For marketers, AI-generated image tools open up new opportunities for content personalization and campaign engagement… but only when used in a strategic and targeted way.
AI-generated image platforms like Flow, runwayAnd DALL E are becoming a valuable part of marketing, design and UX toolkits. But with all the options available, it’s sometimes hard to understand Where And How These tools actually provide added value – and where they fall short.
In a current one Marketing against the grain ConsequenceKieran and I discuss common challenges teams face when using AI imagery tools, practical use cases for maximum performance, and best practices for integrating AI imagery into your workflow.
Three key challenges when introducing AI-generated images
AI image generation applies solid Potential – but its introduction is not without hurdles. Kieran and I see three challenges in particular that prevent widespread use:
1. Hesitation due to privacy and security concerns.
Employees are hesitant to engage with AI tools, often due to concerns about privacy, data security, and the current limitations of AI. Many also feel that AI may not yet be “good enough” for their needs.
Addressing these concerns begins with Clear communication about the strengths and limitations of AI. When teams understand where AI can provide value – and where it cannot – they are more likely to engage with it in a realistic way.
2. False expectations.
Users often expect AI to “do everything,” which leads to frustration when it falls short, especially for tasks that require precision. As Kieran pointed out, employees sometimes treat AI like a “magic box,” which can lead to disappointment.
From Manage expectations and train teams on how AI works bestmarketers can focus on the achievable, practical applications that make an impact.
3. Need for guard rails.
Given this broad potential, many people find it difficult to find the right applications for AI. As Kieran highlighted during the show, a defined, structured approach – like clear AI prompts or targeted use cases – helps ease adoption by giving employees a clearer sense of purpose.
Likewise the use guided prompts or simplified interfaces For certain tasks, it may be easier for teams to explore AI without feeling overwhelmed.
Three use cases for AI-generated images in marketing
Despite these challenges, AI imaging tools can have a big impact when applied to targeted use cases. In our experience, AI-generated imaging tools can be used to:
1. Increase ad performance.
In my opinion, creating tailored ad variations is one of the most effective uses of AI-generated images. Custom images that precisely match specific ad copy help marketers deliver a more personalized experience across different platforms.
In our testing at HubSpot, we found this approach significantly increases conversions, making it an invaluable tool for efficiently scaling our advertising campaigns.
2. Increase email engagement.
AI can also increase engagement in email marketing by generating unique images tailored to each message.
Combined with AI-generated text, these visual elements create a curated and relevant experience for readers, adding a layer of personalization that keeps content fresh and increases the chance of building a deeper and more memorable connection with the audience.
This approach works particularly well when you need to create different visuals for different segments or campaigns at scale.
3. Save time editing.
AI is also valuable for image editing, helping marketers quickly adapt images to different audience needs.
For example, a technology company could use AI to modify product screenshots by adding a customer’s logo or highlighting certain features.
This tactic allows brands to deliver a more personalized visual experience without the time and effort of manual edits, making it a powerful option for scalable, audience-specific content.
Best practices for implementing AI images
To maximize the value of AI-generated images, you need to know where and how to use them. These pointers will ensure that your approach remains practical and results-oriented.
✔ Define clear use cases. Because AI can be overwhelming, define specific applications (e.g. customer support or ad variations) where it is most likely to be successful rather than trying to apply it universally.
✔ Focus on volume instead of perfection. AI excels at creating multiple variations rather than single “perfect” images. If you need a flawless image, stick to traditional methods.
✔ Educate teams about the strengths and limitations of AI. To improve adoption, set clear expectations and provide guidance on where AI will provide the greatest benefit. This can help address resistance due to privacy and reliability concerns.
✔ Stay authentic. Avoid using AI-generated images to represent real people or customers as this could damage trust. Save AI images for conceptual or product-oriented visual representations.
To learn more about how marketing leaders can integrate AI-generated images into their teams and workflows, check out complete episode from Marketing against the grain.
This blog series is a partnership with Marketing Against the Grain, the video podcast. It delves deeper into the ideas of marketing leaders Kipp Bodnar (CMO of HubSpot) and Kieran Flanagan (SVP, Marketing at HubSpot) as they craft growth strategies and learn from outstanding founders and peers.