Across the healthcare landscape, the pressure to reduce costs while improving patient outcomes is intensifying. For many payers, the promise of agentic AI is clear, but the road from pilot to scaled impact remains cloudy.

This fifth blog installment of our Mastering AI Agents in Healthcare series explores how one national payer overcame that challenge. Faced with operational inefficiencies in utilization and care management, the organization partnered with Productive Edge to develop a targeted pilot that uncovered $2.4 million in annual savings potential and built the foundation for broader enterprise adoption.

This case study outlines the structured approach that made that outcome possible and will show you how you can replicate the same results. From AI Action Planning to a phased rollout strategy, it illustrates how leaders can move beyond experimentation toward intelligent systems that reduce manual work, improve accuracy, and scale confidently.

The Problem: High Costs, Manual Work, and Missed Opportunities

Rising costs, burned-out teams, and fragmented care. That was the reality for Utilization Management (UM) and Care Management (CM) teams at a leading national payer. The mission was clear—deliver more value, more efficiently. But the tools weren’t built for today’s complexity.

• Overutilization was common. Service plans often exceeded approved limits—driving unnecessary costs.
• Manual monitoring drained resources. Staff spent up to 60% of their time tracking usage across disconnected systems.
• Data overload slowed decisions. Care managers lacked tools to synthesize data, leading to delays and reactive interventions.
• Process complexity grew. New care models (like home-based care) didn’t fit old workflows, causing bottlenecks and errors.

With systems designed for yesterday, the organizations struggled to meet the demands of tomorrow. As a result, patients kept waiting. Costs mounted. And innovation stalled. 

Turning AI from Buzzword to Business Driver 

While most organizations get lost in the noise, chasing hype with no clear path to ROI, this payer chose a smarter route.

They partnered with Productive Edge for a hands-on, expert-led AI Action Planning Workshop. The goal: zero in on the operational pain points that mattered most and build an AI roadmap that drives real impact.

In just one working session, cross-functional leaders from operations, IT, compliance, and the C-suite aligned around a clear strategy to:

• Pinpoint high-friction workflows draining productivity and driving costs
• Score each AI opportunity by ROI potential and implementation feasibility
• Prioritize quick wins that would prove value fast and build momentum for scale

This wasn’t a whiteboard session. It was a blueprint. The outcome? A clear starting point: reimagining service plan management in utilization management with agentic AI.

Leveraging the AI Prioritization Matrix

Not every AI idea is worth pursuing. At least not right now. That’s why the team used a purpose-built prioritization matrix to separate high-impact, high-feasibility opportunities from those that need more time or infrastructure.

Each use case was scored across two critical dimensions:

Impact Factors

• ROI potential and cost savings
• Productivity lift
• Quality of care improvements
  Member experience gains

Feasibility Factors

• Data availability and cleanliness
• Integration complexity
• Technical readiness
• Training and change management needs

The result? A clear, visual way for decision-makers to align on quick wins, defer complex bets, and ensure every AI investment is set up to succeed.

This matrix didn’t just guide decisions—it accelerated them. And it’s a model any healthcare organization can use to stop spinning wheels and start scaling smarter.

Use Case: Fixing Service Plan Management

The pain was clear: Staff needed to pull data from multiple systems, interpret dense documents, and navigate unclear workflows just to create or review a service plan.

The solution was focused: Build an AI assistant that aggregates all relevant information, guides staff through the correct steps, and provides real-time support along the way.

The Results:

• 50% reduction in time spent on service plan tasks
• Fewer delays from missed steps or documentation errors
• Higher staff satisfaction from eliminating tedious, manual work

This wasn’t just automation. It was intelligence, embedded directly into the workflow.

The Rollout: Fast, Phased, and Feedback-Driven

To move from concept to impact fast, the payer adopted a three-phased implementation strategy built for speed, learning, and scale. 

Phase 1: Proof of Concept

A tightly scoped pilot focused on routine service plans. The goal? Validate technical feasibility and gather frontline feedback—fast.

Phase 2: Controlled Expansion

The rollout extended to a broader user group in a single state. Here, the team pressure-tested usability, refined workflows, and built the foundation for wider adoption.

Phase 3: Scale

With insights in hand, the organization moved to full deployment—supported by structured change management, training programs, and cross-team alignment. Using this high-velocity implementation roadmap, the client was able to minimize risk, maximize engagement, and create a clear path to enterprise-wide value.

A Solid Foundation for Intelligent AI Scalability

With early proof points in hand, this leading payer didn’t pause but doubled down with a clear vision: embed agentic AI deeper into utilization and care management, not to replace people but to work alongside your team to unlock new levels of productivity, reduce friction, and fight burnout.

In utilization management, AI agents will:

• Accelerate prior authorizations with smart decision support
• Flag likely denials before they happen with predictive intelligence
• Power real-time claims adjudication by monitoring and resolving exceptions on the fly

In care management, AI assistants will:

• Equip care managers with synthesized data, proactive recommendations, and hands-off task automation
• Surface care gaps before they become crises
• Track what works, learn from what doesn’t, and continuously optimize outcomes

This isn’t automation. It’s augmentation—with intelligence that scales, adapts, and empowers the people who make care possible.

Lessons Every Healthcare Leader Should Steal

What made this initiative successful? Beyond the technology, it came down to mindset and execution. Here are the key lessons for anyone building an AI strategy:

1. Start With Specific Workflows

Don’t aim to “do AI.” Aim to fix something broken. The team focused on one bottleneck—service plan management—and knocked it out of the park.

2. Prioritize Quick Wins That Matter

Fast success builds trust. Instead of chasing shiny, futuristic AI use cases, the team looked for pain points they could solve immediately.

3. Align on the Front End

Stakeholder alignment wasn’t an afterthought. It was step one. That alignment created the buy-in and ownership needed to execute fast.

4. Design for Scale From Day One

Even in the pilot, the solution was built with future expansion in mind. That made the transition from pilot to production seamless.

5. Invest in Change Management

AI requires new ways of working. The team built role-specific training, created feedback loops, and identified champions across departments to support adoption.

This wasn’t just a successful pilot. It was a proof point—a tangible example of how agentic AI can move from abstract promise to real-world impact in healthcare operations. With a $2.4M savings opportunity uncovered and a clear path to scale, this leading payer didn’t just implement AI—they operationalized it.

For healthcare leaders facing similar pressures, the blueprint is clear:

• Start with a specific problem.
• Prioritize outcomes over hype.
• Design for scale from the start.
• Empower your teams—not just your tech stack.

Agentic AI is a growth enabler today, and when deployed with precision, it has the power to reduce administrative burdens, boost staff productivity, and improve care outcomes in ways legacy systems never could.

Get the details, metrics, and implementation framework behind this payer’s success and discover how you can replicate it by downloading the complete use case guide Transforming Utilization and Care Management with Agentic AI.  

Then, continue your journey to Mastering AI Agents in Healthcare series with our next blog on  Navigating the Rapidly Emerging Agentic AI Vendor Solution Landscape, which sheds insight into the market forces accelerating adoption, shares a framework to evaluate solution providers, and explores the real-world applications transforming payer and provider operations. If you missed the previous installment, read it now to see How to Transform Revenue Cycle Management with Agentic AI.