Scaling Outcome-Based AI Pricing: Zendesk CRO on Trust, Automated Resolutions, and CFO-Grade ROI

Scaling Outcome-Based Pricing with AI: What Sales Teams Can Use Today

AI is pushing enterprises to rethink rigid seat-based pricing. Zendesk's Chief Revenue Officer, Chris Donato, argues the real barrier isn't math-it's trust. Buyers want control, visibility, and proof of ROI. Sales leaders who deliver that win.

The big misconception: outcome-based pricing is unpredictable

Many assume usage- or outcome-based pricing creates uncertainty. It doesn't have to. With the right controls-usage dashboards, forecasts, and threshold alerts-customers gain more visibility than a seat-based plan ever offered. Seat-based still fits some teams, but automation-driven workflows benefit from flexible, usage-oriented models.

Make it predictable by putting customers in control: give them usage tracking, budget alerts, and clear levers to adjust consumption. Pair that with ongoing enablement so buyers understand how pricing maps to real operational gains.

Define the outcome: "automated resolution"

Zendesk uses a simple, objective benchmark: an automated resolution. An issue qualifies when an AI agent fully resolves it-no human intervention-within 72 hours. The definition adapts to each channel but follows consistent principles: accurate intent detection, correct solution, and no escalation. Quality reviews keep the metric meaningful.

Where this is going: Zendesk expects 80% of support queries to be fully automated by AI within a few years, and 100% to involve AI in some way. Pricing anchored to outcomes keeps value measurable as automation scales.

Sell AI ROI to CFOs without hype

• Start with outcomes: commit to metrics that matter-faster resolution, lower cost per ticket, reduced backlog, improved retention.
• Prove it in a pilot: 30-90 days, one use case, clean baseline, shared success criteria.
• Translate to finance: tie outcomes to cost per transaction, headcount savings or reallocation, churn impact, and forecast accuracy.
• Maintain transparency: share benchmarks, provide live usage data, and forecast ranges with alerting for thresholds.

The risk in outcome-linked revenue-and how to avoid it

If you tie revenue to narrow or subjective metrics (a single CSAT snapshot, a one-off handle time), you encourage short-term thinking. Instead, anchor on objective, mutually agreed outcomes. "Automated resolutions" work because they're binary, auditable, and linked directly to the provider's tech and service.

Build trust with clear definitions, transparent measurement, and joint accountability. That's how you avoid misaligned incentives and keep the partnership fair.

Where automation meets human expertise

A global cosmetics retailer deployed an AI agent for high-volume, repetitive chats. Results:

• 60% of chats resolved on first contact by AI with no human involvement
• ~5 minutes saved per ticket and 360 agent hours per month
• Reported 369% ROI in under a year

The pattern is consistent: AI handles triage and common tasks; humans take complex, emotional, or brand-defining moments. The goal isn't replacement. It's a system where automation lifts human work and data informs how you balance the two.

How AI is changing the SaaS sales motion

Buyers are sharper about AI's capabilities and limits. Sales teams must move beyond features and guide customers through data privacy, responsible automation, and long-term viability. Precision discovery and credible solution design still win-the bar is just higher.

Top performers act like advisors: align on outcomes, de-risk with pilots, quantify impact, and set up a cadence for continuous improvement. AI also makes prospecting smarter, enabling tailored outreach and timing that maps to buyer intent.

Sales playbook: make outcome-based pricing work

• Segment pricing: keep seats where human concurrency drives value; use usage/outcomes for automation-heavy flows.
• Co-define success: write the outcome definition (e.g., automated resolution within 72 hours) into the order form and success plan.
• Add controls: usage dashboards, budget caps, alerting, and forecast ranges. Offer commit + overage or prepaid bundles with rollover.
• Pilot with intent: baseline current costs, run 30-60-90 day pilots, and publish a joint scorecard weekly.
• Compensation alignment: pay reps on contracted value and leading indicators (e.g., committed automated resolutions) to avoid sandbagging.
• Risk-sharing: set floors/ceilings, clawbacks for quality slips, and "ratchet" terms that reward sustained performance.
• Executive reporting: ship a CFO-friendly pack-cost per ticket trend, automation rate, SLA adherence, and forecast vs. actuals.
• Trust posture: have security, privacy, and data handling documentation ready for procurement.

Tooling checklist for credibility

• Accurate meters for usage and outcomes (with audit logs)
• Self-serve dashboards and alerts for consumption thresholds
• Scenario-based forecasting and budget controls
• APIs for billing integration and chargeback
• Quality review workflows and periodic sampling

Helpful resources

• Usage-based pricing benchmarks to inform packaging and forecasting.
• AI courses by job role for upskilling sales and customer success on AI fundamentals, pricing, and ROI storytelling.