On February 27th, we hosted a vibrant roundtable with several CX leaders—Kenji, Ashley, Erica, and Sarah—on all things AI. Our mission was to look beyond the buzzwords, sharing firsthand perspectives on where AI genuinely helps, where it can flop, and how we can all implement it without losing that human touch customers crave.

Why AI matters in CX

AI is more than a fad—it's a long-term companion

Everyone on the panel agreed that AI is absolutely sticking around, but the trick is using it the right way. Yes, it’s powerful, but it’s not a cure-all. If you adopt AI just because it sounds trendy, you risk pouring time and budget into a half-baked initiative that never addresses your real needs. A more effective strategy? Zero in on a specific pain point—maybe repetitive password-reset requests or after-hours inbound volume—and aim the AI squarely at that challenge. By solving genuine issues instead of playing buzzword bingo, you end up with an AI approach that both leadership and frontline teams can trust.

Shifting work from reactive to relational

A huge win with AI is how it can handle the mind-numbing, lower-level tasks that burn out human teams over time. Think of questions like, “Where’s my order?” or “How do I reset my password?”—important inquiries, but often repetitive. Turning these over to AI frees up your staff to do what humans excel at: empathetic conversations, complex troubleshooting, and building those real connections that drive loyalty. Instead of replacing agents, AI can elevate their roles, pushing them into more consultative or strategic duties. That’s how you transform CX from an afterthought into a powerhouse for relationship-building.

Why timing and transparency matter

Even the slickest AI can flop if you roll it out at the wrong moment or keep your team in the dark. Successful AI adoption hinges on two things:

• Choosing the right time: You don’t want to layer on AI if your knowledge base is a mess or your processes aren’t well-defined. Clean up your docs and workflows first. Only then can AI learn from (and amplify) accurate information rather than repeating outdated or incorrect answers.
• Involving your team: Agents shouldn’t find out about a new AI tool two days before launch via a random Slack message. By collaborating early—explaining the tool’s purpose, showing how it assists rather than replaces them—you’ll ease any fears and cultivate real champions of your new approach.

Key challenges to AI adoption

Implementing AI can be transformative, but it also comes with hurdles that even seasoned CX teams sometimes overlook. If you don’t invest in solid groundwork or keep your team aligned, your AI project might stall out before it ever hits its stride. Below are the main pitfalls to watch for—and how to navigate them.

Documentation & data quality

Ai depends on high-quality source material. If your help center or product documentation is outdated, the AI will simply repeat stale information—and do so at lightning speed. Clean data, up-to-date references, and consistent content standards are non-negotiable foundations for any successful AI initiative.

Ongoing maintenance

Remember, AI isn’t a set-it-and-forget-it feature. You’ll need someone (or an entire mini-team) to continuously monitor performance, retrain models, and update any references. If you neglect this upkeep, your chatbot, analytics tool, or QA system can quickly spiral into irrelevance. If you don’t feel equipped to invest in that way, consider utilizing an Augmented AI platform, like Crescendo AI, that handles all ongoing maintenance and training for you.

Team training & buy-in

Long-tenured agents may fear AI is poised to replace them, while newer hires might rely on it so heavily that they lose deeper product knowledge. Early and transparent communication—backed by hands-on training—helps calm anxieties, encourage collaboration, and ensure AI supplements your team instead of sidelining them.

Measuring impact

Metrics like deflection or raw CSAT only provide part of the picture. If customers bail on a chatbot in frustration, that might still appear as a “deflection”—yet it’s hardly a win. A deeper analysis of resolution quality, sentiment, or escalation rates can offer far richer insight into whether your AI is actually moving the needle.

Practical (and often overlooked) AI use cases

Implementing AI in customer experience often makes people think of chatbots first, but there’s a whole range of lesser-known applications that can supercharge your team’s efficiency. Here are five powerful yet under-appreciated ways that leaders on the webinar indicated they use AI to transform support operations.

• Quality assurance (QA)
• Data analysis & insights
• Meeting notes & summaries
• Localization & translation
• Content moderation

AI can be a real game-changer in these areas because it saves teams from tedious, repetitive manual work and cuts down on human error. For instance, AI-powered QA solutions can parse through thousands of support tickets in seconds—checking tone, correctness, and compliance. Instead of sampling a small fraction, you suddenly have eyes on every interaction, which helps you spot coaching opportunities and celebrate stellar work more often.

Data analysis & insights tools similarly streamline your detective work. They’ll sift through massive volumes of customer feedback, zero in on patterns (like recurring bugs or language issues), and hand you the knowledge you need to act. Rather than leaving a big pile of data to gather dust, AI systems highlight trends you might have missed.

Meeting notes & summaries tools are great for letting you talk face-to-face (even virtually) without one eye on the “record” button. By automatically transcribing and summarizing discussions, AI frees you to fully engage in brainstorming or decision-making. Localization & translation features go a step further for global teams, turning previously separate language silos into one cohesive support flow. Finally, content moderation solutions can flag copyrighted or harmful material instantly, empowering reviewers to handle edge cases more efficiently.

Evolving CX roles & career growth

Many companies are awakening to the fact that CX is a strategic asset, not just an entry-level stepping stone. As AI sweeps through support teams, it also creates space for innovative roles and new paths to grow your career.

First, there’s a growing need for in-house AI owners—people who understand the nuts and bolts of machine learning but also “get” the human side of customer experience. That might look like a support operations developer or an AI training specialist, roles devoted to fine-tuning chatbots, analytics engines, or QA platforms. These specialized positions are popping up all over the place, allowing teams to own their AI stack rather than outsourcing it all.

Second, the emergence of AI-fueled insights gives CX pros a seat at the table in shaping product roadmaps, brand strategy, and cross-functional collaboration. When support teams can show concrete data on recurring pain points or new market opportunities, they stop being the “break-fix” department and become an essential voice. That shift from reactive to strategic positions CX leaders as architects of smarter, more cohesive customer journeys.

Finally, this is also a chance for veteran agents to flex deep institutional knowledge in new ways. They already speak the brand’s language and understand the product from multiple angles. With AI handling grunt work, these experienced pros can focus on big-picture improvements, advanced escalations, or ethical considerations around how AI communicates. In other words, the deeper AI goes, the more valuable a truly human perspective becomes.

Final takeaways

1. Start with the right problem. Don’t chase AI for its own sake. Pinpoint a true pain point—like content moderation or time-consuming analytics—and tackle that.
2. Prep your foundations. A knowledge base that’s out of date makes for a clueless chatbot. Clean up your docs and data first.
3. Plan for maintenance. AI needs regular feeding and care. Budget time and resources to keep models up to date.
4. Focus on people. Reassure your team that AI is a tool, not a guillotine. Let robots handle the robotic tasks so humans can do the empathetic, creative work.
5. Measure holistically. Look beyond “deflection” or raw CSAT for a full picture. A deep understanding of resolution quality and sentiment will guide you much better.

AI holds the potential to flip CX from a purely reactive channel to a proactive, insight-driven advantage. When you thoughtfully weave AI into your workflows—whether in administrative tasks, real-time knowledge sharing, or quality checks—you free your support teams to do what they do best: solve issues with empathy, creativity, and expertise. That’s how we get the best of both worlds—a better customer experience, and a more fulfilled, future-ready support team.

Want to dive deeper?
Connect with any of our panelists on LinkedIn or find them in CX communities like Support Driven or Elevate. We’d also love to see you at our upcoming webinar on “Agentic AI,” where we’ll keep exploring how AI can partner with humans (instead of competing against them) to deliver next-level support experiences.

Thank you to everyone who joined, shared questions, and kept this conversation lively. We can’t wait to see how you’ll build on these insights in your own AI journey!

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