Cascadia Global Ventures

I deploy Zapier to connect LLM models to the specific lifecycle category tools. Then define one specific repetitive task where automation would save time or money for the org on that application. For example, in daily sales forecasting based on updates in the CRM from field Account Executives. Then I write the LLM with Zapier’s API NLA to execute the task. Connect it to the required inputs and outputs: CRM, Email, Calendar, and then Test completely before deploying it.

The next step is a  sales operations proof of value using  Zapier + LLM workflows to automate CRM, email, and calendar triggers across business units go to market sales motions. Second, deploy Lavender.ai to improve email clarity, tone, personalization, and deliverability and Regie.ai for supported persona-based sequences and consistent messaging. Third, use either Apollo.io or Seamless.ai for high-quality prospect data and fast enrichment   This creates a full AI-augmented outbound engine for the  sales and marketing organizations.

Fourth, you need to validate how MQL’s are defined, scored, and routed into the sales funnel working with marketing operations to tune scoring criteria, qualification rules, and enrichment workflows. Fifth track and score where leads are converted or stalled, how SDR handoffs affected pipeline quality, and how automation tools influenced velocity across each funnel stage. Your largest gains will come from qualification and handoff stages, where automation reduces variance and increases signal quality.

The results your organization will achieve:

• MQL → SQL: 12–20% improvement (better scoring, cleaner enrichment, fewer low-fit leads).
• SQL → Discovery/Qualified: 18–30% improvement (AI agents enforce qualification criteria and SDR → AE handoff consistency).
• Discovery → Proposal/Eval: 15–25% improvement (persona-aligned messaging and automated follow-ups reduce stall-outs).
• Proposal → Closed Won: 5–12% improvement (stronger objection handling and tighter forecasting triggers).