Kavak replaced underutilized, employee-facing AI tools with autonomous agents now handling over 90% of customer interactions. This deliberate transition required a painful year of flat growth, as the company chose to absorb short-term performance dips to build a scalable, agent-first future.
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For mature companies struggling with AI inference costs, the solution isn't feature parity. They must develop an AI agent so valuable—one that replaces multiple employees and shows ROI in weeks—that customers will pay a significant premium, thereby financing the high operational costs of AI.
The rapid evolution of AI is forcing startups into successive, exhausting pivots. Founders who just integrated AI into their roadmaps are now being told they need an "agentic version" without a traditional UI, creating strategic fatigue and emotional strain for teams struggling to keep pace with platform shifts.
With infinitely scalable AI agents, cost and time per interaction are no longer primary constraints. Companies should abandon classic efficiency metrics like Average Handle Time and instead measure success by outcomes, such as percentage of tasks completed and improvements in Customer Satisfaction (CSAT).
Jason Lemkin's company, SaaStr, transitioned from a go-to-market team of roughly 10 humans to just 1.2 humans managing 20 AI agents. This new, AI-driven team is achieving the same level of business performance as the previous all-human team, demonstrating a viable new model for sales organizations.
Traditional product metrics like DAU are meaningless for autonomous AI agents that operate without user interaction. Product teams must redefine success by focusing on tangible business outcomes. Instead of tracking agent usage, measure "support tickets automatically closed" or "workflows completed."
Adding a chat interface or minor "AI features" won't unlock new budget. To capture significant AI spend, your product must either replace human headcount, make users dramatically more effective, or provide an order-of-magnitude productivity increase.
The evolution of Tesla's Full Self-Driving offers a clear parallel for enterprise AI adoption. Initially, human oversight and frequent "disengagements" (interventions) will be necessary. As AI agents learn, the rate of disengagement will drop, signaling a shift from a co-pilot tool to a fully autonomous worker in specific professional domains.
Initially, being the "AI guys" led to endless custom requests across departments. The scalable breakthrough was shifting their model from doing the work to teaching customers how to use their platform to build agents, empowering them to solve their own problems.
An attempt to use AI to assist human customer service agents backfired, as agents mistrusted the AI's recommendations and did double the work. The solution was to give AI full control over low-stakes issues, allowing it to learn and improve without creating inefficiency for human counterparts.
The paradigm shift with AI agents is from "tools to click buttons in" (like CRMs) to autonomous systems that work for you in the background. This is a new form of productivity, akin to delegating tasks to a team member rather than just using a better tool yourself.
