How Hylant is transforming employee benefits with AI and robotic automation
Skip the buzzwords. Hylant is winning by fixing the slow, error-prone work that burns time and erodes client trust. The COO, Mark Nixon, puts it simply: "We've actually made quicker inroads through RPA than with AI initially." That clarity is what's moving the needle in employee benefits.
RPA first: faster reporting, cleaner billing, fewer member issues
For large self-funded employers, monthly reporting and analytics can swallow hours. Hylant's bots now pull the reports, and the analytics team reviews for quality instead of doing the grunt work. That shift turns "report delivery" into "client insight."
Post-open enrollment, RPA reconciles invoices against benefits enrollment reports. It flags employees who were accidentally left off plans and catches billing mismatches before they hit payroll. What used to take hours now takes minutes-resulting in tighter reporting, accurate billing, and fewer member disruptions.
AI where it actually helps: document comparison and instant answers
AI shines on document-heavy work. Hylant compares annual contracts and certificates of coverage against last year's versions or quoted proposals and auto-flags inconsistencies. That's real control over renewal drift and carrier changes.
On the front end, AI embedded in the agency management system answers client questions fast. Ask, "Is voluntary life portable or convertible?" and it scans the right documents to respond immediately. What used to require digging through PDFs now happens in seconds-without leaving the client waiting.
The integration gap: EB vs. P&C and the Epic reality
The sticking point isn't ideas-it's integration. Vendors tend to focus on either employee benefits or property/casualty, not both. Add the need to play nicely with Applied Epic, and the provider list shrinks fast.
The risk is building a "junk drawer" of point tools. Hylant is deliberately avoiding that trap by prioritizing impact and team efficiency over novelty. Fewer tools, deeper integrations, clearer outcomes.
Culture as an implementation filter
Technology only works if your people can live with it. Leadership at Hylant asks one question: will this make employees' lives better or worse? Sometimes the answer is short-term pain-new systems, data cleanup-for long-term lift.
That clarity protects margins and morale. It also forces discipline: fix data, standardize processes, then automate. In that order.
What insurance leaders can copy this quarter
- Pick three high-friction workflows: monthly client reporting, invoice-to-enrollment reconciliation, and contract/certificate comparisons.
- Automate the pull, reconcile, and flag steps with RPA; keep humans over QA and exceptions.
- Add AI where documents pile up: redline comparisons against prior-year binders and proposals; policy Q&A sourced from the AMS library.
- Integrate with your AMS (e.g., Applied Epic) before you add another standalone tool.
- Set a "no junk drawer" rule: every tool must integrate, expose metrics, and retire at least one manual step.
Metrics that prove it's working
- Cycle time: hours per monthly reporting package (target: minutes, not hours).
- Quality: reconciliation accuracy and number of members restored before carrier billing.
- Service speed: first-response time for coverage questions from the AMS.
- Cost to serve: labor hours saved per 100 lives; error rework rate down and staying down.
- Adoption: % of documents run through AI comparison before client delivery.
Implementation guardrails
- Start with stable, rules-based tasks for RPA; reserve AI for language-heavy comparisons and retrieval.
- Track exceptions aggressively; use them to fix upstream data and forms.
- Limit vendors; require Epic-ready integrations and clear data governance.
- Train client-facing teams to interpret AI outputs, not just copy/paste them.
Why this approach works
RPA clears the sludge-reporting, reconciliations, file pulls-so humans can deliver analysis. AI then compresses the research time on documents and client questions. Together, they shift your team from "busy" to "useful."
If you're evaluating next steps, start where the friction is obvious and the data lives in predictable places. Use RPA to standardize and speed up. Layer AI for precision on documents and instant answers inside your AMS. For deeper industry context and playbooks, see AI for Insurance.
Helpful reference
For a neutral primer on the tech itself, see Robotic process automation. It's not flashy-but applied the way Hylant is doing it, it frees up the hours you actually need for clients.
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