Don’t Hide the Humans: Why a First Contact Resolution Approach to AI Will Be Key in Customer Care   

As AI becomes more embedded in customer care, one concern continues to surface—not from companies, but from customers:

Will I still be able to talk to a person if I need to?

The answer, when AI is deployed thoughtfully, should be a resounding yes. But in the rush to automate, many brands are missing a critical point: not all customers are ready to be handed off to a bot.

To consumers, the issue isn’t whether AI can replace human agents. They’re concerned that brands will take away human assistance as a customer service option.

Some companies are realizing this as they try to reduce headcount in their contact centers. Gartner predicts that by 2027, 50% of organizations that expected to significantly reduce their customer service workforce will abandon those plans. Companies are questioning whether they can implement a “digital-only,” AI-driven service model soon. And, seeing the diverse demands of customers, they’re questioning whether they should.

Customers want fast answers, yes. But they also want control. They want to know that if the AI doesn’t get it right, or if their issue is too complex for a chatbot to resolve, they can still reach someone who can. That’s why a first contact resolution (FCR) approach to AI—one that prioritizes getting customers to the best outcome as quickly as possible, regardless of channel—is the winning move right now. It also gives businesses a north star for integrating AI more thoughtfully in their customer care, anchoring their decisions in whatever channel or technology (or person!) will get the customer to the right resolution, the first time.

What Customers Actually Want from AI

There’s no question that consumers are growing more comfortable with AI interactions. Consumer adoption of GenAI more than doubled in a year’s time, according to Deloitte, with 38% of respondents now saying they’ve experimented with the technology or used it for tasks beyond experimentation.

In fact, we found that 85% of telecom consumers say they prefer AI over a live agent in at least one customer service channel. But that doesn’t mean they’re ready to give up human support altogether—or that they trust companies to get the balance right. In that same study, 61% of consumers said a seamless handoff to a live agent is one of the top traits they look for in an AI tool.

That tracks with what Gartner found in a recent survey: The most cited customer concern about AI in customer service (60%) is that it will become more difficult to reach a person for support. Notably, the same survey also had 64% of consumers saying they’d prefer that companies not use AI in customer service at all.

We don’t think the issue is that customers reject AI. It’s that they reject AI that gets in their way. When it’s used to streamline the experience, AI becomes a welcome part of the journey. The question is how a business should execute that.

Why a Focus on First Contact Resolution Works in AI for Customers and Companies

How can companies to scale support efficiently while still delivering the kind of human connection that builds trust and loyalty?

For starters, AI is great at handling high-volume, low-complexity tasks. It can deflect routine inquiries, route calls in the contact center based on intent, and provide instant answers around the clock. That’s where speed and scale come in.

But when a customer’s issue is nuanced, emotional or urgent, they want to talk to someone who can listen, adapt and solve. AI is growing increasingly capable of handling those issues, too, but it’s where human professionals shine.

Understanding when to use AI versus human support is just one piece of a larger customer experience strategy. It requires businesses to prioritize resolution above all and minimize the friction of switching channels. The reality is that customers switch channels, so it’s critical to get them to the right place as early as possible for the right resolution. As Gartner found, each additional channel increases the likelihood they will churn. Customers who had a service issue with a brand were asked whether they would stay loyal to that brand or switch to a competitor. Among customers whose issue was resolved in a single interaction, 56% said they’d stay loyal. But among customers whose issue wasn’t resolved in a single interaction, that loyalty figure fell to 36%.

Ultimately, an FCR-focused approach helps businesses meet customers where they are—whether that’s a Gen Z user who prefers chat or a Baby Boomer who still wants to talk to a person. The goal is to get each customer to their best, quickest resolution.

For those customers who still want to talk to a person, AI can enhance those interactions, too. Businesses are investing in real-time agent assist technology; this often takes the form of bots that tell the human agent what’s going on with the customer they’re talking to and recommends ways to resolve their issue. That’s a solid contact center use case to help improve FCR among those agent contacts.

But what about an AI use case that reduces those agent contacts and makes them more effective at the same time?

Here’s an example of that:

Case Study: A Real-World Win-Win of AI + Human Support

One of the world’s largest technology companies faced a familiar problem: too many support calls were landing in the wrong place. Its IVR system, even after a recent upgrade, was still misrouting 60% of inbound calls—especially in the online store support flow. That meant customers were getting bounced between agents, which included not just product support teams but also sales reps, who picked up these misdirected customer calls and spent valuable time trying to solve issues they weren’t trained for.

To fix it, the company partnered with CSG to implement conversational AI and intent-based routing through Xponent.

The goal wasn’t to eliminate human support. It was to make sure:

1. The contact center supplied human support only when customers truly needed it

AND

2. Customers always got connected with the right agent group to solve their issues.

Xponent’s AI used natural language processing to understand customer intent and route calls accordingly. If a customer’s issue could be resolved through self-service, the system guided them to the correct knowledge base. If not, it connected them to the right agent group the first time. The company’s own AI models were integrated and tuned by CSG’s engineers, pushing intent accuracy from 80% to as high as 98% for certain inputs.

CSG then expanded this program to over 40 languages in the tech company’s markets across the world.

The results were immediate and significant:

• 43% reduction in calls to live agents
• 42% improvement in customer self-service
• 34% drop in agent transfers
• 40 languages deployed in three days

And most importantly, the human option never went away—it just became easier to reach when it was truly needed.

You can read the full case study here.

This is what customer-first design looks like in practice: AI that enhances the experience without taking it over. And it’s why companies trust CSG to help them get the balance right.

What to Do Next: Start With the Right Strategy

If you’re leading a CX, contact center, or IT team, the question isn’t whether to use AI—it’s how to use it well. And that starts with the customer journey, not the technology.

Before rolling out new tools, map out where customers actually need help. Look for the moments where AI can reduce friction without removing the human option. That might mean using AI to deflect routine questions, translate into local languages, route calls more accurately, or surface relevant content faster. But it shouldn’t mean making it harder to talk to a person.

The right strategy also means choosing partners who understand both sides of the equation: the tech and the trust. That means understanding what AI can do and what your customers will accept.

That’s the FCR approach, and the companies that get it right will be the ones that move fast without leaving their customers behind.