Say Hello to Your New Robot Overlord!
SEO involves three main components: technical elements, content, and link-building. I believe that AI, like ChatGPT, can accelerate SEO content and strategies into the future. From creativity to productivity, we break down the endless possibilities of content creation with ChatGPT, and leave you with some thoughts about how we expect this fledgling tech sector to evolve.
You’re Not David Ogilvy, and That’s Okay
In our field, creating valuable content on a consistent basis is crucial for search engine optimization (SEO) success. But let’s be real, not everyone is a great writer. You may excel at generating high-level concepts and ideas and explaining them verbally, but struggle putting pen to paper. Rather than wordsmithing sentences over and over, let ChatGPT give your prose the kickstart it needs so you can stay focused on the big-picture message at hand. As the technology progresses, I’m eager to see how users can train it to be more personalized to our thoughts and sound authentic and not robotic.
*Full disclosure: For fun, we asked ChatGPT to “write a fun headline about ChatGPT and SEO” for this article.
Human Capital for the Big Idea, Bots for Everything Else
Let’s get one thing straight – I’m not looking to AIs like ChatGPT to replace the human experts in their respective fields but rather to enhance our efficiency. And let me tell you, this is where the real magic happens with ChatGPT. Forget about those long meetings to summarize key points for copywriters to craft, ChatGPT can generate customized copy in a matter of seconds. All you need is a little practice to prompt it properly, and bam! You’re making a quantum leap forward in your productivity. And that means more time for the experts to focus on communicating and executing on the high-level ideas they’re known for. Going back to the SEO example, ChatGPT isn’t just making the process of articulating ideas more efficient, it’s optimizing the sum of its parts. For example, a think tank may have academics who produce 100-page white papers on public policy topics. However, the website may have a poor synopsis of the paper that has been published in an academic journal, hindering its SEO performance. If I could use an AI algorithm to summarize the paper into 500 words, the professor would not have to spend hours condensing a 100-page paper. This would save time and allow for more efficient use of resources.
Swipe Right for Your Data Pattern Match
Chat AIs can also learn from data patterns and incorporate information at a faster rate than human analysts. Period. One exciting application of this coming into focus with ChatGPT involves multivariate testing or A/B testing for landing pages. This involves producing ad copy directing users to a landing page and tweaking the landing page to maximize conversions by testing different headlines. We used to have someone manually create different variations, but what if AI could generate those headlines and then use the data from the testing to continually improve them? This would allow the AI to get smarter and smarter with each test and find the most effective language to use. The potential for such technology is huge, as the human brain can never match the pattern recognition ability of an algorithm. This is an area that has not been explored much yet, but the potential is immense.
The Race to Integrate Chatbot AI: Not All Are Created Equal
In conclusion, it is important to call out the distinction between machine learning and AI to better understand where Chat AI is headed more broadly as an industry. AI cannot know something unless it’s taught or trained. Once it has a baseline of knowledge, it can start making connections that it wasn’t trained for before. For instance, you can train the AI to look at click-through rates by finding data on the Internet and feeding it into the system’s understanding. So, how will Google, which has vast data on click-through rates, respond? Meanwhile, the creators of these systems might not reveal how their AI was trained for fear that their competitors will use the same training mechanisms. These knowledge sets will become more specialized as different competitor AIs become available. Perhaps Google will offer an AI more focused on targeting advertising, while another company’s AI will be known for creating original art, analyzing a person’s email and purchase history, or even automatically identifying year-end tax deductions. The winner in each area will depend on the knowledge sets being used. I imagine AI algorithms will eventually be similar to people, with some better at certain things than others. And who knows? Maybe one day you’ll be able to send AI off to college to get trained in a new discipline.