This piece explores how Human-in-the-Loop (HITL) models create more reliable and context-aware solutions by combining the efficiency gains of AI with the contextual judgment of human expertise. 

While Artificial intelligence has transformed a variety of industries, judgment remains essential for these solutions to be effective. AI will continue to become faster and more capable, but its full business potential can only be realized when human expertise is integrated into workflows, ensuring accountability and fostering trust with customers and regulators. 

Consider self-driving cars: despite decades of research and development by top engineers, fully autonomous vehicles operate in only a handful of cities, and only after being tuned carefully to those local conditions. In many scenarios, they still rely on remote human operators to step in when the system encounters unusual situations or lacks sufficient context.  

Another example comes from customer service, where an AI chatbot might automatically process a cancellation, whereas a human agent can detect nuances. Say there’s a family emergency; humans can respond with empathy and have the flexibility to consider alternate solutions. 

Even with partially automated workflows, strategically placed human oversight can unlock significant business value. These examples highlight how human judgment is leveraged within AI solutions, specifically for edge cases and complex decisions. 

What are Human-in-the-Loop (HITL) models? 

Human-in-the-Loop (HITL) models are a strategic approach to addressing these challenges in AI development and deployment. HITL models insert human intervention and expertise directly into the AI process.  

Human expertise can be leveraged throughout an AI project, as noted above, including for curating and labeling training data, validating and correcting model outputs, and real-time oversight to AI generated responses, to name a few. Rather than leveraging humans merely as a fallback for AI solutions, businesses should adopt a strategic framework that integrates HITL approaches to build more trustworthy and effective solutions, maximizing the value of their AI investments. 

Human Oversight in Customer Service: More Than a Safety Net 

AI-powered chatbots have become all too common in customer service. The ability for an AI chatbot to handle routine inquiries with accuracy has become enticing for a variety of services. However, what happens when conversations become emotionally charged or contextually complex?  

For example, automation is often not enough on its own, and Lyft has recognized this by incorporating a HITL strategy in their AI chatbot deployment that has cut the average customer service resolution time by 87%. Their customer care assistant is now integrated with Claude, running on Amazon Bedrock, where they direct customers to human specialists using automated escalation processes.  Humans are often able to better interpret the tone and ambiguity in messages to respond to customers with empathy, improving customer sentiment. 

Further to this point, a 2025 survey published by the International Journal of Research in Computer Applications and Information Technology (IJRCAIT) found that creating and developing clear escalation triggers led to a 36.5% reduction in handling time for escalated tickets. These triggers route complex messages directly to human agents, successfully leveraging the strengths of both AI chatbots and the problem-solving expertise of human customer service agents.    

Klarna, a financial services provider, recently felt the challenges of an AI chatbot deployment with little human oversite. Removing humans from the customer service process has led to a decrease in service quality, prompting the company to announce a recruiting initiative to restore quality human support. This example shows how maintaining brand trust and integrity of customer interactions can be difficult when relying solely on AI chatbots. Leveraging a HITL model in your AI implementation can act as an oversight mechanism and safeguard against AI errors, ensuring better customer satisfaction. 

Knowledge Management: AI Organizes, Humans Curate 

Organizations often rely on a large set of business documents containing high volumes of data. Spreadsheets, reports, contracts, forecasts, and a variety of other documents are stored in a central repository. AI solutions are often leveraged in environments with large file volumes, with the ability to locate and display documents in a fraction of the time it would take humans. AI solutions deployed without considering a human-in-the-loop model often produce misapplied tags, since they lack the business context and judgement needed to handle edge cases.  

File taxonomy, for instance, should be less of an IT task, and more of a business decision. Without human intervention, issues tend to arise when documents containing similar information or document versions are tagged by AI classifiers. To prevent downstream issues or errors, human expertise must be leveraged.  

Microsoft’s SharePoint platform offers an AI taxonomy tagging service that automatically applies terms from an organization’s Term Store into managed metadata columns. Without customization, experts can manually review AI suggestions and make corrections as needed. This solution can be leveraged to create a HITL model when combined with Syntex’s document understanding models and Power Automate workflows.  

This combination allows organizations to evaluate AI confidence values and route low-confidence or business critical suggestions to experts for approval and edits, supporting a mature AI and data strategy. All human corrections should be captured to retrain and refine the model over time. Implementing a human-in-the-loop can create a collaborative strategy that lets AI solutions handle scale and volume while amplifying human expertise.  

Final Thoughts: Human Intelligence is the Strategic Edge 

Organizations often design and build AI solutions with a key goal of increasing efficiency and reducing costs, a component of which is often through an assumed reduction in their workforce needs. As we’ve discussed, without the human intelligence required to apply emotional intelligence, validate outputs, or adjust model inputs, businesses put themselves at risk of falling short of intended expectations for their AI strategy and investments.  

Building solutions that can operate at scale, maintain quality, and drive better outcomes will be a key differentiator for organizations looking to maximize the value of their AI. Whether it’s AI chatbots or file taxonomy, implementing a human-in-the-loop approach allows organizations to balance and leverage the strengths of both humans and AI solutions. 

Clarkston’s AI experts partner with organizations to design and build effective AI strategies that leverage both the value of human oversight and the inherent benefits of AI solutions.