Enterprise interest in AI is sky-high, but actual adoption? Not so much.

44% of organizations reported piloting AI in 2025, but only about 10% have moved those pilots into full production deployments. And just 42% of large enterprises currently have AI actively in use.

It’s clear that while proofs-of-concept multiply, few make it out of the sandbox. And when they do, they’re rarely in customer-facing functions. Why? One word: trust.

In customer service, AI isn’t a behind-the-scenes tool. Every response is public. Every conversation shapes the customer’s perception of your brand. That visibility makes the stakes higher and the margin for error lower.

At ada, we’ve seen firsthand what holds enterprise leaders back from scaling —and what finally gives them the confidence to move forward. It always comes back to trust and safety. Not just in the tech, but in the systems that govern it.

This post explores why trust is the linchpin of AI adoption in customer service, and what it actually takes to earn it.

The real risk in AI isn’t what it does, it’s what you can’t see

There’s no shortage of AI tools in the market. But behind every demo, there’s a black box: What data was used to train this system? How are outputs validated? What happens when things go wrong?

For most enterprise leaders, the problem isn’t capability, it’s confidence.

They’ve seen what AI can do. But they haven’t seen enough of how it behaves under pressure, or what guardrails are in place when something breaks. It’s not just a technology concern, it’s a governance gap.

Trust can’t be declared. It has to be demonstrated. And that requires independent oversight, continuous testing, and real transparency—not just a privacy policy and a line about “responsible AI” in a pitch deck.

This is where most AI vendors fall short. And it’s where ada has taken a fundamentally different approach .

In customer service, trust has a higher bar

AI isn’t just helping agents behind the scenes anymore. In customer service, AI is the agent. It’s the voice, the tone, the resolution.

And when something goes wrong, it’s your reputation.

Unlike internal tools, AI agents in customer service operate in high-stakes environments: responding to frustrated customers, navigating sensitive topics, and resolving account-level issues.

One hallucinated refund policy. One bad tone. One exposed data point. That’s all it takes to erode customer trust.

If AI is going to act on your behalf, it has to be safe by default—and auditable by design.

What enterprise-grade trust and safety actually looks like

At the surface level, trust in AI often looks like technical performance: accurate answers, fast response times, seamless handoffs. But real trust—the kind that earns executive buy-in and protects your brand—has to go deeper.

Enterprise-grade trust is layered. It spans systems, safeguards, and strategies. It means designing your AI agents with failure in mind, not just success.

And in customer service, where every output is customer-facing, there’s no such thing as a harmless mistake. That’s why trust and safety need to be more than aspirational. They need to be operationalized.

At ada, we’ve built our approach around key foundational principles:

• Security that scales: Certifications like SOC 2 Type II, HIPAA, and GDPR are table stakes. Real security means ongoing penetration tests, LLM-specific risk reviews, and controls that evolve as threats do, including jailbreak testing and red-teaming that specifically target the model.
• Data protection by design: ada’s Zero Data Retention (ZDR) model means customer data is never used to train LLMs. You get full transparency into what’s stored by ada, why, and for how long, and access is tightly controlled, logged, and regularly audited.
• Safeguards that filter, not just fix: Hallucination blocking, knowledge-grounded verification, and non-compliance filtering help ensure every AI-generated answer reflects your policies, tone, and truth.

The result is a system that doesn’t just promise safety—it makes safety observable, at scale.

Beyond certification: A platform built for trust

For some organizations, certifications are the end goal. For us, they’re just the baseline.

Ada is proud to be the first AI customer service platform certified under AIUC-1: the world’s first standard focused specifically on AI agent safety, security, and reliability that requires an audit of both AI governance practices and technical safety testing. We didn’t just meet the bar, we helped build it.

After enabling thousands of enterprises to succeed with AI agents and powering billions of conversations, ada was a natural fit to contribute to AIUC‑1 as a Founding Technical Contributor and ensure the framework meets the unique demands of CX use cases in enterprises.

One of the world’s largest social media platforms put our approach to the test. Before committing, their team conducted custom adversarial testing on top of our AIUC-1 certification. The result: confident, global-scale deployment with safeguards that met their standards for trust, privacy, and compliance.

Our philosophy is simple: trust is earned through architecture. That’s why we’ve embedded safety at every layer of our platform so that our customers can deploy AI agents with confidence, not caveats.

From transparency to trust transformation

As AI becomes a foundational part of customer experience, enterprises face a pivotal question: not can we automate more, but can we trust what we automate?

The answer depends on how seriously your systems take safety.

At ada, we believe AI agents can be your best customer-facing employee, but only if they’re built, governed, and improved like one. That means giving enterprises full visibility, full control, and full confidence at every step of the journey.

If you’re looking to scale AI in customer service, trust shouldn’t be the barrier. It should be the reason to start.