Healthcare leaders are under increasing pressure. Staffing shortages persist, chronic conditions are becoming increasingly complex, and data remains scattered across disparate systems. At the same time, value-based care models are gaining traction, pushing teams to deliver better outcomes with fewer resources. In this landscape, care and disease management teams are being asked to do more than ever, without the time, tools, or support to scale.
In a recent episode of the AI in Business podcast, Raheel Retiwalla, Chief Strategy Officer at Productive Edge, joined host Matthew DeMello to explain how Agentic AI is stepping in to ease these pressures. But not by replacing care teams. Instead, it helps them work faster, smarter, and with greater confidence by reducing busywork and surfacing key insights when and where they’re needed most.
Here are three takeaways from the conversation:
1. Agentic AI is already in use, and it's delivering results. Agentic AI isn’t theoretical. It’s being used today by healthcare organizations in the U.S. and internationally. Raheel shared examples of live deployments where agents are:
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Tracking real-time benefit utilization to help avoid authorization denials and service gaps
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Detecting non-adherence in medication data and enabling proactive follow-up
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Mining unstructured data like discharge notes to flag rising-risk members for early outreach
These aren’t major technology overhauls. They work with existing tools and require minimal lift to launch. By starting with small, targeted use cases, health plans are seeing improvements in care continuity, cost control, and member engagement, without having to wait years for a return on investment.
2. It's about augmentation, not replacement. Raheel made it clear: the goal isn’t to replace humans. It’s to help them. Agentic AI surrounds current systems, such as EHRs, CRMs, and intake tools, and enhances them with the right information at the right time. It doesn’t require ripping out or rebuilding workflows. Instead, it supports them.
For example, an agent might:
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Alert a care manager that a member is about to exceed their authorized physical therapy sessions
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Recommend outreach to a member showing signs of medication gaps
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Draft scripts or messages tailored to the individual’s situation, reducing guesswork and rework
This allows care teams to spend less time chasing data and more time acting on it. It brings focus, speed, and scale to high-friction workflows.
3. There's a maturity curve that leaders can follow. Raheel broke down a progression for organizations getting started with AI:
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Begin with summarization: aggregating and condensing key information from across systems
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Advance to recommendation: highlighting what matters most
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Then move into nudging and activation: delivering those insights through the right channel
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Finally, enable execution: supporting task completion and decision-making
This staged approach enables organizations to achieve initial successes, such as reducing the time spent preparing review books, and gradually build toward more advanced capabilities, including automating elements of disease management enrollment or care plan generation. It’s not about turning on everything at once. It’s about building capability over time.
For any healthcare executive wondering where to begin with AI, this episode offers a grounded, practical roadmap. It’s full of examples from real-world implementations and highlights where organizations are already seeing traction.
Listen to the whole conversation on the AI in Business podcast, hosted by Emerj:
Want to see how Productive Edge is helping care teams modernize with AI? Visit productiveedge.com/ai-agents
