Everyone has seen AI get something wrong, like a chatbot suggesting tacos in the middle of a finance question. That’s funny in a demo. In benefits, a wrong answer can mean an employee skips coverage they needed.
AI is now part of how employers run enrollment, and decision support is where it does the most visible work. This guide covers what it does well, where it goes wrong and what brokers and HR teams should ask before buying it.
What AI does well in benefits
The job is narrow: take a complicated set of plans and tell one employee, in plain language, which ones fit their situation. Most employees spend a few minutes on enrollment and keep what they had, so the explanation has to be personal and short enough that people read it. Aon describes AI’s value in HR as turning large amounts of data into experiences tailored to each person, and in enrollment that’s the useful part.
It also takes work off HR. SHRM reports that AI is taking over repetitive HR tasks so teams can spend more time on strategy. During enrollment, the repetitive task is answering the same benefits questions hundreds of times, often questions HR isn’t allowed to answer for an employee anyway. Speakers at the HR Tech Conference made a similar point about personalization changing how employees use HR systems (HR Executive).
UK law firm Ashfords frames the tradeoff well: AI brings faster processes and better data to HR, but it has to be adopted carefully because of what it can cost the human side of the work.
Five common AI pitfalls
Generic recommendations
Many AI systems give everyone roughly the same answer. Employees can tell when their situation wasn’t considered, and they stop trusting the tool.
No explanation
When a tool can’t say why it recommended something, users get a black box. In benefits that’s worse than no recommendation, because the employee has to commit money to a choice they don’t understand.
Bias in the data
Models trained on historical data can repeat the biases in it. In health and benefits decisions, that can mean unfair outcomes for some groups of employees.
No way to correct it
If nobody can see what the tool is recommending, nobody learns when it’s wrong, and the mistakes repeat across the whole group.
Plans it doesn’t know
Benefit plans differ from employer to employer. A model that doesn’t know the specific plans in front of the employee will give answers that are irrelevant or wrong.
TechnologyAdvice lists similar risks for AI in HR, including bias, inaccurate results and exposure of private data.
How Help Me Choose Benefits handles them
Recommendations built on the employee’s answers
Employees answer anonymous questions about their household, finances and health, without giving any personally identifiable information. HMCB matches those answers to the employer’s actual plans, so the recommendation reflects their situation instead of an average employee’s.
An explanation with every recommendation
Every recommendation says why a plan or coverage fits someone in the employee’s situation, drawing on the plan details and the broker’s and employer’s input. It also says when a benefit isn’t needed.
Trained on each employer’s plans
Brokers and plan administrators add public and private details about each plan, so the AI works from the specifics. They can test it with sample employee profiles before launch and adjust the logic.
People who know the group control the logic
Brokers and employers set the recommendation logic for their own group, and the dashboard shows what’s being recommended while enrollment is open. If a recommendation looks wrong for that workforce, the people who know the group can see it and change it, instead of waiting on a vendor’s model.
Questions to ask before you buy
- How does the tool explain each recommendation to the employee?
- Who controls the recommendation logic: you, the employer, or only the vendor?
- What employee data does it need, and where is it stored?
- Does it know the employer’s actual plans, including voluntary lines?
- Can you see what’s being recommended while enrollment is open?
- How long does it take to go live, and does it need an integration?
Vague answers usually mean vague recommendations. Try our employee demo and judge ours the same way.
To see how HMCB answers these questions, start with HMCB for brokers or HMCB for HR teams.
