By kbodnar@hubspot.com (Kipp Bodnar and Kieran Flanagan)

For marketers, AI-generated imagery tools open up new possibilities for content personalization and campaign engagement…but only when applied in strategic, targeted ways.

AI-generated imagery platforms like Flux, Runway, and DALL-E are becoming a valuable part of marketing, design, and UX toolkits. But with all the options available, it’s sometimes difficult to understand where and how these tools actually bring value — and where they fall short.

In a recent Marketing Against the Grain episode, Kieran and I discuss common challenges teams face when using AI image 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 holds massive potential — but its adoption isn’t without hurdles. In particular, three challenges preventing widespread use that Kieran and I often see are:

1. Hesitation due to privacy and security concerns.

Employees hesitate to engage with AI tools, often due to concerns around privacy, data security, and AI’s current limitations. Many also feel that AI might not yet be “good enough” for their needs.

Addressing these concerns starts with clear communication about AI’s strengths and limitations. 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 falls short, especially for tasks requiring precision. As Kieran pointed out, employees sometimes treat AI as a “magic box,” which can set them up for disappointment.

By managing expectations and educating teams on how AI works best, marketers can shift focus toward the achievable, practical uses that make an impact.

3. A need for guardrails.

With such broad potential, many people struggle to find the right applications for AI. As Kieran highlighted during the show, a defined, structured approach — like having clear AI prompts or targeted use cases — helps ease adoption by giving employees a clearer sense of purpose.

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

Three AI-Generated Image Use Cases in Marketing

Despite these challenges, AI image tools can have a strong impact when applied to targeted use cases. In our experience, AI-generated imagery tools can be used to:

1. Boost ad performance.

In my opinion, one of the most effective applications of AI-generated imagery is creating tailored ad variations. Custom images that closely align with specific ad copy help marketers deliver a more personalized experience across different platforms.

In our tests at HubSpot, we’ve seen that this approach significantly boosts conversions, making it an invaluable tool for efficiently 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 …read more

Source:: HubSpot Blog