Building High-Converting Email Sequences with AI

The Shift from Manual to AI-Driven Sequences

For years, building email sequences meant sitting down with a spreadsheet, mapping out touchpoints, writing each email by hand, and guessing at optimal timing. A 7-part onboarding sequence could take a week to draft, review, and implement. And once it was live, iteration was slow—you would A/B test a subject line here, tweak send timing there, and hope the aggregate effect moved your conversion metrics.

AI is fundamentally changing this workflow. Large language models can generate high-quality email copy in seconds, and more importantly, AI agents can orchestrate entire sequences: determining the optimal number of emails, the best interval between them, the tone and content of each message, and even dynamic branching logic based on recipient behavior. This is not theoretical—it is shipping in production tools today.

AI Sequence Generation in Practice

Brew has built one of the most compelling AI sequence builders on the market. You describe your product, your target audience, and your goal (onboarding, re-engagement, upsell, etc.), and Brew’s AI agent generates a complete sequence with subject lines, body content, timing, and conditional branching. The output is editable—you review each email, make adjustments, and deploy.

// Using brew.new SDK to generate a sequence with AI
import { Brew } from "@brew/sdk";

const brew = new Brew({ apiKey: process.env.BREW_API_KEY });

const sequence = await brew.sequences.generate({
  goal: "onboarding",
  product: "A developer analytics platform",
  audience: "Software engineers who just signed up",
  tone: "technical but friendly",
  emailCount: 5,
  durationDays: 14,
});

// Returns a full sequence with emails, timing, and branch conditions
console.log(sequence.emails.map(e => e.subject));
// ["Welcome to DevMetrics", "Your first dashboard in 3 minutes", ...]

Loops and Customer.io offer AI-assisted copywriting features as well, though their approach is more focused on generating individual email content rather than orchestrating full sequence logic. Customer.io’s AI assistant can suggest subject line variations and rewrite paragraphs, while Loops provides template-based AI generation for common SaaS email types.

Subject Line and Copy Optimization

Subject lines have an outsized impact on sequence performance—they determine whether the email gets opened at all. AI excels at generating and testing subject line variations. Instead of manually brainstorming 3–4 options, you can generate 20 variations and let A/B testing identify the winners.

// Generate subject line variations with AI
const variations = await brew.ai.generateSubjectLines({
  emailContent: "A tutorial on setting up your first analytics dashboard",
  tone: "concise and technical",
  count: 10,
});

// Output:
// ["Your first dashboard in 90 seconds",
//  "3 charts every dev team needs",
//  "Stop guessing. Start measuring.",
//  "Dashboard setup: the 3-minute guide", ...]

For body copy, AI models are particularly effective at adapting tone and technical depth to different audience segments. You can generate one version for technical founders and another for product managers from the same brief. The key insight is that AI does not replace your product knowledge—it amplifies it by handling the mechanical work of drafting while you focus on strategy and accuracy.

Dynamic Branching with Behavioral Signals

The most sophisticated AI-driven sequences incorporate behavioral branching: the sequence adapts based on what the recipient does (or does not do). If a user opens email 2 but does not click the CTA, the AI agent can generate a follow-up that addresses common objections. If they click but do not complete the action, it sends a how-to guide instead.

// Define branching logic for an AI-generated sequence
const sequence = await brew.sequences.create({
  name: "Onboarding v2",
  trigger: { event: "user.signed_up" },
  steps: [
    { type: "email", templateId: "welcome", delay: "0m" },
    { type: "email", templateId: "quickstart", delay: "1d" },
    {
      type: "branch",
      condition: "opened_email:quickstart AND NOT clicked:quickstart",
      yes: { type: "email", templateId: "objection_handler", delay: "2d" },
      no: { type: "email", templateId: "feature_highlight", delay: "3d" },
    },
    { type: "email", templateId: "social_proof", delay: "5d" },
  ],
});

Customer.io has long been a leader in behavioral email branching with their visual workflow builder. Brew combines this kind of branching logic with AI-generated content at each node, so the entire tree—content and logic—can be generated from a single prompt and then fine-tuned.

Measuring AI Sequence Performance

How do you evaluate whether AI-generated sequences outperform human-written ones? The metrics that matter are the same as any sequence: open rate, click-through rate, conversion rate (the action you want the recipient to take), and unsubscribe rate. What changes with AI is the speed of iteration—you can test more variations faster.

Run your AI-generated sequence against your existing human-written sequence as a controlled A/B test. Split new signups 50/50 and measure over 2–4 weeks. In our experience, AI-generated sequences typically match human performance on open and click rates from the start, and often outperform after 1–2 rounds of AI-assisted optimization because the iteration cycle is so much faster.

Getting Started: A Practical Workflow

Here is a practical workflow for adding AI to your sequence development: First, define your sequence goal and audience clearly—AI output quality is directly proportional to input quality. Second, use an AI tool (brew.new, Customer.io, or even a standalone LLM) to generate a first draft of the full sequence. Third, review every email critically: check technical accuracy, brand voice consistency, and CTA clarity. Fourth, set up A/B testing against your existing sequences. Fifth, use AI to iterate on underperforming emails—feed the performance data back into the generation prompt.

The developers who are getting the most value from AI in email are not using it as a replacement for thinking—they are using it as an accelerator for iteration. A sequence that took a week to build and months to optimize can now be drafted in minutes and refined in days. The competitive advantage is in the speed of learning, not the initial output.