This week’s Ask an SEO question about affiliate strategies comes from Mike R:
“How is AI changing affiliate marketing strategy in 2025? I’m concerned my current approach will become obsolete but don’t know which new techniques are actually worth adopting.”
Great question, Mike. I’m seeing a few trends and strategies that are changing, for the better and for the worse.
When AI is used properly in the affiliate marketing channel, it can help businesses and brands grow.
If any of the three types of businesses (defined below) in affiliate marketing use it in a way that AI and large language models are not ready for “yet,” it can backfire.
I’m answering this question in three parts, as I’m unsure which side of the industry you’re on.
For the record: The affiliate channel is not at risk (i.e., affiliate marketing is not dead) because affiliate marketing is more than a content website that creates a list or writes a review, and coupon sites intercept the end of sale.
Affiliate marketing is a mix of all marketing channels, including email, SMS, online and offline communities, PPC, media buying, and even print media.
It is not going to be as impacted by AI as SEO and content marketing – and in many ways, it will likely grow and scale from it.
1. Affiliates (Content Creators, Publishers, Media Houses, Etc.)
Affiliates are the party that promotes another brand in hopes of earning a commission.
Here’s some of what I’m seeing regarding the use of AI and its impact on affiliate revenue.
Programmatic SEO And Content Creation
Programmatic SEO is not new, and using LLMs to create content or lists is burning what were quality sites to the ground.
It is almost never a good idea; it doesn’t matter if AI can spin up content and get it publish-ready in minutes.
In the early 2000s, affiliates and SEO professionals would use pre-AI article spinners to create massive quantities of content from one or two professionally written and fact-checked articles, then publish them to blogs and third-party publishing platforms like Squidoo.
This is equivalent to affiliates publishing their content on Reddit or LinkedIn Pulse to rank it.
The algorithms caught up and penalized the affiliate websites. Squidoo and some of the third-party platforms managed to stay afloat as they had trust and a strong user base for a while.
Next, PHP became the go-to for programmatic SEO, and affiliates would generate shopping lists or pages with unique mixes of products and descriptions via merchant data feeds and network-provided tools. Then, these got penalized. Again, nothing new.
Media companies have been getting penalized and devalued for years for this, and plenty of content creators, too.
If an affiliate manager is telling you to use LLMs to create content, or someone is using LLMs and AI to do programmatic SEO, look for advice elsewhere.
I’ve watched multiple quality sites fall since ChatGPT, Perplexity, and others began writing and spinning their content.
Content And Creator Value
In traditional affiliate marketing, if an affiliate is not making sales, even if they send quality traffic, they get ignored. LLMs have changed this 100%.
I’ve seen affiliates, including bloggers, YouTubers, forums, and social media influencers, are being sourced and cited by AI systems.
If the brand is not on the content being used for fact-checking (grounding) and sourcing, the brands begin to disappear from outputs and results. I’m seeing this firsthand.
Not getting traffic or sales, or being number seven to 10 on a list, now has value. The citations and mentions from the resources that LLMs trust can help your brand gain visibility in AI.
Affiliates can and should begin charging extra fees for these placements until the LLMs begin penalizing or ignoring pay-to-play content.
We’re likely a couple of years away from their algorithms being anywhere near that advanced, so it is a prime opportunity while Google is reducing traffic to publishers via AI Overviews.
Coupon Sites For Top And End-Of-Sale Touchpoints
I think coupon sites are going to take a substantial hit, as AI is starting to create its own lists of coupons that work.
It also includes where and how to save, where to shop, and current deals on specific products. For example, “I want to buy a pair of Asics Kayano 32 men’s running shoes and get them on sale. Where can I find a deal?”
Right now, Google’s AI Overviews are populating lists of where to find deals, and it is showing the coupon sites as the sources to the right. These sites are likely getting clicks now.
I’ve seen ChatGPT pull the codes directly and preventing the need to click to the coupon website and set their affiliate tracking. It does show the website it came from, though – just no reason to click since you get the code in the output.
One interesting thing is that ChatGPT may pull in vanity codes.
The output from ChatGPT featuring these could give an influencer who was sourced for the code or a coupon site credit for their sales, throwing attribution off, because it was the coupon that triggered the commission, even though the user was using the LLM.
The influencer did not have anything to do with this transaction, but they’ll be getting credit.
The brand may now pay more money to the influencer, when, in reality, it should be ChatGPT – that is where the customers are, not the influencer.
By showing where to find the deals and which deals are available by product (not brand), AI eliminates one of the deal and coupon site’s top-funnel traffic strategies to brands.
The biggest hit I see coupon sites taking is ranking in search engines for “brand + coupon” for the last-second click from someone who is already in the brand’s shopping cart.
If Google AI Overviews creates its own coupon lists as the output, like ChatGPT is doing, there is no reason to click on a coupon website and click their affiliate links.
But, don’t count deal and coupon sites out. They still have email lists and social media accounts that can drive top-funnel traffic, and they can reintroduce customers who have forgotten about you by utilizing their own internal databases of shoppers.
2. Affiliate Manager And Affiliate Management Agencies
These are the people who manage programs by recruiting affiliates into the program, giving the affiliates the tools they need, and ensuring the data on the network is tracked and accurate so the brands being promoted have the sales and touchpoints they’re looking for.
Content Sites That Lost Traffic
Some managers hit the panic button because they relied on content sites and publishers who have SEO rankings, but AI Overviews is using affiliate and publisher content and not sending the same amount of traffic to the publishers.
This reduces the number of clicks and traffic. The publishers are still driving traffic, but it is coming in via Google and not the affiliate channel.
With that said, affiliate managers can shift their focus to channels not as impacted by AI Overviews, including:
- Discord.
- Platforms like Skool.
- Social media groups.
- YouTube channels.
- Influencers.
Fraud Sign Ups
From speaking to others, it appears that high-quality publisher accounts are being created en masse as fronts for fraud and fake affiliate accounts.
I’ve had conversations with people hired by the fake affiliate account who are being paid to talk to the affiliate manager, so it makes these sites look even more legit. We’ll have back-and-forth emails, and in some cases, a call.
Once the traffic and sales start, it turns out to be stolen credit cards or program violations. In some instances, the person or websites they applied with no longer exist.
Interestingly, when they activate a year later, thinking you forgot about them, magically, the site reappears when they know you’re not checking.
Always evaluate a site, and if the content is being generated by LLMs or AI, it may be best to reject it and reduce the risk of a fake account.
AI content may rank temporarily, but this is not a long-term strategy. If your brand is being written about by AI and spun out to a site via programmatic SEO, there is a reasonable chance that the details won’t be as factual or as on-brand as they should be.
An affiliate who cannot take the time to create good content and use AI to edit, versus using AI to create and then edit, should not be trusted in your affiliate program.
Non-Factual Information And False Claims
When your affiliates are generating content or fact-checking via LLMs and AI, they’re not doing their jobs as your partners to promote your program factually, with correct talking points, and following brand guidelines.
There’s a reasonable chance that incorrect claims about financial products, medical treatments, or even books to buy and read will be in the content you, as a brand, are paying to have made.
Even if you’re paying on a performance basis, you are approving this content to be live and represent your brand. This is why affiliates in your program using AI to create content are at high risk.
Set rules and enforce them so that your brand cannot be included in any AI-created content, or remove the affiliate from your program until they’re ready to treat your brand or your clients’ brands with the same care as you do.
Partner Matching And Approvals
One interesting use of AI for affiliate management is merchant and affiliate matching using machine learning and AI by agencies and larger brands.
Just because a partner does well in one vertical or with one affiliate program that has a similar audience, it does not mean it is a good match for others.
- One program may allow end-of-sale touchpoints while the other does not. The top partners that use low-value clicks should not be allowed in a similar program that does not (or will not) match it. If the programs are on auto-approve or using AI to approve affiliates that do well in specific verticals, the TOS is likely no longer being enforced.
- A partner may make a ton of T-shirt sales in one program, but their audience may not respond to the colors, social causes, or price points of another merchant. If the affiliate is part of AI matching and starts to lose money because they got matched to new T-shirt shops, they may start to move on from the affiliate or focus less because they’re making less money and getting bad recommendations from the agencies and managers.
- If the program trusts AI to do matching, but has restrictions like requiring advertising disclosures or using factual information, the machine learning likely won’t be able to check for this, and partners that are not a fit can get in.
- Automating approvals because they pass an AI review or scan is risky, as AI will miss things that an experienced affiliate manager will find, like advertising disclosures in the wrong space and false claims in the industry or space in content.
One exception to using AI for matching is to build a list of potential partners from a database. But automatically approving that list because the output creates a list is problematic.
Each affiliate that is recommended still needs to be vetted by hand to make sure they meet the requirements of the new program.
Recruitment And List Building
Some of the best uses of AI, especially LLMs, have been building lists of potential partners.
You can train GPTs to validate the lists, remove current partners so you don’t accidentally email or call them, do a gap analysis, and even customize the recruitment email to a very strong degree.
No, it isn’t perfect, but you can save hours each week from the manual tasks of discovery, validation, and outreach.
The recruitment emails still need to be reviewed and sent manually, but it is a massive time-saver.
We manually review every email before it goes out and have to do a decent chunk of rewriting, but we’re saving large amounts of time, too.
We also pre-schedule the emails using a database tool, but we’ve slowly begun implementing new discovery and drafting methods, and they’re turning out to be fantastic.
I was a non-believer in AI for this at first, but now I’m about ready to double down, especially as the systems advance.
3. Affiliate Networks
These are the tracking and payment platforms that power the affiliate programs.
Affiliates rely on them to accurately record sales and release payments.
Affiliate managers use them to track progress, simplify paying partners around the world, and generate reports based on the key performance indicators (KPIs) their company uses.
Better Controls
All of the networks we’re working on have an influx of AI-generated sites. I’ve talked to agencies and managers on the ones we don’t work on, and they’re seeing the same.
The networks would be wise to add filters and create an alert for affiliate managers to let them know if the affiliate is human or AI, meaning that AI would be a website and promotional method without quality control.
There are no advanced controls in place on any networks that I’ve seen specifically for AI affiliates. But most networks do have compliance teams to which you can report fake accounts.
From the networks I’ve talked to, they’re working on solutions to help detect and reject these sites, but it is a massive problem because they’re being generated at high volumes, and some are really hard to detect.
The spammers and scammers are getting smarter, and AI has given them a new advantage.
Partnership Matching
This is a double-edged sword. Networks have more data than any affiliate agency, and they may be best suited to try partner and program matching algorithms.
They can create a list of programs that an affiliate may want to test, or a list of partners a program manager can pay to recruit based on program goals and dimensions.
The downside is that programs spend countless hours recruiting partners for their programs. Networks doing matching and recruitment take that work and give it for free to that program’s competitors.
A second downside is that affiliates get bombarded with program requests, and this can cause that to skyrocket, making it harder to get them to open emails, including program updates and newsletters.
Once they start ignoring emails because of too many, you may not get compliance issues fixed or promotions that would normally have benefited both parties.
Reporting
One of the most beneficial things a network can do, but none are currently doing on a mass scale (some are starting to, and it’s looking promising), is to use AI to create custom reports for affiliate programs. These could be charts and graphs on trends over XYZ years.
Another is a gap analysis of products that get bundled together by type of affiliate, and then which similar affiliates already in the program don’t have a specific SKU in their orders.
The manager can recommend pre-selling the SKU within the content that drives the sale, or adding that specific SKU as an upsell to any customer who came from that affiliate’s link, based on the affiliate ID passed in the URL.
It can show trends where there are cross-channel (SEO, email, PPC, SMS, etc) touchpoints and how it modifies seasonally, annually, and if the goal creates more or less sales for the affiliate channel or company as a whole.
One important thing to remember is that not all affiliate networks offer true cross-channel reporting. Multiple only offer it once the user has clicked an affiliate link.
Final Thoughts
AI is going to be amazing and horrible for each of the three entities above that make up the affiliate marketing channel.
If used correctly, it can save time, increase efficiency, and create more meaningful strategies.
At the same time, it could result in violations of a program’s Terms of Service (TOS), steal traffic from publishers, and harm multiple types of businesses.
More Resources:
- Part 1: How To Launch, Manage, & Grow An Affiliate Program Step-By-Step
- 7-Point Checklist To Find the Best Affiliate Programs
- SEO In The Age Of AI
Featured Image: Paulo Bobita/Search Engine Journal
