Vision Insights Industry 2035: The Future Belongs to the Architects of Data, Insights, and Decisions

When we talk about the Insights Industry of 2035, we should first clarify the perspective from which we view the future. My own daily work rarely involves traditional primary research. I do not deal with questionnaires, synthetic respondents, or AI-moderated interviews. Instead, I work predominantly with existing data sources: social listening, search data, reviews, earned media, AI visibility, and other digital behavioral data.

It is precisely in this area that we are currently observing developments that offer an astonishingly clear glimpse into the future of our industry. In my view, the major changes of the coming years will not stem primarily from new research methods, but from access to data, the quality of that data, how it is processed, and the role of insights within increasingly automated corporate processes.

The End of Data Democracy: The New Gatekeepers

One development stands out as particularly defining: Access to relevant data is becoming more difficult. The more essential data becomes for generating sustainable insights, the more strictly it is controlled, protected, and monetized. Platforms like Reddit and X, which used to have very open interfaces, have now regulated them much more strictly. Meta has pursued a restrictive approach to external data access for years.

We are now seeing similar tendencies among the major LLM providers. The recent example of Reddit being deprioritized by ChatGPT illustrates this impressively. While we might explain to a client in a project what is happening on Reddit within their category, that same content can suddenly be deprioritized by a language model like ChatGPT. New digital gatekeepers are emerging, influencing what information is even perceived at all.

For the insights industry, this results in a clear consequence: Knowledge of access points and the establishment of reliable data pipelines are evolving from operational craftsmanship into a central strategic competitive advantage. Those who possess the best proprietary data pipelines will deliver the most valuable insights in an AI-dominated world.

The Search for the Human: AI Slop and Synthetic Noise

Simultaneously, the volume of available content is exploding. On social networks and across the internet, it is becoming increasingly difficult for users to distinguish whether content is AI- or consumer-generated. This phenomenon is not limited to public platforms: Respondents use LLMs to generate answers, simply copy-pasting them into questionnaires.

Our clients do not pay to understand the perspective of an AI – they want to understand what real people think and do. The real challenge is no longer gathering data but making the consumer visible behind the AI. The detection of "AI slop" and strict quality controls within data pipelines must increasingly take center stage to separate authentic signals from synthetic noise. This is a key priority for Ipsos already today.

Dashboard Fatigue: Why Clients Want Answers, Not Dashboards

At the same time, the way insights are consumed is changing. We are moving away from standardized dashboards and static PowerPoint presentations, toward integrated data pipelines and flexible user interfaces.

We already see this in practice with large, globally operating corporations: Even when we develop highly customized, powerful SaaS platforms for clients and train them intensively, the number of active users often drops after a few months. The reason is not the platform itself but changing user behavior. Decision-makers simply do not have the time in their daily routines to dig through complex dashboards or analyze data themselves. Today, clients expect interface experiences and "agentic solutions" similar to those they are already used to from LLMs: They want to ask specific business questions and receive direct, well-founded answers.

Furthermore, it is no longer enough to highlight isolated digital KPIs. The mere realization that "social awareness" or "AI visibility" is increasing is meaningless to clients if these metrics do not correlate directly with overarching business goals - such as sales figures. Business impact must be inherently integrated into the systems' answers. The creation of such highly customized, HTML-based interfaces at the project level is now significantly simplified by Generative AI.

The Agent-to-Agent Economy: Insights as Triggers, Not End Products

However, the real transformation is not happening in the user interface, but in what happens after the insight is generated. Traditionally, our work ended with a finding, based on which humans made decisions. By 2035, this process will look fundamentally different.

Insights will be integrated directly into other AI systems. We are seeing the rise of an Agent-to-Agent Economy, where insights are no longer merely consumed but immediately processed. Insights agents deliver findings to marketing agents, which in turn feed them to content or commerce agents.

Because business-critical decisions will continue to be made based on these findings, the quality standards for our work remain exceptionally high. A flawed insight in an automated system multiplies rapidly and leads to poor strategic decisions. Reliability and the quality of the underlying data pipelines are therefore paramount. Human intelligence will supervise this process.

AI Visibility: The Machine as the New Consumer

It is not just companies that are changing how they make decisions; consumers are changing their paths to purchase as well. LLMs are increasingly acting as information filters between the consumer and the brand. Alongside the human consumer, a second relevant target audience is emerging: the AI assistant.

A look at China illustrates what this mechanism already looks like in practice today. For highly relevant and critical topics, such as in the healthcare sector, the leading language models hardly rely on noisy social media data anymore. To provide reliable answers, these AIs draw their information directly from specialized, authorized expert platforms. For companies, this means they must secure partnerships and ensure their official content is maintained precisely in these verified networks that feed the language models. AI Visibility is therefore the beginning of a fundamental shift in the understanding of consumer intelligence.

Conclusion: From Researcher to Architect

All these developments are radically altering the profile of the insight worker. Methodological excellence remains important, but the future expert will be defined primarily by technical know-how, creativity, solution orientation, pragmatism, and the courage to consistently integrate GenAI into all workflows. These cultural impacts are already visible today and will continue to shift.

Simultaneously, structures on the client side are changing. It is highly probable that companies will reduce headcount in the hope of becoming more efficient through AI—and specifically by shifting budgets toward IT departments. Paradoxically, this internal shortage of personnel opens up external opportunities: Long-term, certain research functions will be increasingly outsourced to specialized agencies.

The future value of an agency will lie in building its own unique data pipelines and data pools, even independently of specific individual client commissions. These can then be seamlessly connected to client systems via agents.

The insight expert of 2035 will be neither primarily a data collector nor a traditional researcher. They will become the architect of trustworthy decision-making foundations in an agentic economy. The most important task of our industry will not be to deliver more data, but to ensure that authentic signals lead to the right decisions.

Dieser Beitrag erschien unter gleichlautendem Titel am 10.09.2026 auf marktforschung.de.