Verizon CEO Dan Schulman has publicly committed to replacing a substantial portion of the carrier's customer service workforce with AI agents, citing a 1,280 basis point improvement in customer satisfaction scores from recent pilots. Schulman's framing positions this as inevitable disruption—"I don't see how that isn't possible"—whilst simultaneously arguing that AI enables superior personalisation and end-to-end experience consistency across touchpoints. The strategic logic is clear: by automating routine inquiries, Verizon can redirect human agents toward complex, empathy-driven interactions that competitors find difficult to replicate. This represents a deliberate bet that AI-human hybrid models will outperform either modality alone, with the carrier already testing this approach in live customer service operations.
The implications for CX teams are bifurcated and consequential. Forrester's parallel forecast—that AI will halve customer service headcount by 2030—suggests Schulman's position reflects industry consensus rather than Verizon-specific strategy. For teams currently managing large contact centre operations, the question becomes not whether automation arrives but how quickly it scales and what skill gaps emerge in the transition. The remaining human roles will concentrate on genuinely complex cases, meaning support team leads must immediately begin identifying which interactions genuinely require human judgment versus those currently treated as complex due to poor process design. Simultaneously, new roles in AI training, data governance, and prompt engineering will emerge, creating a talent arbitrage problem: organisations cannot simply redeploy existing agents into these specialist positions without substantial upskilling. For CX professionals already embedded in platforms like Zendesk or Salesforce, this raises an urgent architectural question—are your current knowledge bases, routing logic, and quality frameworks sufficiently mature to serve as training data for agentic systems, or will poor data quality become a bottleneck that undermines automation ROI?
The competitive pressure is asymmetric. Large carriers with Verizon's scale can absorb the investment and experimentation costs required to build proprietary AI capabilities; smaller vendors and mid-market operators cannot. This creates a widening gap between enterprises that can afford to build bespoke agentic systems and those dependent on third-party platforms, where feature parity in AI automation may become table stakes within 18–24 months. For CX leaders, the strategic imperative is clarity on whether your organisation's differentiation depends on human service quality (in which case you're competing against Verizon's investment) or on operational efficiency and data leverage (in which case you must move faster on automation than your peers).
Verizon CEO: AI will take over ‘a large percentage’ of customer service CX Dive