AI is revolutionizing customer operations, streamlining tasks, and delivering efficiencies that have the potential to displace traditional BPOs (Business Process Outsourcing) and disrupt customer operations. However, despite its immense promise, AI still faces significant limitations. It cannot yet operate fully autonomously, especially in use cases where quality and precision are critical. Challenges such as hallucinations (when AI generates incorrect or fabricated information) and biased decision-making pose substantial risks to organizations. When AI is tasked with handling thousands or millions of tasks and customer interactions, these errors can snowball, leading to reputational damage, loss of customer trust, regulatory breaches, and revenue loss.
To harness AI's full potential, it must work alongside human workers in a hybrid system that maximizes the efficiencies of automation while preserving human oversight and accuracy. One critical element of this hybrid model is a well-designed escalation system that ensures AI knows when to hand off tasks to humans - maximizing productivity without sacrificing precision.
When should AI escalate tasks to humans?
Successfully integrating AI and human agents requires knowing when AI should hand off tasks to humans.
There are two primary scenarios where this handoff is necessary:
1. High-value or high-risk tasks
Certain tasks demand human judgment or approval due to business rules, regulatory requirements, or potential impact. For example, high-value transactions or tasks involving sensitive customer information may need to be escalated to a human. Mishandling these tasks could lead to compliance issues, legal complications, or significant reputational damage.
2. Low confidence in AI reponse
AI works by assessing patterns in data and generating predictions based on what it has learned. Confidence scores tell you how certain the AI is about its response. A high confidence score means the AI has encountered similar situations before and is certain about its response. But when the AI’s confidence score is low, it suggests ambiguity or a lack of data, meaning the AI’s response could be wrong or incomplete.
When handling customer operations tasks, an AI system should only ever proceed autonomously when its confidence score is high. When the AI’s confidence in its decision is low, it should escalate the task to a human to ensure accuracy and protect the customer experience.. Allowing AI to act on uncertain results could damage customer satisfaction and harm your brand’s credibility.
Designing intelligent escalation paths
To ensure your hybrid AI-human system functions seamlessly, it’s important to design clear, efficient escalation paths.
Here are the key best practices for designing intelligent escalation paths:
Define clear escalation triggers
Establish precise rules for when AI should escalate tasks to human agents.
- High-value or high-risk tasks: Include explicit rules in your prompts for specific scenarios that require human hand-off or approval. For example, you might instruct the AI model to escalate any task related to a transaction exceeding a specific monetary value, or involving sensitive data.
- Low confidence scores: Set a minimum threshold that prompts the AI to hand off tasks when it is unsure or lacks sufficient data to make a reliable decision. This threshold will differ based on business needs—some tasks may require near-perfect accuracy, while others can tolerate lower confidence levels.
By clearly defining these triggers, you ensure the AI knows when to seek human intervention and when it can proceed autonomously.
Ensure accurate confidence scoring
Escalation decisions hinge on accurate confidence scores. If AI misjudges its confidence level, it can escalate tasks unnecessarily or miss errors, which could harm customer satisfaction and brand trust.
Methods for assessing AI confidence
1. Raw model probabilities
The model generates a numerical score between 0 and 1 (or 0% to 100%) indicating its certainty about an output. For instance, when evaluating a product for fraud, the model might output:
- P(fraud) = 0.07
- P(genuine) = 0.93
The model is 93% confident that the item is genuine. However, these scores are based on training data, meaning the AI can be overly confident if the data is inadequate or flawed.
While this approach offers precise, continuous scoring that's useful for setting thresholds, research shows that RLHF-trained models (reinforcement learning from human feedback) can be poorly calibrated and consistently over-confident. Additionally, not all LLM providers grant access to these scores.
2. Verbalized confidence
In this approach, prompts are designed to instruct the AI to verbalize its confidence within its text output, such as: "I'm 80% confident" or "Confidence: high."
This method allows for additional controls, such as specifying when the AI should express uncertainty (e.g., “If the image is blurry, don't assign a confidence above 80%”). This can be more accurate than raw probabilities in some cases but lacks the precision needed for setting granular thresholds.
Experiment with both methods and assess whether the AI’s confidence scores align with your expectations and use case requirements.
Enable seamless handoffs
It’s not enough for AI to simply escalate tasks to humans; the handoff must be smooth and efficient. Make sure that when a task is handed off, the human agent has all the relevant context, including:
- Previous interactions
- AI analysis
- AI confidence scores
With all this information, human agents can seamlessly take over, picking up where the AI left off and finishing tasks efficiently.
Incorporate human quality assurance (QA)
Ongoing QA assessments are key to improving the AI’s escalation process. Start by conducting daily checks to understand how the system is performing, focusing on the following areas:
- Errors in AI-handled tasks: This may indicate overly high confidence scores.
- High escalation rates for specific tasks: This could suggest low confidence scores due to insufficient training data.
- Borderline cases: Review instances where AI’s confidence score is near the threshold to further understand and improve training data and confidence scoring.
- Long resolution time for escalated cases: Long escalation times could mean agents are not being given enough context from the AI agent.
Use this feedback to improve your AI’s confidence scoring and prompt design. Once the system stabilizes, move to weekly QA assessments to fine-tune the model.
Deploy your own AI-human model
A well-designed hybrid AI-human system can revolutionize your customer operations, combining AI’s speed and scalability with human expertise to drive efficiency without compromising satisfaction. As your business grows, this approach becomes essential for maintaining trust, accuracy, and quality.
Effective task escalation is just one piece of the puzzle. Success also requires optimizing workflows for AI and human agents, establishing low-effort feedback loops, and progressively automating more tasks while preserving high standards of accuracy.
For a comprehensive guide to implementing this hybrid approach, check out our white paper: Scaling online marketplace operation: The power of AI-human collaboration.
