With a deep background in analytics and performance optimization, Milena Traikovich is an expert at helping businesses build effective campaigns that nurture high-quality leads. As a veteran in the demand generation space, she has a unique perspective on the operational shifts required to successfully integrate new technologies. Today, she shares her insights on what it truly takes to make AI a powerful, and safe, ally in email marketing. We’ll explore why treating AI as core infrastructure is more important than seeing it as just a content generator, discuss the critical guardrails needed for compliance, and dive into the practicalities of prompt engineering and performance measurement.
Many marketers view AI as a content creation tool. How does shifting this mindset to treating AI as core marketing infrastructure, focusing on data governance and quality, change the setup and long-term success of email campaigns? Please walk us through the first practical steps.
That shift in mindset is everything. When you see AI as just a content-spinner, you miss the entire foundation that makes it work. Treating it as infrastructure forces you to confront the real, unglamorous work that precedes any meaningful results. The first practical step isn’t writing a prompt; it’s a data audit. You have to consolidate your records and ensure all engagement history can be mined from a single source, usually your CRM. Without this, the AI is flying blind. It can’t distinguish an early-stage lead from a hot prospect, so it can’t generate relevant content. The next step is defining your deal stages with absolute clarity within that system. This groundwork feels slow, but it’s what allows the AI to understand the sales funnel and actually support progression, turning it from a novelty into a strategic asset.
AI systems can generate and send emails with incredible speed, which can create compliance risks. What specific consent verification processes and operational guardrails should teams establish before deploying AI-powered workflows to avoid issues with unsolicited mail? Can you share a few examples?
The speed of AI is both a blessing and a curse. It can scale your efforts instantly, but it can also multiply your compliance mistakes at the same rate. Before a single AI-generated email goes out, the first guardrail is a thorough review of your existing opt-in policies and consent records. You need to know, without a doubt, that you have permission to contact these people. Operationally, a great example is building a two-stage quality assurance process. The first stage is a content check for clarity and accuracy. But the second, and arguably more important, stage is a dedicated compliance check. This means having a human verify that the data used for personalization aligns with the consent given by that specific recipient and adheres to local regulations, which can vary wildly. Another simple but effective guardrail is to create fallback behaviors in your prompts for when consent levels are unknown or denied, ensuring you always err on the side of caution.
When choosing an AI, marketers often weigh integrated CRM-native assistants against third-party models. What are the key decision-making factors in this choice, and what are the primary trade-offs a team should consider regarding vendor lock-in, integration resources, and testing capabilities?
This is a classic “ease vs. flexibility” trade-off. A CRM-native AI is often the path of least resistance. It can immediately reference all your contact data, deal information, and campaign history without any complex integration. For a smaller organization without a dedicated IT specialist, this can be the deciding factor. The significant downside, however, is vendor lock-in. You’re tethered to that platform’s capabilities, which might not be best-in-class. A third-party model, on the other hand, gives you incredible freedom. You can choose a model that’s more specialized for your industry or just generally more capable, and you can A/B test different language models against each other. The trade-off is the technical overhead. Connecting it to your data requires resources and expertise, which not every team has. The decision really hinges on your team’s technical resources and your long-term desire for flexibility and optimization.
An “assisted content curation” approach is often recommended over full automation. Could you describe a practical, two-stage quality assurance workflow that balances AI efficiency with essential human oversight? What common AI-generated errors, such as invented statistics or inconsistent tone, should this process catch?
Absolutely. Full automation is a recipe for disaster. “Assisted content curation” is the only sustainable path. A practical workflow starts with building modular content libraries—pre-approved introductions, product descriptions, and calls to action that the AI can assemble. This gives it a strong, on-brand foundation. Then comes the two-stage QA process. Stage one is the marketer’s review: Does this email make sense? Is the message clear, accurate, and on-brand? This is where you catch embarrassing errors like an inconsistent tone of voice, anodyne messaging that sounds robotic, or exaggerated claims. The second stage is a compliance and data-use review, which is critical. This person checks for things like invented statistics—which AIs are notorious for—and ensures any personalization doesn’t cross privacy lines. This is especially crucial for campaigns that mention pricing or operate in regulated industries where oversight isn’t just good practice, it’s a business necessity.
Effective prompting is crucial for quality AI output. Beyond just a topic, what specific details—like recipient lifecycle stage, CRM field names, or desired calls to action—should a marketer include in a prompt to generate truly relevant and distinct copy for different campaign goals?
Prompting is truly an art form, and generic prompts yield generic results. To get something truly useful, you have to be incredibly specific and translate your marketing goals into the AI’s language. A great prompt goes far beyond “write a nurture email.” It should specify the recipient’s lifecycle stage, like “welcome email for a new MQL.” It should also include specific CRM context, even dictating raw field names to pull from. For example, you might instruct it to reference a prospect’s recent engagement with pricing information to craft a sales acceleration message. Most importantly, every prompt must have a crystal-clear desired call-to-action. Is the goal to encourage a first action, build understanding with a case study, or reinforce value for a renewal? Each of these goals requires a completely different prompt, guiding the AI to produce content that is sharp and effective, not just broadly engaging.
AI doesn’t eliminate the need for performance measurement. When comparing AI-generated content against human-written alternatives, what key metrics should marketers track to determine if the technology is genuinely improving outcomes versus simply reducing production time? Please give some specific examples.
This is so important. Efficiency is great, but it’s meaningless without effectiveness. AI doesn’t get a pass on performance, so you have to track its impact rigorously. The classic A/B test is your best friend here. Run AI-generated content against your human-written control and watch the key metrics. Don’t just look at open and click-through rates. You need to track deeper, more meaningful outcomes. For example, if you’re using a CRM-native AI, you can directly link specific content variants to business results. Are the leads nurtured by AI-generated emails converting to sales-qualified leads at a higher rate? Is the content accelerating movement through the sales pipeline? These are the metrics that show whether AI is truly improving outcomes. If all you’re seeing is that it’s faster to produce emails with the same or worse performance, then you’re not getting a real return on your investment; you’re just speeding up mediocrity.
What is your forecast for AI in email marketing?
My forecast is that the conversation will shift dramatically from the novelty of AI-generated text to the operational discipline required to make it successful. The tools will become more sophisticated and better integrated, but the winners won’t be the ones with the flashiest model. The winners will be the marketing teams that treat AI adoption as a fundamental operational change. They will be the ones who invest heavily in data quality, build robust review and compliance processes, and master the art of performance measurement. Success won’t be determined by the sophistication of the AI itself, but by the strength of the human-led strategy that governs it. It requires careful planning, rigorous control, and constant evaluation, just like any other significant change in business.
