AI in Benefits Administration: Define the Intent Before You Choose the Tools

The use of artificial intelligence (AI) is accelerating rapidly and some benefits leaders may feel growing pressure to implement AI in benefits administration. However, it’s important for benefits leaders to understand some of the basic elements of AI and identify the intent on using it. Establishing your objectives for using AI in benefits administration may help provide clarity in choosing a benefits administration provider that can support your goals. Successful benefits administration providers will be able to cleanly connect multiple data sources to provide purposeful integration of data-driven technology and AI. 

This blog will cover some key aspects of how Benefitfocus has incorporated automation into our benefits administration service for over 25 years, and how we’re incorporating AI in a purposeful and responsible way. We provide some suggested questions to ask that can help refine your objectives for using AI in benefits administration. You’ll also learn how AI is applied to health care data analytics to provide actionable insights and why this can help address the root causes of certain benefits needs. 

Using AI to Help Simplify Benefits Enrollment and Administration 

Benefitfocus has been providing automated responses returned through rules and algorithms years before AI became a trending topic in modern business culture. Today's conversations might sway more towards generative and agentic AI, but these may not be the optimal choices for some benefits administration use cases. Benefitfocus applies rules and algorithms to data, enabling our software to determine enrollment eligibility, connect employees to optimal healthcare plans and perform tax and other calculations. AI in benefits administration is evolving quickly but has existed longer than people may realize. These capabilities were intentionally built into Benefitfocus solutions from the beginning, with a mission of simplifying benefits for administrators and employees. 

3 Questions to Clarify Your AI Objectives 

Benefits leaders may possess a sense of urgency to quickly implement or optimize AI for benefits administration, but it’s important to first consider your objectives. Be intentional and think about specific results you want to achieve with AI, rather than rushing to implement a solution that may not support your goals. Here are three questions that can help you and your team define your AI objectives: 

1. What administrative efficiencies do we want to achieve? Maybe you’re looking to reduce the workload on your human resources (HR) team by automating data reconciliation or offering a self-service tool to answer employee questions about their benefits. Identify the benefits administration activities that are time consuming or repetitive and consider whether AI could help reduce this burden.
2. Are we looking to drive changes in employee health and wellness behaviors that could help lower longer-term healthcare spend? Some employers may want to optimize plan choices, such as increasing enrollment in high-deductible health plans (HDHPs) or increasing employee contributions to health savings accounts (HSAs). Others may want to focus on increasing employee engagement with point solutions or reducing claims costs. Think about your goals around health and financial outcomes and how AI can help support them in the employee experience.
3. Can our benefits administration provider support our objectives? This question may require more effort to answer. Consider performing a thorough assessment of your benefits administration provider, looking deeper into their approach to using AI. Some providers may offer a suite of “AI-powered” solutions, but this doesn’t necessarily translate into real outcomes. Ask your benefits administration provider about their strategy on tracking measurable outcomes with their AI solutions, which may help reveal whether they’re using AI in a purposeful way. 

Asking these questions to clarify your AI objectives can help uncover hidden pain points in your benefits administration processes. As a result, your AI strategy will be built on a informed and focused foundation with meaningful goals for your benefits program. 

Integrating AI to Power Health Care Data Analytics 

Opinions on AI can vary, but there’s general agreement on the idea that AI is only as effective as the quality of data that drives it. AI can be a powerful tool to uncover data insights, whether you're analyzing enrollment trends across your employee population or claims cost by demographic. These insights can be used to evaluate a plan’s performance and identify cost-saving opportunities. 

At Benefitfocus, we apply advanced analytics and machine learning models to client medical, pharmacy, dental and vision claims data. These models are used for tasks such as identifying clinical and cost patterns, categorizing services, detecting trends or outliers, and supporting risk and performance measurement. They help us to identify insights, such as: 

• High-cost conditions and risk segments – Claims data can identify employees who have or are at risk of developing certain chronic diseases, without exposing individual employee identities. These chronic conditions may account for an impactful chunk of your benefits spending. If gaps are identified in care plans, targeted communications can be used to make sure employees are getting the care they need.
• Emerging trends and care opportunities – With at least two to three years of historical claims data integrated during implementation, we can spot data trends and analyze root causes. For example, if data shows increased emergency room (ER) use over several years, Health Insights analysis can uncover whether ERs are being used for conditions that could be taken care of in a less expensive setting. With the average ER visit costing over $2K¹, implementing something like a telemedicine program can realize year-one savings of several thousand dollars.
• Predicted cost and utilization patterns – Health Insights has integrated the Johns Hopkins Adjusted Clinical Groups^® (ACG^®) predictive modeling engine and benchmark data to allow users to accurately identify members with chronic conditions, assess plan risk and predict future costs. This allows users to create and monitor member populations, monitor case management and evaluate ROI on wellness, onsite clinic and case management programs. 

Health care data analytics is a powerful practice and integrating AI can provide insights on why certain costs are rising and where to make impactful decisions that create better health and financial outcomes. 

AI in the Employee Experience 

Analyzing health care data is essential to evaluate your population’s health, identify gaps in care and cut wasteful spending. However, it’s also important to consider how to address the challenge of benefits engagement. Creating optimal health and financial outcomes for your employees can be an uphill battle if benefits engagement remains low. Integrating AI into the employee experience can be an effective strategy to help boost engagement and support desired health and financial outcomes. Here are some examples of how we’re integrating automation and AI into the employee experience: 

• Incentives Dashboard – The same analytics and machine learning used to identify claims trends and target employees at risk can be used to create incentives to increase employee benefits engagement and change behaviors. Employers can choose to offer sweepstakes entries or assign points for actions such as choosing a primary care physician, joining a wellness program or getting a flu shot.
• Personalized insights and nudges – Claims data drives an employee experience that is truly personalized to their needs. Employees see a personal insights feed on their home page with timely, relevant information and clear direction. And when data reveals opportunities to improve health and wellness, we can include nudges and reminders for employees to take action. These actions could include scheduling a wellness check or considering a switch from a brand name drug to a generic.

• Chatbot assistant* – This feature uses a closed-architecture knowledgebase to provide responses that are relevant to the employee’s actual benefits—not one‑size‑fits‑all answers. Responses include deep‑link navigation to take employees directly to the right place in Benefitplace to complete tasks—reducing clicks, confusion, and time spent searching. 

  *The chatbot assistant is currently limited to select clients, with wider access expected in 2026 or 2027. 

Responsible AI as a Strategic Choice 

Benefitfocus is committed to using AI responsibly. Our thoughtful approach to AI focuses on clear purpose and responsible use. We protect personal information through a secure data governance framework designed to safequard sensitive data and support responsible use. Access is tightly controlled and AI results are generated from approved, accurate data sources.

Connect with Benefitfocus to learn how our approach to AI and automation helps benefits leaders simplify administration, engage employees and make data‑driven decisions with confidence.