Affiliate marketing is entering a new phase. As artificial intelligence changes how people discover products, it is also beginning to reshape the publisher landscape.
Product discovery is increasingly happening through conversational and AI-driven interfaces - from ChatGPT and Google’s AI-powered search experiences to AI shopping assistants integrated into ecommerce platforms. Instead of browsing static pages or typing simple keyword queries like “best running shoes men,” users can now ask questions such as “I need a lightweight running shoe for long runs under €150 - what should I buy?” and receive tailored recommendations instantly.
This shift is influencing how publishers surface products and how brands reach potential customers. Many established partner models - including cashback, coupon, and CSS publishers - are already adapting by using AI to improve product feeds and data structures, so they can perform effectively in more automated discovery environments.
At the same time, new publishers are emerging whose platforms are built with AI at the core of the user experience. In this guide, we refer to these partners as AI-first publishers.
For affiliate managers and ecommerce teams, the important question is simple: what does this shift mean in practice?
This guide explains:
what AI-first publishers are
how they differ from traditional publisher models
and how brands can evaluate and test these emerging partnership opportunities.
What Is an AI-First Publisher?
An AI-first publisher is a partner whose platform is built around artificial intelligence as a core layer for product discovery and recommendations.
In these environments, AI interprets what users are looking for and matches that intent with relevant products in real time. Instead of browsing static pages or filtering through categories, users interact with systems that generate personalised product suggestions based on their needs, preferences, or questions.
“Importantly, AI-first does not describe a specific publisher category. Rather, it describes how a platform is built. Any publisher model - from cashback and voucher sites to content publishers or shopping assistants - can become AI-first if artificial intelligence sits at the centre of how users discover and evaluate products,” explains James Maley, Head of International Partner Development & Network at Tradedoubler.
AI-Enhanced vs AI-First
Many publishers already use AI. The key difference is how deeply it is embedded in the platform.
AI-enhanced publishers use AI to improve performance within their existing model.
AI may support activities such as:
Product feed optimisation
Automated bidding and targeting
Title and attribute enrichment
Personalised recommendations
In these cases, AI improves efficiency and campaign performance, but users still interact with the platform in familiar ways - browsing pages, searching for deals, or navigating product categories.
AI-first publishers go a step further. Here, artificial intelligence shapes the discovery experience itself. Users interact with systems that interpret their needs and generate relevant product suggestions in real time.
Typical signals of AI-first platforms include:
AI-driven discovery – platforms interpret user intent through queries or conversations rather than traditional keyword searches
Real-time product matching – AI analyses multiple signals to recommend products dynamically
Conversational or interactive interfaces – discovery may happen through chat-based assistants or recommendation tools
Continuous learning – AI models improve recommendations over time based on user behaviour
For brands, this shift changes how visibility is created. Success in AI-driven environments depends less on traditional placements or keyword rankings and more on factors such as structured product data, feed quality, contextual relevance, and how effectively AI systems can interpret and recommend products.
Examples of AI-First Publishers
The following examples illustrate how AI-first discovery models are already emerging across the affiliate ecosystem.
JoinDig: Visual & Instant Deal Discovery
JoinDig leverages AI-assisted search and recognition to help shoppers find the best prices and cashback opportunities instantly. Shoppers can take a photo, upload a link, or enter a search term and JoinDig automatically compares prices across retailers and shows the best options, all while earning cashback on purchases.
Although its primary focus remains price comparison and rewards, the emphasis on natural input (e.g., image or link search) and instant matching showcases how AI-first interaction principles can reshape discovery.
Twains: Conversational Engagement & Conversion
Twains uses AI-powered chat experiences to turn conversations into conversions. It creates digital twins of partner influencers or brand chatbots that engage audiences directly via conversational channels like WhatsApp, driving personalised interactions and, ultimately, higher engagement and conversion opportunities for brands.
These tailored AI interactions allow users to ask questions and receive dynamic responses - making discovery and decision-making more conversational and interactive than traditional browsing.
Envolve Tech: AI-Powered Virtual Shopping Assistant
Envolve Tech uses generative AI (built with Google Gemini™) to create a chat-based shopping experience integrated directly into ecommerce sites. This AI-first assistant engages visitors with natural, conversational product discovery, answers questions in real time, and helps guide users toward purchase decisions - all through an interactive AI layer that sits alongside traditional site navigation.
These publishers showcase how AI can change where and how product discovery happens - moving from static browsing to more dynamic, conversational, and context-aware interfaces.
Opportunities of Working with AI-First Publishers
AI-first publishers can offer meaningful advantages when aligned with the right objectives. While still evolving, they introduce new ways for brands to engage consumers in AI-driven discovery environments.
1. High-Intent Discovery
Conversational interfaces often capture users at the moment they are actively seeking recommendations. Instead of passively browsing, users describe their needs directly - signalling clear intent.
For brands, this can lead to:
More relevant product exposure
Stronger alignment between user intent and recommendations
Potential improvements in conversion quality
2. Personalised Product Matching
AI systems can process multiple signals simultaneously (preferences, budget, use case, and behavioural data) to generate tailored suggestions in real time.
This is particularly valuable for:
Large product catalogues
Complex or feature-heavy products
Fashion and lifestyle categories
Brands with wide price ranges or product variations
When supported by structured and high-quality product data, AI-first publishers can surface relevant products that might otherwise be overlooked.
3. Early Positioning in Emerging AI Ecosystems
AI-driven discovery environments are still developing. Brands that test AI-first partnerships early may benefit from:
Lower competition within these environments
Learning advantages before broader adoption
Stronger positioning in conversational and AI-assisted commerce
This is especially relevant as AI-powered search and shopping interfaces continue to evolve.
What Brands Should Consider Before Partnering
AI-first publishers offer opportunity, but they require structured evaluation. As with any emerging models, strategic alignment and testing are key.
1. Measurement & Attribution
Conversational and AI-driven environments may differ from traditional publisher setups. Brands should clarify:
How conversions are tracked
How attribution is handled
How AI-generated recommendations are measured
2. Scale & Maturity
Many AI-first publishers are still in early growth stages. Volume may initially be lower compared to established publisher types.
A controlled test approach with defined KPIs and realistic expectations is often the most effective way to assess impact.
3. Brand Fit & Governance
Because AI systems generate responses dynamically, brands should understand:
How products are selected and prioritised
How brand messaging is represented
What compliance and oversight mechanisms are in place
AI-first publishers are not a replacement for established publisher types. They represent an expansion of the affiliate marketing ecosystem. The key is identifying where they complement your broader strategy and approaching them with structured testing and clear commercial goals.
How to Work with AI-First Publishers: A Practical Framework for Brands
Rather than treating AI-first publishers as a trend to “try,” brands should integrate them into a structured testing and evaluation process. Below is a practical framework to guide that process.
1. Define Your Objective First
Before identifying partners, clarify what you want to achieve.
Are you looking to:
Drive new customer acquisition?
Increase visibility in AI-driven discovery environments?
Improve product discovery for complex catalogues?
Test conversational commerce experiences?
2. Assess Product & Data Readiness
AI-first environments rely heavily on structured data. The quality of your product feed directly influences how well AI systems can interpret and recommend your products.
Review:
Product titles and descriptions
Attribute completeness (size, colour, specifications, materials, etc.)
Pricing accuracy and stock updates
Category tagging and taxonomy consistency
3. Clarify Tracking & Attribution Setup
Because AI-first publishers may operate within conversational or dynamic environments, confirm technical alignment early.
Ensure clarity on:
How tracking links are implemented
How conversions are attributed
Attribution windows and deduplication logic
Reporting transparency
4. Start with Controlled Testing
Begin with a structured pilot and define:
Clear KPIs (CPA, ROAS, new customer rate, AOV, engagement metrics)
Test duration
Baseline comparisons
Success thresholds
5. Evaluate Strategic Fit, Not Just Volume
AI-first publishers may not initially match the scale of established voucher or cashback partners. Evaluation should focus on strategic contribution, not only short-term volume.
Consider:
Audience alignment
Product suitability
Quality of engagement
Contribution to diversified publisher mix
6. Iterate and Optimise
If early results are promising, refine and expand.
Potential optimisation levers include:
Feed improvements
Product prioritisation
Commission adjustments
Creative alignment
Messaging refinement within AI interfaces
Ready to Apply This Framework?
To help you put this into practice, we’ve created an AI-First Publisher Testing Toolkit - a structured, fillable framework designed for affiliate and ecommerce teams.
The toolkit includes:
An AI-readiness checklist
A partner evaluation matrix
A pilot campaign planning template
Download the toolkit to evaluate potential partners, design pilot campaigns, and test AI-first partnerships in a structured and measurable way.
Navigating the Next Phase of Affiliate Marketing
As discovery becomes more conversational and AI-driven, the publisher ecosystem continues to evolve. AI-first publishers represent one of the most visible signals of that evolution. At the same time, established publisher types are integrating AI into their existing models, demonstrating how adaptable and innovation-driven the affiliate industry is.
Working with AI-first publishers requires clarity, structure, and experimentation. Brands that approach these partnerships strategically - with defined objectives, clean data, and controlled testing - are best positioned to benefit from emerging AI-driven discovery models.
If you are exploring how AI-first publishers could fit into your affiliate strategy, we’d be happy to discuss how these emerging partners align with your objectives. Get in touch to explore a tailored approach to testing AI-first opportunities within your programme.