WordPress’s AI Landscape
While investigating the AI technology in WordPress, I discovered that there is a clear difference between the approach followed by WordPress itself and the one used by the entire ecosystem. Instead of incorporating an artificial intelligence solution into the core platform, WordPress creates open infrastructure, which enables developers to use different instruments.
Shipped Capabilities
- Block Editor (Gutenberg): A highly adaptable visual editor that allows for structured and reusable content building.
- Full Site Editing: The ability to edit the entire site and customize templates without any need for coding.
- Improvements in Performance & Editor: Constantly evolving improvements in usability, performance, and workflow management.
Emerging AI Ecosystem
- ClassifAI: Provides automatic generation of alt texts and categorization of images and other content to save time and improve accessibility.
- AI Writing Assistants: Helps draft, rewrite, summarize, and translate content for marketing departments to manage bigger editorial calendars without any loss of human supervision.
- AI Categorization and Tagging: Facilitates organization and searches of large content repositories.
Most important is that WordPress adheres to open standards and allows control by the users themselves. WordPress offers the framework but enables innovation for the community, providing freedom rather than tying the creator to one particular AI solution.
What I Learned from AI Leaders
Three ideas from the AI Leaders Program have shaped how I approach analytics and technology.
Responsible AI
Outputs from AI must be interpreted in a business environment and not blindly trusted. When I analyzed the Google Trends data, I realized that high volumes in searches might indicate popularity, media coverage, or even controversy. This consideration would give more meaningful insights for marketing.
Human-in-the-Loop Decision Making
In my IBM Cognos churn analysis project, it was seen that while analytics tools reveal certain patterns, they do not make decisions about the business process. Once I found out there was more customer churn within certain customer segments, I made a decision based on analyzing the business environment and other alternatives.
Iteration and Continuous Learning
The development of AI technology is happening at such a rapid pace that experimentation and improvement are crucial. Instead of pursuing perfection in each endeavor, I see every project as an avenue for testing my assumptions and improving my methodology.
My AI Adoption Philosophy
I would describe my approach towards AI as being both curious and methodical.
Rather than jumping at any new release, I always try to use tools that solve some problem for me. Whenever I want to implement some AI tool in my work, I need to answer myself three questions:
- Does it solve any meaningful problem?
- Am I able to verify its output?
- Does it make my decisions better or help me learn something?
To stay up to date, I have my discovery system based on LinkedIn industry discussions, There’s an AI for That, and Product Hunt. I look through all the available tools, see how they fit to my active projects, test them on some small scale and estimate their usefulness.
It can be seen from my attitude to AI, that the main thing I learned from AI Leaders is that responsible AI involves more than just implementation of any new technologies quickly. It means being curious and thinking critically while working with AI.
In my future work as a marketing analytics expert, I hope to show that the most valuable insights come from the right combination of data analysis, interpretation and decision making.
William Dvorak | Marketing Analytics & AI Portfolio 