Why AI hasn't fulfilled the promise of truly flawless transitions to human experts yet?
Why AI hasn't fulfilled the promise of truly flawless transitions to human experts yet?
The surprisingly simple reason: Queues no longer work!
by: Ronald Rubens
With billions of dollars invested in AI, the technology industry agrees on one thing: the future lies in human-AI collaboration. Yet, Gartner revealed staggering statistics earlier this year about AI in the customer experience domain:
- 60% of Customers still have concerns that with AI, it will become increasingly more difficult to reach a person when it matters
- 59% of customers using self-service fail to resolve their issues
- Customer experience erodes by more than 50% when customers struggle to connect with the right human agent.
These insights highlight a critical gap in delivering the flawless, human-centered experiences customers increasingly demand.
Just the other day, I experienced firsthand how crucial it is to have a skilled human agent step in and resolve an issue on the first attempt. It was a frustrating encounter with the largest webshop in the Netherlands, and it left me thinking about how far we still have to go in delivering exceptional customer experiences.
Here’s what happened: my personal information had been falsely used by someone posing as a fake vendor. This fraudster not only abused my personal and business details but also hijacked my phone number, leaving me to deal with a flood of angry calls from customers facing delivery issues. It was a clear case of fraud—one that, in a perfect world, should have triggered immediate compliance and fraud alerts.
Instead of receiving proactive support, I found myself stuck in a frustrating “doom loop” with their chatbot. The bot couldn’t grasp the complexity of my situation and failed to escalate it to the right expert. When I finally reached a human, I was endlessly redirected between agents—none of whom were empowered or equipped to resolve the issue.
The result? Customer experience eroded with every passing attempt. This highlighted a critical gap: while automation and AI are valuable in customer service, they often fail to seamlessly transition conversations to the right skilled human agents.
The contact center vendor used by the webshop is also deployed by a bank in the Netherlands, where a voice-bot is used to determine customer needs. When I needed to reactivate my credit card, I clearly said, ‘Please reactivate my credit card.’ What followed was 40 frustrating minutes of listening to an endless ringtone, interrupted only by a recorded voice repeating, ‘All our agents are currently busy, but your call is important.’ Finally, when I was connected, the agent told me she couldn’t help and that I needed to speak with another department.
Despite both enterprises relying on technology from the same Gartner Magic Quadrant leader, poor integration between the bot and the contact center left the system unable to recognize or predict when a human expert was needed—or to empower agents to act when it mattered most. Instead, I was stuck in queues—a frustrating dead end that wasted valuable time and left me without a resolution.
So, what’s the issue with queuing you might ask?
The challenge lies in the very design of traditional contact center platforms – including those in Gartner’s leadership quadrant. Most contact center or CCaaS systems are built on legacy queue management models, predominantly first-in-first-out (FIFO). These systems struggle to integrate with advanced AI agents and conversational AI platforms. Without this integration, they fail to deeply understand customer intents, let alone prioritize routing based on those intents and most importantly business impact.
As a result, when requests become more complex – or when multiple intents, emotion and other customer data needs to be paired with the right skills, attributes and proficiency of human experts – queues are simply incapable of handling a level of fine-grained matching, let alone proactive triaging. Moreover, the lack of clear visibility into the urgency or importance of intents, combined with agents being logged into multiple queues, makes it impossible to accurately predict wait times.
These platforms also fail to dynamically manage surges in customer demand. Without effective pairing and triaging, queues grow—and so do inefficiencies. Supervisors are forced to intervene, manually reassigning tasks, which increases costs and strains resources. At the same time, SLA loopholes give a false sense of efficiency while customer experience continues to erode.
This combination of poor integration, rigid queue-based systems, and reliance on manual oversight creates a cycle of inefficiency—driving up costs, overburdening teams, and leaving customers and employees dissatisfied.
It is time for a fresh approach – bringing the right expert into the conversation
One thing that AI is really good at is to collect, interpret, and leverage vast amounts of data – lots of data ! It can analyze intents, determine sentiment, and even predict churn. This data powers automated dialogues, workflows, and more. But here’s the missed opportunity:
the same data isn’t being used proactively to optimize pairing and triaging when a human expert is truly needed
Instead, interactions are thrown into a queue, leaving customers powerless—stuck waiting without any visibility into how long it will take to connect with the right expert or agent capable of resolving their issue on the first attempt.
So, how can we create a truly flawless collaboration between AI agents and human experts – one where AI-generated data is harnessed for proactive and predictive human-in-the-loop support?
SentioCX was founded three years ago with the mission to achieve exactly that. As a pure-play proactive human-in-the-loop orchestration engine, we’ve invented a revolutionary new algorithm and were granted the industry’s first U.S. patent for predictive intent-based routing.
The issues we address to create flawless transitions to Human Experts while optimizing business impact include:
1. Unlocking self-service stalemates
45% of customers who started in self-service said the company didn’t understand what they were trying to achieve. The reason is that most conversational AI platforms are focuses on transactional conversations which can be automated. However, when intents become complex or when vulnerable customers require immediate assistance, it’s critical to involve the right human expert with the appropriate priority. SentioCX leverages real-time intent and sentiment analysis to prioritize and pair each customer interaction with the most suitable human expert (when necessary or desired), ensuring optimal business impact and a personalized experience.
2. Dramatically improving first-time resolution through Intent-based pairing
SentioCX pairs intents with relevant agent attributes—such as skills, proficiency, and authorization levels—to create the best possible match between customers and human agents. This approach not only improves first-contact resolution but also leads to shorter and predictable wait times (as covered in point 3). By positively impacting both the customer and employee experience, SentioCX drives measurable improvements in key performance indicators like customer churn.
3. Pro-active and continuous triaging
A key element of SentioCX’s solution is its Business Impact Prioritization Framework. Beyond pairing and assigning importance and urgency to intents for effective triaging, SentioCX leverages emotion detection and specific data from AI agents such as churn prediction, fraud alerts, or compliance triggers.
This data allows SentioCX to continuously identify and prioritize critical moments when customers require access to human experts—particularly in scenarios where personalized assistance can make a significant impact, such as preventing churn. Even when all agents or experts are busy, conversations remain within the self-service or AI agent. Meanwhile, SentioCX dynamically adjusts priorities based on changing conditions or sentiment, ensuring escalation happens automatically when needed.
The below screenshot of SentioCX’s management console depicts the Business Impact Prioritization Framework, which can be easily adapted to incorporate multiple attributes, such as churn prediction, intent, sentiment and customer score for proactive triaging purposes.
4. AI Supervisor Agent for self-regulation based on business impact
As pro-active triaging aligns with the business impact prioritization framework, SentioCX has introduced the concept of an AI Supervisor Agent. This intelligent agent not only provides actionable insights from critical data but also automatically adjusts priorities and routing through self-regulated service levels, ensuring proactive triaging and optimal resource allocation. The result is a reduction in manual supervisor intervention by over 90%.
Additionally, the platform can automatically and temporarily deactivate lower-priority intents when service levels are compromised, redirecting focus to higher-priority tasks. It can automatically choose to halt hand-offs, prioritizing interactions with the AI Agent, suggest alternative self-service or asynchronous options to users (if human assistance is unavailable), or bring on-call human agents into the conversation as needed.
In summary, the AI Supervisor Agent provides full visibility in all interactions and business impact parameters to influence proactive triaging. By continuously pairing intents and other key ‘signals’ with agent attributes – prioritized dynamically in real time – it creates a unified workforce of humans and AI agents working seamlessly together.
By bringing the right expert into the conversation, SentioCX is able to eliminate static queues. It ensures that customer needs are met with precision and timeliness, driving exceptional outcomes. Additionally, every interaction, escalation, and outcome feeds into a third-party AI agent feedback loop for continuous optimization and refinement.
For more information or a demo on SentioCX, please contact us