How Uber runs its inbound lead flow on autopilot.
Uber's Revenue Technology Manager on the AI agents that qualify and route every inbound lead, so reps only work real deals. Live walkthrough and open Q&A.
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What you'll walk away with
Where to start. Why Uber for Business began with high-volume, repetitive inbound work, and how to find your own first use case for fully autonomous AI agents.
Know exactly where you stand, and why 95% of AI projects fail. The 4 levels of AI autonomy, why most teams stall between L1 and L2, and what L3 (AI autopilot) looks like inside a revenue org at scale.
How agents qualify autonomously. They email inbound leads for budget and authority, then route each warm, qualified lead to the right rep.
How the agent earned the right to run on its own. Every email reviewed at first, then the agent ran solo, with humans only on the edge cases.
What Uber for Business would do differently. Why starting with autonomous outbound was too ambitious, and why it comes after inbound, not first.
A look under the hood. A live walkthrough of a pre-built Relevance multi-agent system: the agents, the processes, the data flows, the visual canvas. Then open Q&A.
Unlock the agentic ROI you promised your board
Relevance's platform maps the path from assisted AI to full autonomy. Real business impact is driven in L3/L4.
Delegate busywork like research and drafting to a copilot like Cowork.
Teach your copilot your playbooks. Powerful, but hard to govern.
Proven playbooks become governed agents that run autonomously.
Your agents run evals and swap models themselves. You lead strategy.
