Don't expect an AI agent to invent a successful sales process. First, have your human team identify and document what works—effective emails, scripts, and objection handling. Then, train the AI on this proven playbook to execute it flawlessly and at scale. The AI is a scaling tool, not a strategist from day one.

HubSpot quickly realized that for AI to be transformative, it must move beyond individual productivity hacks. The goal is 'institutional productivity,' where AI systems meaningfully improve core business outcomes like growth or P&L metrics, which represents the real unlock for businesses.

By replacing manual inbound follow-up from BDRs with HubSpot's native prospecting agent, 3Play Media dramatically improved performance. This allowed their remaining BDR to focus exclusively on calls and meetings, increasing overall efficiency and output without backfilling two open roles.

Don't just "turn on" an AI sales agent and expect results. The only path to success is to first identify what works with your human reps—the scripts, the process, the data. Then, you must manually train the AI on that proven playbook, iterating and refining its performance daily for at least a month. The AI automates success; it doesn't create it from scratch.

Companies are abandoning static enablement platforms because a central AI can connect methodology and content directly to real-time sales scenarios. Instead of searching a library, reps get hyper-personalized, context-aware guidance (e.g., 'what discovery questions to ask now'), making enablement dynamic and immediately applicable.

Unlike older sales tools, AI agents shouldn't be handed to individual SDRs to manage. This approach leads to failure. Instead, centralize the strategy: a core team must own agent training, contact routing, and performance tuning to ensure a consistent and effective GTM motion across the entire organization.

A year ago, the best strategy was using distinct, specialized agents for different sales tasks (e.g., cold outbound vs. reviving ghosted leads). As AI models have improved, it's now more effective to consolidate these functions into a single, more capable agent that can handle multiple tasks.

Instead of one-off prompts, feed your AI a persistent knowledge base with company data, sales playbooks, and territory info. This "intelligence layer" provides crucial context, enabling the AI to perform complex, tailored sales tasks effectively and consistently.

Off-the-shelf AI go-to-market tools fail because they are purely transactional. ElevenLabs' CRO built custom AI agents for SDRs, proposals, and customer success that assist humans by drafting personalized messages, which are then reviewed, sent, and used to fine-tune the models, leading to actual revenue generation.