Creating consistent, useful content for a WordPress site is one of the biggest challenges for small businesses.
This Scenario makes use of the Make AI Agent (New) and tools to generate blog posts for wordpress sites and also publish to social media, in this case a Linkedin Page.
Most business owners know they should publish more, improve their website visibility, and stay active on LinkedIn. The problem is that writing, researching, choosing images, formatting posts, publishing to WordPress, and then creating social media content can take hours.
I built a Make scenario that turns this into an automated AI blogging workflow.
The goal is simple:
Generate a practical, high-quality WordPress article, select a relevant image, publish the article, and create a LinkedIn post — all from one AI-powered workflow.
The scenario is called GCA_Blogger_Linkedin (Upgrade) (Expanded) and it uses Make AI, WordPress, HTTP, LinkedIn, Make AI Web Search, Make AI Extractors, Data Stores and Gmail.
What does this automation do?
This Make scenario acts like an AI content production assistant.
It can:
- Research fresh AI-related topics using Make AI Web Search
- Generate a full WordPress blog article
- Create a standalone LinkedIn post
- Search Pexels for a relevant featured image
- Check whether that image was already used
- Download and upload the image to WordPress
- Add image title, alt text and description
- Publish the WordPress post
- Publish the LinkedIn post
- Review the final post and image quality
- Send email alerts when something passes or fails
The scenario is active and scheduled to run daily around 06:00.
Tools used
The main tools in the workflow are:
- Make AI Agent — plans and writes the blog and LinkedIn content
- Make AI Web Search — researches latest AI tools, features, releases and news
- Pexels API via HTTP — finds relevant images
- HTTP Download File — downloads the selected image
- Make Data Store — stores used Pexels image IDs to prevent duplicates
- WordPress — creates media items and publishes posts
- LinkedIn — publishes company image posts
- Make AI Extractors — describes and evaluates images
- Gmail — sends pass/failure notifications
Step 1: Research the topic
The automation starts by using Make AI Web Search to research current AI topics, tool updates, features, releases and practical business use cases.
This is important because the articles are not just generic AI content. The system is instructed to write for South African small business owners and operators, focusing on practical value instead of hype.
Expected result:
A researched topic that can be turned into a useful article for small businesses, with enough context to make the content relevant and current.
Step 2: Generate the WordPress article and LinkedIn post
The main AI agent is instructed to act as a:
“WordPress blogger, LinkedIn post writer, and visual content strategist for AI in South Africa and how AI impacts small businesses.”
It creates three things:
- A full WordPress blog post
- A native LinkedIn post
- A relevant image strategy for the article
The WordPress article follows a strict structure:
- Understanding the Technology
- Why This Matters for Small Businesses
- Real-World Business Applications
- Impact on Websites and Online Presence
- Things Businesses Should Consider
- Final Thoughts
The article is required to be 700–900 words, formatted in HTML, with a 55–70 character title and a 140–160 character excerpt.
The LinkedIn post is not just a copied summary. It must stand on its own, include practical examples, mention the full article on GreenCircuit, and include a conversational CTA.
Expected result:
A complete WordPress-ready article and a separate LinkedIn post designed for business readers.
Step 3: Find a relevant image
The scenario searches Pexels through an HTTP request.
The prompt specifically avoids weak or generic stock images. It tells the AI not to use generic AI, robot, circuit-board, handshake, or abstract “digital transformation” images unless there is no better option. Instead, it prefers images showing real business activity such as shops, restaurants, offices, workshops, online orders, tablets, customer service and practical operations.
Expected result:
A business-relevant image that visually supports the article instead of looking like generic AI stock photography.
Step 4: Prevent duplicate images
Before using a Pexels image, the workflow checks a Make Data Store to see whether that image ID has already been used.
If the image is new, the workflow records:
- Pexels image ID
- Usage status
- Search query used
- Image filename
This is stored in the Data Store so future runs can avoid reusing the same image.
Expected result:
The blog does not repeatedly use the same Pexels images across different articles.
Step 5: Upload the image to WordPress
Once the image is selected and downloaded, the scenario creates a WordPress media item. The media upload includes the file, title, alt text, caption and description.
This matters because image metadata helps with accessibility, content quality and SEO.
Expected result:
A properly uploaded WordPress media item with useful alt text and description.
Step 6: Publish the WordPress post
The WordPress module then creates the post with:
- Title
- Slug
- Status
- HTML content
- Excerpt
- Featured media
The scenario uses the WordPress createPost module to publish the article.
Expected result:
A fully published WordPress blog post with a featured image.
Step 7: Publish to LinkedIn
After the WordPress article is created, the workflow publishes a LinkedIn company image post.
This post is designed to be useful on LinkedIn even if the reader does not click through to the article. It includes a practical business insight, examples, takeaway, link to the article, CTA and hashtags.
Expected result:
A LinkedIn post that supports the WordPress article and drives traffic back to the site.
Step 8: Quality review and correction flow
The scenario also includes a review agent. This agent evaluates the published WordPress post, LinkedIn post and media. It can retrieve the draft/post, get the LinkedIn post, fetch media, describe the image, and check whether the output passes quality expectations.
If the result passes, an email notification is sent.
If it fails, another branch can improve the post, replace the image, update the WordPress post, create a corrected LinkedIn post, delete the old LinkedIn post, and send a failure/update notification.
Expected result:
The automation does not just publish blindly. It includes a quality-control loop.
Example output
One execution produced an article titled:
How March 2026 AI Updates Help South African Small Businesses
The workflow selected a Pexels image of people packing online orders, uploaded it to WordPress, used it as featured media, and created a LinkedIn post. The execution log shows the WordPress title, content, excerpt, image metadata, Pexels image ID and LinkedIn post data.
The same execution recorded token usage, which is useful for monitoring cost and optimisation.
Why this is useful
This kind of automation is useful because it combines content creation, publishing and distribution into one workflow.
For small businesses, agencies or niche content sites, this can help with:
- Publishing consistently
- Reducing manual content work
- Keeping website content fresh
- Improving topical relevance
- Creating LinkedIn visibility
- Avoiding repeated images
- Maintaining a practical editorial structure
- Adding a review layer before trusting the output
My next improvements
The next upgrades I would consider are:
- Add Google Search Console feedback into the topic selection
- Add internal links to older WordPress posts
- Add category and tag selection based on the article topic
- Add automatic schema markup
- Add a human approval stage for higher-risk content
- Add a cost-control layer for AI token usage
- Add a content calendar in Airtable or Google Sheets
- Add support for multiple WordPress sites
Final result
The final result is an AI-powered WordPress blogging system built in Make.
It researches a topic, writes the article, selects the image, checks for duplicate image use, uploads the media, publishes the WordPress post, creates a LinkedIn post, and then runs a quality review.
For anyone managing a WordPress site, this shows how Make can go beyond simple publishing automation and become a full AI-assisted content production workflow.
