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. 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, policy enforcement, and payroll reporting belong in the deterministic layer. Those decisions need to follow established rules and produce outcomes that can be explained later. Whether a reimbursement is taxable should not depend on how closely it resembles previous expenses or what a model considers most likely. It should follow the applicable IRS tax logic.
The same is true when determining who is eligible for a program, how an expense should be classified for tax purposes, or how an approved reimbursement should be reported through payroll. Those outcomes need to follow established rules because correctness and consistency are part of the product itself.
We sometimes describe this internally by saying that we refuse to “agentify the compliance layer.” Giving an autonomous model authority over those 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 in the layers around that compliance core, where it can interpret information, reduce repetitive work, identify possible issues, and give people more useful context without owning the final outcome.
Which Lifestyle Spending Account platforms use AI to autofill or verify expense receipts?
Receipt submission is a good example of where AI can improve the experience without taking over a compliance decision.
When an employee uploads a receipt in Compt, AI reads details such as the vendor, date, amount, and category and uses them to populate the expense form. The employee can then review the information before submitting instead of manually entering details that are already visible in the image.
The distinction between completing the form and deciding the outcome is important. AI helps the employee provide the necessary information, but it does not determine whether the expense should be treated as taxable. That classification runs through Compt’s deterministic tax logic, 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?
Receipt autofill removes the need for your employees to retype information that is already visible on the receipt. Real-time policy checking addresses a different source of friction: helping employees understand whether their submission meets the employer’s requirements before they send it.
As the employee completes the claim, Compt checks the submission against the employer’s policy. If required information is missing, an expense appears to fall outside the policy, or the receipt does not contain what is needed, the system flags it, and the employee can address the issue immediately rather than learning about it later through a rejection or an email from HR or Finance.
That gives employees more confidence that they are submitting the claim correctly and reduces avoidable back-and-forth for the people reviewing it. The AI can help interpret what appears on the receipt and explain the issue clearly, while the policy decision itself remains tied to the employer’s established rules.
How does AI-assisted claim review work in benefits administration software?
Some claims are straightforward. Others involve unusual documentation, unclear circumstances, or policies that leave room for judgment. In those cases, AI can give the reviewer useful context without taking the decision away from them.
Compt’s AI-assisted claim review offers a preliminary assessment for the reviewer to consider alongside the receipt, the employer’s policy, and the circumstances of the claim. The AI provides another input into the review process, but a human makes the final decision.
The important question is whether the AI’s recommendation is reliable for the specific kind of claim being reviewed. One way to understand that is through agreement rate, which measures how often the AI’s preliminary assessment matches the decision a human reviewer ultimately makes. Looking at that rate by category is more useful than relying on one blended number because performance may vary significantly between straightforward reimbursements and claims involving less familiar documentation or more nuanced policies.
That measurement also creates a responsible path toward greater automation. A narrow, well-understood type of claim may eventually become a candidate for autonomous approval, but it should not reach that point because the feature demos well or autonomy sounds impressive on a roadmap. It has to earn the right to automate.
That means measuring performance over time, understanding the exceptions, testing the system against human decisions, and setting a high standard before removing human review. Anything novel or uncertain continues to go to a person, while tax and eligibility logic remain deterministic regardless of how much the surrounding workflow evolves.
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, and when a person remains involved and why.
That clarity is important because broad feature descriptions can conceal very different uses of AI. “AI-powered receipt processing” might mean that a model copies the date and amount into a form. It could also mean that the model decides whether the expense is eligible or taxable. The first removes repetitive work. The second gives a probabilistic system authority over a compliance decision.
Those uses should not be grouped together simply because they both involve AI.
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.
Compt’s Customer Success bot has successfully resolved 89% of support questions without a human involved.
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 boundary between AI assistance and final decision-making.
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.
What Compt AI features are we exploring next?
The ideas we’re exploring will continue to follow the same principle: AI can make the experience easier, surface useful information, and support better decisions, while the deterministic compliance layer remains untouched.
Enabling agents to take defined actions safely
Model Context Protocol, or MCP, creates the possibility for AI systems to call defined tools and take structured actions instead of only producing text.
In Compt, that could eventually allow an agent to prepare or submit a claim on an employee’s behalf, or retrieve reporting information for additional analysis. Those capabilities could remove administrative work, but they also raise important questions about authority and access. Before an agent can act inside a benefits workflow, its permissions, available actions, and boundaries need to be clearly defined.
The goal is not to give an agent unlimited freedom inside the platform. It is to make a narrow set of useful actions possible while preserving control over what the agent can access and change, and what still requires direct confirmation from a person.
Continuing to improve receipt review
There is also room to make receipt review more useful without moving tax or eligibility decisions into the probabilistic layer.
A more capable system could identify likely errors, flag missing information, or point out details that need a person’s attention while the employee is still completing the submission. That could reduce avoidable rejections and help employees understand what they need to correct without making the process unnecessarily rigid.
The balance is important. Employees should have enough flexibility to submit legitimate expenses that do not fit a perfectly predictable pattern, while HR and finance teams still retain the purpose and control built into the program.
Surfacing predictive insights
AI can also help employers better understand how people are using their benefits.
For example, platform behavior may suggest that certain employees are not using their professional development funds. That information does not explain why, but it can help an HR team recognize where employees may need more guidance, clearer communication, or support deciding how to use the benefit.
The same approach could help identify which teams are underusing particular programs, which categories are becoming more popular, or where engagement patterns are beginning to change. These are insights for HR to investigate rather than compliance decisions for a system to execute, which makes them a more appropriate use of probabilistic AI.
Combining datasets with partners
There may also be future opportunities to work with partners to uncover stronger insights than either dataset could provide alone.
Combined information could help HR teams understand which groups are underusing benefits, which categories are trending, or which teams are recognizing one another most frequently. Used thoughtfully, those insights could help employers respond earlier rather than waiting until the end of the year to discover that a program was not reaching the people it was meant to support.
Any work in this area would need to meet the same standards that shape the rest of the platform. Data handling, access, transparency, and the requirements associated with SOC 2, GDPR, and ISO 27001 remain part of the decision-making process from the beginning, not something added after the insight has already been built.
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Why does this approach matter for HR leaders and partners?
For HR and People leaders, the value of AI should be visible in the amount of unnecessary work it removes. Receipt autofill reduces manual entry, real-time policy checks catch fixable problems earlier, and AI-assisted review gives administrators more context when a claim genuinely requires human judgment.
The result is not that HR disappears from the process. Not at all. It’s that people spend less time on repetitive steps and more time on the decisions that require an understanding of the employee, the policy, and the purpose of the program.
The deterministic layer also gives HR and Finance teams a clearer answer when a decision is questioned. 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 applicable rule instead of a model’s inference.
For partners, there is an opportunity to combine information and build stronger insights, but that opportunity depends on using data thoughtfully and transparently. Clear boundaries around access, decision-making, and accountability are what make deeper collaboration possible without asking customers to accept a black box.
In both cases, trust comes from knowing what the AI is responsible for 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?
- Who makes the final call when a decision affects an employee?
- How do you measure the AI’s performance against human judgment?
- What happens when the AI and a human reviewer disagree?
- 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, recommending an action, or making a compliance decision. Don’t sign anything until you know.
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 create a compounding effect by making each step in a workflow faster, clearer, and easier to complete. It can reduce repetitive work for employees, catch problems before they reach an administrator, and give HR teams better information about how their programs are working.
There are also places where introducing AI would make the system less consistent, less explainable, and more difficult to defend. 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 tax-compliant. AI can help us deliver on that belief more completely when it amplifies human judgment and removes unnecessary work without taking control of decisions that need to remain consistent.
AI can assist. It can interpret. It can surface patterns and make the experience easier. But a person or an auditable rule should still own every outcome that matters.
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
The decision to embed AI into an employee benefits software platform should begin with the consequences of getting the answer wrong. When an outcome can create legal, financial, payroll, or employee trust issues, the final decision should come from a deterministic rule or a qualified person. AI is better used around those decisions, where it can organize information, reduce manual work, and give people more context without owning the outcome.
What does responsible AI use look like in HR and payroll software?
Responsible AI use in HR and payroll software means being specific about what the model is doing and honest about its limits. Customers should know which outcomes follow fixed rules, which involve an AI recommendation, and which still require human review. Vendors should also measure performance within each workflow rather than assuming that a model performing well in one area will be equally reliable in another.
Which employee benefits platforms are transparent about the limits of their AI features?
Compt’s approach is to keep tax, eligibility, policy enforcement, and payroll reporting in the deterministic layer while using AI around those decisions. Transparency is easier to judge by the quality of a vendor’s explanation than by the presence of an “AI-powered” label. Look for platforms that clearly separate AI assistance, deterministic rules, and human decision-making and that identify which features are currently available versus still being explored.
Why do some HR platforms avoid using AI for tax and compliance decisions?
Tax and compliance decisions need to be repeatable, explainable, and defensible. A probabilistic model can produce a likely answer, but tax treatment and eligibility are governed by defined rules rather than by what happened in similar cases. Keeping those decisions deterministic allows the platform to apply the same logic consistently and trace the result back to the rule that produced it.
How should HR software handle IRS rules and eligibility logic, automated or human-reviewed?
IRS rules and clearly defined eligibility requirements can be automated in HR software through a deterministic rule engine. Automation itself is not the problem; the important distinction is whether the system is applying a documented rule or inferring an answer from patterns. Human review remains important when a situation is novel, documentation is unclear, or the employer’s policy requires judgment.
Which Lifestyle Spending Account platforms use AI to autofill or verify expense receipts?
Compt uses AI to read uploaded receipts and populate details such as the vendor, date, amount, and category for the employee to verify. The platform can also check the submission against the employer’s policy before it is submitted. The AI assists with the form and the review process, while tax classification and policy outcomes remain tied to deterministic rules.
How does AI-assisted claim review work in benefits administration software?
In Compt, AI provides a preliminary assessment and additional context for a person reviewing the claim. The reviewer considers that information alongside the receipt, the employer’s policy, and the specific circumstances before making the final decision. The purpose is to help the person review the claim more efficiently, not to replace their judgment on unusual or uncertain cases.
Which benefits platforms combine AI recommendations with human approval for claims?
Compt uses this model for AI-assisted claim review: the AI offers context, and a human makes the final decision. When evaluating other platforms, HR leaders should ask what happens when the model and the reviewer disagree, whether that disagreement is recorded, and how the vendor uses those results to evaluate the model’s performance.
What questions should HR leaders ask vendors about their AI governance and safety practices?
When considering employee benefits software vendors, ask what the AI decides, what remains governed by deterministic rules, and who has final authority when an outcome affects an employee. It is also worth asking how the vendor measures performance, what happens when the model is wrong, which features are live versus exploratory, and what controls govern access to employee data. A vendor should be able to answer at the level of the individual workflow rather than relying on broad descriptions of its AI strategy.
How can HR teams evaluate whether a vendor’s AI claims are substantive or just marketing?
Ask for details about the action the AI performs. A feature that reads a receipt and completes a form carries different implications than one that determines taxability or approves a claim. Substantive AI claims should come with a clear explanation of the model’s role, the audit trail, the human or rule-based controls around it, and how the vendor evaluates whether the feature is actually performing as intended.
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.
