AI in Graphic Design Process - Evaluation

In my quest to harness AI for design evaluation, I ventured into using two notable tools: ABtesting and LogoMark. Here's a closer look at the intricacies of my experiences:

ABTesting.AI

ABtesting.ai is engineered to optimize websites using AI, aiming to enhance conversion rates through automated A/B testing. However, my initial interactions were less than smooth. The seemingly straightforward process of submitting a website for analysis turned into a challenge, with a persistent loading indicator that never seemed to resolve, regardless of which website I tried or how many attempts I made. Beyond this, another challenge loomed. To fully harness the platform's capabilities, users had to embed a custom code snippet into their Webflow projects. Unfortunately, given the limitations of the free Webflow version I was using, this would be an insurmountable obstacle. Additionally the signup page for the whole website was unavailable for several days I was trying to test this tool. Apart from this I wasn't able to find any other A/B testing tools. 

 


LogoMark

Turning to LogoMark by Brandmark.io, the promise was an AI-driven evaluation of logos, assessing parameters like uniqueness, clarity, and overall visual appeal. Eager to see the AI's verdict, I input my typography logo. To my surprise, the platform returned no feedback as it struggled with recognizing the text, leaving me without any scores or insights. Not one to give up easily, I decided to input only the planet imagery component of my design. This time, LogoMark responded with scores close to 90 across various categories. However, these numbers came without context or a transparent breakdown, making me question their true utility.

 



Bing
On another occasion, I revisited Bing Image Recognition once again, hoping it might shed light on the value of my final designs for the brand. While Bing did recognize and commend specific elements of the designs, it often missed certain subtleties. Interestingly, it subsequently offered recommendations to enhance the designs, based on the very details it initially overlooked.

 






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