Pace's AI Agents To Streamline Prudential's ILI Operations
Prudential's Individual Life Insurance (ILI) business has selected Pace, the "agentic workforce for insurance," to simplify service delivery at scale. Pace's AI agents are already live, handling policy servicing and supporting quality assurance-offloading thousands of hours of manual work.
"Our work with Prudential is an example of how AI can be used as a strategic advantage. We are thrilled to support their exceptional team with AI agents in production today that deliver best-in-class accuracy and speed at significant scale," said Pace chief executive officer, Jamie Cuffe.
Sara Atkinson, vice president of customer acquisition for Prudential's ILI business, said the automated systems free teams to focus more on customer relationships-supporting the company's goal of delivering industry-leading client and customer experiences.
What's live and delivering value
- Automated policy servicing across high-volume, rules-based workflows.
- AI agents move across internal applications, interpret documents, and place phone calls where permitted.
- Quality assurance support to stabilize consistency and reduce rework.
- Shift from pilot to a multi-year agreement after clear gains in speed and operational efficiency.
Why this matters for operations
- Throughput: Shorter cycle times on policy servicing, fewer handoffs, better queue health.
- Quality: Higher first-pass yield and tighter adherence to SOPs and scripts.
- Cost: Lower cost per transaction versus manual processing or traditional BPO for targeted workflows.
- Workforce focus: Teams spend more time on exception handling and customer relationships.
How to evaluate a similar rollout
- Define 3-5 measurable outcomes (e.g., cycle time -30%, first-pass yield +10 pts, backlog -40%).
- Prioritize stable, rules-heavy, high-volume processes with clear success criteria.
- Set guardrails: PII handling, call scripting, approvals, and clear escalation paths.
- Use human-in-the-loop for exceptions and edge cases; automate confidence-based routing.
- Instrument everything: logs, audit trails, and evidence for internal audit and regulators.
- Plan change management and training so frontline teams trust and use the system.
- Run vendor due diligence on security, model updates, uptime SLAs, and incident response.
Key metrics to track
- Cycle time, queue aging, and on-time SLA adherence.
- First-pass yield, exception rate, and rework volume.
- QA audit accuracy, error severity distribution, and remediation time.
- Customer wait time, CSAT/NPS (where applicable), and complaint rates.
- Cost per policy/transaction and hours returned to the business.
Operational risks and controls
- Incorrect outputs: enforce confidence thresholds, sampling, and second-checks on sensitive actions.
- Phone interactions: script compliance, consent, disclosures, and recordings where lawful.
- Data privacy: minimize data access, restrict retention, and segregate environments.
- Model drift: periodic recalibration, regression tests, and rollback plans.
- Change control: versioned prompts/policies, approvals, and traceable releases.
Getting started
- Start with one or two high-impact processes and time-box a pilot (6-10 weeks).
- Stand up an ops x risk x tech working group with a named owner for each KPI.
- Document "stop rules" for edge cases and define clear human escalation paths.
- Codify what works, then scale horizontally to adjacent workflows.
Pace notes its agents support mission-critical operations for major insurers, handling work traditionally sent to business process outsourcers. For governance frameworks in financial services AI, see the MAS FEAT principles for fairness, ethics, accountability, and transparency in AI use here.
Want to upskill your team on automation and AI in operations? Explore curated resources on automation practices and tools here.
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