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The AI tools for ecommerce and content creators that actually earn a permanent spot in our workflow are shockingly few, and this episode is my Profitable Audience co-host Toni Herrbach and I walking through exactly which ones survived a year of daily use across our businesses. Some of them cut hours out of our week. Others looked cool for a month and got kicked out.
I run an ecommerce store (Bumblebee Linens), a blog, a YouTube channel, and this podcast. Toni runs a design and content business plus her Happy Housewife brand.
We use AI daily and disagree on plenty. This is the real accounting.
Here is our tool-by-tool rundown, the workflows that stuck, and the traps we hit along the way.
Key takeaways
- ChatGPT is the workhorse. Best uses that stuck: writing code for one-off admin tasks, turning existing blog posts into YouTube scripts, generating 10 candidate titles or subject lines to mash into one, and finding spreadsheet formulas faster than Google.
- Paid ChatGPT ($20/month) is worth it for GPT-4 quality plus plugins. The web-browsing plugin and the Amazon-review-summary plugin are the two most useful in a content workflow.
- Midjourney and other AI image generators are hit-or-miss. Toni skips them and pays a designer. I stopped using Midjourney after outsourcing images.
- Adobe’s Podcast AI audio enhancer saves recordings shot outside, in echoey rooms, or on the wrong mic. Occasionally injects a garbled voice artifact on one channel; test before you rely on it.
- Opus Clip is great at pulling interesting clips from long-form video, but its clips underperform on YouTube Shorts because they lack a hook. Fix: record a fresh 5-second hook to prepend to each clip.
- Klaviyo’s subject line AI and Tailwind’s title AI both got benched. Too many iterations, not brand-nuanced enough. Just prompt ChatGPT instead.
- The Google Search Generative Experience cites its sources inside the AI answer, which is a preview of where organic ranking is going.
Which AI tools for ecommerce and content creators earned a permanent spot in our workflow?
The AI tools that actually earned a permanent spot in our ecommerce and content workflow are ChatGPT (paid plan), Adobe Podcast for audio cleanup, and Opus Clip for extracting shorts from long-form video, and even those need heavy hand-holding to be useful. Every other tool we tested got dropped inside a few weeks.
The pattern that came through the entire conversation: AI is a leverage tool, not an autopilot. You have to know the domain well enough to prompt it and to catch its mistakes.
Toni’s summary of her own experience: for anything factual (recipes, correct code, hard business facts), she still trusts human sources. For anything fluid where iteration is expected (subject lines, titles, script outlines), ChatGPT is faster than Google.
How I use ChatGPT to write code for one-off ecommerce admin tasks
I use ChatGPT to write small pieces of code for one-off admin problems on Bumblebee Linens, and it works because I already know how to read code, debug it, and phrase prompts precisely. If you cannot do those three things, the code output will bite you when it does not work and you have no way to diagnose why.
The morning we recorded this episode, Bumblebee Linens got hit with 100,000 hits from a single IP inside a minute and went down. My ChatGPT workflow for the fix:
- Prompt one: “What is the command line to figure out how many hits are coming from a specific IP address from these access logs?”
- Prompt two: “What is the command line to add someone to the firewall banlist on this OS?”
- Prompt three: assemble the pieces into a cron job that runs every five minutes and auto-bans abusive IPs.
Could I have asked ChatGPT to write the entire cron job in one prompt? Probably. But code has a thousand ways to do the same thing, and I want the specific way I know how to maintain.
That is the “know how to read code” prerequisite in action.
Where ChatGPT coding falls apart for non-coders
The failure mode is not that ChatGPT writes obvious garbage. It writes plausible-looking code that misses a corner case. When I point out the corner case, it responds “oh yes, you are correct, that case will not work” and rewrites the code, which is charming and also useless if you did not know to catch it in the first place.
For small, contained tasks (add a class to a Shopify theme, sort products by stock status, animate a hover state), a non-coder can copy the output and get lucky. When it does not work, you have to know how to debug. Otherwise you are stuck.
The safe non-coder use case is the one Toni mentioned: paste working example code you found on a tutorial site into ChatGPT and ask it to adapt it to your situation. Starting from something that already works is a lot lower risk than generating from scratch.
How to turn a blog post into a YouTube script with ChatGPT
Turning a blog post into a YouTube script with ChatGPT is Toni’s favorite use case because quality is high (the source content is yours), speed is dramatic (5 minutes vs. 30-45), and iterations are minimal. She feeds in the existing post, prompts for a script format, and then asks to extend the word count if needed.
Her example: a 1,000-word blog post extended to a 1,750-word YouTube script in a couple of prompts. The extra 750 words are more filler than her original, but the script hits YouTube’s length target and reads in her voice because the source content is hers.
My workflow is the reverse. My blog posts are typically 5,000 words, so I have to trim aggressively before ChatGPT even sees it. I edit down to the through-line first, then feed the shorter version and ask for a script.
The paid ChatGPT plugins worth using
The two ChatGPT plugins that earn their keep in a content workflow are the web-browsing plugin and the Amazon review-summary plugin.
Web browsing lets you paste a URL and ask ChatGPT to summarize the page, which skips the copy-paste step from the browser. The Amazon review plugin takes an ASIN and returns the top complaints from the reviews, which is gold if you write product reviews or run an Amazon influencer channel and need to make sure your video answers real buyer concerns.
The paid ChatGPT tier is $20 a month and unlocks GPT-4 plus the plugin library. If you use ChatGPT for content or code more than once or twice a week, it pays for itself.
What we tried, dropped, and would not recommend
Not every AI tool survived our workflow test, and the ones we dropped are worth naming so you do not waste weeks on them. Here are the tools we tried, kept using for a while, and eventually benched.
Klaviyo subject line AI and Tailwind title AI: benched
Klaviyo’s AI subject line generator does not respect brand nuance. My ecommerce brand has a specific voice, and Klaviyo’s output reads generic. It is easier to prompt ChatGPT with a few examples of my voice and get a shortlist that fits.
Same story for Tailwind’s title/description AI. Too many iterations to get something usable. If the underlying engine for these tools is the same (and it likely is), you are better off prompting the general-purpose model directly with your context loaded in.
Midjourney: fun to play with, hard to use in production
Midjourney can generate lifestyle backgrounds for product photos and stylized editorial images, but getting it to output exactly what you want takes 16 iterations. For anyone who values their time above $10 an hour, paying a designer or using stock photography is faster.
Toni pays a designer for blog images. I stopped using Midjourney after outsourcing to a VA. The exception is product-specific lifestyle backgrounds (our student Dale tried this on his spray bottles), and even there, the results were inconsistent enough that most sellers give up.
Real image AI generally has a giveaway “AI look.” Hands are still the tell. Full-bodied humans are still off. Backgrounds and objects are closer to production-ready than people.
Adobe Podcast AI: the audio-cleanup tool worth the risk
Adobe Podcast (specifically the Enhance Speech tool) is the audio-cleanup AI I use for three types of recordings the tool can save: bad-mic recordings, echoey rooms, and outdoor interviews. When it works, the difference is night and day.
Cases where it saved me:
- An episode I accidentally recorded on my laptop webcam mic instead of my podcast mic. Adobe Podcast fixed the audio to broadcast-usable quality.
- A guest who took the interview outside on a beach with boats and planes going by. Cleaned the ambient noise substantially.
- A guest who moved to a phone-booth-sized “quiet room” with hard walls and heavy echo. Removed most of the echo.
The demonic-voice glitch to watch out for
Toni has hit a repeatable failure mode where Adobe Podcast injects a roughly 17-second garbled voice artifact into the middle of a processed track. It has happened to her three times, and she has stopped using the tool.
My hypothesis: it seems to happen when you feed the tool a track with multiple people talking on the same channel, versus a single isolated voice per track. If you always record each speaker to a separate track, the risk goes down significantly.
The workflow rule: process each speaker’s track separately, listen to the entire enhanced track before publishing, and keep the raw unprocessed track as a backup so you can bail if the tool corrupts a segment.
Why Opus Clip shorts underperform on YouTube (and the fix)
Opus Clip is genuinely good at identifying interesting clips from a long-form video, which is the hard part, but the shorts it produces underperform on YouTube because they lack a hook. I ran the tool for six to eight weeks on my long-form YouTube content and most of the shorts got fewer than 1,000 views.
The reason is structural. Opus can pull a compelling clip, but a YouTube Short lives or dies on the first 2 seconds. Without a hook engineered specifically for shorts distribution, even a great clip scrolls right past.
My new workflow: use Opus to identify the clip candidates (the hard part), then batch-record a 5-second hook for each and stitch them together. Extra work, but the hooks are the difference between 1K views and 50K.
The upstream fix: hook every section of your long-form video
A YouTube friend told me the real fix is upstream. If every section of your long-form video has a mini-hook at the start, Opus’s extracted clips will already open with a hook.
For a “5 ways to X” video, that means five hooks: one for each section. The video gets more retention on YouTube itself because sections stay compelling, and shorts extracted from it work out of the box.
I have not fully executed on this yet, but it is the direction I am moving. Plan your long-form video to be shorts-friendly from the outset.
The AI tools worth a mention but not central to our workflow
A few tools we discussed briefly, without heavy production use yet.
- Canva AI (Magic): Toni is excited to test it because everyone she teaches already uses Canva; the AI features are the natural extension. Not yet in her workflow.
- Descript: the eye-correction feature simulates you looking at the camera even when you are reading a script off to the side. Interesting for anyone who hates teleprompter rigs.
- Google Pixel photo-face-swap: the camera phone can now combine faces from multiple group photos into one shot where everyone is smiling. A preview of consumer-grade face editing.
- ManyChat + ChatGPT: ManyChat by itself is scripted, not AI. Wire ManyChat to send an external request to OpenAI and it becomes a real conversational bot. Low on my priority list, but the plumbing is there.
- Google’s Search Generative Experience: Google’s AI answer with source citations, now on top of search results. This is the preview of what SEO looks like next.
The one thing I refuse to feed AI
The one thing I refuse to feed AI models is a full backup of my life’s content, and it is the reason I paused “SteveBot,” a chatbot idea I was building on top of my blog, podcast, and YouTube archives. To make it work well I would have to send every transcript, every post, and every video I have ever made to Microsoft or OpenAI.
That is not a technical objection. Everything you feed a public model becomes training data (or is treated as such by many practical measures). There is no “I trained my own bot on my content.” You trained their bot on your content.
For anyone with proprietary IP, first-hand research, or a body of work that competitors would love to have, this is a real decision. It is not paranoia to think twice about what you upload.
Frequently asked questions
What are the best AI tools for ecommerce sellers in 2024?
The AI tools most ecommerce sellers get durable value from are ChatGPT (paid tier, for prompts, code snippets, and content), Adobe Podcast for audio cleanup, and Opus Clip for extracting shorts from long-form video. Klaviyo’s subject line AI, Tailwind’s title AI, and Midjourney for product imagery are worth trying but tend to get dropped from production workflows because iteration cost is too high.
Is ChatGPT Plus worth the $20 per month for creators?
Yes, if you use ChatGPT more than a couple of times a week. GPT-4 produces noticeably better output than the free tier, and the plugin library (web browsing, Amazon review summary, and others) unlocks real workflow shortcuts. If ChatGPT is your first stop for titles, subject lines, or code snippets, the paid plan pays for itself in saved time.
Can you use ChatGPT to write code if you are not a programmer?
Only for small, contained tasks where you can safely test the output and back out if it breaks. ChatGPT writes plausible-looking code that often misses corner cases, and diagnosing why it does not work requires reading and debugging skills. A safer path for non-programmers is to paste working example code you found on a tutorial and ask ChatGPT to adapt it to your specific situation.
How do I use Opus Clip to actually get views on YouTube Shorts?
Opus Clip finds interesting clips reliably but its shorts underperform because they lack a hook. The fix is to prepend a fresh 5-second hook to each extracted clip before publishing. The upstream fix is to write every section of your long-form video with a built-in hook so Opus’s clips already open compellingly.
Which AI image generator is best for product photography?
None of the current image AI tools reliably produce production-ready product photography. Midjourney can generate lifestyle backgrounds you composite behind your product, but getting the exact output takes many iterations. Most sellers get better ROI by hiring a designer, buying stock, or shooting product photos themselves than by fighting an AI image generator.
Is Adobe Podcast Enhance Speech safe to use on published episodes?
It is usable but not fully safe. Adobe Podcast can inject garbled voice artifacts into a processed track, especially when multiple speakers are on the same channel. The safest workflow is to process each speaker’s isolated track separately, listen to the entire enhanced track before publishing, and keep the raw unprocessed audio as a backup.
Should I be worried about feeding my content into ChatGPT?
If your content is public and low-sensitivity (existing blog posts, YouTube transcripts, standard business copy), the exposure is low. If your content is proprietary research, unpublished IP, customer data, or a body of work competitors would like access to, treat every prompt as a potential training-data contribution. There is no way to build a genuinely private AI on top of a public model’s API.
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