For marketers, AI-generated imagery tools open up new possibilities for content personalization and campaign engagement … but only when applied in a strategic and targeted way.
AI-generated imagery platforms like Flux, Runway, and DALL-E are becoming an essential part of marketing, design, and UX toolkits. But with all the options available, sometimes it’s hard to know where and how These tools actually bring value – and where they fall short.
In new Marketing Against the Grain episode, Kieran and I discuss common challenges teams face when using AI imaging tools, practical use cases for maximum performance, and best practices for integrating AI imagery into your workflow.
Three Key Challenges of AI-Generated Image Adoption
AI image generation continues massive potential – but adoption is not without obstacles. In particular, the three challenges that prevent widespread use that Kieran and I often see 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.
Solving the problem starts with clear communication about the strengths 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 real ways.
2. Misaligned expectations.
Users often expect AI to “do everything,” which leads to frustration when it fails, especially for tasks that require precision. As Kieran points out, employees sometimes think of AI as a “magic box,” which can lead to disappointment.
According to manage expectations and educate the team on how AI works bestmarketers can shift their focus to actionable practical uses that make an impact.
3. A need for guardrails.
With such a wide potential, many people struggle to find suitable applications for AI. As Kieran highlighted during the show, the structured approach he established – like having clear AI directions or targeted use cases – helps ease adoption by giving employees clearer goals.
In addition, use guided prompts or simplified interface for certain tasks can make it easier for the team to explore the AI ​​without feeling overwhelmed.
Three Use Cases of AI Generated Images in Marketing
Despite these challenges, AI imaging tools can have a powerful impact when applied to targeted use cases. In our experience, AI-generated image tools can be used to:
1. Improve ad performance.
In my opinion, one of the most effective applications of AI-generated imagery is creating customized ad variations. Custom images that align with specific ad copy help marketers provide a more personalized experience across multiple platforms.
In our tests at HubSpot, we’ve seen that this approach can significantly improve conversions, making it an invaluable tool for efficiently expanding advertising campaigns.
2. Add 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 opportunity to connect with your audience in a deeper and better way.
This approach works well when you need to create different visuals for different segments or campaigns at scale.
3. Save time for editing.
AI is also invaluable for image editing, helping marketers quickly adjust visuals to suit the needs of different audiences.
For example, a technology company can use AI to modify product images by adding a client’s logo or emphasizing certain features.
This tactic allows brands to deliver a more personalized visual experience without the time and effort required for manual editing, making it a powerful option for scalable and audience-specific content.
Best Practices for AI Image Implementation
Maximizing the value of AI-generated images means knowing where and how to use them. These tips will make your approach practical and results-focused.
âś” Define a clear use case. Because AI can be so versatile, determine which specific applications (such as customer support or advertising variations) are most likely to succeed, rather than trying to apply it universally.
✔ Focus on volume rather than perfection. AI excels at creating multiple variations rather than one “perfect” image. If you need a flawless image, stick with the traditional method.
âś” Educate the team on the strengths and limitations of AI. To increase adoption, set clear expectations and provide guidance on where AI is most useful, which can help overcome resistance due to privacy and reliability concerns.
âś” Keep it original. Avoid using AI-generated images to represent real people or customers, as this can damage trust. Save AI imagery for conceptual visuals or product focus.
To learn more about how marketing leaders can integrate AI-generated imagery into their teams and workflows, check out the full episode Marketing Against the Grain.
This blog series is in collaboration with Marketing Against the Grain, a video podcast. Dig deeper into the ideas shared by marketing leaders Kipp Bodnar (CMO HubSpot) and Kieran Flanagan (SVP, Marketing at HubSpot) as they unpack their growth strategies and learn from their famous founders and peers.
