The fourth episode of The Partner Marketing Podcast, hosted by Matthias Stadelmeyer, CEO of Tradedoubler, provides a deep exploration of artificial intelligence (AI) and its potential to reshape Partner Marketing. Matthias is joined by Rob Berrisford, founder of affiliate.ai, to discuss the current state of AI in the industry, its inherent challenges, its potential applications, and the transformative role it could play in creating efficiencies and fostering innovation. Over the course of the episode, Matthias and Rob analyze AI’s capabilities, limitations, and the cultural and technical prerequisites necessary for its successful adoption in Partner Marketing. Let’s dive right in!
The current state of AI in Partner Marketing
Matthias: “Hello, Rob. A warm welcome to the Partner Marketing Podcast. We will be speaking about AI. We will be speaking about Partner Marketing. And, a bit more specifically, we will be speaking about AI in Partner Marketing. What would you say? Is the status of AI in Partner Marketing the same as in all other industries? Or are we maybe a little bit ahead?”
Rob: “Thanks for having me, Matthias. I think the experimentation in AI, which is largely where the sector is right now, is limited to the really big guys, the frontier model providers, and a whole bunch of startups who are testing and learning what to do. When we started affiliate.ai, we thought there'd be a rapid arms race in Partner Marketing to use this technology. We've seen a lot of the networks and agencies experimenting in the space, but very little has made it to customers so far."
Rob begins by addressing the current state of AI within Partner Marketing, describing its role as underdeveloped. While interest in AI is growing, its adoption in Partner Marketing is in its infancy, with limited practical applications available to end customers. Rob notes that experimentation is widespread, driven largely by frontier AI model providers such as OpenAI and Anthropic, as well as startups exploring niche use cases. However, these efforts remain isolated, and the industry as a whole has yet to integrate AI solutions into its core operational frameworks.
The widespread enthusiasm around AI stems from its potential to revolutionize processes, but Rob acknowledges that much of this excitement has yet to translate into tangible results. Predictions that AI-powered chat interfaces would replace traditional methods of interaction, such as web searches or website navigation, have not come to fruition. While these conversational AI interfaces were initially envisioned as the future of digital interaction, Rob explains that users have shown a preference for existing tools over these new alternatives.
One of the fundamental obstacles to AI adoption in Partner Marketing is the inconsistency of AI systems. Rob highlights how large language models (LLMs) operate on probabilistic frameworks, which can lead to errors or “AI hallucinations.” These inaccuracies, while tolerable in creative contexts, are unacceptable in data-driven fields such as Partner Marketing, where precision is essential. As a result, AI remains underutilized for strategic tasks that require consistent and reliable outputs.
Exploring the challenges of AI implementation
Matthias’ and Rob’s conversation turns to the challenges of integrating AI into Partner Marketing. Rob emphasizes that these challenges are multifaceted, involving technical, cultural, and structural barriers that must be addressed for AI to achieve its full potential in the industry.
One major challenge lies in the complexity of the Partner Marketing ecosystem, which involves three primary groups: Brands, Publishers, and networks. Each of these stakeholders has distinct objectives and operational processes, creating a diverse set of requirements for AI solutions. Brands, for instance, are focused on driving revenue and scaling partnerships, while Publishers prioritize creating engaging content and generating traffic. Networks, on the other hand, act as intermediaries, managing relationships and facilitating transactions between Brands and Publishers.
Developing AI tools that can cater to the diverse needs of these stakeholders is a daunting task.
Data privacy and security concerns further complicate AI adoption. Partner Marketing is a highly collaborative field that requires extensive data sharing among stakeholders. Brands often work with hundreds or even thousands of Publishers, exchanging sensitive transactional data that must be protected. Rob points out that the reluctance to share this data with AI models, due to fears of breaches or misuse, is a significant barrier to integrating AI into Partner Marketing workflows.
Another challenge is the current limitations of AI models. While AI excels at automating repetitive tasks, it struggles with more complex functions, such as strategic planning and decision-making. Rob explains that the intelligence of most AI systems today is comparable to that of a first-year university student—sufficient for predefined tasks but inadequate for nuanced problem-solving. As a result, AI is best suited for tasks like generating reports or conducting basic analyses, rather than replacing experienced marketers or account managers.
Rob: “If you've trained the model on how to use Tradedoubler, how to validate sales, and how to get your invoice, then it's very easy for them to parrot that data back to you. What those models can't do right now is any significant kind of chain of thought or strategic thinking. You could pass it, and we've experimented with this, playbooks on how to run an affiliate program, but what the models will do at the moment is just parrot those playbooks back to you. (...) Right now, the intelligence of large language models is at sixth form, first year of university level. These models aren't at the same level as your senior account directors who drive the strategic thoughts on your affiliate program.”
The Partner Marketing industry also faces a lack of specialized tools to support AI integration. Although general-purpose tools like OpenAI’s GPT APIs provide a foundation, there is a need for industry-specific solutions that can handle the unique challenges of Partner Marketing. For instance, transitioning data from structured formats, such as databases, to unstructured formats that AI can analyze effectively remains a technical challenge.
Rob also highlights the cultural resistance within organizations as a significant barrier to AI adoption. Many companies are unprepared for the rapid pace of AI development and struggle to adopt the agile, experimental mindset required for successful implementation. Traditional planning methods, such as creating detailed roadmaps, are ill-suited to the dynamic nature of AI, which demands flexibility and a willingness to adapt as new technologies and techniques emerge.
Potential applications of AI in Partner Marketing
Despite these challenges, Rob and Matthias explore several areas where AI could make a transformative impact in Partner Marketing. These applications span various aspects of the industry, from workflow automation to content creation and strategic insights.
One of the most immediate opportunities lies in automating repetitive tasks. Rob explains that AI can handle routine functions such as generating weekly or monthly performance reports, identifying anomalies in campaign metrics, and flagging underperforming Publishers. By automating these tasks, marketers can focus on more strategic activities, such as building relationships with partners or developing growth-oriented strategies.
For Publishers, AI offers the ability to create high-quality, personalized content that resonates with their audiences. Rob highlights how AI tools can analyze audience behavior and preferences, identify trending topics, and generate tailored content that drives traffic and engagement. This capability is particularly valuable in today’s competitive digital landscape, where Publishers must constantly innovate to retain and grow their audiences.
Brands can use AI to simplify the process of identifying and onboarding new Publishers. By analyzing performance data and compatibility metrics, AI can recommend Publishers that align with a Brand’s goals, reducing the time and effort required for partner discovery. This capability enables Brands to scale their partner programs more efficiently while maintaining quality.
Networks, too, can benefit from AI’s capabilities in outbound sales and prospecting. Rob explains how AI-driven tools can analyze vast datasets to craft hyper-personalized outreach campaigns. These campaigns are more likely to resonate with potential Clients or Publishers, increasing the success rate of prospecting efforts.
While current AI systems are not yet capable of providing high-level strategic guidance, they can support decision-making by identifying gaps in partner programs and suggesting areas for improvement. With human oversight, these insights can help marketers refine their strategies and optimize performance.
Rob: “You have to think about what's good and bad about large language models right now and bake that into your use case. And if you understand that large language models create efficiency but are inconsistent, then you have to use that to your advantage. A lot of stuff that we've been doing at affiliate.ai and other companies is experimenting with first-round weekly reports and first-round monthly reports. Running the data, looking for red flags, looking for who's up and down, and passing that data to a senior account manager to check over. We're seeing a lot of success and efficiency there, and you can handle the inconsistency problem by adding a human layer on top of the outputs.”
Why experimentation is essential in AI adoption
Successful AI adoption requires a culture of experimentation. Rob explains that companies must be willing to test new ideas, gather feedback, and iterate quickly. Unlike traditional technologies, which often follow predictable development cycles, AI evolves rapidly, making it essential for organizations to remain agile and adaptable.
Rob shares his own experiences with experimentation, noting that some of his initial assumptions about AI proved incorrect. For example, he initially believed that marketers would prefer to interact with their data through conversational interfaces. However, user feedback revealed that this approach did not meet their needs, prompting a shift in strategy. These experiences underscore the importance of validating assumptions through real-world testing and being prepared to pivot based on results.
Rob: “The most important thing if you are building with an engineering team is having the right culture of experimenting, moving, and checking quickly. One of the biggest mistakes that affiliate.ai made was to presume that people wanted to chat with their data. I thought they wanted to chat with their data, they told me they wanted to chat with their data, but right now, no one actually wants to chat with their data. And so, if you are building new things that you don't have a playbook from what people have done before, you need to move quickly and experiment to prove that it's a real thing. It's very easy in AI to lose 12 months of building something you realize doesn't add enough value.”
To foster a culture of experimentation, Rob advises companies to equip their teams with the tools and resources needed to explore AI-driven solutions. This includes investing in training, providing access to cutting-edge technologies, and encouraging collaboration between technical and non-technical teams.
The future of AI in Partner Marketing
Rob is optimistic about the future of AI in Partner Marketing, predicting significant advancements in the coming years. He anticipates improvements in model intelligence and memory, which will enable AI systems to handle more complex tasks and deliver highly personalized experiences. For instance, future AI tools could analyze a user’s past interactions and preferences to recommend tailored solutions, such as customized marketing strategies or optimized partner programs.
There is also a trend towards a greater focus on improving the accuracy of AI systems. Current models often produce inconsistent results, limiting their utility for high-stakes applications. Efforts to address these accuracy issues will expand the range of use cases that AI can support, making it a more reliable tool for marketers.
As AI systems become more sophisticated, Rob predicts that they will play an increasingly central role in both personal and professional workflows. For example, AI could function as a personal assistant, managing tasks such as scheduling, communication, and data analysis. This integration has the potential to significantly boost productivity and efficiency, transforming how marketers and organizations operate.
Rob: “A whole wave of work is happening within the large language models to understand the neural pathways. A lot of the problems we see with accuracy are difficult to solve because we don't know how they came to the answer that they came to, and so I think over the next 6-12 months, there'll be a greater push for accuracy within the responses. This opens up a whole world of use cases, as we spoke about earlier, that AI just can't touch right now. And then there's the memory bit; the more comfortable we get with interacting with AI models, the more likely I am to pass on my history of all of my family holidays so it can do a better job at creating your own personal assistance. I think that could be incredible if you get the accuracy and the memory to hand over your calendar and a lot of your inbox to an AI assistant, the step change in productivity could be astronomical.”
Practical advice for companies adopting AI
Rob and Matthias conclude their discussion with practical recommendations for companies considering AI adoption. Rob emphasizes the importance of starting with clear, measurable objectives, such as increasing revenue, improving campaign performance, or expanding partnerships. By aligning AI initiatives with these goals, companies can ensure that their efforts deliver tangible value.
He also highlights the need for robust data infrastructure. Clean, well-organized data is essential for effective AI applications, and companies must invest in technologies that enable seamless data integration and analysis.
To support experimentation, Rob advises collaborating with leading AI providers, such as OpenAI or Anthropic, to access state-of-the-art tools. However, he cautions that legal and regulatory considerations must be addressed early to avoid delays.
Finally, Rob recommends starting small and scaling gradually. By focusing on manageable projects, organizations can build confidence in their AI capabilities and demonstrate ROI before committing to larger initiatives.
The episode concludes with a strong sense of optimism about AI’s potential to transform Partner Marketing. Matthias and Rob agree that while challenges remain, the industry is well-positioned to leverage AI’s capabilities to enhance efficiency and foster innovation. By embracing AI, companies can not only streamline their operations but also maintain their competitive edge in a rapidly evolving digital landscape.
If you would like to hear more about AI in Partner Marketing, you can find the podcast episode on Spotify, Apple Podcasts, our website, and all other podcast platforms.
About The Partner Marketing Podcast
The podcast brings together thought leaders and professionals from across the globe. In each episode, our host, Matthias, sits down with guests to discuss the evolving world of Partner Marketing and share personal stories.
For more details, please visit www.tradedoubler.com/podcast
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