AI in Customer Experience: How AI Is Changing the Way Businesses Serve Customers
SVP, Global Support Services
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AI has become a fundamental part of delivering standout customer experiences. Businesses across industries are already tapping into AI tools to streamline support, personalize interactions, and uncover insights that used to take days to surface.
In my role leading a contact center team and working at a company focused on building AI-driven customer intelligence, I’ve had a front-row seat to how transformative this technology can be—for both customers and the teams supporting them. In this post, I’ll break down some real-world ways AI is reshaping the support landscape.
AI is reshaping what great CX looks like
See how Dialpad brings AI to every stage of the customer journey — from self-service to real-time agent support to the insights that drive better decisions.
AI in customer experience FAQs
AI in customer experience refers to technology that operates on customer interaction data, in real time, across channels, to improve how those interactions go. It includes AI that customers interact with directly, like voice and digital agents, as well as AI that supports human agents during live conversations, evaluates interaction quality, and turns conversation data into actionable intelligence.
AI improves customer experience by making self-service more capable, giving agents better real-time support, surfacing customer signals at scale, and enabling more consistent quality across interactions. The cumulative effect can be faster resolution, more consistent service, and a tighter feedback loop between what customers experience and what teams can act on.
Common examples include AI voice and digital agents that handle customer inquiries autonomously, real-time coaching tools that surface answers to agents during live calls, AI-inferred CSAT scoring that evaluates satisfaction across every interaction, and automated QA scorecards that evaluate agent performance without manual review. Dialpad customers like BNI, Proliance Surgeons, and FinditParts offer concrete examples of what these capabilities deliver in practice.
The most meaningful metrics are CSAT (particularly when inferred across all interactions rather than survey-dependent), self-service resolution rate, average handle time, QA coverage and score consistency, and downstream business metrics like retention and expansion. Deflection rate and efficiency gains are useful but incomplete: the stronger signal is whether customers are actually getting their issues resolved, and whether that's showing up in satisfaction and retention data.