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Zendesk CTO Jason Maynard says AI has moved from a vendor-led pitch to a board-level priority for many customer-support organizations. The shift changes what teams need to measure and manage: not just whether an AI agent handles a conversation, but whether it resolves the request, follows approved procedures, and hands off appropriately when it cannot.
What has changed in customer experience?
In an interview published by Computer Weekly on 15 August 2025, Maynard said Zendesk’s conversations with customers had “totally flipped”: companies increasingly arrive with board-backed AI initiatives rather than waiting for a supplier to persuade them to consider AI.
The business case has two connected parts. Faster, real-time answers can reduce customer waiting and dissatisfaction; automating suitable work can also reduce labor costs. Zendesk applies different algorithms across AI agents, Copilot, automated quality assurance (QA), and predictive workforce management, rather than treating every CX task as one generic AI use case. Maynard said Zendesk had more than 10,000 customers using its AI products in 2025.
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What is Zendesk’s AI pricing model?
Zendesk has moved away from pricing AI agents solely by seat. Its pricing page says AI agents are included in Suite and Support plans, with charges based on successful outcomes. That means the AI-agent component is oriented around resolutions, while the underlying Zendesk plan still matters; it does not mean the entire support platform is necessarily priced only per resolution.
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Zendesk documentation defines an automated resolution as a customer request successfully resolved by an AI agent without escalation to a human. The definition distinguishes resolution from deflection: a conversation that ends without reaching an agent is not necessarily a successful outcome if the customer’s request remains unresolved.
In a 26 March 2025 announcement, Zendesk said each charged resolution would be verified by the AI agent and independently confirmed by a dedicated evaluation model, with spam and routine exchanges excluded. The materials cited here do not establish a per-resolution rate, regional pricing differences, or the detailed billing terms for every plan, so teams should check the current terms for their account before forecasting costs.
How are Zendesk AI agents different from chatbots?
The distinction in Zendesk’s account is not simply that an AI agent uses a large language model. Zendesk describes its agent as a group of about five specialized large language models, each responsible for a different task, such as creating an ontology, planning a conversation, or generating dialogue. The company says it benchmarks new models and abstracts model changes from customers.
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More importantly for support operations, Zendesk positions the agent within a wider system of knowledge, integrations, procedures, governance, and measurement. A basic bot may answer from supplied content; the workflows Maynard described can also look up information in another system or carry out an approved action in a downstream system. Zendesk’s 2026 Resolution Platform announcement describes a unified platform spanning AI agents, knowledge, actions and integrations, governance, and measurement.
That does not mean every agent automatically has access to every business system or can take every action. The organization must define the procedures, connect the required systems, and decide what the agent is allowed to do. Human escalation remains relevant when the request cannot be handled successfully by the automated workflow.
What does a bot manager do?
Maynard calls the emerging operating role a “bot manager”; organizations may use titles such as AI architect or automation architect. The role is a technical product manager for the AI-agent experience, coordinating the knowledge and content the agent uses, the engineering endpoints it connects to, and the procedures and actions it follows in back-end systems.
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This changes the work of support leadership. Managing an AI service requires maintaining the system and its feedback loop—not only staffing a team of people. Maynard compared the shift to marketing’s move toward more technical, data-driven work between 2000 and 2010, saying CX is undergoing a similar reskilling.
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How should teams design and govern AI agents?
Maynard described a lifecycle of design, testing, deployment, and feedback. The practical starting point is to understand the most common reasons customers contact support, then define what the agent should do for each kind of request.
Classify the work by the systems and permissions it needs
| Procedure tier | What the agent does | Example from Maynard’s description |
|---|---|---|
| Knowledge-based response | Answers a straightforward question using approved knowledge. | Simple question and answer. |
| Lookup | Retrieves information from another system. | Checking information in an ERP. |
| Write action | Changes or creates a record in a downstream system. | Creating a Jira bug. |
These tiers imply different integration and governance needs. An answer drawn from approved knowledge is not the same operational risk as reading customer or order data, and neither is equivalent to writing to a business system. Teams should test each procedure at the level of access it requires and specify the conditions for escalation.
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Measure quality across the workflow
Zendesk says its AI QA tool can score 100% of human and AI interactions for completeness, tone, and compliance risk. Maynard contrasted that with traditional manual sampling of roughly 5–10% of interactions. The claim describes Zendesk’s stated tool capability; it is not evidence that every organization has enabled it or that automated scoring alone replaces review of high-risk cases.
Choose a meaningful outcome metric
Zendesk CEO Tom Eggemeier said at the 26 March 2025 Resolution Platform announcement, “The only metric that matters in customer service is resolution.” For an operating team, resolution should be considered alongside quality, compliance, appropriate escalation, and customer experience: an automation rate by itself cannot show whether the customer’s need was met.
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Maynard described MCP as an approach for conversational interaction with APIs and A2A as a way for service agents to share context with other agents. He also cautioned that the current integration plumbing remains manual and brittle. These approaches may shape how systems connect, but they do not remove the need to design and maintain the actual procedures, permissions, and handoffs in a support workflow.
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Will AI replace BPO customer-service work?
Maynard identified tier-one, high-volume, lower-complexity work as particularly suited to AI automation. Some customers are considering reducing business-process-outsourcing (BPO) contracts and using AI agents for tier-one support. Organizations with large in-house support teams may instead begin with Copilot to help existing staff.
The outcome depends on where automation value accrues. A BPO provider could use AI to lower its own costs and improve margins, or a buyer could seek to capture savings by changing the contract or moving work in-house. As Maynard put it, customers are asking whether their BPO vendor is using AI to increase its margins or whether the customer receives the savings. The interview describes these as choices customers are considering, not a settled prediction that AI will eliminate BPO roles or contracts.
What customer expectations may come next?
Zendesk’s 2026 CX Trends report, published by Zendesk in 2025, says 76% of consumers would choose a company that allows text, voice, and visuals in one conversation, while 83% of CX leaders believe Voice AI can significantly evolve CX. These figures indicate interest in joined-up, multimodal service and Voice AI; they do not establish that every customer prefers those channels or that every organization is ready to deploy them.
Zendesk’s 2026 autonomous-service strategy says its service experience spans messaging, email, voice, and external AI platforms. For support teams, that broadens the design problem: the same request may begin in one channel and continue in another, so the organization needs consistent knowledge, procedures, governance, and measurement across the customer journey.
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