AI in Email Marketing: Use Cases, Challenges & Opportunities

A mature and proven marketing channel, email remains at the heart of customer retention for ecommerce brands. But with inboxes more crowded than ever, the challenge is for retailers to create personalised, relevant and timely communications that truly resonate.

This is where AI is proving transformative. By combining automation with predictive insight, email marketing platforms enable teams to deliver smarter, more human-like experiences at scale. Instead of manually segmenting audiences or crafting endless content variations, marketers can now leverage AI to analyse behaviour, predict intent and create dynamic content that adapts to each recipient in real time.

In this article, we explore:

• how leading email marketing platforms are integrating AI;
• the most popular email AI use cases for ecommerce brands;
• the challenges to watch out for when leveraging AI in email marketing; and
• tips for successfully harnessing AI to enhance your email marketing strategy.

Email marketing AI: How are leading platforms using it?

AI is rapidly becoming a defining feature of modern email marketing platforms. Leading providers are accelerating their investments in machine learning and generative technology, embedding AI across the entire campaign lifecycle: from segmentation and planning, to content creation, optimisation and analysis.

For instance, Klaviyo recently launched its new AI Agent, K:AI, positioning it as an autonomous assistant for ecommerce teams. It uses data to create on-brand campaigns and flows, resolve customer issues and automatically optimise performance – no prompts required.

Just some of Klaviyo’s other built-in AI tools include:

• Image Remix – Allows users to create and update images with plain language prompts inside the Klaviyo email editor.

• Channel Affinity – This predicts the best channel for communicating with each customer based on real behaviour, routing messages where appropriate without any manual segmentation.

• Personalised Send Time – Empowers brands to deliver messages at the optimal time for each individual subscriber.

• Segments AI – Uses customer data to inform comprehensive segments.

Mailchimp has similarly expanded its AI capabilities. Its tools now generate on-brand copy, subject lines and product recommendations, whilst also suggesting A/B test variations based on historical engagement data. The result is faster campaign creation and smarter decision-making for smaller marketing teams that need to do more with less.

HubSpot has embedded AI assistants directly within its CRM workflows, helping users to draft emails, summarise customer interactions and even resolve service queries. This creates a more connected, conversational experience across marketing, sales and support.

At the enterprise level, Salesforce’s Einstein AI powers features such as predictive lead scoring, next-best-action recommendations and AI-driven journey orchestration. This allows marketers to automate highly complex customer journeys based on real-time intent signals.

Meanwhile, platforms like Dotdigital, ActiveCampaign and others are using AI to enhance personalisation and timing through predictive analytics, dynamic content blocks and send-time optimisation. These capabilities are helping marketers balance automation with creativity, ensuring that messages feel relevant without feeling robotic.

It’s important to note that with the rapid proliferation of “AI-powered” features across the email marketing tech landscape, there’s also a growing risk of ‘AI washing’ – where platforms overstate the sophistication or autonomy of their tools. Not every feature labelled as “AI” necessarily represents true machine learning or generative capability; some are simply rule-based automations rebranded for marketing appeal.

We’d always recommend approaching AI claims with a critical eye, considering whether a platform and its tools will help you to meaningfully improve business outcomes.

Use cases for AI in email marketing

AI has multiple use cases across email marketing. For ecommerce brands, its value lies in personalisation, prediction and performance optimisation.

Here are some of the most valuable and widely adopted applications of AI within email marketing today.

1. Predictive analytics & insights

By learning from historic purchase data, browsing behaviour and engagement signals, predictive models can forecast when a customer is likely to buy again, which products they’ll consider, and if they’re at risk of churning from a subscription.

This allows brands to proactively target individuals with relevant offers, timely replenishment reminders, early access to launches, or personalised incentives based on their predicted needs.

As well as predicting buying behaviour, AI can also automatically flag ‘at-risk’ email subscribers who are showing early signs of email disengagement, enabling brands to intervene before deliverability is affected. These users can be targeted with re-engagement campaigns or automatically suppressed to maintain a healthy sender reputation.

For example, Klaviyo’s Marketing Analytics uses AI to automatically detect patterns and shifts in customer behaviour and campaign performance. It groups customers based on variables such as purchase frequency, recency of site visits and how quickly they engage with emails. If something unusual occurs – like a sudden drop in open rates or a spike in unsubscribes – the system can surface this insight automatically, without anyone needing to dig through the data manually. This enables ecommerce teams to quickly identify what’s working, what’s not, and make informed adjustments to timing, content or audience targeting to improve performance.

Klaviyo can also recommend appropriate actions, such as when to send a win-back email, how to reward loyal customers, or which high-value segment to prioritise during peak trading.

By bridging the gap between data interpretation and decision-making, AI-driven predictive analytics empower marketers to act on insights faster and more confidently – closing the loop between performance, learning, and optimisation. For ecommerce retailers, this kind of predictive intelligence can improve conversion rates and customer lifetime value.

2. Smarter segmentation

Traditional segmentation is often limited to static data like demographics or order history. AI segmentation, by contrast, can process thousands of multi-dimensional behavioural and intent data points to group customers more intelligently – creating nuanced micro-segments that humans wouldn’t necessarily spot.

These segments help brands communicate in a more tailored, relevant way. For example, AI might identify a subset of shoppers who are most likely to respond to discounts during sales periods, or customers who consistently buy from premium collections without discount prompts. Each segment can then be nurtured with appropriate messaging, from loyalty incentives to value-driven product recommendations.

Uncovering hidden audience patterns like this can lead to an increase in email engagement, conversion rates and repeat purchase rate.

3. Content generation & dynamic adjustments

Generative AI is streamlining the creative process behind email production. Tools can now assist with writing subject lines, preview text, body copy and alt text, helping marketers produce polished content faster. The key is to treat this AI-generated content as a starting point, refining outputs to ensure alignment with your brand’s distinctive tone and messaging.

AI can also dynamically adjust email content according to recipient data such as engagement level, location, purchase history or website activity. For example, a fashion retailer could send the same campaign template to every subscriber, but use AI to vary the featured products, sizes and visuals depending on what each customer has previously viewed or purchased. The result is a 1:1 shopping experience at scale – without the manual workload for marketing teams.

4. Send time & frequency optimisation

Timing can be the difference between an opened email and one lost in the inbox. AI tools can analyse engagement patterns to determine the optimal send time for each recipient, rather than relying on a single blanket schedule.

Similarly, AI can assess how often a subscriber interacts with a brand’s communications, then fine-tune frequency to maximise engagement without overwhelming the user. Over time, this kind of adaptive optimisation helps sustain deliverability and ensures that messages arrive when customers are most receptive – not when the brand happens to hit “send”.

5. A/B testing at scale

Testing is a cornerstone of good email strategy, but manual A/B testing can be slow and limited in scope. AI can automate and scale this process, simultaneously testing dozens of variations of subject lines, imagery, layouts or calls-to-action.

It then analyses performance in real time, identifies statistically significant winners, and automatically deploys the best-performing versions to the rest of the audience. This continuous optimisation loop means brands can improve performance at speed – without waiting for manual analysis or gut-based decision-making.

Over time, AI-driven testing builds a rich data set about what resonates with specific customer groups, informing future campaigns and creative direction.

A practical example of this in action comes from Swanky’s work with a long-term client where we used Klaviyo’s AI Optimisation Test, an automated A/B testing tool that uses machine learning to find the highest-converting settings for sign-up forms, email send times, and campaign content, without manual effort. In this specific case, we were trying to identify the most effective timing for displaying email list sign-up forms to site visitors.

Rather than relying on static delay settings, the AI continuously tested a range of timing options against the existing form setup, learning which moments drove the highest engagement. This automated approach not only reduced the need for manual experimentation but also helped maximise subscriber acquisition by ensuring forms appeared precisely when users were most likely to convert.

Read more about our work and portfolio as an official Klaviyo Agency Partner.

Challenges of using email marketing AI

Despite the advantages, adopting AI in email marketing is not without its challenges. To maximise its value, brands must navigate both technical and ethical considerations.

1. Maintaining brand voice & personality

Over-use of AI-generated copy can lead to a flattening of brand individuality, causing many brands to use the same homogenised tone. This strips away the human nuance that makes communication feel genuine, and that customers connect with.

This human tone is central to trust and loyalty. Subtle cues such as humour, empathy or cultural references are difficult for AI to replicate convincingly, which makes human editing crucial in maintaining a brand’s distinctive voice across email.

Where once subject lines and preview text were prime real estate for creative expression, some brands are now finding that their carefully crafted messages are being overridden by inbox providers’ AI-generated versions. This makes it even more challenging to maintain brand voice, with many marketers feeling like they are losing control of the way their brands are being represented in inboxes.

2. Data quality

AI is only as effective as the data it’s trained on. Incomplete, inconsistent or siloed customer data can undermine the accuracy of predictive models and the quality of personalisation. For example, if product, CRM and behavioural data aren’t properly synchronised, an AI system may send irrelevant product recommendations or misjudge a customer’s purchase intent.

Before implementing AI-driven campaigns, brands should ensure robust data hygiene and a well-integrated tech stack where data flows seamlessly between tools. Platforms like Shopify and Klaviyo are making this process easier through deeper native integrations, but human oversight remains essential. Marketers should regularly audit data flow and accuracy to ensure the foundation of their AI strategy is sound.

3. Privacy & compliance

As AI personalisation becomes more sophisticated, so too do the data privacy implications. Many AI tools rely on analysing customer interactions, purchase behaviour and engagement patterns – activities that must comply with strict data protection regulations like GDPR and CCPA.

Customers expect brands to understand their preferences, but they also expect transparency about how their data is used. Retailers must therefore ensure they have clear consent frameworks in place, limit the use of personally identifiable data in model training, and communicate openly about data handling practices.

A brand that mishandles customer data not only risks legal repercussions but also damages the trust that underpins long-term retention. Building a transparent, privacy-first culture around AI adoption is critical for sustainable success.

4. Accuracy

While generative AI has improved rapidly, it remains imperfect. From subtle grammatical errors to factual inaccuracies, AI-generated content can introduce small mistakes that, if unchecked, undermine professionalism and brand credibility.

In regulated industries such as health, finance or cosmetics, this risk is amplified. An AI-generated claim about product benefits, for example, could cross compliance boundaries or mislead consumers. Even in less sensitive sectors, inaccuracies can weaken brand authority over time.

To mitigate this, AI content should always be treated as a first draft, not a finished product. Human review processes should remain in place for all customer-facing copy, ensuring factual accuracy, tone consistency and legal compliance.

Advice for leveraging AI in your email marketing strategy

As AI capabilities accelerate, the goal isn’t to resist the technology, but to implement it thoughtfully, with strong oversight and a clear strategic framework. With the use cases and challenges we’ve discussed in this article in mind, here are some key tips for successfully harnessing AI in email marketing:

• Start with a strong data foundation. Ensure your customer data is clean, consistent and centralised. Prioritise integrating your ecommerce, CRM and marketing systems before layering AI on top.

• Maintain human oversight, always. Think of AI as an assistant, not the author. The brands seeing the best results today are those using AI as a collaborative tool: combining machine speed with human quality control to deliver efficient yet trustworthy communication.

• Implement guardrails. These are safeguards that will ensure AI systems operate safely, responsibly and within defined boundaries – preventing harmful, biased or inaccurate results.

• Be transparent. Disclose your use of AI to build trust where appropriate. Adhere to consent/privacy laws and always respect opt-outs.

• Define success metrics. AI should serve measurable business objectives. Track engagement, conversion and revenue metrics to quantify the value of AI-led optimisation.

• Test, learn and iterate. Start with small, controlled experiments. A/B test AI-powered subject lines, predictive segments or send-times in specific campaigns, then analyse performance before scaling up.

Looking for email marketing specialists?

Whether you’re new to email marketing AI and wondering how it can fit into your email marketing strategy, or you want to elevate your existing use of email AI, we can help you deliver exceptional results.

Please get in touch to speak with our team of email marketing specialists.

And if you’re looking for further email marketing support, you might find these articles helpful:

• Best Practices For Your Klaviyo Email Design
• Mastering Klaviyo Flows: 12 Email Automations That Every Ecommerce Brand Needs
• How to Create a Clean Email List & Why it Matters For Ecommerce Brands