Category: AI for Marketing
-
The AI Image Generator Boom: Why the Market Is Poised to Hit $1.09B by 2032
A look at the explosive AI image generation market and what it means for marketers, creators, healthcare professionals, and educators.
-
Google Just Took Image AI from “Cool” to “Whoa” with Gemini 2.5 Flash Image
Google’s new image generator, Gemini 2.5 Flash Image has some serious advantages over other image creators. Here’s what you should know.
-
IBM Just Dropped a Multimodal Model And Document AI Will Never Be the Same
IBM just dropped Granite-Docling-258M, an open-source multimodal model designed specifically for end-to-end document conversion.
-
You Can Finally Share ChatGPT Projects (and Yes, It’s as Good as It Sounds)
A look at ChatGPT’s new Projects feature, which allows you to share projects with colleagues. These tips will change the way you work.
-
SEO Is Dead. Long Live AI Search: What CMOs Need to Know Before 2026
ChatGPT drives 87% of AI traffic and 82% of AI-driven sales. AI is replacing the usual Google search. Here’s what you need to know.
-
Google’s Gemini Is Moving Into Chrome
Google is bringing Gemini into Chrome as part of how the browser works. Here is a look at the good, the bad and the privacy concerns.
-
Streamline Marketing Strategies: ChatGPT’s Branching Explained
ChatGPT’s new “Branch in New Chat” features gives marcom leaders extra superpowers. Here is what you need to know.
-
AI Isn’t Replacing Content Strategists, It’s Elevating Them
How AI elevates your content strategy and content creation, automation and personalization, in lieu of OpenAI’s $300k content strategist role.
-
AI Influencers Are Rising, But Can They Replace Human Authenticity?
A look at the rise of AI-generated influences, the audiencers they attract and the impact this has on marketing communications.
-
PR’s AI Revolution: From Hesitation to Full Adoption
The PR industry is finally adopting generative AI, especially as earned media is a key driver in GEO and brand visibility in AI models.
-
Maximizing AI Performance: Fine-Tuning and RAG Explained
Why you need RAG and fine-tuning. Both rely on additional data to improve model performance, but they use that data differently.