In the last two years, AI has moved from theoretical potential to operational reality in market research. But what we’re witnessing isn’t just the adoption of new tools.

It’s a fundamental shift in how companies access, generate, and activate insights driven not only by AI, but by the rise of DIY platforms, the evolution of online research, and the fragmentation of consumer signals across digital ecosystems.

If approached correctly, this shift can finally unlock what many organizations have long promised but rarely delivered: real consumer understanding, built from within.

What we see today is a pivotal moment: organizations must choose whether to continue outsourcing their consumer understanding or build the internal capability to make it a true strategic asset.

Here’s how AI and online tools are changing the rules and what companies need to do to stay ahead.

The new cost model: challenge everything

AI and online tools are reducing the cost of data collection and processing. This changes the financial logic of research. Companies can question the cost structures of traditional providers.

But lower costs do not automatically create better outcomes. Savings must be reinvested in building internal capability. DIY platforms are only as effective as the people framing the questions and interpreting the results.

Without a strong internal system, companies risk saving money on research execution but losing ground on insight quality.

From large-scale studies to agile, strategic learning

For decades, market research was structured around large, periodic projects: brand trackers, usage and attitude studies, segmentation work. These projects were valuable, but slow, expensive, and often outdated by the time results reached decision-makers.

AI breaks this cycle. Automated survey platforms, natural language processing, and predictive models now enable faster, more iterative learning. Hypotheses can be tested in days, not months. New pack designs or messaging can be validated with consumers quickly and affordably.

This isn’t the end of foundational studies but it changes their role. Large projects are now complemented by lighter, faster research that supports daily business needs.

The bottleneck shifts from data collection to asking sharper questions and driving action.

The myth of the AI autopilot

It’s easy to believe AI can deliver insights at the push of a button. It can’t.

AI can process data faster. It can find patterns. But it can’t define business problems, detect political tensions behind a brief, or replace human intuition when interpreting weak signals.

More importantly, AI only works with what it can see. It cannot identify gaps in the data or predict shifts outside historical patterns. If your consumers are about to change, if a market is about to move, AI alone won’t warn you.

This is why companies must strengthen their ability to think critically, not just automate tasks.

Rethinking roles, responsibilities, and reach

The structure of insight functions must evolve. Many companies still operate models built for a slower world: centralized teams holding the tools, marketing teams sending briefs, agencies executing projects.

Today, companies need:

• Embedded insight capability across functions Teams closest to the market, product, brand, sales must be able to run and interpret simple research themselves.
• Shared consumer understanding Insights must circulate freely across teams and regions, not be locked in isolated reports.
• Strategic framing and activation The real value now lies at both ends of the process:
  • Upfront: framing the right business questions
  • At the end: turning insights into clear, timely actions

The middle: data collection and processing is increasingly automated. The differentiation comes from the thinking that happens before and after.

A new system, not just new tech

Technology is an enabler. But without the right system, it becomes just another tool.

Companies need systems that connect tools, processes, roles, and decisions. Systems that prioritize flexibility, resilience, and relevance, not size or complexity.

Building this kind of system requires clear priorities, phased improvements, and a focus on linking consumer understanding directly to business outcomes.

It’s time to act

The future of market research isn’t about faster reporting. It’s about better thinking.

AI will continue to evolve. It will improve how we model, summarize, and predict. But it will not replace the need for strong business framing, critical interpretation, or strategic action.

Organizations that continue to outsource their consumer understanding risk falling behind not just in insight quality, but in the speed and relevance of their decisions.