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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchZendesk CTO Jason Maynard says customer-experience AI has moved from a vendor-led pitch to a board-level priority. For support teams, that shift is changing how they evaluate AI: not by how many conversations a bot deflects, but by whether it resolves customer requests, how safely it can act across business systems, and who captures the resulting savings.
Why customer-experience AI is changing
In a Computer Weekly interview published on 15 August 2025, Jason Maynard, Zendesk’s chief technology officer for Asia-Pacific and North America, described a reversal in the way companies approach AI. Instead of Zendesk having to persuade customers to consider it, he said customers were arriving with board-level AI initiatives already in motion.
The business case has two connected aims: resolve customer requests faster, reducing waits and dissatisfaction, and automate work to lower labor costs. Zendesk says more than 10,000 customers were using its AI products when Maynard made the claim in 2025. The company applies different algorithms to products including AI agents, Copilot, automated quality assurance and predictive workforce management; these tools address different parts of service operations rather than representing one interchangeable AI feature.
What Zendesk’s AI pricing model means
Zendesk has moved AI agents away from a purely per-seat pricing logic. Its current pricing page says AI agents are included in Suite and Support plans, with pricing based on successful outcomes. The documentation term for an outcome is an automated resolution: a customer request successfully resolved by an AI agent without escalation to a human. Zendesk’s documentation does not establish a specific per-resolution rate here, so the model should not be read as a stated universal price.
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In its Resolution Platform announcement, Zendesk said each charged resolution is verified by the AI agent and independently confirmed by a dedicated evaluation model. The company also said spam and routine exchanges are excluded. That qualification matters: the intended charging unit is a verified resolution, not every message, conversation, or deflection.
How AI agents differ from basic chatbots
The distinction in Zendesk’s approach is the work an agent can perform. A basic chatbot may answer from a set of knowledge articles; Zendesk describes procedures that can also retrieve information from another system or take an action in a downstream application. Its 2026 Resolution Platform strategy brings together AI agents, knowledge, actions and integrations, governance, and measurement. Zendesk says the strategy extends autonomous service across messaging, email, voice, and external AI platforms.
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In the 2025 interview, Maynard described Zendesk’s AI agent as a constellation of about five specialized large language models. Different models handle tasks such as building an ontology, planning a conversation, or generating dialogue. Zendesk says it abstracts model changes from customers and benchmarks new models as they become available. That is a description of Zendesk’s architecture, not a guarantee that every AI product or deployment works the same way.
Maynard characterized MCP as an integration approach for conversational API interaction 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. Teams should therefore evaluate actual connection, permission, and handoff behavior in their own systems rather than assuming that an integration standard makes implementation automatic.
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What a bot manager does
Maynard calls the emerging operating role a “bot manager”; organizations may use titles such as AI architect or automation architect. The role resembles a technical product manager for the AI-agent experience, coordinating the knowledge and content an agent uses, engineering endpoints, procedures, and actions in back-end systems.
This changes the support leader’s job. Managing a human team remains important, but scaling an AI service also requires maintaining its technology and feedback loop: deciding what it should handle, checking whether procedures work, and improving the system when interactions reveal gaps. Maynard likened the wider change in CX to the technical and data-driven shift in marketing between 2000 and 2010, saying the industry is undergoing a similar reskilling inflection point.
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How support teams should govern AI agents
Maynard describes a lifecycle of design, testing, deployment, and feedback. Zendesk’s approach begins by analyzing the organization’s top customer-intent drivers, then defining what the agent is permitted and equipped to do. A practical procedure should distinguish among these three levels:
- Knowledge-based answers: Resolve straightforward questions using approved content.
- System lookups: Retrieve information from another system, such as an enterprise resource planning (ERP) platform.
- Write actions: Change a downstream system, such as creating a Jira bug.
These levels carry different operational consequences. A write action can change business records, so its procedure and access should be designed and tested accordingly; the interview does not prescribe a universal permission model. Teams should define human escalation paths and review whether the agent’s instructions, connected systems, and allowed actions remain appropriate as procedures change.
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Zendesk says its AI quality-assurance tool can score 100% of human and AI interactions for completeness, tone, and compliance risk. Maynard contrasted that coverage with traditional manual sampling of roughly 5–10% of interactions. This is Zendesk’s stated capability and comparison, not evidence that every organization has deployed full coverage or that automated scoring replaces human oversight.
What the shift means for BPO and support work
Maynard sees tier-one, high-volume, low-complexity support as especially suited to AI automation. Some customers are considering reducing business-process-outsourcing (BPO) contracts and using AI agents for that work. Organizations with large in-house support teams may instead begin with Copilot, which assists existing staff rather than replacing the service operation outright.
The key business question is who receives the savings. A buyer may want lower service costs, while a BPO vendor may use automation to reduce its own delivery costs and protect or increase margins. Maynard framed this as a question of where the value from AI accrues, not as proof that BPO work will disappear. The answer depends on contract economics, which tasks can be automated reliably, and whether human support remains necessary for exceptions and complex cases.
Why resolution is becoming the central CX metric
Zendesk CEO Tom Eggemeier said at the company’s 26 March 2025 Resolution Platform announcement: “The only metric that matters in customer service is resolution.” That emphasis is reflected in the automated-resolution pricing unit, but businesses still need to assess whether customers actually get useful outcomes and whether escalation works when automation is insufficient.
Zendesk’s 2026 CX Trends report 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 are findings reported by Zendesk, not universal forecasts. They point to a broader design challenge: providing continuity across channels while preserving a clear route to a person when the AI agent cannot resolve a request.
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