From crafting communication to detecting fraud, artificial intelligence (AI) can be a powerful tool for employers and benefit plan sponsors. Presenters and authors for the International Foundation of Employee Benefit Plans and International Society of Certified Employee Benefit Specialists (ISCEBS) have offered several ideas at recent conferences and in publications for the use of AI.
Here’s a look at some of their suggestions.
Communication—Making Benefits More Accessible
AI can improve benefits communication by translating and simplifying content, adjusting the tone and format to fit the audience, and brainstorming ideas, according to Sara Carroll, CEBS, a 2025 ISCEBS Employee Benefits Symposium presenter.
For example, Carroll, who is the senior manager of total rewards with Evolve, which is based in Denver, Colorado, said a large language model (LLM) such as ChatGPT could be asked to clarify language regarding any benefit offering. The prompt could ask the tool to adjust the language to a sixth-grade reading level, use plain language and adhere to the company’s typical tone. “It’s not only easier to read, it’s empowering,” Carroll said. “When people understand their options, equity improves.”
AI could further adjust the content to be used in different formats, such as emails or social media posts.
Carroll cautioned that AI-generated information should be reviewed for accuracy and should never be distributed without human review. Benefits professionals who use an open LLM also should be mindful of the data they enter into the system, Carroll said in an email interview. Open LLMs are more budget-friendly than a closed, paid LLM, but they are more likely to produce inconsistent data and have fewer security parameters, she added.
“Vendors are creating closed LLMs for HR use specifically that are much friendlier to the type of work we do in terms of security and compliance. Closed LLMs tend to produce more accurate results and to be more user-friendly,” she said.
Open Enrollment
AI is not new and neither is the application of AI to employee benefits or human resources. Benefit plans have used decision-support tools to help plan participants choose their benefits based on certain factors. But the technology has vastly improved.
AI can use a combination of plan data and member input to help members choose health plans, explained Julia Posacki, who presented on the topic of practical uses of AI for health plans at the 71stAnnual Employee Benefits Conference. Posacki is chief strategy officer for SmartLight Analytics in Plano, Texas.
For example, using an AI tool, a participant could input information about expected events during the year, such as marriage or the birth of a baby. The tool could incorporate data, including the participant’s past health care claims, drug utilization history, financial information and geography to suggest the best health plan for them.
Posacki mentioned, however, that plan members could have concerns about their data being shared with an AI tool. Another question is whether the tool would suggest the right plan for the member or for the plan sponsor.
Personalization
“Plan members might already be receiving information about different programs that you have to offer them,” Posacki said. “The more information they get that is not specific to them, the more unlikely they are to pay attention to it.”
AI can help benefit plans become more targeted in meeting a plan member’s needs and in identifying how different benefits, such as disease or lifestyle management programs, could be useful to them. Plan members are more likely to engage if they receive information that is specific to their needs, she said.
Plan Administration
Plan sponsors can use AI to gain better oversight of their plans, Posacki added. This could include writing service agreements with vendors, such as third-party administrators and pharmacy benefit managers (PBMs). AI can also help plan sponsors validate contract performance guarantees, ensure accuracy of claims payments, validate reasonableness of fees charged to the plan and understand plan expenditures.
Data Analytics
By speeding up the process, AI has vastly improved data analytics, which is the process of inspecting, cleaning, transforming, interpreting and modeling data to discover trends, patterns and other information.
Posacki said that by using data analytics to analyze health care claims, a plan might determine that a member is likely to become a high-cost claimant in the future. The information could be sent to vendors to help determine how they might be able to intervene and help the member.
Leave Administration
In their article in the November/December issue of Benefits Magazine, authors Melanie Payton and Chrissy Theiss explained that AI could assist with leave management tasks such as taking in requests, tracking leaves, reviewing medical documentation, communicating updates to the employee and their manager, and ensuring that pay is appropriate. Payton and Theiss, who are consultants at Brown & Brown, wrote that AI can analyze medical records, claims history and workforce trends and identify red flags such as missing or conflicting information. For example, a claims manager could use AI to read medical records to find the relevant information from a doctor’s note and extract and summarize the critical details.
Chatbots, deployed by either the employer or third-party vendor could help communicate with employees. Well-executed chatbots understand natural language, allowing employees to ask questions and receive answers based on plan documents.
In the future, the authors predicted that AI could detect early signs of burnout—such as frequent absences, erratic log-in times or stress signals—and trigger wellness outreach and suggest appropriate leave options or support resources.
It could also help with compliance monitoring to ensure that employers stay in compliance with evolving labor and employment laws.
Preventing Fraud, Waste and Abuse
AI’s ability to sift through large amounts of data quickly is an important tool for detecting fraud, waste and abuse, Posacki remarked.
AI can scan health care claims to find billing problems such as double billing, claims paid for members who are no longer covered under the plan or fraud schemes. “It might seem elementary that your claims are being processed correctly, but claims processing is extremely complex,” Posacki noted.
In an article in the July/August issue of Benefits Magazine, author Laurent Laor described how an AI-driven biometric authentication system also can be used to protect plan member data by detecting issues such as forged IDs or false documents.
The Internal Revenue Services (IRS) is already using such a system to secure taxpayer accounts, according to Laor, chief executive officer of Viveka Health. The article states that taxpayers accessing IRS systems are asked to verify their identities by uploading a government-issued ID and taking a selfie, which the system checks using AI-powered liveness detection to ensure authenticity. Laor wrote that benefit plans can use a similar process for their members to sign up and log into their benefits apps, which is more secure than the typical username-and-password approach.
Oversight Needed
While the applications may be widespread, authors and speakers warned employers and plan sponsors to exercise caution when using AI and to be mindful of their fiduciary duties to plan participants.
- Employers and plan sponsors should develop and implement AI usage policies with clear guidelines for how AI can and cannot be used in the workplace.
- AI-generated benefits decisions should not be made without human oversight. Employees and plan participants should be informed about how AI is being used.
- Protected health information (PHI) should not be entered into AI tools.
- Plan sponsors also need to ensure that their vendors are using AI appropriately. Any data shared with vendors that are using AI should be deidentified.
“AI without appropriate oversight becomes a liability factor,” Posacki said.
For more information about AI and employee benefits, check out the International Foundation’s AI/ChatGPT toolkit.
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