For years across the market research industry, ‘quality’ has been a hot topic. It’s been used as a differentiator, a promise, a marketing tagline. In 2026, that framing will no longer hold. Quality data is a baseline expectation, enforced not just by researchers, but by procurement teams, legal stakeholders and chief marketing officers who increasingly view data integrity as a form of enterprise risk.

Fraudulent practices have become more sophisticated with new tools and methods. AI has introduced new ambiguity around respondent behaviour and data provenance. Decision-makers are relying on insights in faster, more consequential ways than ever before. The result is intolerance of uncertainty and demand for proof.

The industry has become stricter each year in what ‘acceptable’ data looks like, and that definition will extend well beyond traditional checks for bots or inattentive respondents in 2026.

From technical problem to organisational mandate

Historically, data quality challenges have been treated as technical issues to be solved downstream: add another validation layer, tighten sampling rules, apply stronger filters after the fact. While those tools still matter, they are no longer sufficient on their own.

What’s known is that data integrity cannot be managed reactively. It must be governed proactively. That means organisations need to think about fraud prevention and data quality standards not just as operational functions, but as organisational ones.

Soon we will see trust and safety emerge as a formal discipline within insights teams. Much like privacy, compliance, or information security, it will have clear ownership, defined processes and executive visibility. The days of fraud mitigation being “everyone’s responsibility”, and therefore no one’s, are coming to an end.

This evolution mirrors what other data-driven industries have already experienced. When stakes rise, governance follows.

Why expectations are tightening now

Several forces are accelerating this shift. One is buyer sophistication. Clients today are far more aware of how insights are generated, where risk can enter the system and how poor data quality can cascade into flawed decisions. They are asking tougher questions about sourcing, verification and safeguards, and expecting clear answers.

Another driver is AI itself. While AI has unlocked powerful efficiencies across research workflows, it has also complicated the definition of authenticity. Respondents may use AI tools to generate or enhance answers in ways that are difficult to detect with traditional methods. These responses may pass surface-level checks while still undermining the signal researchers are trying to capture.

This doesn’t make AI inherently problematic. But it does raise the bar for transparency and verification. When the line between human and machine input becomes harder to discern, confidence must be earned through stronger controls, clearer documentation and more deliberate oversight.

Trust as a system, not a claim

One of the most important changes ahead is a move away from treating trust as something that can be asserted, and instead treating it as something that must be operationalised.

That means building systems where data lineage is clear, sourcing practices are documented and data quality decisions are consistent, rather than ad hoc. It means defining what acceptable risk looks like, and what it does not look like, before data is collected, not after it is delivered.

It also means recognising that speed and scale, while valuable, are not neutral forces. When volume becomes the primary success metric, the opportunities for fraudulent activity go up, and data quality inevitably suffers. In contrast, organisations that embed trust and safety principles into survey design, respondent engagement and sampling strategies are better positioned to deliver insights that hold up under scrutiny.

In many cases, this will require cultural change as much as technical investment. Teams will need permission to prioritise integrity over throughput, and leadership will need to reinforce that those choices are the foundation for growth.

What this means for the industry

The formalisation of trust and safety functions will not happen overnight, and it will not look identical across organisations. However, the direction is clear. Managing data quality is no longer optional, negotiable, nor assumed. It needs to be defined, measured and governed.

When stakeholders trust the data they are working with, they are more willing to experiment, adopt new methodologies and rely on insights for high-stakes decisions.

In 2026, the organisations that succeed will be those that treat trust as more than a talking point. With trust embedded as infrastructure, these companies will help ensure that research remains credible, defensible and worthy of the decisions it informs.

Patrick Comer is chief executive at Cint