let’s apply the technology to our industry. With respect to benefits engagement, AI shows tremendous promise. Some of the most noteworthy areas include:
Personalized content creation. Generative AI allows the development of highly personalized and creative content for benefits engagement campaigns. This ensures communications resonate with individual employees, fostering increased engagement and understanding.
Efficient information accessibility. AI, particularly LLMs, facilitates quick access to information. Chatbots can interact with employees, providing instant answers to routine questions, and, in the future, they have the potential to guide them through complex benefits options, streamlining the information retrieval process.
Reduction of variability. AI can dramatically improve benefits communication, ensuring that every employee receives accurate and consistent information. This consistency is crucial in building trust and minimizing confusion among employees.
Data-driven decision-making. AI analytics have the potential to revolutionize employer benefit strategies. By harnessing diverse datasets – including individual employees’ personal histories, broad demographic information, preferences and behaviors – industry experts can deliver data-driven recommendations, resulting in higher adoption, better outcomes, fit and improved satisfaction.
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While AI can raise the bar on benefits engagement, there is no escaping multiple challenges that come with this technology. Consider the following:
Lack of sentience, empathy and emotional intelligence (EQ). Where decisions carry significant cost and importance, the absence of genuine emotional intelligence in AI becomes a critical factor. Employee benefits decisions often require a nuanced understanding of individual needs, concerns and preferences. AI’s inherent lack of sentience and empathy impedes its ability to navigate these complexities effectively. Given the substantial impact of benefits decisions on individuals, qualities such as empathy, understanding and a human touch play pivotal roles in ensuring informed choices. The limitations of AI in these aspects highlight the value of preserving human-centric qualities for more nuanced and beneficial decision-making in the realm of employee benefits.
Risk of hallucinations, misinformation and regulatory risks in the highly regulated insurance market. AI systems may generate content that lacks factual accuracy or context, leading to the spread of misinformation. In regulated markets like insurance, this poses not only reputational risks but also significant regulatory liabilities. The potential implications of AI-generated content in the insurance market are yet to be determined by insurance commissioners, adding a layer of uncertainty to an already complex landscape.
Privacy and security concerns. The use of AI involves handling sensitive employee data. Ensuring robust privacy and security measures is paramount to preventing data breaches and maintaining employee trust.
Low engagement and latency. In applications requiring quick responses, the current challenge of latency, defined as the delay in data transmission, will significantly impact outcomes. These delays, inherent in AI’s current state, compromise confidence and credibility, leading to high consumer abandonment rates of above 20% – presumably, decisions resulting from confirmation bias. While future advancements in hardware, such as faster processors and more efficient neural network architectures, should mitigate these issues, the current state should be a cause for concern.
Cost. While not publicly available, an estimated 30% of employees actively engage with stand-alone AI decision support. And with abandonment rates (i.e., leaving the process before completion) in the 20% range, only about 16% of employees appreciate the value of the recommendation engines. This statistic means 84% are presumably defaulting to their previous year’s elections or making decisions based on confirmation biases, laying waste to a strategic objective of those putting the plans together. This makes AI solutions using PEPM models very expensive. Over time, this should change. While today’s AI solutions require significant financial investments in specialized hardware, software and skilled professionals, costs are coming down, as are barriers to entry. While few industry entities will endeavor to create tools themselves, there undoubtedly will be a flood of new entrants offering solutions.
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Given the pros and cons of AI applications in the employee benefits space, what can organizations looking to embrace this technology do without putting themselves at risk? A strategic approach to this issue includes the following four steps:
Step 1: Educate stakeholders. Employers must prioritize the education of stakeholders involved in implementing AI for employee benefits engagement. It starts with ensuring that decision-makers, employees and other relevant parties have a clear and comprehensive understanding of AI, its capabilities and potential impacts. That means conducting workshops, training sessions and informational campaigns to demystify AI concepts, dispel misconceptions and foster a shared knowledge base. By cultivating an informed stakeholder community, your employer clients will establish a solid foundation for collaborative decision-making and a smoother integration of AI into the benefits landscape.
Step 2: Implement ethical practices. As with any new endeavor, teams should approach AI deployment with ethical considerations. To this end, it is essential to establish explicit guidelines and standards for the ethical use of AI, emphasizing principles such as privacy, fairness and transparency. That means ensuring AI algorithms and models are designed and monitored to ensure accuracy, mitigate biases and address the risk of misinformation. Employers should routinely review and update ethical practices to align with evolving industry standards and regulatory requirements. Prioritizing ethical considerations builds trust among stakeholders and mitigates potential risks associated with the deployment of AI in benefits engagement.
Step 3: Combine AI with human expertise. While AI can enhance efficiency and remove some variability, it should complement, not replace, human experts who can provide context, and leverage emotional intelligence and their nuanced understanding. Therefore, it is wise to develop strategies that encourage employee populations to engage on both fronts. This combination will increase comfortability with emergent technology without sacrificing the impact of a more comprehensive and effective benefits engagement strategy that leaves employees feeling cared for and supported.
Step 4: Exhibit patience. Employers must acknowledge that successful AI integration is a gradual process rather than an instantaneous transformation. This is why it’s important that they exhibit patience throughout the implementation, recognizing that AI technologies will evolve and adjustments to strategy will be necessary. It helps to foster a culture that embraces learning from each implementation experience, allowing for continuous improvement. Employers also must address challenges iteratively, seeking feedback from stakeholders and refining strategies accordingly. Patience is paramount in navigating the complexities of AI, ensuring its deployment is successful and sustainable in the long term.
Along the dynamic landscape of technology, the pervasive influence of AI on employee benefits engagement and decision support demands careful consideration of its promises and challenges. As AI, embodied in LLMs and generative AI, enters our professional sphere, its potential to revolutionize benefits communication is undeniable. From personalized content creation to efficient information accessibility and data-driven decision-making, the promises are compelling. However, navigating the pitfalls, such as the lack of sentience, misinformation risks, privacy concerns, latency issues and associated costs, requires a strategic approach. Educating stakeholders, implementing ethical practices, combining AI with human expertise, and exhibiting patience form the pillars of this approach.
Organizations can unlock AI’s transformative potential in benefits engagement and ensure sustainable and responsible integration by understanding key AI terms, recognizing promises and pitfalls and embracing a systematic strategy. As the AI journey unfolds, it is not merely a technological shift but a cultural evolution, and with the proper strategic foundation, the benefits can far outweigh the challenges.
