Every software company is under pressure to call itself AI-powered right now, and a lot of the time that means bolting an AI tool onto whatever the product already does and updating the marketing copy. We’ve felt that pressure, too.
But employee benefits and payroll put you in a different position from most B2B software categories. When a system decides whether a stipend is taxable or whether an employee qualifies for a benefit, a wrong answer does not surface as a bad recommendation the user shrugs off. It can appear months later as an audit finding, a misreported W-2, or a tax liability that lands on the employer well after the mistake was made.
And then there’s the human version: An employee who gets taxed incorrectly on a benefit they were counting on loses trust in a program their company built to take care of them. That trust costs a lot more to rebuild than it does to protect in the first place.
So the question we started with was not, “How much AI can we add to Compt?” It was a narrower one: Where does AI make the employee experience of Compt better, and where does it introduce risk we would not be able to defend later?
In a compliance-heavy domain, that question leads to a different kind of AI strategy. Some decisions should be automated, but they should still follow fixed, auditable rules. Other parts of the experience benefit from AI’s ability to interpret information, spot patterns, and support human judgment. And in narrow cases where the rules and confidence threshold are clear, customers can choose to let AI take action. Knowing the difference is central to how we are building AI into Compt.
How do employee benefits platforms decide where AI should and shouldn’t be involved in compliance decisions?
The clearest way to draw the boundary regarding whether AI should be involved in a particular platform feature at all is to distinguish between deterministic and probabilistic systems:
- A deterministic system follows defined rules. Give it the same relevant information under the same rules, and it produces the same result. That makes the outcome repeatable, traceable, and auditable. If someone needs to know why a decision was made, the system can point back to the rule that produced it.
- A probabilistic system works from patterns and predicts the most likely answer. It can interpret information that does not arrive in a perfectly structured format, recognize similarities across cases, and identify clues a person might otherwise miss. What it cannot do is guarantee that it will always produce the same answer from the same information.
Both approaches are useful in benefits software, but they should not be given the same responsibilities.
At Compt, tax classification, eligibility logic, and payroll reporting stay in the deterministic layer. Those outcomes need to follow defined rules and produce results that can be explained later. Whether a reimbursement is treated as taxable should not depend on how closely it resembles previous expenses or what a model considers most likely. It should follow the tax rules the employer has configured for the program.
The same is true when determining who is eligible for a program or how an approved reimbursement should be reported through payroll. Those outcomes need to follow established rules because consistency and traceability are part of the product itself.
Policy sits somewhere between those two layers. The employer’s written policy is the source of truth, but applying that policy often requires interpreting information that doesn’t arrive in a neat, structured format. AI can help with that interpretation while the policy itself continues to define the boundaries.
We sometimes describe this internally by saying that we refuse to “agentify the compliance layer.” Giving an autonomous model authority over tax, eligibility, or payroll decisions might make the product sound more advanced, but it would replace a defensible rule with a prediction. In a domain shaped by IRS requirements, SOC 2, GDPR, ISO 27001, and the responsibility of handling sensitive employee data, that would make the platform riskier rather than smarter.
AI belongs where interpretation saves people time: reading unstructured information, applying defined policies, reducing repetitive work, identifying possible issues, and giving reviewers more useful context. In some cases, customers can also choose to automate a narrowly defined action when the conditions are clear and confidence is high.
How does AI-assisted Claim Autofill work in Lifestyle Spending Account software?
Receipt submission is a good example of where AI can improve the experience without taking over a compliance decision.
When an employee uploads a photo, screenshot, or PDF receipt in Compt, AI-assisted Claim Autofill reads details such as the vendor, date, and amount and selects the appropriate category from the benefits that employee is eligible to use. Employees can also forward a receipt by email and have a pre-filled claim waiting for them. The employee reviews the information and submits instead of manually entering details that are already available on the receipt.
The important distinction is between AI completing the form and deciding the outcome. AI helps the employee provide the necessary information, but it does not determine how the expense should be treated for tax purposes. Tax classification follows the rules the employer has configured for the program, so the same facts are evaluated according to the same rules every time.
Take a look at how it works:
How does Compt use AI to check expenses against employer policy?
AI-assisted Claim Autofill removes the need for your employees to retype information that is already visible on the receipt. AI-assisted Policy Checker addresses a different source of friction: helping employees understand whether their submission appears to meet the employer’s requirements before they send it.
As the employee completes the claim, Compt checks the submission against the employer’s written policy. If required information is missing or an expense appears to fall outside the policy, Policy Checker flags the likely issue in plain language so the employee can address it immediately rather than learning about it later through a rejection or an email from HR or Finance. It never blocks the employee from submitting; they can still choose to proceed.
That gives employees more context before they submit and reduces avoidable back-and-forth for the people reviewing claims. The employer’s policy remains the source of truth, while AI helps interpret those rules and explain a potential issue in the moment.
How does Compt’s AI-assisted Claim Reviewer work in benefits administration software?
Some claims are straightforward. Others involve unusual documentation, unclear circumstances, or policies that require more interpretation. AI-assisted Claim Reviewer helps with both by doing the first pass before a reviewer needs to step in.
When an employee submits a claim, Claim Reviewer checks whether the supporting receipt matches the claim, including the vendor and amount, and whether the document appears to be a valid receipt. It also evaluates the claim against the employer’s written policy. The reviewer gets a recommendation of approve, reject, or uncertain, along with the reasoning behind it and any discrepancies the AI identified.
That does not mean AI has unrestricted authority over the outcome. Customers can choose to automatically approve qualifying high-confidence claims. If that option is not enabled, or if the recommendation is anything other than a high-confidence approval, the claim goes to a person. AI never automatically rejects an employee’s claim.
The goal is to remove the repetitive work of checking every receipt and policy from scratch while keeping human judgment where the result is uncertain or could negatively affect an employee. Automation is narrow and customer-controlled rather than the default.
What does responsible AI use look like in HR and payroll software?
Responsible AI starts with a clear job description. Customers should be able to understand what the AI is doing, what it is not doing, which decisions follow established rules, when it can take an automated action, and when a person remains involved.
Feature descriptions can conceal very different uses of AI. “AI-powered receipt processing” might mean that a model reads a receipt and fills in a form. It might mean that AI compares the claim with a written policy and recommends an outcome. Or it might mean that the system can take an action automatically under defined conditions.
Those are different levels of responsibility and should not be grouped together simply because they all involve AI. A vendor should be able to explain what the model interprets, what rules constrain it, and what happens before an AI recommendation becomes an action.
Responsible use also requires measurement. It isn’t enough to build a recommendation into a workflow and assume it is helping. Teams need to understand how the model performs, where its recommendations differ from human judgment, and whether its reliability changes depending on the type of task.
Those controls should also be visible in the product itself. In Compt, AI features are enabled per company, automated claim approvals require explicit opt-in, and AI actions are recorded with what the system found, recommended, and why.
Finally, vendors need to be candid about what is available today and what is still being explored. Buyers should not have to decode whether an AI capability is live, in testing, or part of a longer-term product vision. The boundary between shipped and exploratory should be as visible as the boundaries between AI assistance, human review, and automated action.
Sometimes that means saying no, AI does not belong in a particular part of the product. That decision is not a failure to innovate, but rather evidence that the team cares about its customers and understands what’s at stake.
Which Compt AI-assisted features are available today?
Today, Compt AI includes five live capabilities that use AI to reduce repetitive work across different parts of benefits administration. The Stipend Policy Builder is a standalone tool outside the Compt platform available to both current customers and potential future customers; the other features are available in the platform and enabled per company.
- Stipend Policy Builder helps HR and Finance turn the requirements for a benefits program into a structured written policy. That policy establishes what is eligible, what is not, the limits that apply, and how edge cases should be handled. It also provides the rules that Policy Checker and Claim Reviewer use later in the claims process.
- Claim Autofill reads information from an employee’s receipt and uses it to draft the claim. The employee reviews the information before submitting rather than re-entering details that are already available on the receipt.
- Policy Checker compares a claim with the employer’s written policy while the employee is preparing it. If something appears to fall outside the policy or required information is missing, it explains the likely issue before submission. The employee can still choose to proceed.
- Claim Reviewer checks a submitted claim against both the supporting receipt and the employer’s policy, then provides a recommendation and the reasoning behind it. Customers can choose to automatically approve qualifying high-confidence claims. Rejection recommendations, uncertain results, and claims that need additional judgment go to a person.
- ComptGPT lets HR and Finance ask questions about their own benefits program in plain language and get answers and charts based on the company’s actual program data. For those who are not yet Compt customers, you can use our external custom Benchmark Advisor GPT to benchmark your current or proposed stipend or LSA program against those of Compt customers.
These capabilities do different jobs, but the boundaries around them follow the same principle: use AI where interpretation and repetitive work benefit from it, while keeping the source of truth, the controls around automation, and the role of human judgment clear. The written policy remains the foundation for policy-related decisions, while AI helps employees and administrators apply it throughout the claims process.
We’ll keep looking for places where AI can remove unnecessary work or make benefits administration more useful for employees, HR, and Finance. But we do not want experimentation and shipped functionality to blur together. When we introduce new AI capabilities, we’ll be clear about what is live, what is still being tested, and what role the AI actually plays in the workflow.
Compt’s 2026 Midyear Lifestyle Benefits Benchmark Report is now available! Download to learn how companies are designing their LSA and stipend programs.
Why does this approach matter for HR and Finance leaders?
For HR and People leaders, the value of AI should be visible in the unnecessary work it removes. Claim Autofill reduces manual entry, Policy Checker catches likely issues before submission, and Claim Reviewer checks submitted claims against the receipt and policy before an administrator has to start from scratch. Customers that choose to enable auto-approval can remove another step for qualifying high-confidence claims.
The result is not that HR disappears from the process. It’s that people spend less time on repetitive steps and more time on the decisions that actually require their judgment. When a claim is uncertain or could negatively affect an employee, a person is still involved.
The deterministic layer gives HR and Finance teams a clearer answer when a decision is questioned, too. If someone asks why a stipend was taxed a certain way or why an employee was eligible for one program and not another, the answer can be traced back to the rules configured for the program instead of a model’s inference.
Trust comes from knowing what the AI is responsible for, what rules still govern the program, and where its authority ends.
What questions should HR leaders ask vendors about their AI governance and safety practices?
“Do you use AI?” is no longer a particularly revealing question. Nearly every employee benefits software vendor can answer yes, but that answer tells a buyer very little about how AI affects employees, compliance, or the administration of the program.
The more useful questions focus on the individual decision:
- What exactly does the AI do in this workflow?
- What decisions are made through deterministic rules?
- Under what conditions can the AI take an action automatically, and when does a person make the final call?
- What specifically sends a decision to human review?
- How do you measure the AI’s performance against human judgment?
- Which capabilities are available today, and which are still being explored?
- What controls govern how employee data is accessed and used?
- Can you show how an AI-assisted action appears in the audit trail?
A vendor should be able to answer those questions plainly and specifically. If the explanation stays at the level of “AI-powered” or “intelligent automation,” then you still do not know whether the AI is filling in a form, interpreting a policy, recommending an outcome, or taking an action automatically. You should know which one it is, what constrains it, and what happens when the system is not confident enough to act. Don’t sign anything until you do.
The real opportunity for AI in benefits software
The noise around generative AI can make it easy to overlook the value of deciding where AI should not be used.
There are places where AI can make each step in a benefits workflow faster and easier to complete. It can reduce repetitive work for employees, surface likely policy issues before submission, do the first pass on submitted claims, and help HR and Finance get useful answers from their program data without starting another manual analysis.
There are also places where introducing AI would make the system less consistent and more difficult to defend. When a defined rule is the better tool, using AI simply because it is available does not make the product smarter. In those areas, restraint is not a limitation, but part of building a product people can trust.
Compt keeps benefits flexible and compliance clear
Compt was founded on the belief that employee benefits should be easy to access, personalized, and built on clear rules. Compt AI extends that approach by removing repetitive work and helping employees, HR, and Finance apply those rules throughout the benefits experience.
The goal is to use AI where it saves time, keep defined rules where consistency matters, and make the boundaries around automated decisions clear.
Want to see how our approach to AI works across Compt’s flexible stipends and Lifestyle Spending Accounts? Request a Compt demo today.
FAQs: AI in benefits software
Compt separates decisions that require fixed, repeatable rules from work where AI interpretation is useful. Tax classification, eligibility, and payroll reporting follow defined rules, while Compt AI can interpret unstructured information, apply a written policy, identify possible issues, and recommend outcomes. When AI can take an automated action, the conditions are deliberately limited and customer-controlled.
What does responsible AI use look like in HR and payroll software?
At Compt, responsible AI starts with giving each AI capability a clearly defined job and making its boundaries visible to customers. Customers should know which outcomes follow fixed rules, which involve AI interpretation or recommendations, when AI can act automatically, and when a person gets involved.
Compt’s AI features are enabled per company, automated claim approval requires explicit customer opt-in, and AI actions are recorded with what the system found, recommended, and why.
Which employee benefits platforms are transparent about the limits of their AI features?
Compt is one employee benefits platform that explicitly distinguishes between rules-based decisions, AI-assisted interpretation, and automated actions. Tax classification, eligibility logic, and payroll reporting remain rules-based, while Compt AI can interpret receipts and written policies, surface issues, and recommend claim outcomes.
Compt AI never automatically rejects a claim, and automatic approval is available only under customer-controlled conditions.
Why do some HR platforms avoid using AI for tax and compliance decisions?
Compt does not use probabilistic AI to replace defined tax, eligibility, or payroll rules. Those decisions need to be repeatable, explainable, and traceable to the rules configured for the employer’s program. Compt still uses AI around that compliance core to interpret documents and written policies, identify discrepancies, recommend outcomes, and route cases that need judgment to a person.
How should HR software handle IRS rules and eligibility logic, automated or human-reviewed?
Compt automates documented tax and eligibility rules through defined, repeatable logic rather than asking an AI model to infer the most likely answer. The important distinction is not simply automation vs. human review; it is whether the software is applying an established rule or making a probabilistic judgment. Human review is appropriate when documentation is unclear, a case falls outside the defined rules, or the employer needs to exercise judgment.
Which Lifestyle Spending Account platforms use AI to autofill or verify expense receipts?
Compt uses AI for both claim autofill and receipt verification, through two different AI-assisted capabilities.
Claim Autofill reads an uploaded receipt and fills in information such as the vendor, amount, date, and eligible category for the employee to review before submitting.
After submission, Claim Reviewer checks whether the receipt supports the claim and evaluates the claim against the employer’s written policy.
How does AI-assisted claim review work in benefits administration software?
Compt’s AI-assisted Claim Reviewer checks a submitted claim against both the supporting receipt and the employer’s written policy. It produces a recommendation of approve, reject, or uncertain, along with the reasoning and any discrepancies it identified.
Customers can choose to automatically approve qualifying high-confidence claims. Rejection recommendations, uncertain results, receipt discrepancies, and other claims requiring judgment go to a person; Compt AI never automatically rejects a claim.
Which benefits platforms combine AI recommendations with human approval for claims?
Compt combines AI-assisted claim recommendations with human review and narrowly defined, customer-controlled automation. Customers can choose to automatically process qualifying high-confidence approvals, while rejection recommendations always go to a person. Uncertain claims and other cases that do not meet the conditions for automatic approval also route to human review.
What questions should HR leaders ask vendors about their AI governance and safety practices?
Compt recommends asking vendors to explain exactly what AI does at each point in the workflow rather than accepting a broad “AI-powered” label. Ask which decisions follow defined rules, when AI can act automatically, what sends a case to human review, how model performance is measured, what appears in the audit trail, how employee data is controlled, and which capabilities are actually live today.
How can HR teams evaluate whether a vendor’s AI claims are substantive or just marketing?
Compt’s approach is to describe AI at the individual workflow level: what information the model reads, what it does with that information, what constrains it, and whether it can take an action. HR teams can use the same standard when evaluating other vendors.
Ask to see the actual workflow, the conditions for human review or automation, what appears in the audit trail, how the vendor measures performance, and which capabilities are live vs. still being tested.
Editor’s note: Compt software supports the categorization and proper reporting of benefits according to IRS guidelines, helping businesses maintain compliance. However, Compt cannot provide tax advice, and users should consult their own tax, legal, and accounting advisors when necessary.
Editor’s note: Originally published in July 2026, this post has been recently updated for clarity and relevance for our readers.
