AI can help with email drafts, personalization, audience analysis, send-time suggestions, and campaign reporting. Use it to support a defined marketing goal—not to run unchecked campaigns. Start with one task, review the message and automation rules yourself, then test the result against a control where possible.
What AI can do in an email workflow
Email marketing tools may combine two kinds of AI. Generative AI creates or adapts content, such as subject lines and email copy. Predictive AI analyzes historical information to suggest patterns or actions. Salesforce describes this distinction and outlines potential email applications in its AI in email marketing guide; these are vendor-described capabilities, not proof of a particular campaign outcome.
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- Drafting and variations: Create a first draft or propose alternative copy for a specific audience or offer.
- Personalization: Adapt content using customer or campaign data, where the platform supports it and has permission to process that data.
- Segmentation and timing: Analyze available first-party data to help identify audience groups or suggest when to send.
- Testing and analysis: Help develop test variants or summarize campaign performance for further review.
- Automated nurture: Apply personalization within a sequence triggered by a recipient’s actions or status. HubSpot documents marketing-email generation and a nurture-agent feature for automated emails, subject to the product’s access requirements (AI email creation; nurture-agent personalization).
AI support does not remove the need to decide who should receive a message, what action it should encourage, and how success will be measured.
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- Choose one bounded task. For example, use AI to draft a welcome email or suggest copy variations. Begin with a task that has a clear success measure instead of enabling every available feature at once.
- Write the campaign brief. Specify the audience, offer and its terms, the desired action, brand constraints, and the outcome you will track. Use only data you have permission to use and that the platform is configured to process.
- Configure the feature and workflow. Use the platform’s documented drafting, personalization, or predictive feature. Access may depend on settings, permissions, or product eligibility; consult the relevant product documentation rather than assuming every account has the same controls.
- Review the content and the path to send. Check factual claims, dates, discounts, links, language, recipient eligibility, sequence triggers, and frequency. HubSpot advises users to verify generated content, especially facts, statistics, and non-English content. No universal send-frequency limit or review checklist is established by the cited guidance, so set rules appropriate to your audience and campaign.
- Test one meaningful change. Compare the AI-assisted version with a control when possible, changing one variable at a time. Salesforce’s guidance says, “When you do your A/B email testing, don’t test multiple things at once.” Choose a measure aligned with the campaign goal, then keep, revise, or remove the automation based on the observed result.
- Check the provider’s data terms. Before entering customer details into an AI feature, review its data settings, retention and training terms, access permissions, and processing disclosures.
How to judge results without assuming a lift
Feature descriptions explain what a product says it can do; they do not establish that a campaign will get more opens, clicks, conversions, or revenue. Define the relevant outcome before a test, use a control group where practical, and avoid attributing a change to AI if other campaign variables changed at the same time. Salesforce recommends testing against a control and not changing several things in one A/B test.
#1 Best Overall
Litmus’s 2026 guide, published February 3, 2026, reports that 70% of email marketers expected up to half of their email operations to be AI-driven by the end of 2026. That is a survey finding about respondents’ expectations, not a measured outcome. The same guide reports a 340% increase in marketers using generative AI in 2025; its cited summary does not specify the denominator, so the figure should not be read as the share of all marketers or as evidence of campaign effectiveness.
What to check when choosing an AI email tool
There is no universally best platform established by the available product documentation. Compare tools against the job and safeguards you need:
- Does it assist with drafting, prediction, personalization, testing, analysis, or several of these?
- Can it use the first-party data you need and fit your existing email or CRM workflow?
- What settings and permissions govern generation and personalization, and what review controls are available?
- Can you run a controlled test and connect the result to your campaign goal?
- What are the provider’s specific terms for data access, retention, and model training?
Privacy claims are product-specific
Google states that Gemini in Gmail does not use personal Gmail messages to train foundational models. Google Workspace Help makes a similar statement for Workspace content used with the Gemini features it lists. These statements apply to the named Google products and features, not to AI email tools generally. Review the terms and settings for the exact provider and feature you plan to use: Google’s April 7, 2026 explanation of Gemini in Gmail privacy and Google Workspace Help on Gemini data protection.
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