Smart moves: How AI is reshaping the affiliate playbook

Since ChatGPT launched three years ago, AI has become one of the most talked-about buzzwords. We’ve seen a tidal wave of AI-powered apps enter the mainstream – from virtual girlfriends to graphic design tools – flooding the consumer space. While public opinion remains mixed, it appears to be thawing at last. A recent YouGov survey found that over half of the UK’s 18–24-year-olds now use AI weekly. In July, the British government signed a deal with OpenAI to roll out its products across justice, defence and education.

In the iGaming space, Google’s launch of AI Overviews has sparked concern among SEO experts, reigniting debate over the future of organic traffic. But as perceptions shift and Gen Z becomes the dominant consumption force, what does AI mean for affiliate product pipelines? We speak to three innovators about how AI is being smartly deployed in their businesses and what it might mean for the sector’s future.

AI x product: comparison

In the wider price-comparison space, incorporating AI tools to enhance user experience has become increasingly popular. UK-based consumer finance site Comparethemarket, for instance, uses AI to ensure compliance and generate customer diaries. Travel metasearch engine Kayak has implemented AI features for comparing agency service quality, recognising itinerary screenshots and streamlining personalised deal-hunting.

Inspired by platforms like Kayak and MoneySavingExpert, Rob Lawrence, founder of AI venture studio Good Engine, recently launched Good Choice, an affiliate-focused search engine for comparing iGaming bonuses. This initiative was driven by the notoriously opaque and complex nature of gambling rewards.

“[IGaming] offers are designed to look attractive at the headline level but hide key limitations in the small print,” Lawrence explains. "Our mission is to build products and services that deal with ‘dark patterns’ trapping users and help media owners regain trust lost due to low transparency.” Players rarely have time to dissect pages of T&Cs and pinpoint friction points such as “wager 40x before withdrawal” or restrictions like “UK-only players.”

Good Choice aims to “rethink the affiliate model” by prioritising transparency and providing clear, personalised information before conversion. It structures messy operator data to surface genuinely fair offers tailored to users’ preferences like fast payouts, best odds and minimal wagering. Central to its operation is a proprietary pipeline involving fine-tuned language models trained on real offer terms, rule-based logic to catch missing information and human oversight for compliance.

While still in early stages, initial adoption is promising, with users spending more time engaging and interacting more deeply with featured offers. Lawrence is expanding a partner ecosystem, inviting affiliates and media owners to incorporate the widget through a staged rollout.

AI x product: prediction

On the prediction front, AI-powered tools are rapidly gaining popularity among affiliates seeking bettors hungry for data-driven insights. Companies like Game Lounge, with its recent Betlookr launch, and Oddschecker are already heavily investing in predictive betting tools for their loyal users. 

Richard Acosta, co-founder of Vor Interactive, saw an opportunity in this growing space. His company developed proprietary algorithms analysing historical match data, market odds and player statistics, generating over 2,000 predictive data points per event. Data is delivered via APIs or packaged into the VorTurbo.com widget for easy affiliate integration.

Though only in development for about a year, these algorithms have achieved 83% accuracy over 18 years of back-tested results across leagues like the NFL, NBA, IPL cricket, soccer and Masters tennis. Currently, the widget is live with Odds Shark for MLB in the US, and in Italy with 123scommesse and Hub Affiliations.

Acosta notes that the user experience is just as important as the backend. Affiliates can adjust the widget’s operator order, design and tracking links. Meanwhile, the AI selects relevant bets, generates match summaries in local languages. “We are really excited about where we can go with the widget and the other products we are building,” Acosta says.

Challenges: from tech to trust

But building with AI isn’t straightforward. For Lawrence, the biggest technical challenges lie in structuring inconsistent affiliate data feeds and making AI interpretations explainable to clients. 

“Explaining to regulators, partners and users how an AI 'reads' a legal document like a T&C – and what that means in practice – is non-trivial,” he says. In addition, in regulated markets, keeping AI output aligned with advertising standards and evolving local laws requires ongoing effort. 

There’s a commercial balancing act. “Transparency sometimes highlights negative terms that could suppress conversion in the short term but build long-term trust,” Lawrence admits. “Managing that tension requires design nuance and stakeholder buy-in.”

Acosta has faced challenges of a different sort. While the tech has performed well, market adoption has been uneven. “A lot of people are resistant to new tools, especially if they’ve already got something that’s working and profitable,” he says. “We’ve had meetings where people say, ‘We tried something new before and it went badly. Now we don’t want to change anything.’” 

Acosta advocates gradual onboarding, beginning with limited rollouts for testing, feedback and fine-tuning before wider deployment. Ultimately, successful AI integration still requires significant human oversight, only scaling up once everything aligns perfectly.

AI x content: the lingering stigma

Perhaps most controversial is AI-generated content. While some assume AI content creation simply involves inputting a brief prompt into ChatGPT, the reality is more nuanced. Ioana Maria Dragomir, digital marketing consultant and AI strategist, observes that although the industry is at “an interesting turning point with AI in content production”, there is “still a stigma attached, as if using AI is the easy way out or that it leads to inferior work.” 

Few affiliates openly acknowledge their use of AI. In a LinkedIn post, Gentoo Media’s publishing director Emma-Elizabeth Byrne revealed, “AI supports our content planning, draft production, linguistic adaptation and search optimisation,” with human editors shaping final outputs.

“We are practical about AI. We use it where it adds value, and we continue to test and refine how it integrates with our systems. But the effectiveness of our content still depends on editorial judgement, compliance alignment and human accountability,” Bryne wrote. 

As Dragomir explains, in a typical setup for an effective AI-driven content system, the human writer provides “seed” information, like their take on a sporting event. That input is then processed by a chain of AI agents: one creates a first draft, another edits it based on the brand’s tone of voice, and a third optimises it for SEO.

Each AI agent is programmed with specific tasks and trained on the brand’s historical content. “The bigger your database, the better your AI output,” she says. “Writers who used to spend 45 minutes now would only spend 5 to 10 minutes at the beginning to provide the sed content, and 5 to 10 minutes at the end to make sure the final copy is correct.”

Dragomir also highlights the potential of AI-powered chatbots, which can provide tailored game suggestions based on user queries, and AI-driven A/B testing tools that optimise landing pages and email flows for better conversions. In each case, she emphasises that AI is there to support humans in “a partnership”, especially when producing content at scale.

“When AI is used well – as a true partner rather than just a tool – it doesn’t replace quality; it elevates it,” Dragomir says. “I’d love to see the industry move towards a mindset where creating content with AI is something to be proud of, because it demonstrates strategic thinking and a commitment to excellence at scale.” 

What comes next?

Nevertheless, AI isn’t a magic bullet. A good adoption of AI in affiliate product lines, as Lawrence notes, aims to “solve a genuine user or business pain point” and “ is transparent about its output and logic”. In contrast, a bad adoption tends to use “off-the-shelf models with no tuning”, use AI as a “back box” gimmick with no clear value and overrely on humans to correct outputs. 

Lawrence anticipates the rise of composable affiliate platforms, where AI handles data ingestion, creative optimisation and funnel analytics behind the scenes. His team is already developing Good Sense, an infrastructure layer that automates onboarding and structured data generation, and is exploring real-time AI agents for customer communications across email and SMS.

The job landscape is also set to shift. Acosta predicts the rise of AI-specific roles – “technical people who make sure different pieces of AI software work together and function as a human would expect”. Dragomir sees the emergence of dedicated AI content specialists who understand not only how to prompt AI, but how to coordinate writing, image and video tools across channels. 

“Human involvement is still very relevant and necessary at this stage in AI’s evolution,” Acosta says. “As productivity increases, some roles will become redundant, but others will be created. So it is going to be a big period of change and transition over the next two or three years.” 

As the popular saying goes: AI won’t take your job, but someone who knows how to use it might. For affiliates, the ones who embrace it strategically rather than superficially will shape not just their own growth, but the direction of the industry as a whole.