GenAI customer service costs may overtake human agents by 2030 - here's how to adapt
Gartner projects that by 2030, the cost per resolution for generative AI in customer service will top US$3. That would put many AI-only resolutions above the cost of offshore human agents in B2C environments.
The drivers: higher data center and inference costs, vendors shifting from subsidized growth to profit, and more complex AI use cases that require expensive talent and governance. Translation: full end-to-end automation won't be the cheapest path for most teams.
Why AI gets pricier than people
- Compute inflation: GPUs, energy, and network capacity push inference costs up.
- Vendor economics: less discounting, more margin, more add-on fees (evals, observability, support).
- Complexity creep: advanced orchestration, RAG, and custom safety layers need expert engineering.
- Governance overhead: model audits, data controls, and compliance reviews add ongoing cost.
Gartner's takeaway: leaders will use AI to drive better engagement, not just to cut headcount. Fully automated support will be too expensive for many organizations.
Regulations will push more conversations to humans
By 2028, Gartner expects assisted service volume to rise 30% as customers exercise the right to speak to a human. Regulations like the EU AI Act will make "talk to a human" the default choice for many customers.
That means maintaining (or rehiring) human agents and paying closer attention to staffing plans. If you under-staff, wait times climb and CSAT falls.
What this means for your 2026-2030 roadmap
- Adopt a hybrid model: use AI for triage, guidance, and agent assist; route high-risk or emotional cases to skilled humans.
- Rebuild your P&L: model cost per resolution by path (AI-only, hybrid, human). Include inference, orchestration, data pipelines, evaluations, safety, and retraining.
- Set the right metrics: track containment rate, escalation rate, AHT, CSAT, recontact rate, and total cost per resolution. Tie outcomes to LTV and repurchase.
- Invest in agent experience: deploy copilots for summarization, knowledge suggestions, and quality flags. Train agents on prompt writing and judgment calls.
- Design for compliance: clear "request a human" options, fast handoffs, and auditable consent/PII controls.
- Tune vendor mix: match model class to risk level, use multi-model routing, negotiate outcome-based contracts, and avoid lock-in where possible.
- Upgrade outsourcing strategy: prioritize partners that run hybrid pods, offer strong QA and analytics, and handle complex conversations gracefully.
Benchmarks to watch
- AI cost per resolution vs. US$3 threshold (and vs. offshore human benchmark)
- Containment rate trend under "right to human" rules
- Median wait time to human and first available time
- CSAT by path (AI-only, hybrid, human)
- Human request ratio by channel and intent
- Agent turnover and coaching coverage
Where AI still pays off
Gartner expects 10% of Fortune 500 firms to double customer service spending by 2030 to create hyperpersonalized, proactive experiences. The value shifts from raw cost cutting to revenue: higher LTV, better retention, more repeat purchases, and stronger brand loyalty.
Ninety-day action checklist
- Build a cost-per-resolution model using your real volumes and intents.
- Pilot human-first routing with AI agent assist on two high-impact queues.
- Stand up a compliance review for "right to human" flows and audit logs.
- Negotiate model/inference commitments and escape hatches with vendors.
- Launch a copilot trial for summarization and knowledge suggestions.
- Define escalation SLAs and staff buffers for peak hours.
If your team needs focused upskilling on AI-assisted support workflows, browse these courses by job to build practical capability fast.
For broader context on research in this space, see Gartner's Customer Service & Support practice here.
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