Over the past decade, customer service automation has evolved from brittle scripts to remarkably fluent systems. Today, AI customer service agents can interpret nuance, personalize interactions, and manage multi-step conversations across every channel.

But without the right operating model to support them, most still leave customers waiting, even when 90% of customers expect a reply within 10 minutes. And for all the pilots and proof-of-concepts launched across the enterprise, teams are still asking the same question.

Where’s the impact?

That’s why the next chapter of AI in customer service won’t be defined by technology alone, but by an operating model — including proven processes and expertise— that transforms it into a real asset. One that supports, governs, measures, and actually delivers.

AI adoption without an operating model was doomed from the start

Most AI strategies today are built on a foundation of legacy tools: technologies designed for yesterday’s problems patched together in today’s stack. Less than 4 in 33 AI pilots ever make it to production. And for the ones that do, 95% fail to deliver ROI —not because the models themselves don’t work, but because the systems around them don’t.

Chatbots lived in one product. Email automations in another. Human agents switched platforms, channels, and interfaces with every escalation. So did the customer.

AI was a bolt-on, layered over a fragmented tech ecosystem, never quite solving the problem beneath: service delivery at scale.

Most AI deployments fail not because the models can’t perform, but because the infrastructure around them doesn’t exist. To manage AI like a high-performing team member, you need the systems to support it:

• AI that’s grounded in context: Not just fluent, but rooted in real customer service data, aligned with your policies, and capable of resolving complex customer needs with confidence.
• Full control over performance: Visibility into what the AI customer service agent is doing, why it’s doing it, and how it can improve—with the tools to test, measure, and iterate in real time.
• Seamless connection to your ecosystem: AI should be embedded in the workflows where real work happens.
• Built-in mechanisms for accountability and growth: Because AI isn’t a set it and forget it tool. It needs coaching, feedback, and the ability to evolve—just like the rest of your team.

It’s now painfully clear that the answer isn’t more AI, it’s knowing how to make AI actually work. Because while the technology keeps improving, the outcomes still haven’t caught up. Build this infrastructure, and AI stops being a pilot and starts becoming a strategy.

Everyone’s chasing better AI, but few are building for it

Truth is, most organizations aren’t built to support AI this capable.

Today’s AI can interpret vague, messy customer input. It can reason across systems. It can personalize responses in real-time. It can even trigger workflows and take action—if it’s set up to do so.

Oftentimes, you drop a language model into the tech stack, give it access to your knowledge base, and hope for the best. No grounding. No visibility. No safety net. And definitely no system for coaching, testing, or evolving the agent over time.

To manage AI like a high-performing team member , you need a system that includes:

• A platform built for performance: Enterprise-grade infrastructure that powers real automation, with transparency, safety, and the ability to reason, take action, and improve.
• A practice that drives continuous improvement: A structured operating model that guides how AI is deployed, governed, and evolved, with tools to manage performance, adapt agent behavior, and align outcomes to business goals.
• A team that knows how to scale it: Dedicated experts who can help you integrate AI into workflows, build internal capability, and ensure your agents evolve with your business.

Without all three, you’re not scaling AI—you’re babysitting it.

What it looks like when the operating model works

AI doesn’t transform customer service on its own. It takes an operating model—one that treats AI like your most valuable customer service employee. Not a feature, not a side project, a fully embedded, performance-driven function in your organization.

Ada ACX is that model. It gives enterprises the structure to build, manage, and scale AI agents with the same intention and accountability as a human team, aligning every interaction to your brand, goals, and policies.

Using an operating model like Ada ACX empowers AI customer service agents to go the extra mile, reducing cost-to-serve through automation, engaging customers with personalized, consistent support, and expanding your reach across channels, languages, and regions.

And as AI takes on more responsibility, ACX ensures you stay in control. With built-in governance, compliance, and visibility, it’s designed to help you scale safely—without compromising confidence or oversight.

1. A platform built for enterprise performance

The ACX Platform is built from the ground up to meet the complexity, scale, and safety demands of enterprise customer service. It combines:

• A CX-tuned reasoning engine that fuses customer history, policy, and knowledge context to generate accurate, compliant, and brand-aligned responses—not just surface-level LLM output.
• A command center that gives organizations complete control over building, deploying, analyzing, and optimizing their AI agents to continuously improve their performance, ensuring every interaction reflects their brand voice—while efficiently resolving complex customer issues.
• Consistent, omnichannel service across languages and channels messaging, voice, email, and more, while maintaining customer identity and context.
• APIs and SDKs that connect to custom channels, business systems, and extend the capabilities of AI agents.
• Enterprise-grade compliance, trust, and safety standards, enabling you to confidently deploy and scale AI agents.

2. A practice that drives measurable improvement

The ACX Practice offers a proven blueprint for transforming customer experience with AI, guiding organizations from early adoption to enterprise scale. It also:

• Offers training resources to enable proficiency in managing AI agents
• Gives you the framework to map maturity across key capabilities, like task automation, personalization, proactivity, and human-AI collaboration.
• Empowers you to benchmark your progress against leaders in your industry, so you can evaluate where you stand and how to accelerate AI transformation.

3. A team that makes it stick

Even with the right platform and processes, success doesn’t scale without people. The ACX Experts program ensures enterprises aren’t doing it alone. Here’s what that looks like:

• Dedicated consultants and engineers working alongside your team to help embed AI into your workflows.
• Support with integration efforts, expanding agent capabilities, and helping you build the roles, rituals, and rhythms that make AI sustainable long term.
• Help ensuring internal teams know how to manage performance, track impact, and drive adoption—without relying on vendor hand-holding forever.
• Connecting you with peers to share best practices and learnings.

In short: We don’t just help you launch. We help you build the muscle to run AI like a business unit.

The future belongs to those who successfully operationalize AI

In 2025, every customer service leader is being asked: What’s your AI strategy? But the real question should be: What’s your system for making AI work?

Because AI performance doesn’t come from a model. It comes from management, infrastructure, maturity, and building for AI the same way you build for people: with clarity, accountability, and the tools to improve over time.

So if you’re tired of pilots that stall or tools that underdeliver, stop chasing smarter AI, and start using a system that makes AI smart where it counts.