John Henry still leading the race vs AI in customer service
AI hasn't cleared the contact center. According to new Gartner research, only 20 percent of customer service and support leaders reduced agent staffing because of AI. Most teams are using AI to handle more volume without cutting people.
More telling: 55 percent kept the same headcount while managing higher customer demand, and 42 percent are hiring for new, AI-enabled roles. As Gartner's Melissa Fletcher put it, "Customer service and support leaders should avoid framing AI initiatives solely around headcount reduction... plan for new roles, leverage central resources, and communicate transparently about AI's impact."
What leaders are actually doing
- Holding headcount steady to absorb increased volume and reduce backlog.
- Shifting agents to higher-value work while AI handles repetitive intent types.
- Hiring specialists to operationalize AI instead of over-indexing on bots.
- Pausing backfill in some teams (25 percent) as they retool workflows.
Roles on the rise
- AI Strategist / Program Manager
- Agent Assist Analyst
- AI Automation and Process Analyst
- Conversational AI Designer
- AI Analyst and Trainer
Why the "agentless" vision keeps slipping
Gartner expects half of the organizations aiming for major AI-driven workforce cuts to reconsider by 2027. The reason: most companies don't have the readiness to sustain fully automated service at scale. Even where chatbots or IVAs can remove volume, progress is incremental and depends on clean data, process clarity, and strong change management.
90-day plan for support leaders
- Weeks 1-2: Map top 20 intents by volume and failure rate. Flag quick-win automations and high-impact agent assist use cases.
- Weeks 3-6: Pilot agent assist for 1-2 queues (templates, summaries, knowledge surfacing). Track AHT, FCR, QA scores.
- Weeks 7-10: Launch targeted self-serve for 3-5 intents with strong containment potential. Set clear deflection thresholds.
- Weeks 11-13: Stand up an "AI in Support" working group. Define roles, governance, and a backlog tied to business KPIs.
Metrics that prove impact
- Containment rate by intent (and customer reopen rate)
- Agent handle time and after-call work
- Resolution quality (QA) and first contact resolution
- Customer effort score and NPS by channel
- Time-to-publish and article reuse for knowledge
- Cost per contact and cost per resolved intent
Signals from the broader labor market
Recent labor data out of Yale's Budget Lab suggests no clear economy-wide hit to cognitive jobs since late 2022, despite the hype around generative AI. On the other hand, a BearingPoint survey of 1,000 executives points to expected overcapacity by 2028 as productivity climbs. Translation: jobs aren't disappearing overnight, but staffing models will change as AI matures and process debt gets paid down.
How to prepare your team now
- Invest in agent assist first. It's the fastest path to quality and speed without customer-facing risk.
- Prioritize data hygiene: intent taxonomy, knowledge freshness, CRM fields, and tag discipline.
- Create an RACI for AI changes. Product, data, legal, and CX should work from one backlog.
- Reskill frontline experts into the new roles above to keep domain knowledge close to the tooling.
- Set expectations: automation gains will arrive in steps, not leaps.
AI is clearly helping support teams do more with the same number of people. The win goes to leaders who pair automation with smart org design, measurable pilots, and honest communication about what's coming next.
Gartner: Customer Service & Support Research
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