Feature
At this year’s Customer Contact Week in Las Vegas, there was one theme you couldn’t escape: AI.
The hype was real — but so was the growing sense of realism. While the buzzwords were everywhere, so were the deeper conversations about what AI actually takes to implement and where it really delivers value.
Across interviews with researchers, strategists and tech leaders, one message came through clear: AI isn’t a magic fix. It’s a tool. And whether it helps or hurts depends on the groundwork organizations are willing to put in.
AI Rollouts Should Start With Employees
“Without a real reckoning around labor and the power of the worker… that worst-case scenario could happen.”
- Nicole Kyle
Managing Director & Co-Founder, CMP Research
Most organizations begin with employee-facing AI tools before deploying them in customer interactions, said Nicole Kyle, managing director and co-founder of CMP Research. “That allows them to work out some kinks and understand more about the journey on a relatively lower risk population than rolling it out to customers.”
Still, she emphasized the importance of internal change management, noting that AI should be positioned as “an augment to [employees’] ability to be successful in their job, as opposed to a replacement.” The goal, she said, is to support workers, not sideline them.
Despite the benefits that AI brings to the workplace, Kyle also pointed to the human toll caused by AI-driven automation. When organizations deploy AI tech, “What’s left over for the human agent… is complex stuff,” she said, adding that burnout is on the rise even though frontline agents aren’t necessarily leaving their jobs. “They’re staying… but disengaged,” she said.
While many hope AI will free up time for more meaningful work, Kyle remains skeptical: “Without a real reckoning around labor and the power of the worker… that worst-case scenario could happen.” Emotional intelligence, she added, may still be a differentiator for human workers — but even that, she said, can be mimicked well enough by machines to satisfy most customer service expectations.
Related Article: Building the Skills to Succeed as an AI-Augmented Worker
Smart Contact Centers Hit Pause Before Scaling AI
Businesses are rushing to integrate AI. But there’s a growing need to slow down and get strategic, said Colleen Callaway Eager, senior strategist, commerce & customer care practice at Perficient.
“We need to think about this a little bit. What problem are we actually trying to solve?” she asked. Rather than blindly adopting AI for every process, organizations should map real use cases, involve cross-functional stakeholders and prioritize governance before rolling out tools. “IT shouldn’t make the decision alone. Customer service shouldn’t make the decisions alone. It really impacts the whole business.”
One key theme: start with internal use cases, especially tools like agent assist. “It’s living in your domain,” Callaway Eager explained. “You have a lot more control about how it works, how you train your models, what you're solving for.”
She sees agent-facing AI not only as a lower-risk entry point, but as a cultural bridge. “It allows the company culture to get comfortable, to say, ‘Okay, we’re going to start using AI.’” It also directly improves customer outcomes by making human agents “faster, better, smarter.”
Beyond picking the right starting point, AI success also requires a cultural shift and sustained iteration, Callaway Eager stressed. “For AI to work, it’s not something you just train a model and walk away from.”
AI Isn’t a Strategy — It’s a Tool (If You Use It Right)
There’s a major disconnect in the way companies are approaching AI, according to Patrick Beyries, head of product and innovation at NeuraFlash. Too many are leaping into implementation without defining the business problem first.
People are “jumping to the answer without really knowing the question,” he said. Instead of asking which AI to use, leaders should be asking why they need it at all. Generative AI, for example, may not be the right tool for tasks that require consistency — predictive AI might serve better in those cases. The over-fixation on hype, he warned, is leading to a market that feels “commoditized,” even though the technology is still transformational.
For organizations that want to drive results, Beyries recommended investing early in the customer journey — especially self-service.
“If you can resolve [a customer’s issue] quicker, then it’s a win-win,” he said. “You win on the cost side and you win on the customer side.” Self-service frees up agents for complex interactions, while lowering cost and boosting customer satisfaction.
But metrics will require a rethink. Beyries is blunt: “Disassociate your love of handle time, because that is not going to be a metric that matters.” As automation takes care of easy tasks, humans will handle only the hard ones — so rising handle times may actually reflect progress.
Instead, leaders should track metrics like:
- Customer satisfaction
- Issue resolution speed
- AI agent interaction quality
Related Article: 10 AI Customer Experience Statistics You Should Know About
Fix the Knowledge Base, Then Add the Bot
“AI has been so ineffective that we've never really been confronted with what happens when it works well. Now, it does work.”
- Brian Cantor
Managing Director, Digital, Customer Management Practice
Organizations that succeed with AI are the ones that start with foundational work, said Brian Cantor, managing director, digital, at Customer Management Practice. That means cleaning up fragmented systems, consolidating data and fixing broken knowledge bases before rolling out automation.
“The more we automate, the more we use AI to handle these interactions, the more you lose that sort of human oversight over what's happening at times,” he said. If your bot pulls an outdated or incorrect knowledge article, it doesn’t just frustrate the customer — it erodes trust in the system.
As AI improves, the pressure increases to get this right. “AI has been so ineffective that we've never really been confronted with what happens when it works well. Now, it does work.”
And so the question, Cantor said, is: Are you an organization that has readied your team, rethought your objects, rethought your metrics for that transformation? “If you have, then you’re going to start to see a lot more success and really get to that human-AI synergy.”
That rethink includes building trust — both with employees and customers. Cantor pointed to a persistent bias: customers still believe the best support comes from live phone agents, even when AI tools are accurate and fast. What it’s going to take, he said, is overcoming those past biases and disappointments.
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Transparency and optionality here are key: “The number one way to get customers to trust the chatbot is to ensure they have an easy way to get to a live agent.” Companies that confidently surface both options — and clearly state what the AI can and can’t do — signal that they respect the customer’s time and choice.
AI Success Isn’t Magic — It’s Management
If there’s one thing leaders agreed on at CCW, it’s that success with AI isn’t about chasing trends — it’s about solving real problems with the right foundations in place. That means a clear understanding of when to use AI and when to lean on human strengths. AI may be evolving fast, but without strategy, structure and buy-in across teams, even the most advanced tools will fall flat.
The companies seeing real value aren’t just investing in new tech. They’re rethinking roles, retraining employees and redefining what good customer experience looks like in an AI-enhanced world. As one expert put it: AI isn’t a strategy. But with the right planning, it can be a powerful extension of one.
