AI can save time on email copy, but a fast draft is only part of the job. If choosing between versions, correcting facts and rebuilding the email consumes the time you saved, the production process has not become faster.
For a retention team, the useful question is which tasks reach an acceptable result with less total work. Start with a specific handoff, then count the effort through approval.
Does AI save time on email marketing? It can, but measure the whole job: preparation, prompting, editing, building, checks and approval. Compare total human minutes for equivalent, approved emails. Keep the quality standard fixed and include failed attempts. A faster first draft alone does not establish a saving.
What does the evidence actually show?
There is direct evidence of shorter copy production in a particular email-marketing setting. In Dubé and Xu’s Wine Access study, published in 2026, the first 2024 trial used a pre-trained language model. An editor, who was not permitted to edit the copy, fact-checked its output and regenerated it until it was acceptable. The researchers report that this process took no more than 10 minutes, against about 3 hours for the retailer’s usual research and writing.
That comparison concerns one retailer’s copy workflow. It is not a benchmark for producing an entire campaign, or a forecast for a team using a general-purpose chatbot.
Use it as a reason to test a defined production task, with review included. The separate question of whether AI-written email improves retention needs customer-outcome evidence; a time log cannot answer it.
Which work remains after AI generates the email?
Check what the specific feature actually finishes. For example, Klaviyo’s Email AI section generator, available on paid accounts, leaves product or feed selection, images, alt text and button or link URLs for manual completion. A generated section is not a completed send.
The platform’s Composer guidance also assigns review and approval to the user, including checking prices, discounts and product information. These are documented responsibilities for those features, not a claim that every AI tool works identically.
Before testing your own workflow, write down what “finished” means. For an ordinary promotional email, a useful acceptance checklist is: approved offer and facts, suitable voice, correct destination links, completed layout and a reviewer willing to approve the send. Keep that checklist the same for the usual and AI-assisted processes.
Otherwise, you risk comparing a finished human email with an AI draft that still needs work.
How should you measure the time saving?
Compare total human minutes through approval for equivalent emails, including prompting, editing, checks and failed attempts. Keep the quality standard fixed. This is a suggested production log, not an industry benchmark.
Net minutes saved = usual-process human minutes − AI-assisted human minutes.
Sum the work across everyone involved. If the writer saves time but the reviewer spends longer repairing the result, both changes belong in the calculation. Log elapsed waiting time separately: a draft waiting overnight for approval is a different constraint from an hour spent editing.
Here is an illustrative example with invented times, for equivalent emails that meet the same approval standard:
| Production stage | Usual process | AI-assisted process |
|---|---|---|
| Prepare the brief and approved facts | 10 minutes | 10 minutes |
| Write the draft, or prompt and generate it | 30 minutes | 8 minutes |
| Select and edit the copy | 10 minutes | 17 minutes |
| Build the email and check it | 20 minutes | 20 minutes |
| Review, revise and approve | 10 minutes | 10 minutes |
| Total human effort | 80 minutes | 65 minutes |
The draft stage saves 22 minutes, but extra editing consumes 7. The net saving is 80 − 65 = 15 minutes. If AI-copy editing instead takes 35 minutes, the total becomes 83 minutes: 3 minutes longer than the usual process.
Count time spent on discarded generations and abandoned attempts, including an eventual manual rewrite. Excluding those would make the process look better than the work log supports.
Also record whether the email passed the agreed checks. An unfinished or rejected email is not an equivalent output, however little time it took.
What should you hand off to AI first?
Start by testing a small job, such as shortening approved copy or drafting a section from verified facts. Keep factual approval with the responsible person. These are candidate handoffs to evaluate, not a proven ranking of time savings.
| Candidate handoff | Input to supply | Decision to retain |
|---|---|---|
| Shorten existing copy | Approved draft and what must remain | Whether the meaning and offer conditions survived |
| Suggest subject-line options | Final email, offer and voice example | Which option accurately represents the email |
| Draft a product section | Verified product facts and its purpose | Whether the claims and emphasis are appropriate |
| Adapt an approved message | New audience context and explicit changes | Whether the adaptation fits that audience |
Keep offer selection and factual approval with the person accountable for the campaign while you test the writing task. There is no need to delegate the whole campaign to find out whether a smaller handoff helps. The broader AI email marketing explainer separates content generation from other uses of AI.
Try a reusable brief like this:
- Audience and purpose: who will receive the email and what this message should help them do.
- Approved information: product facts, offer conditions, dates and destination link; flag anything still missing.
- Voice: an approved example and the specific qualities to retain.
- Deliverable: the section or rewrite required, plus facts and conditions that must not change.
- Review rule: mark missing information instead of inventing it; name the person who checks the result.
This is an input template, not a guarantee of accuracy. Check the output even when the prompt explicitly tells the model not to invent facts.
When should you keep, narrow or stop the AI workflow?
Compare similar jobs with the same acceptance criteria. Rotate the method across comparable briefs, and record campaign complexity and time spent learning the tool. Treat an early small trial as directional; one easy campaign does not establish the saving for a difficult launch.
Use the log to choose the next change:
| What the log shows | Next action to test |
|---|---|
| Drafting is faster and total effort falls at acceptable quality | Keep that handoff and check whether the saving repeats |
| Editing repeatedly erases the draft saving | Classify the edits: missing facts, voice, structure or changed brief; repair the input or narrow the task |
| Build work dominates and copy is already quick | Test the actual bottleneck before adding another writing tool |
| Approval waiting dominates elapsed time | Examine the approval handoff separately from writing speed |
| The final output still needs a complete rewrite | Return that task to the usual process while retaining any smaller useful step |
Count reusable setup too. In another illustrative calculation, a brief and template that take 90 minutes to prepare need 90 ÷ 15 = 6 comparable emails to recover that effort if each saves 15 minutes. Include later maintenance; do not assume the saving continues unchanged.
Finally, distinguish reclaimed capacity from cash savings. Finishing sooner may free someone for another job without reducing payroll or software spending. Decide what the saved time will be used for before describing it as a cost reduction.
The useful outcome is a specific, repeatable handoff: this task, with these inputs and these checks, takes less total effort. Keep that result narrow enough to trust.