Issue #258 All About Email

Hi, {{first_name|friend}}. 👋

Welcome to Issue #258 of All About Email!

Last week, we discussed the new Apple Mail app, which shipped with iOS 27. The change I focused on is how Mail helps people find an email after it’s disappeared down their inbox.

This week, we are following up on Issue #256, in which I examined the finding that advanced AI adopters are 54% more likely to follow WCAG and 52% more likely to comply with the European Accessibility Act.

I hadn’t read the full report then, and I said I’d revisit my questions once I had.

I’m doing exactly that this week. There’s useful extra context, but does it change how I’d interpret those figures?

Let’s go! 👇

💡 Now that I’ve read the report, I’ve got the methodology:

🙋‍♂️ Having not seen the full report, I had three questions in Issue #256.

Let’s look at those now, remembering that data is important, but so is data context; we shouldn’t just accept figures presented to us.

The report says 28% selected advanced AI adoption, indicating that AI was deeply integrated into their email marketing workflows and decision-making processes.

That gives us an approximate idea of the group’s size. However, we still don’t have the exact respondent counts behind the accessibility comparison.

We can see the AI adoption question and its answer options. There’s also a question about how companies approach accessibility requirements.

🚨 The accessibility chart on page 19 appears to have a labelling error: it repeats the hiring-skill categories from page 11, with different percentages.

The surrounding text explicitly reports that 26% of organisations follow WCAG and 21% follow regional mandates such as the EAA. But the chart labels don’t match those findings, so we can’t reliably reconstruct the accessibility answer options from this page.

The methodology doesn’t describe an independent assessment of the emails sent by these teams.

💡 So the distinction from Issue #256 still matters: reporting that you follow accessibility standards isn’t the same as demonstrating that your finished emails meet them.

That doesn’t mean respondents are wrong. It means we need to be careful about what their answers establish.

The report states that 26% of organisations follow WCAG standards, while 21% follow regional mandates such as the EAA.

Those are useful figures to have alongside the headline comparison. But there’s a detail missing.

💡 When the report says advanced adopters are “54% more likely” to follow WCAG, that passage doesn’t explicitly identify who they’re being compared with:

We shouldn’t assume the overall rate is the starting point and calculate an advanced-adopter rate from it.

🚨 And these remain figures about reported practices. They don’t tell us how many finished emails passed an accessibility assessment, or which problems subscribers encountered.

This was one of the most useful qualifications in the report.

When discussing regional differences in AI adoption, the authors acknowledge that these might reflect genuine sophistication, earlier investment, or differences in how teams interpret “advanced”.

💡 That matters when we’re using the label to explain better results.

Think about two teams:

Both might describe their adoption as advanced, but their workflows could be quite different.

Those are my examples, rather than a breakdown the report provides.

🤔 They illustrate why I’d still want to know which tasks improved, how the results were checked and what changed for subscribers.

Knowing that a team uses lots of AI doesn’t answer those questions.

The report also says advanced AI adopters are 75% more likely to achieve email ROI above 45:1.

It discusses better targeting, personalised content and faster learning, then connects the group’s production-speed advantage with stronger financial returns.

🤔 Those explanations might be right. But the analysis presented doesn’t separate AI’s contribution from other differences between the teams:

🚨 We can’t tell from these findings how much each contributed. That also applies to my alternative explanation in Issue #256. I suggested that stronger processes might help explain the accessibility gap.

The full report leaves that possibility open. But it doesn’t prove it.

A little, in terms of context. It doesn’t fundamentally change my advice.

We know more about the survey, how respondents classified their AI adoption and the overall accessibility figures.

🚨 We still don’t have evidence showing that introducing AI caused the reported accessibility difference.

For your own programme, I’d start with one specific task you want AI to improve:

💡 You can then make a more useful decision as to whether that particular AI step earns its place in your workflow.

✅ Keep the consistent checks and comparisons we discussed in Issue #256, and look for new problems and improvements.

Big thanks to Anand Subramanian for sharing the link to Litmus’s full report, as I couldn’t get it myself after submitting the form twice and tagging Litmus on socials. 🫠

I said I’d revisit the findings once I had the full report, including whether it changed my interpretation. It has added useful detail. It has also helped me be more specific about what’s still missing:

🤷‍♂️ Whether AI will improve your emails’ accessibility will depend on both the quality of the AI and how you use it.

But you don’t have to wait for another report to investigate what’s happening in your own programme.

🤔 So, I’ll leave you with this:

Hit reply, {{first_name|friend}}. I’d love to hear what you found.

That’s it for this week. 👋

Simon

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