--- title: "Email marketing’s history can guide AI governance" url: https://adtech.lol/articles/99b87518-1821-4ba0-ba8a-19d32c7d39e7 source_url: https://martech.org/email-marketings-history-can-guide-ai-governance/ source_site: martech author: "Ryan Phelan" published: 2026-10-16T00:00:00+00:00 tags: ["Email marketing", "AI governance", "Marketing ethics"] publisher: AdTech Radar --- # Email marketing’s history can guide AI governance > Attribution, accuracy, and authenticity offer a starting point for responsible AI use — and for protecting your brand's credibility. ## Email marketing’s history can guide AI governance ### Attribution, accuracy, and authenticity offer a starting point for responsible AI use — and for protecting your brand's credibility. By [Ryan Phelan](https://martech.org/author/ryan-phelan/), Executive Leader, Email & Lifecycle Marketing Published on 2026-10-16T00:00:00Z • Last updated on 2026-10-16T00:00:00Z • 11 minutes read --- ### AI is moving faster than email AI is where email was at the start of the 21st century, when rapid development created the need to set limits, create rules for responsible behavior, and develop a set of generally accepted best practices. Email marketers learned the importance of establishing their own standards as governments and technology providers developed rules for the channel. As AI adoption accelerates, those lessons can help guide the establishment of ethical standards for its use. As I’ve explained in earlier MarTech columns, email marketers faced a familiar challenge: How to deal with new technology without a unified framework of government rules and regulations. High-level thinking about the ethics of this new technology can be overwhelming. We’re learning to use this technology and developing its rules at the same time. Much of the AI-related discussion in the news centers on who gets to decide what the rules should be, whether it’s the AI companies or — God forbid — the government. Email had this same problem in the pre-permission age, thanks to unfettered email address acquisition and firehoses of messaging, which nearly sank the channel until responsible people realized we needed to govern ourselves better. That was before Congress followed EU countries’ examples and passed the CAN-SPAM Act. At the same time, email practitioners and industry thought leaders worked across brands, tech providers, and industry interest groups to develop a code of ethics based on what we’d learned from email marketing and previous work in marketing and technology. It’s not written in a single codebook. But this generally accepted set of practices cascaded to companies that power email, such as ESPs and marketing technology providers. Now we have laws, ESP and martech acceptable-use policies, blocklist rules, and organization-level codes of conduct, along with a library of books, articles, training workshops, and conference presentations. They don’t agree on the details, but they map a path. ### AI is moving faster than email At no other time in history have we gone from idea to execution and final product so quickly. We thought we moved fast with email, but AI development is outpacing email at light speed. The email industry developed its code of conduct over two decades, and it’s still developing. ChatGPT launched in 2022, and its adoption took off immediately. Some forms of AI and machine learning existed for years, but they didn’t have the reach and budget-friendly cost. This leads to another parallel: how AI’s growth can be good and bad at the same time. **Fast AI growth is good.** I can get stuff done faster. I can come up with an idea, take the dog for a walk to think about the possibilities, then sit back at my computer, fire up my AI assistant, and boom! I have a plan. **Fast AI growth is bad.** AI and how we use it are evolving faster than our understanding of the impact. If we’ve learned anything over the last 100 years or so, it’s that making big changes without considering the consequences can create disaster. It’s not just about the burgeoning growth of data centers to power AI, although we need to think about that, too. It’s about our personal use of AI and the need to govern ourselves before someone makes those decisions for us. ### Let’s organize the discussion on ethics right here This is happening all over, but just as in email’s early days, conversations are fragmented, ad hoc, and dominated by special interests and radically opposing viewpoints, from “AI will save us!” to “AI will kill us all!” What are we doing now to be ethical actors? I’ve always maintained that we have to govern ourselves first. Now we have to take that from governance to ethics. Let’s do that now. #### The three As of ethical AI use ### 1. Attribution I could fill a library with all of my blog posts, white papers, industry articles like this one, company reports, keynote speeches, customer guides, webinar presentations, PowerPoints, and who knows what else that I’ve created over the last 20-plus years. So could many of my fellow email thought leaders. Along the way, two people coined phrases that — at least among the old guard — almost instantly became part of the lexicon: - Loren McDonald, an early email pioneer, mashed the concepts of mobile, social, and email messaging into a single concept, “mocial,” in 2011. In 2009, he also originated the phrase “Average is the new bottom,” meaning average benchmarks are no longer acceptable. - Kath Pay created and named “holistic email marketing,” which centers marketing strategy around customers and their needs, using email as the primary channel to engage and guide them through their brand journeys. These phrases became inextricably intertwined with their creators. If another marketer used them without acknowledging their creators, we recognized that. “Yeah, Loren talks about that.” Or “Kath came up with that concept. Stop stealing her stuff!” Now, go to Google and search “mocial.” Or “What is holistic email marketing?” The AI Overviews sum up the concepts accurately enough, but they don’t name the creators. You must click on the attribution links or scroll down through layers of links to find results that mention names. Let’s say I want to write about ESPs and headless commerce. I’ll ask my good buddy Claude some questions. It’ll do the research and bring me a perspective that answers my questions. But it doesn’t tell me who came up with the ideas it found. Some AI models, including Google Gemini, cite URLs so you can track down their sources. But are you going to do that if you want Claude to deliver a fully formed article that you can slap your name on? The problem is that AI’s results consolidate information from many sources. It might give you the source, but not the person who originated the thinking. If I publish that article without attributing my sources, AI could treat me as the authority on that topic. The next time someone writes about ESPs and headless commerce, their LLM will regurgitate my thoughts and perspectives without naming me. That writer will present my article as their idea. Now we’re even farther downstream in attribution. Aside from potentially perpetuating outdated or incorrect thinking, this raises the ethical problem of presenting someone else’s thoughts as your own — a form of plagiarism. ### 2. Accuracy Let’s dispel the illusions that AI is smarter than we are and that we should assume everything it tells us is true. We know AI makes up stuff, that it hallucinates or lies. Poorly worded or biased queries or prompts, which I’ve discussed in previous columns here on MarTech, often drive AI’s fairy tales. I’ve also seen this in action. I’m building an agentic model right now. I discovered that one of the model’s agents was making up things, such as inflating the number of people we interviewed for a project and then reporting that incorrect information in a client report. That leads me to create another rule: “Don’t put in anything that the facts don’t support.” ### 3. Authenticity I confess: I let AI write some of my emails. That’s because I suck at it. I hurry. I leave things out. I need to be sure my clients have the full picture and that I’ve considered everything that needs to go into that message. I’ll ask ChatGPT or Claude to clean up a message before I send it. Now, does the email reflect my words or Claude’s? Claude knows my voice and tone because I spent weeks training it on my personal and professional voices. It also knows our previous discussion. But the email reflects Claude’s work, influenced by me. I review every AI-assisted email before it goes out to be sure it says what I intended. Source: [https://martech.org/email-marketings-history-can-guide-ai-governance/](https://martech.org/email-marketings-history-can-guide-ai-governance/)