Most AI strategies for support are focused on the left side of this spectrum.
The problem: When you optimize for efficiency, you train your organization to see customer-facing functions as costs to minimize. AI becomes a tool for headcount reduction.
Block just cut 40-50% of staff citing AI efficiency. Stock jumped 20%. Wall Street rewarded the cost optimization.
Companies on the right side are playing a different game.
They’re deploying the same AI tools—but measuring success differently:
- Revenue protected from at-risk accounts
- Proactive engagement to prevent customer issues
- Adoption velocity improvements
- Friction patterns identified and eliminated
- Customer skills enhanced
They’re showing AI helps customers succeed—which protects the revenue that drives NRR.
Here’s what this means for you:
Your team touches customers who generate 75% of your company’s revenue. You have technical expertise customers trust. You help customers overcome friction, drive adoption, spot churn signals.
If your executives measure you on deflection and cost, you optimize for efficiency—and look like overhead AI can replace.
If they measure you on customer outcomes, you optimize for value—and prove you protect revenue AI can’t.
Same team. Same AI tools. Different positioning. Different contribution to growth metrics.
The question isn’t whether to deploy AI in support.
The question isn’t whether to deploy AI in support.
The question is whether you’re using it to cut costs—or to protect and grow the revenue you already have.