As AI continues its rapid integration into the enterprise, customer experience and support teams are navigating a distinct set of challenges. While experimentation is often encouraged in other functions, CX operates under different expectations: precision, empathy, and virtually no margin for error.
In our latest Two Truths and AI fireside chat, CX and support leaders came together for a candid conversation about what’s working, what’s not, and how AI is reshaping our understanding of productivity, trust, and team enablement.
5 Takeaways for CX Leaders
1. “Productivity is about having a very focused impact with minimal friction.”
Redefining productivity in CX starts with outcomes, not activity. Customer-facing teams are often measured by how much they do: response times, ticket volume, hours logged. But real productivity comes from progress on what matters most—delivering standout customer experiences, solving root problems, and building trust. That kind of impact demands ruthless prioritization, clear alignment, and an environment where issues can be surfaced openly.
To move in that direction, leaders should examine how surface-level metrics connect to customer and business outcomes. As AI accelerates workflows, it’s essential to ensure increased speed translates to better results. Monitoring performance is key—but so is inviting open feedback from teams to uncover risks, friction points, and opportunities for deeper impact.
Read more: How Leaders Measure AI’s Success
2. “The fear of getting it wrong is real.”
CX teams are adopting AI more cautiously—and for good reason. According to Grammarly’s recent annual report, AI adoption in CX is slower than in other functions. The hesitation makes sense. Customer interactions require emotional nuance and precision—areas where AI still feels unproven. Combine that with fragmented tools, strict compliance requirements, and often nebulous AI use policies, and CX reluctance becomes rational.
To unlock adoption, CX leaders can start by reducing fear. Broad access to new AI tools is helpful, but it needs to be paired with clear guardrails. It also requires safe spaces to experiment, whether through sandbox environments or low-risk internal use cases. Lastly, you need to normalize the learning curve. Showcasing and celebrating early adopters can support here, turning AI power users into ambassadors who help upskill the larger team.
3. “Take away the tedious so teams can focus on what matters.”
AI should elevate the human side of CX, not replace it. The most valuable AI use cases in CX don’t have to be flashy—they can be practical. Automating meeting notes, surfacing account insights, or simplifying access to customer data frees teams to focus on the work only humans can do: listening, empathizing, and building real relationships.
The goal isn’t automation for its own sake. It’s about reducing busywork so your people can show up with more presence, clarity, and creativity. And like any good tool, AI still requires human judgment, especially when the stakes are high. Teach your teams to treat AI as an assistant, not an authority.
Read more: The Global CX Communication Playbook
4. “You can’t scale what people don’t trust.”
Governance, clarity, and enablement drive adoption at scale. Trust is the foundation of every customer interaction—and it’s also the foundation for AI adoption. Without confidence in how data is handled and what the tools are doing, teams hesitate to engage. The most successful organizations aren’t just giving their teams tools; they’re also giving them confidence in the tools they use. You can build trust with enterprise-grade protections like data loss prevention and encryption, and by making your policies clear from day one. In addition, offer role-specific training that goes beyond how-to guides and speaks to real-world use cases.
Learn more: Grammarly’s Answers to Your AI Vendor Questions
5. “If AI adds complexity, it’s not helping.”
Great CX depends on simplicity, and so should your AI strategy. Customer experience work is inherently fragmented. Teams bounce between platforms for CRM, support tickets, adoption metrics, and communications. AI, if not thoughtfully deployed, can add to that chaos—especially when tools live in silos or come with steep learning curves.
Before bringing in a new AI solution, ask: Will this reduce context switching or add to it? Can it connect to the tools we already use? Will it simplify work, or just shift it elsewhere? The best AI doesn’t just solve problems—it removes friction, connects workflows, and helps teams stay in flow.
CX is where your brand’s promise meets reality. And in a world of rising expectations and tighter budgets, doing more with less isn’t just a goal—it’s a mandate. The opportunity before us isn’t just about automating tasks. It’s about creating the space, clarity, and confidence teams need to deliver work that truly matters.
