marketingtechnology
Chief Technology Officer Driving CX Assurance and AI Innovation
From the survey results, the number one reason customers ghost brands is not being able to reach a human agent. Why do you think it still matters so much even as AI gets more advanced?
Customers want confidence that their problem will actually get solved. A human agent represents that safety net. The data shows that 71% of consumers still prefer to begin customer services with a live person, and the number one dealbreaker is being unable to reach one at all.
Even as agentic AI improves, customers know there are complex situations where nuance, judgement, or empathy matter. Billing deputes, travel disruptions, or anything tied to money or personal data often carries emotional weight. People want the option to escalate to someone who can take ownership of the issue.
What kind of hybrid models between AI and human service do you think work best, and why?
The most effective hybrid models treat AI as the first layer of assistance rather than the final authority. Agentic AI is excellent at handling high volume tasks like account lookups, status checks, and other structured requests. When automation handles them well, human agents gain more time to focus on complex cases that require judgement and empathy.
Where hybrid models fail is when the escalation paths are unclear. The survey shows that many customers want to escalate immediately or after a single failed bot interaction.
A strong hybrid model depends on CX assurance. Organizations must validate that the journey works from the customer’s perspective and that the handoff from AI to human support is smooth and reliable.
The survey suggests younger generations are more open to AI handling issues if it’s seamless. Do you see this shaping long-term AI strategy?
Yes, but mainly in how companies design their customer experiences. Younger customers tend to prioritize speed and convenience. More than half (56%) of Gen Z say they would choose an AI over a human interaction if it resolved their issue seamlessly.
However, generational expectations vary widely. Older customers often prefer speaking with a human, especially when the situation involves sensitive information or financial decisions.
The long term strategy should focus on flexibility. Brands should provide multiple ways to resolve an issue and allow customers to choose the path that works for them.
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Nearly half of consumers quit a brand after just a couple of bad experiences. How do you measure and improve AI’s effectiveness to avoid those moments?
Many organizations measure technical success rather than customer success. If the bot responds and the system records a completed interaction, the dashboard may show everything working correctly. Meanwhile the customer might have repeated themselves, been routed incorrectly, or received an answer that did not solve the problem.
Improving AI effectiveness requires measuring the full journey. Teams need visibility into whether the system understood the request, provided the correct information, and resolved the issue without unnecessary friction.
CX assurance plays a role here. Continuous validation across channels allows organizations to identify where journeys break and correct those issues before customers experience them.
There is a perception gap around AI capabilities versus reality, like consumers thinking humans resolve issues faster. How can tech teams help close that perception gap?
The perception gap comes from experience. Many customers have interacted with automation that failed to understand their question or trapped them in a loop. Those early experiences shape expectations long after the technology improves.
The only way to change perception is through reliability. When AI consistently resolves issues quickly and accurately, customers start to trust the channel. By continuously validating customer journeys, teams can detect and correct breakdowns before they damage customer confidence.
What’s your approach to building trust in AI-driven experiences rather than just rolling them out quickly?
Trust comes from discipline in how systems are deployed and monitored. Many organizations feel pressure to launch AI quickly. They introduce automation into customer journeys without fully validating how those systems behave under real conditions.
A better approach starts with governance and testing before launch, followed by continuous monitoring once the system is live. AI systems evolve as data changes and models learn, so reliability must be validated continuously.
CX assurance provides that layer of oversight. It helps organizations confirm that AI interactions remain accurate, compliant, and aligned with the intended customer experience.
A major takeaway is that poorly validated AI can harm reputation and loyalty. How do you ensure your AI systems are tested and governed before they go live?
Testing AI requires going beyond a limited set of scripted scenarios. Traditional quality assurance might validate a small number of expected interactions. Real customers behave very differently. They interrupt conversations, change topics, and ask questions in unexpected ways.
Effective CX assurance intentionally pushes AI systems through these use cases. Teams test unusual phrasing, multi-step conversations, and cross channel interactions to see how the system responds.
It’s important to identify weaknesses before customers encounter them. This approach reduces risk and ensures the experience behaves safely under real world conditions.
Looking ahead, what do you think needs to change in AI-powered CX to delight customers rather than frustrate them?
The next stage of AI powered CX will depend on reliability across the entire customer journey. Many organizations focus primarily on the intelligence of the agentic AI model. In practice, the most common failures occur in the surrounding workflow such as knowledge accuracy and/or escalation paths.
Customer journeys also move across multiple systems and channels. Each transition introduces potential friction if context is lost or the experience resets.
To deliver experiences that truly delight customers, companies need to design those journeys end to end and validate them continuously. CX assurance helps ensure that every step works as intended, even as systems evolve and customer behavior changes.
