- Sruthi Madhavan, VP of Strategy & Transformation, and Dr. Navin Sethi, Chief Medical Officer
There’s no shortage of conversation about AI in healthcare, but much of it misses the point. The driver of many “pre-AI” systems in place today has been not so much to proactively support clinical teams or improve patient outcomes, but to reactively meet reporting requirements, enforce rules, or manage risk. As a result, healthcare technology often has prioritized compliance over care.
Healthcare organizations clearly recognize AI’s potential—84 percent expect it to influence clinical decisions, and 80 percent anticipate reduced labor costs through automation. Yet those projections often focus on AI’s capabilities rather than its actual impact on care delivery.
Early evidence from clinician feedback highlights persistent barriers to AI adoption, including poor integration into workflows and usability challenges. These issues are often overlooked until deployment is already underway.
The real question isn’t whether AI can process information or automate routine tasks. It’s whether it can meaningfully support care teams and improve the way decisions are made and delivered.
For many health plans, the impact remains unclear. Despite major investments in AI‑enabled platforms, clinical and operational teams often face challenges using the tools effectively in practice. A systematic review of AI implementation in healthcare found that workflow misalignment, usability issues, and lack of actionable outputs frequently limit adoption. Risk scores may appear without clear next steps, dashboards can overwhelm rather than inform, and what’s marketed as intelligence can quickly become noise.
That disconnect reveals something important: AI only becomes valuable when it directly contributes to better care.
When Technology Distracts from Care
In theory, AI is meant to ease the burden on clinical teams and streamline operations. But too often, it’s introduced as an overlay on top of disconnected systems and manual workflows. Rather than redesigning processes to improve care, organizations end up automating what is already inefficient.
This approach creates siloed apps, competing dashboards, and task automation that fails to account for the bigger picture. Instead of solving problems, it adds layers of complexity—introducing alerts without context, duplicating documentation, and making it harder for teams to work across systems.
From a clinical standpoint, this leads to two core issues that directly affect the delivery of care:
- Lost time. Nurses and care coordinators spend more time navigating fragmented systems than engaging with members or guiding care plans.
- Lost trust. When AI-generated insights fail to align with how care is actually delivered or lead to actionable next steps, care teams may gradually disengage. Studies show that a significant portion of physicians hesitate to rely on AI tools that lack clinical interpretability or disrupt established workflows.
The ripple effects go beyond inconvenience. Disjointed workflows can delay care plan reviews, slow authorizations, and disrupt discharge coordination. Member satisfaction drops. Quality measures suffer. And health plans are left investing in tools that automate activity without improving outcomes.
These aren’t just operational hurdles. They affect an organization’s ability to meet its clinical goals and deliver timely, coordinated care.
AI’s Role in Orchestrating Smarter Care
There is nothing inherently valuable about AI unless it makes care more consistent, more timely, and most of all, results in better outcomes. When applied intentionally, AI has the potential to support clinical teams and streamline administrative operations. Its value is not in replacing decision-making but in accelerating it, and in reducing the manual burden that often slows down both care delivery and backend processes.
A recent Gartner report estimates that, while agentic AI is expected to power 15 percent of work decisions by 2028, more than 40 percent of these projects will be canceled by 2027. The reasons are telling: unclear return on investment, escalating costs, and poor risk controls. Gartner also warns of a rising trend of “agent washing,” where vendors repackage chatbots or rule-based automations as agents, despite lacking true coordination or autonomy.
That is why orchestration matters. When AI is embedded into the workflows that teams already use and connected across the administrative and clinical functions that make up those workflows, it becomes a true accelerant. AI can connect data to action, reduce unnecessary handoffs, and guide users toward the next best step in a coordinated, efficient way.
When implemented with this orchestration mindset across related processes, departments, and teams, AI agents can:
- Ensure timely handoffs to the right next process steps, such as between payer and provider during a prior authorization request
- Leverage insights into proactive, best-next actions or care plans, rather than forcing teams to guess what comes next
- Reduce time spent on manual triage, redundant documentation, or administrative coordination
- Fit naturally into existing processes, rather than requiring users to navigate a separate layer of tools or logic
This level of coordination is especially critical now as health plans search for new ways to offset rising costs and mitigate the impact of declining reimbursement rates.
The point is not to abandon AI. It is to ground it in real operational and clinical needs, so it becomes a tool that simplifies complexity, rather than adds to it.
Putting AI to Work Where It Matters Most
At Zyter|TruCare, we’ve built technology that injects AI directly into established workflows and “orchestrates” AI agents to work together in order to break down process silos and actually make cross-team processes faster and more efficient. Rather than aiming to just automate, the focus is on aligning technology, workflow, and clinical expertise to reduce friction and improve care delivery.
This approach brings together three essential capabilities:
- A modular digital platform that integrates utilization, care, and disease management into a single system. This unified foundation improves visibility, connects siloed processes, and enables care teams to operate more efficiently.
- An AI orchestration layer that optimizes the digital platform foundation, by adapting to context, proactively identifying intervention opportunities, and guiding cross-team task execution using clinical logic. It goes beyond isolated use cases to deliver intelligence across core workflows.
- Expert services that support internal teams with experienced clinical and operational resources. These services help translate insights into measurable value, not just during implementation but over time.
These components work together to streamline operations, drive consistency, and improve outcomes, while actually lowering costs. In fragmented environments where gaps in data or process create delays or compliance risks, this model provides structure and clarity across departments and disciplines.
Key capabilities are embedded to support the day-to-day work of health plans:
- Next steps are automatically generated based on member acuity, allowing staff to move from insight to action with greater speed.
- Documentation gaps are identified early, helping prevent delays in prior authorizations or appeals.
- All activity is captured in a structured, auditable format, reducing manual cleanup and supporting compliance readiness.
Compliance is not treated as a separate checkpoint. It is built directly into the workflow, so teams can act quickly while staying aligned with regulatory requirements.
As health plans manage tighter margins, increasing oversight, and rising expectations, they need more than automation. They need tools that enable clinical precision, operational consistency, and scalable impact.
Whether it’s guiding a care coordinator through a complex case, streamlining clinical intake, or surfacing proactive interventions, this approach supports better performance and better outcomes without adding unnecessary complexity.
From Concept to Impact: What Health Plans Really Need
Ultimately, health plans looking to reduce friction, improve consistency, and act earlier in the care journey don’t need more AI—they need thoughtfully designed tools embedded in a coordinated model that delivers where it counts.
Connect with our team to learn how Zyter|TruCare applies agentic AI with purpose, simplifying operations and improving outcomes for plans, providers, and members alike.
