Rather than simply eliminating jobs, the rise of AI agents is creating a need for new, specialized roles. Positions like "Go-to-Market Engineer" and "AI Marketing Ops Specialist" are emerging to oversee, coach, and orchestrate these agents, signaling a transformation—not a reduction—of the GTM workforce.
To foster adoption and prevent resentment, Qualified gives its human sales reps quota credit for meetings and pipeline generated by the AI agent if it falls within their territory. This reframes the AI as a helpful collaborator that contributes to their success, rather than a competitor for valuable inbound leads.
A common misconception is that AI agents are "set it and forget it" technology. In reality, they require daily coaching, especially in the first 30-60 days. Using scorecards, giving feedback, and continuously training them on new offers and content is crucial for maintaining brand voice and ensuring high performance.
To ensure its AI SDR performed at an elite level, Qualified based its capabilities, speed, and knowledge on their best-ever human SDR, Blake. This "best performer" benchmark became the north star for product development, ensuring the agent was designed to replicate proven success from day one.
Instead of a risky "flip the switch" deployment, companies should gradually introduce AI SDRs. Start by having them augment humans (e.g., nights/weekends), then move to front-line greeting, then handling most MQLs, and finally, operating as the sole inbound SDR. This builds confidence and manages change effectively.
Historically, SDR teams often report to Sales, leaving marketing with indirect influence over converting demand into meetings. By deploying an AI SDR that works for the marketing team 24/7, CMOs regain direct control over the critical MQL-to-meeting conversion process, putting them "back in the driver's seat" of their pipeline number.
Instead of a broad AI overhaul, CMOs should identify their most acute pain point in the inbound funnel—like slow lead follow-up or poor event lead conversion. Deploying an AI agent to solve that specific, high-impact problem first builds momentum, proves value, and de-risks wider adoption.