Analyze how judgment-free AI interactions reduce customer friction, recover lost revenue, and bridge the trust gap in modern digital service.
Amitha, to start us off, could you share the pivotal moments in your career journey that led you to your current role as Chief Product Officer at Cyara?
It has really been a progression of building in communications and customer experience, and then leaning into where the industry was heading. I started at Oracle, then spent several years at Vonage working on APIs across voice, video, and messaging, where I saw how customer interactions were becoming more software-driven and intelligent. Over time, it became clear that while companies were investing heavily in automation and AI, they struggled to ensure those experiences actually worked reliably in production. That’s what drew me to Cyara, the opportunity to focus not just on building AI-driven experiences, but on making sure they deliver consistently and earn customer trust.
Why do you believe a significant portion of consumers, particularly younger generations, feel more comfortable discussing sensitive issues with an algorithm rather than a human being?
A big part of this comes down to how younger generations have grown up interacting with technology. Millennials and Gen Z are true digital natives. For them, communicating through a screen, whether message, social platforms, or apps, is often the default. That changes how they approach sensitive situations. When you remove the human element, you remove the perceived judgment. There is no tone to interpret, no reaction to read, and no pressure to explain yourself perfectly the first time. That creates a level of emotional distance that can actually make it easier to ask uncomfortable questions or admit confusion. We see that clearly in the data. Nearly one-third of consumers have used a chatbot because they felt too embarrassed to talk to a human, and that jumps significantly for younger groups. More broadly, over a third say they feel less judged interacting with AI than a live agent. Digital-native users are used to on-demand, self-directed experiences. They want to engage when it suits them, move at their own pace, and avoid unnecessary friction. AI fits naturally into that expectation.
What specific types of customer inquiries or industries are seeing the highest “embarrassment barrier” where AI is now the preferred first point of contact?
The strongest pattern is around issues that feel awkward, financially stressful, or mildly adversarial. Consumers are more comfortable using AI for subscription cancellations, complaints about poor service, late bill payments, overdraft fees, and declined payments. These are the kinds of interactions where people may feel embarrassed, frustrated, or simply not want to explain themselves to another person.
Financial services, healthcare, and legal or government-related interactions remain high-sensitivity categories. In Cyara’s research, 65% of consumers said they would never trust an AI bot with financial or account security issues, 53% said the same for healthcare information, and 50% for legal or government paperwork. So the opportunity is not equal across sectors. Travel is also involved. Typically emotionally charged as people value speed, convenience and accuracy. Only 30% said they would never trust AI to handle travel disruptions, which suggests that in some categories, customers are open to AI as long as the path to resolution is clear and fast.
Cyara’s research shows nearly half of Millennials and Gen Z use bots to avoid judgment; how should product leaders design AI personas to maintain this sense of safety without feeling cold or robotic?
Product leaders tend to focus on tone, but what really matters is how the AI behaves. Most bots are designed around clean, linear conversations, while real customers interrupt, change their minds, and bring emotion into the interaction. If the system isn’t built for that, it quickly starts to feel rigid and frustrating. The priority should be making sure the AI understands intent, keeps context, and helps the customer move forward without extra effort. When that foundation is there, the experience feels easy and safe without needing to overdo the bot personality.
How does the availability of a “judgment-free” AI tool directly impact a company’s bottom line regarding those 25% of customers who previously avoided contact entirely?
Cyara’s research found that one in four consumers avoided contacting a company because of embarrassment, but would have been more likely to reach out if AI support had been available. That means some companies are losing opportunities to resolve issues, retain customers, recover revenue, or prevent churn before the interaction even begins.
In practical terms, a judgment-free AI entry point can reduce silent churn. It can help brands capture service moments that would otherwise disappear. A customer who is too embarrassed to ask about a failed payment, a fee, a cancellation, or a complaint may simply leave if there isn’t an easier way to engage. If AI makes that first contact easier, the company has a chance to resolve the problem before it loses revenue.
Given that 56% of users lose trust in a brand after one poor AI experience, where is the “fail point” where a helpful bot turns into a liability for the company’s reputation?
The “fail point” is rarely obvious. It’s the moment the customer realizes the bot is confident, but not actually helping them solve their problem. That can look like misunderstanding the request, repeating an answer, providing an answer that sounds right but isn’t, or making it hard to reach a human. At that point, the experience starts to feel like frustration instead of support.
What makes this especially risky is how quietly it happens. The system may show a completed interaction, but the customer walks away more frustrated than when they started. From the customers perspective, the company chose automation and didn’t make sure it worked, which is why 56% say a poor AI interaction reduces trust in the brand. This is where CX assurance comes in. It’s about validating that the journey works the way a customer actually experiences it, across real conversations, edge cases, and handoffs. Without that, issues only show up after customers hit them.
There’s also very little margin for error. Nearly 80% of consumers will escalate after a single failed interaction, so brands don’t get much time to recover. The risk starts the moment the customer feels stuck, misunderstood, or forced to put in extra effort.
What are the core technical or operational pillars that define “reliability” in today’s generative AI landscape?
Just because a bot responds doesn’t mean it’s working. If it misunderstands the question, loses context, or can’t resolve the issue, the experience still fails from the customer’s perspective. Reliability really comes down to whether the AI can consistently understand intent, provide accurate answers, and help the customer reach a resolution without friction.
How can organizations strike the right balance between offering the anonymity of AI and the necessary empathy of a live human agent?
The right balance is a hybrid model built around customer intent and emotional context. AI is well suited for high-volume, structured, lower-emotion tasks where speed and discretion matter. Humans remain essential when the issue is nuanced, high-stakes, or emotionally charged. That includes disputes, vulnerable situations, sensitive account issues, or anything where reassurance and judgment matter as much as the answer itself.
The balance works when organizations let AI be the first layer of assistance without forcing it to be the final say. Customers should be able to start privately and efficiently with AI, but they should never feel trapped with it. Escalation should be obvious, fast, and context-preserving. If a customer already explained the issue to the bot, the human should not make them start over. That is where trust is often won or lost.
In what ways does Cyara’s latest research shift the roadmap for product development when it comes to automated customer experience (CX)?
The direction is increasingly pointing toward a hybrid model of customer experience, where structured, deterministic flows coexist with more dynamic, agentic interactions. Customers don’t think in terms of channels or architectures, they simply expect the interaction to work, whether it starts as a straightforward task or evolves into a more complex conversation. Supporting that requires a stronger foundation than traditional automation approaches. Trust has to be built into the system, with safeguards around accuracy, consistency, and compliance. At the same time, there needs to be continuous validation of the full customer journey, not just isolated interactions, so issues can be identified before they affect real users. And as these experiences span voice, chat, and digital channels, maintaining continuity and context becomes essential. What emerges is a more cohesive, dependable experience, one that prioritizes resolution and reliability over the mechanics of the underlying technology. These are the principles that guide our roadmap.
Looking at the next three to five years, how will the “human-to-AI” interaction evolve as these judgment-free entry points become the standard rather than the exception?
Over the next three to five years, human-to-AI interaction will shift from being a transactional entry point to becoming an integrated layer across the entire customer journey. AI will increasingly handle not just initial queries, but multi-step, outcome-driven interactions that span channels and contexts, with the ability to reason, adapt, and complete tasks end-to-end. However, the defining change will not be full automation, but orchestration, seamless movement between AI and humans based on intent, complexity, and emotional context. Customers will expect continuity regardless of where the interaction starts, with context preserved across AI and human touchpoints.
Quote from the author: Design for trust, not just resolution. As AI takes on a more active role in customer interactions, the risk is no longer whether it can respond, but whether it can be relied on to respond correctly, consistently, and responsibly. AI Trust needs to be built into the foundation ensuring accuracy, detecting hallucinations, validating compliance, and handling edge cases before customers ever encounter them. Customers will engage more readily with AI when it feels safe, predictable, and transparent, especially in sensitive or high-stakes scenarios. The organizations that win will be those that continuously validate real-world interactions, govern AI behavior, and treat trust as a measurable, enforceable outcome, not an assumption.
Amitha Pulijala
Chief Product Officer of Cyara
Amitha Pulijala is the Chief Product Officer of Cyara, where she leads product and strategy for the company’s AI-powered CX transformation. A product and AI leader with more than 15 years of experience, she has built and led high-growth SaaS platforms across enterprise communications and customer experience, including shaping AI and digital CX strategy at Ericsson, Vonage, and Oracle.
