Cresta's CEO advocates for a single AI platform that both assists human agents and powers full automation. This creates a powerful feedback loop: when an AI agent fails, the system observes the human's successful resolution, capturing data to improve the next AI agent iteration.
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Instead of merely 'sprinkling' AI into existing systems for marginal gains, the transformative approach is to build an AI co-pilot that anticipates and automates a user's entire workflow. This turns the individual, not the software, into the platform, fundamentally changing their operational capacity.
Counterintuitively, the path to full automation isn't just analyzing conversation transcripts. Cresta's CEO found that you must first observe and instrument what human agents are doing on their desktops—navigating legacy systems and UIs—to truly understand and automate the complete workflow.
Beyond automating 80% of customer inquiries with AI, Sea leverages these tools as trainers for its human agents. They created an AI "custom service trainer" to improve the performance and consistency of their human support team, creating a powerful symbiotic system rather than just replacing people.
The biggest productivity unlock isn't just making customer support cheaper. It's using AI models to eliminate the need for separate human archetypes for sales (yapper) and support (listener). Companies will bundle these functions into one unified team aimed at a higher-level business goal, like improving CAC.
Intercom's CEO predicts that companies will abandon separate AI agents for sales, service, and onboarding. A single, coordinated "customer agent" is necessary to avoid conflicting goals and create a seamless, high-touch experience for every user.
The goal of AI in customer service isn't human replacement. Instead, use AI agents to handle predictable, repetitive queries instantly. This strategy frees up human staff to focus their time on complex, empathetic problem-solving where a personal connection is most valuable.
Cresta's CEO categorizes customer interactions into three types: those caused by broken processes (eliminate), transactional tasks (automate), and high-emotion issues (augment humans). This framework provides a nuanced approach to AI in customer experience, moving beyond a simple automation-first mindset.
Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.
Customers don't differentiate between sales and support; they just want answers. AI makes it economically viable to handle both inquiry types through a single point of contact. This resolves the common issue of customers calling sales lines for support issues simply because they know a person will answer.
The CEO of Cresta argues that the true ceiling for automation isn't just the AI model's capability. It's equally constrained by the complexity of the business's offerings, the modernity of its IT infrastructure (i.e., API availability), and the digital-savviness of its customer base.