Zendesk views AI as a factor that is pressuring enterprises to rethink traditional seat-based pricing in favour of usage- and outcome-driven models. But according to Chris Donato, the company’s Chief Revenue Officer and President of Global Sales and Field Engineering, the real challenge lies in trust.
In this interview with Frontier Enterprise, Donato discusses common misconceptions about AI’s impact on cost, the risks vendors face in outcome-based pricing, and how sales motions are evolving as CFOs demand clearer ROI.
What’s the biggest misconception about AI in SaaS pricing?
AI is influencing SaaS pricing by shifting value away from rigid seat-based structures. Traditional models suit stable, people-centric environments, but AI-driven automation means smaller teams can manage workloads that would previously have required more staff. With efficiency gains and expanding use cases, flexible, usage-oriented pricing is gaining importance, though seat-based approaches remain relevant for some organisations.
The biggest misconception I see is that outcome-based or usage-based pricing brings unpredictability and a lack of transparency. Some usage-based and outcome-driven models are structured to give customers clearer terms and more control, which is important as AI can drive rapid productivity gains.
To address concerns around unpredictability and transparency, AI solution providers should deliver greater control and visibility to customers. This can involve giving customers tools to track and forecast their AI usage, along with alerts when consumption nears certain thresholds. These practices help organisations manage and predict expenditure with greater accuracy.
Ongoing education and enablement are also crucial. Helping customers understand both the mechanics and benefits of outcome-based pricing and equipping them with detailed, actionable data helps them make informed decisions. When handled thoughtfully, outcome-based pricing can shift the conversation from uncertainty to empowerment, positioning organisations to harness AI’s potential as the technology and their needs continue to evolve.
What are the challenges in scaling outcome-based pricing?
One of the most significant hurdles is establishing clear, consistently measurable outcomes that work across different teams, regions, and legacy technology environments. Business units may have competing priorities or distinct KPIs, which complicates both tracking and attribution.
In the context of AI-powered customer experience, Zendesk defines the key outcome as an “automated resolution,” meaning an issue resolved entirely by an AI agent without any human intervention. To ensure this metric is fair and relevant, the definition is tailored for each interaction channel but follows consistent principles. For example, the AI agent must accurately understand the user’s request, provide the correct solution, and manage the conversation so that the customer does not require further assistance or escalation to a human agent. If a customer returns or the conversation requires human input, it is not counted as an automated resolution. Conversations are also periodically reviewed for quality to keep the metric meaningful.
Looking ahead, we expect a rapid shift. Within a few years, 80% of support queries will be fully automated by AI, and 100% will involve AI in some way. Outcome-based pricing helps keep this future grounded in measurable value as AI and human expertise become more closely connected.
How can you prove AI ROI to a CFO focused on cost centres and churn rates?
When demonstrating AI’s value, it is vital to be clear about the outcomes you want to achieve and how they will be measured. Keeping the focus on outcomes such as faster resolution times, improved retention, or reduced manual workload provides a clear framework for showing results in terms that resonate with financial leaders.
Small-scale pilots are an effective starting point, surfacing measurable gains before scaling further. Once early results are available, it becomes much easier to connect them to financial metrics familiar to a CFO, such as reductions in cost per transaction, operational efficiency, and impact on churn or retention.
Throughout the process, maintaining transparency helps build trust. Providing clear data, benchmarking against industry standards, and using usage-based metrics all support straightforward tracking and forecasting. By grounding the case in targeted pilot success, proven financial impact, and transparency, it becomes easier to secure wider stakeholder support and continued investment in AI.
What risks come with tying revenue too closely to customer outcomes?
While tying revenue to customer outcomes can create alignment and collaboration, it also poses the risk of adopting too narrow a view, particularly if the outcomes are defined solely by short-term or easily measured customer metrics. Focusing only on immediate indicators such as a single CSAT score or resolution time may overlook larger opportunities to foster long-term loyalty, innovation, and sustainable growth.
Rather than relying on customer-defined or subjective metrics, it is more effective to establish standard, objective, and mutually agreed benchmarks for success. For example, Zendesk uses “automated resolutions” as a benchmark: a customer issue is considered successfully resolved when it is handled from start to finish by an AI agent with no human intervention within 72 hours. This approach is applied across channels and use cases, keeping the outcome tied to the provider’s technology and service.
For outcome-based pricing to build trust and deliver sustainable value, both transparency in measurement and accountability are essential. This helps solution providers and customers avoid pitfalls and supports a fair partnership.
How are enterprises balancing automation and human oversight in customer experience AI?
Enterprises are beginning to reimagine customer experience as a collaboration between human intelligence and AI-driven automation, structured to deliver both scale and quality. Instead of asking what they can automate, the leading question has become how they can blend human and machine strengths to improve every customer interaction.
A clear example is a global cosmetics retailer that deployed an AI agent to handle its most repetitive, high-volume queries. The result was a measurable boost in productivity and efficiency, with the AI agent resolving 60% of chats on first contact without human involvement. This freed the customer care team to focus on more complex and emotionally sensitive interactions, such as responding to nuanced needs, building loyalty, and engaging in more personal conversations. These efficiency gains saved roughly 5 minutes per ticket and 360 agent hours each month, leading to a reported 369% ROI in less than a year.
Stronger results tend to come when enterprises use AI to handle initial customer interactions, including triaging, responding, and learning from each exchange, while reserving human agents for more complex cases. Human agents remain essential for handling exceptions, nurturing relationships, and representing the brand. The aim isn’t to replace people but to build a system where automation supports human expertise, and where data continually informs how the two are balanced for business and customer benefit.
How is AI reshaping the SaaS sales motion?
Solution-driven selling in SaaS has always been rooted in understanding client challenges, but AI has intensified and broadened these exchanges. Buyers today are more informed and discerning about AI’s capabilities, risks, and limitations. This raises the bar for sales teams, which must not only uncover customer needs but also demonstrate how AI can deliver measurable impact while addressing concerns around openness, accountability, and strategic value.
AI is also shaping the skills and expertise expected of SaaS sales professionals. Teams are now required to facilitate discussions on data privacy, responsible automation, and the long-term resilience of technology investments. At the same time, AI enables more precise prospect insights, allowing sales teams to tailor their approach and create more relevant, timely interactions.
The fundamentals of SaaS sales remain the same: listening, consultative discovery, and credible solution design, but AI has heightened expectations. Sales leaders are expected to act as credible advisors who can guide customers through change, rather than simply as product experts or order-takers. The result is a more strategic engagement where alignment on outcomes and ongoing partnership are central.
