The Symbiotic Relationship Between Human Expertise and AI Systems
Enterprise support is witnessing the emergence of a powerful dynamic between human expertise and artificial intelligence. Rather than the often-feared scenario where AI replaces human workers, forward-thinking organizations are discovering a more nuanced reality: when properly integrated, human support specialists and AI systems create a symbiotic relationship that enhances both.
At the heart of this relationship is what we call the "knowledge feedback loop"—a continuous cycle where human expertise is captured during live support sessions, transformed through AI summarization into structured knowledge, and then fed into agentic AI systems that become increasingly capable of handling complex support scenarios. This loop represents a fundamental rethinking of how knowledge flows within support organizations and how human expertise can be amplified rather than replaced by AI.
Understanding the Knowledge Gap in Agentic AI
The Limitations of Static Knowledge Bases
Agentic AI systems have made remarkable strides in autonomous problem-solving, but they face a significant challenge: they can only act on the knowledge they've been given. Traditional approaches to building knowledge bases involve manual creation of articles and decision trees—a process that's time-consuming, often incomplete, and quickly outdated as technologies and issues evolve.
ServiceNow's approach to AI agents acknowledges this challenge, noting that their effectiveness depends on having access to "real-time access to enterprise data from any source" and "all the rich business context to make smart decisions fast. However, much of the most valuable support knowledge never makes it into structured databases because it exists primarily in the minds and actions of experienced support specialists.
Where Human Expertise Remains Irreplaceable
Despite advances in AI, human support specialists bring unique capabilities to complex problems. As Denver Naidoo points out in his analysis of agentic AI trends, "We do not believe agentic AI will take jobs, as human resources require human inputs... This is where agentic AI shines: it takes the heavy lifting of manual inputs off HR and recruiting professionals' plates so they can focus more on high-value human interactions and outputs.”
The same principle applies to IT support. Human agents excel at creative problem-solving, empathetic communication, and navigating ambiguous situations—precisely the areas where agentic AI systems struggle. The challenge is capturing that human problem-solving expertise in a way that can inform and improve AI systems.
The Mechanics of the Knowledge Feedback Loop
Phase 1: Capturing Expertise in Action
The knowledge feedback loop begins during live remote support sessions where human specialists troubleshoot and resolve complex issues. Traditionally, the valuable insights generated during these sessions would be lost or, at best, incompletely documented in post-session notes.
Tools like ScreenMeet change this dynamic by enabling seamless remote support directly within ServiceNow. This integration creates the foundation for capturing expertise in action—recording not just what was said but what was actually done to diagnose and resolve issues.
Remote support sessions represent the perfect opportunity to observe and record this procedural knowledge in its most authentic form- during its application rather than through post-hoc documentation.
Phase 2: Transforming Actions into Structured Knowledge
Raw session data alone isn't immediately useful for AI systems. The critical innovation that enables the knowledge feedback loop is AI summarization—the ability to transform unstructured support sessions into clear, structured knowledge assets.
ScreenMeet's AI Summarization technology observes remote support sessions and automatically generates comprehensive documentation of the diagnostic process and resolution. Unlike traditional session recordings that require human review to be useful, these AI-generated summaries extract key information:
- Precise symptoms and their contextual triggers
- Diagnostic steps taken, including those that did and didn't yield results
- The specific resolution actions and their outcomes
- Environmental factors that influenced the problem or solution
This structured information is automatically added to ServiceNow as work notes or knowledge articles, creating a continuous influx of validated support knowledge.
Phase 3: Feeding Structured Knowledge into Agentic Systems
The structured knowledge created through AI summarization becomes the fuel that powers increasingly capable agentic AI systems. Unlike traditional knowledge bases that remain static until manually updated, this approach creates a dynamic knowledge ecosystem that continuously evolves with each support interaction.
ServiceNow's AI Agent framework is designed to leverage this exact type of structured knowledge. As their platform documentation explains, AI agents need both "role" definitions that explain their purpose and "data" that provides the context for decision-making (ServiceNow, 2025). The summarized support sessions provide precisely the type of contextualized problem-solution pairs that agentic AI systems need to become more effective.
This feeding of real-world support knowledge into AI systems creates what Forrester Research calls "experiential learning for AI"—the ability for AI systems to learn not just from predefined training data but from ongoing real-world experiences (Forrester, 2024).
Expanding Knowledge Through Multi-Modal Support
Beyond Screen Sharing: The Value of Multiple Support Channels
The knowledge feedback loop becomes even more powerful when it incorporates multiple support modalities. Modern secure remote support extends beyond simple screen sharing to include:
- Mobile camera access for hardware troubleshooting
- Voice interactions that capture verbal explanations
- Co-browsing sessions that show user navigation patterns
- Chat conversations that reveal how users describe problems
Each modality provides a different perspective on support issues and their resolutions. ScreenMeet's ability to incorporate these various modalities ensures that the knowledge captured and summarized represents a comprehensive view of support scenarios.
This multi-modal approach aligns with cognitive science research on how humans develop expertise. By capturing support knowledge across modalities, organizations create richer training data for their agentic AI systems.
Contextual Understanding: The Missing Piece in AI Support
One of the most challenging aspects of support is understanding the context in which problems occur. Traditional knowledge bases struggle to capture contextual factors, yet these often determine whether a particular solution will be effective.
The integration of live support sessions with AI summarization addresses this gap by documenting not just solutions but the conditions under which they apply. This contextual information is critical for agentic AI systems to make appropriate decisions about when to apply specific resolution techniques.
Measuring the Impact of the Knowledge Feedback Loop
Quantitative Benefits of Continuous Knowledge Enrichment
Organizations implementing the knowledge feedback loop between remote support and agentic AI report significant measurable benefits. According to McKinsey's analysis, this approach typically yields:
- 25-35% reduction in repeat incidents for similar issues
- 15-20% improvement in first-contact resolution rates
- 30-40% decrease in time spent searching for relevant knowledge
- 20-30% reduction in training time for new support specialists
These improvements stem from the virtuous cycle created by the knowledge feedback loop: better knowledge leads to more effective AI agents, which handle routine issues autonomously, which frees human agents to focus on complex problems, which generates new knowledge to improve the AI agents further.
Qualitative Transformation of Support Capability
Beyond the quantitative metrics, organizations experience a qualitative transformation in how support knowledge evolves. Instead of the traditional model where knowledge becomes stale unless manually refreshed, the feedback loop creates a continuously evolving knowledge ecosystem that:
- Automatically identifies emerging issues before they become widespread
- Validates the effectiveness of solutions across multiple instances
- Adapts to changing environments and technologies
- Preserves institutional knowledge even as support teams change
This dynamic knowledge ecosystem represents a fundamental shift from treating support knowledge as a static asset to viewing it as a living system that grows and evolves organically through actual support interactions.
Implementation Strategies for Maximizing Knowledge Flow
Technical Integration Considerations
Creating an effective knowledge feedback loop requires thoughtful integration between remote support tools and AI systems. Organizations implementing this approach should consider:
- API Integration Depth: Ensure that remote support tools like ScreenMeet are deeply integrated with ServiceNow to enable seamless knowledge flow without manual intervention.
- Knowledge Taxonomy Alignment: Develop a consistent taxonomy for categorizing support issues and solutions that works across both human and AI support channels.
- Data Privacy and Governance: Implement appropriate controls to ensure that sensitive information is properly handled during the knowledge extraction process.
- Quality Assurance Mechanisms: Establish processes to validate the accuracy and usefulness of AI-generated knowledge summaries before they influence agentic AI decisions.
Change Management for Support Teams
The knowledge feedback loop also requires cultural and procedural adaptation within support teams. Organizations should focus on:
- Emphasizing Value Creation: Help support specialists understand that their expertise is being amplified rather than replaced, positioning them as knowledge creators rather than just problem solvers.
- Creating Feedback Mechanisms: Enable support specialists to review and refine AI-generated summaries, creating a secondary feedback loop that improves the summarization process itself.
- Recognizing Knowledge Contribution: Develop recognition systems that acknowledge support specialists whose sessions generate particularly valuable knowledge assets.
The Future of Human-AI Collaboration in Support
From Knowledge Sharing to Collaborative Problem-Solving
As the knowledge feedback loop matures within organizations, the relationship between human experts and AI systems will continue to evolve. The next frontier is moving from knowledge sharing to true collaborative problem-solving, where human agents and AI agents work together in real-time to address complex issues.
This collaboration model aligns with Naidoo's vision for the future of agentic AI: "We'll establish an AI hierarchy... organizations can deploy a network of specialized AI agents to operate behind the scenes. These agents can (and should) perform the heavy lifting around tasks such as data processing and analysis, report generation and visualization, and continuous learning.”
Continuous Innovation Through Collective Intelligence
The ultimate promise of the knowledge feedback loop is the creation of a collective intelligence that combines the best of human creativity and AI processing power. By bridging remote support and agentic AI through AI summarization, organizations create an environment where each support interaction contributes to a growing collective knowledge base.
This approach transforms support from a cost center focused on fixing problems to an innovation engine that continuously improves the organization's ability to prevent and resolve issues. As ServiceNow notes, the goal is not just more efficient support but "AI impact you can track" that delivers measurable business outcomes.
The Virtuous Cycle of Knowledge and Action
The knowledge feedback loop between remote support and agentic AI represents a new paradigm in how organizations approach service delivery. By capturing human expertise during live support sessions, transforming it into structured knowledge through AI summarization, and feeding that knowledge into increasingly capable agentic systems, organizations create a virtuous cycle of continuous improvement.
This approach transcends the false dichotomy between human and artificial intelligence, instead creating a symbiotic relationship where each enhances the other. Human support specialists provide the creative problem-solving and expertise that AI systems learn from, while AI agents handle routine issues and offer knowledge support that enhances the effectiveness of human agents.
For organizations seeking to maximize the value of both their human talent and AI investments, implementing tools like ScreenMeet's AI Summarization in conjunction with ServiceNow's agentic AI capabilities lays the foundation for a transformative knowledge feedback loop. The result is not just more efficient support but a fundamentally more intelligent and adaptive support ecosystem that continuously evolves to meet changing needs.
