Prudential's ILI puts Pace AI agents into production for policy servicing and QA
Prudential's Individual Life Insurance (ILI) business has selected Pace to simplify service delivery and improve operational efficiency. The first production systems are live, absorbing thousands of hours of policy service and quality assurance work.
"Our work with Prudential is an example of how AI can be used as a strategic advantage," said Pace CEO Jamie Cuffe. "We're thrilled to support their exceptional team with AI agents in production today that deliver best-in-class accuracy and speed at significant scale."
The engagement began as a pilot and moved quickly into a multi-year production agreement after clear gains in speed and efficiency. "We've taken a step forward on our journey to modernize the customer acquisition process," said Sara Atkinson, vice president of customer acquisition for Prudential's ILI business. "With automated systems, our teams now have more time to focus on customer relationships, which supports Prudential's broader goal of delivering industry-leading experiences for both clients and customers."
What's live today
- Policy servicing tasks handled by AI agents across internal systems and document sets.
- Quality assurance workflows that improve consistency and turnaround time.
- Production-grade scale without adding headcount.
Why it matters for insurers
- Shorter cycle times and higher throughput with the team you already have.
- Staff can shift from repetitive administration to client conversations and complex cases.
- Less reliance on BPOs for routine work while keeping oversight and context in-house.
How Pace works in insurance operations
- Agents operate across internal apps, read and reason over policy files and correspondence, and can place calls to complete tasks.
- Common use cases: submission intake, policy servicing, claims support, and data entry.
From pilot to production: practical steps
- Start with high-volume, rules-driven tasks tied to clear SLAs and audit needs.
- Measure what matters: average handle time, first-pass yield, QA coverage, rework rate, and per-policy service cost.
- Set guardrails early: approval workflows, audit logs, exception queues, and clear escalation rules.
What to watch next
This is a multi-year buildout, so scope will likely expand as results hold. For teams planning 2025-2026 roadmaps, this signals that production AI in life is moving from trials to day-to-day operations.
For more details, read the announcement on Business Wire or visit withpace.com.
Media: press@withpace.com
Business: tristan@withpace.com
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