The automation of interviews and analysis is changing how consumer studies are priced, affecting how often they are conducted and the role of expert advice in the field. For advertisers and service providers, the benefits depend on how many participants can be recruited, how much human work remains, and how the results are actually used. A consumer study typically involves recruiting participants, conducting interviews, analyzing responses, and offering recommendations. Its cost is shaped by the availability of participants, the time teams spend, and the expertise needed to turn responses into useful insights. Artificial intelligence is changing this by automating parts of the process, such as conducting interviews, transcribing responses, and even organizing themes from the data. This shift reduces the time needed for production, allowing research institutes to improve their margins, advertisers to negotiate lower prices, or marketing teams to conduct more research with the same budget. The rise of subscription-based models is also changing how these services are bought and used. Instead of paying for individual studies, companies now often pay for ongoing access, with the value depending on how much they use the service. Platforms like Verso, which recently raised about 5.4 million euros, are examples of this trend. While automation promises faster and cheaper studies, it raises questions about which parts of the process can be automated, which costs remain, and how the value is shared between the service provider and the client. Traditionally, a qualitative interview requires time for preparation, conducting the interview, transcribing the responses, and analyzing the data. Automation can handle parts of this, allowing teams to conduct more interviews, compare different groups, or explore multiple markets using the same tools. However, not all costs disappear. Recruitment of participants still requires time, money, and effort, and finding people from specific groups—like users of a particular product or specialized professionals—can be challenging. While tools can help with selection and invitations, the actual access to these groups, verification of criteria, and payment for participation remain tasks that require human involvement. The time needed and the rarity of certain profiles can affect how easily these studies are conducted. Platforms like Outset illustrate this, offering tools that integrate different services, from general public recruitment to specialized professionals, while also highlighting the challenges of finding rare participants or meeting strict quotas. The shift to subscription models also requires companies to rethink how they manage their budgets. Instead of paying per study, they might pay for ongoing access, with additional costs for recruitment or expert support. The way these services are billed can change the balance of responsibility between the client and the provider. A flat fee per study ties the cost to a specific task, while a subscription model spreads the cost over time, depending on how often the service is used. A usage-based model adjusts the cost according to the volume of research conducted. These different billing approaches influence how companies allocate their resources and how much they rely on external services. Ultimately, the value of these platforms depends not only on their speed and cost but also on the quality of the insights they provide and how well they meet the needs of the client.