3 Missteps with AI Image Generation and How You Should Be Using Them – InstantFollowerz

For marketers, AI-generated image tools open up new opportunities for content personalization and campaign engagement…but only when applied in a strategic, targeted way.

AI generated image platforms such as Flux, Runwayand DALL-E they become a valuable part of marketing, design and UX tools. But with all the options available, sometimes it’s hard to understand where and how these tools actually deliver value – and where they fail.

In a recent Marketing against the grain episodesKieran and I discuss common challenges teams face when using AI imaging tools, practical use cases for maximum performance, and best practices for integrating AI imaging into your workflow.

Three key challenges of adopting an image generated by artificial intelligence

AI image generation holds massive potential — but its adoption is not without obstacles. Specifically, three challenges that Kieran and I often see preventing widespread use are:

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 benefits and limitations of AI. When teams understand where AI can add value — and where it can’t — they’re more likely to engage with it in a realistic way.

2. Misaligned expectations.

Users often expect AI to “do it all,” leading to frustration when it fails, especially for tasks that require precision. As Kieran pointed out, employees sometimes treat AI as a “magic box,” which can lead to disappointment.

By managing expectations and educating teams on how AI works bestmarketers can shift their focus to achievable, practical uses that make an impact.

3. The need for protective fences.

With such a wide potential, many people struggle to find the right applications for AI. As Kieran pointed out during the show, a defined, structured approach – like clear AI queries or targeted use cases — helps facilitate adoption by giving employees a clearer sense of purpose.

Similarly, using guided instructions or simplified interfaces for specific tasks can make it easier for teams to explore AI without feeling overwhelmed.

Three cases of using images generated by artificial intelligence in marketing

Despite these challenges, AI tools for images can have a powerful impact when applied to targeted use cases. In our experience, AI generated imaging tools can be used to:

1. Increase your ad performance.

In my opinion, one of the most effective applications of AI-generated images is to create custom ad variations. Custom images closely aligned with specific ad copy help marketers deliver a more personalized experience across platforms.

In our tests at HubSpot, we’ve seen this approach significantly increase conversions, making it an invaluable tool for effectively scaling our ad 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 visuals create a curated and relevant experience for readers, adding a layer of personalization that keeps content fresh and increases the chances of connecting with your audience in a deeper, more memorable way.

This approach works especially well when you need to create different visuals for different segments or campaigns on a large scale.

3. Save time on editing.

AI is equally valuable for image editing, helping marketers quickly adjust visuals to suit different audience needs.

For example, a tech company can use AI to modify product screenshots by adding a client’s logo or highlighting certain features.

This tactic allows brands to deliver a more personalized visual experience without the time and effort required for manual changes, making it a powerful option for scalable, audience-specific content.

Best practices for implementing imaging AI

Maximizing the value of AI-generated images means knowing where and how to use them. These tips will keep your approach practical and results-focused.

✔ Define clear use cases. Because AI can be overwhelming, define specific applications (such as customer support or ad variations) where it is most likely to succeed, rather than trying to apply it universally.

✔ Focus on volume instead of perfection. AI excels at creating multiple variations instead of single “perfect” images. If you need a flawless image, stick to traditional methods.

✔ Educate teams about the benefits and limitations of AI. To improve adoption, set clear expectations and provide guidance on where AI is most useful, which can help address resistance due to privacy and reliability concerns.

✔ Make it authentic. Avoid using AI-generated images to represent real people or customers, as this can undermine trust. Save AI images for conceptual or product-focused visuals.

To learn more about how marketing leaders can integrate AI-generated imagery into their teams and workflows, see full episodes of Marketing against the grain.

This blog series is in partnership with Marketing Against the Grain, a video podcast. He digs deeper into the insights shared by marketing leaders Kipp Bodnar (HubSpot’s CMO) and Kieran Flanagan (SVP, Marketing at HubSpot) as they uncover growth strategies and learn from notable founders and peers.