Can healthcare payers fully realize the value of Agentic AI without addressing the knowledge gap? The answer hinges on how effectively these AI systems are equipped with accurate, comprehensive and readily accessible information without which their autonomy and intelligence remain constrained.
Agentic AI refers to a cluster of intelligent agents that collaborate in a coordinated sequence to accomplish complex tasks. These agents are interdependent and communicate dynamically, enabling seamless orchestration of decisions and actions within the AI ecosystem itself. These internal collaborations allow Agentic AI to operate autonomously, adaptively addressing challenges and driving outcomes across multifaceted operational environments without requirement of human intervention.
The market for Agentic AI in healthcare is projected to reach $4.96 billion by 2030, growing at a remarkable CAGR of 45.56%. For healthcare payers, this translates into opportunities to automate claims adjudication, enhance member engagement and streamline operations with unprecedented speed and precision. According to McKinsey, strategic AI adoption can lead to up to 12% higher revenue and 25% lower administrative costs. Additionally, Boston Consulting Group (BCG) reports that strategic BPO partnerships can yield 15–40% cost savings across middle and back-office functions.
Despite its promise, Agentic AI is often limited by a persistent knowledge gap. This is compounded by the AI Bubble: a closed environment where AI agents collaborate and orchestrate tasks internally but lack access to diverse, real-world data. For healthcare payers, this leads to shallow automation, flawed predictions and stalled innovation. Breaking the bubble requires unified data, smarter knowledge frameworks and richer inter-agent collaboration to unlock true autonomy and value.
This article explores how knowledge gaps limit Agentic AI, the strategic role of modern knowledge management and how BPM partners are enabling healthcare payers to achieve measurable impact.
why the full potential of Agentic AI isn’t being realized
Despite growing investments in Agentic AI, many healthcare payers are not realizing its full value. The core issue lies in the quality, structure and accessibility of underlying knowledge. Agentic AI thrives on context-rich, connected data but, most payer environments are far from AI-ready. Key barriers include:
- inconsistent document formats: Data is scattered across PDFs, emails, handwritten notes and spreadsheets. This lack of standardization makes it difficult for AI to parse and interpret information effectively.
- fragmented data repositories: Siloed systems and disconnected knowledge assets deprive AI of the full context needed to generate insights and take autonomous actions thereby widening the knowledge gap.
- legacy systems: Outdated, rule-based platforms and rigid dashboards lack the flexibility and integration needed for real-time intelligence. This reduces Agentic AI to surface-level analysis, limiting its ability to drive meaningful outcomes.
To move Agentic AI from passive observer to active decision-maker, payers must rethink how knowledge is captured, structured and maintained. This means transforming raw, scattered data into intelligent, contextual assets that AI can continuously access and act upon securely.
effective knowledge management for agentic AI
Agentic AI’s success lies not only in high-level strategy but also in the precision of its execution. At the very outset of any knowledge management initiative, it is critical to embed keystroke-level SOP updates thereby capturing granular process actions that AI systems can learn from, replicate and evolve with. This is the core foundational element that defines the success of Agentic AI. Building further upon this foundation, the following elements are essential to operationalizing Agentic AI:
- unstructured data is not just transformed but contextualized at the operational level
- standardized models are built with real-world process fidelity, enabling AI to interpret not just what to do, but how to do it
- continuous learning is grounded in actual user interactions, exceptions and feedback loops that reflect the true complexity of enterprise workflows
conclusion
A well-structured and enriched knowledge base is the foundation of intelligent automation, especially in the age of AI. Agentic AI offers healthcare payers the ability to move from static workflows to intelligent, self-driven operations. But without structured, contextual and continuously updated knowledge, its potential remains unrealized.
To truly harness Agentic AI, payers must prioritize effective knowledge management by structuring and standardizing data, building semantic models for intelligent retrieval, and enabling continuous learning and adaptation.
By bridging the knowledge gap, healthcare payers can move beyond automation and into a future defined by intelligent, autonomous operations that deliver measurable impact across the value chain.
how Infosys BPM can help bridge this knowledge gap
At Infosys BPM, we empower healthcare payers to unlock the full potential of Agentic AI by combining deep domain expertise with advanced knowledge management capabilities. Our approach integrates the foundational practices of BPM providers with Infosys-specific innovations to create AI-ready environments:
- documenting tribal knowledge at keystroke level: We capture granular, step-by-step process actions that form the basis of AI learning. This enables Agentic AI to replicate and evolve with real-world workflows, ensuring precision and adaptability.
- designing knowledge models: We build structured frameworks that translate domain expertise into formats optimized for Agentic AI reasoning and decision-making.
- structuring data: Through intelligent document processing, we extract and standardize data from diverse sources such as scanned PDFs, handwritten forms, free-text notes making it usable for AI systems.
- unifying operations: We help payers shift from rigid systems to AI-powered platforms that enable real-time decisions. By integrating key functions such as claims, provider management, member services, appeals and clinical services, Agentic AI can work across silos to resolve issues and improve outcomes with minimal human input.
- knowledge structuring & governance: We provide automated document ingestion, domain-centric tagging and metadata enrichment, all built on a secure, HIPAA-compliant infrastructure with real-time synchronization that ensures AI systems have access to current, contextual knowledge.
With the support of experienced transformation partners like Infosys BPM, payers can accelerate their journey toward AI readiness and build ecosystems where Agentic AI thrives.
Get in touch with our team today to empower your healthcare payer operations with agentic AI.
