Is it the End of Market Research as We Know It? • Behavix Updates

The short answer? Yes — but not in the doomsday, “market research is dead” sense. Instead, we at Behavix believe simply that the age of market research as we have traditionally defined it is ending. The future belongs to a radically different form of insights — one powered by AI, data engineering, and human ingenuity very differently expressed than in the past.

I am frequently asked nowadays in panel discussions, or on calls with both old and new potential investors, young entrepreneurs, industry executives and veterans, about the fact whether the market research profession will die in light of the emerging AI models and applications. I have said this many times, but to make it clear: I don’t believe “MR is dead” in any simplistic way. I do, however, believe the old industry — the legacy workflows, the artificial buckets of methodologies, the overpriced and slow resource-driven models — those are finished. The comfortable middle ground, where proficiency with surveys, panels, and spreadsheet expertise bought you a career, is gone, at least will be soon. The future belongs to people who can orchestrate AI, data, and human judgment in ways others can’t. And that, ironically, is even more ‘human’ than before.

AI isn’t killing market research — AI is transforming it into something new. The old craft of “extract and report” is being replaced by “engineer, automate, and co-create with AI” — and that pivots the entire industry from execution to orchestration.

Market Research: Past Tense is Ending

AI doesn’t replace people, it revalues skills. AI isn’t magic; it’s amplification. Insight professionals used to earn authority by managing data and methodologies. The biggest legends in our industry had big egos, strong thoughts, always ready for an opinion, and they stood behind their companies - whether clients or vendors. In the future, that will be earned by directing AI — judging when its output is valid, correcting its biases, and creatively applying it to business problems, not just reporting on what a dataset says.

In other words, AI transforms everything that was once work in market research — survey programming, data cleaning, coding open text, pattern discovery, segmentation — into a pipeline that essentially runs itself. Now, let’s look at what that actually changes:

AI systems can ingest social media data, transactional logs, behavioural signals, text, voice, video, IoT streams — and synthesize patterns orders of magnitude faster than traditional MR workflows. This goes well beyond simple automation. It’s boundary dissolution: there is no clear line anymore between “data” and “analysis” — AI does both fluidly.

2) Predictive analytics becomes a pre-emptive strategy rather than rear-view reporting.

AI algorithms don’t just describe what happened; they will start to forecast outcomes and provide scenarios. That means businesses can act on insights ahead of the trend — which is a tectonic shift in value.

3) AI can create intelligent proxies — synthetic personas, digital twins (which will be a big topic in the upcoming

SampleCon conference for online survey panels, in Atlanta, I am sure) — that simulate responses and behaviours at scale.

This isn’t futurism anymore — startups and big companies like Ipsos are already doing it. That means you don’t wait for panels and responses; you generate hypotheses and feedback loops in hours, not weeks.

4) AI drastically compresses the feedback loop between insight and action — closing the gap that has frustrated

clients for decades.

Research that used to take months now happens in hours. That changes what clients even expect from insights professionals.

Why the Old “Market Research” Is Not the Future

Here’s where people get confused: “AI makes MR more efficient” is not the transformation. The real transformation is that AI dissolves the traditional MR taxonomy. Classic MR was built around human-time bottlenecks: surveys, panels, focus groups, analysis cycles. AI removes or compresses those bottlenecks — and that means the old mental models die.

Think of something else that used to be slow and manual: software development in adtech. Fifteen years ago, building integrations, APIs, and automated workflows was expensive, slow, and bespoke. Then came modular cloud APIs, agile practices, and programmable infrastructure — and the entire industry changed. Market research is going through the same shift — although the “software” in market research will be very much about AI models plus data pipelines.

The question isn’t “Can AI do surveys?” — it’s “Does it make sense to run surveys at all as a primary value delivery mechanism when AI can derive insights from behavioural signals, digital traces, and real usage data?” And the answer is increasingly no.

This isn’t hypothetical. Global trend reports show that organizations now expect AI to redesign workflows, not just speed them up. According to major global surveys we have seen, the highest value AI practitioners are not just cutting costs — they are reworking their business models and data infrastructure around AI.

AI Isn’t Smart — Without Unique Data and Human Direction

This is where I want to be clear, and why I fundamentally disagree with the simplistic “AI kills jobs and value” narrative:

AI models are only as good as the data they are trained on and the direction they’re given.

Models trained on public data will always produce publicly available conclusions. You cannot build defensible, differentiated value on free, public data alone. Anyone can copy that. It’s like trying to protect a business by republishing Wikipedia — the moat doesn’t exist.

For example, open data sources can teach you about languages, grammar, and structure — thus AI making the replacement of products like Duolingo relatively easy (thus the huge negative impact on its market cap lately) — but they cannot give you unique insights into how and why a specific audience actually behaves in real life, which requires proprietary, unique, private, novel, or new incremental data, as an example*.*

In market research, this means the real competitive advantage lies in privileged access to data that others don’t have — whether that’s zero-party opt-in signals, proprietary behavioural telemetry, or deeply engineered datasets. AI can accelerate analysis, but it cannot invent data that you do not already control.

This is not abstract — it’s strategic. It’s why in the future:

Proprietary data becomes the true currency.
AI becomes a force multiplier, not an equalizer.
Speed of insight becomes a strategic advantage.
Market research companies must evolve into data businesses.

Humans Are Not Being Replaced — But Human Roles Are

Don’t read that wrong. AI doesn’t eliminate the need for people. It eliminates the need for the old kind of work. Just as programmatic trading didn’t replace financial professionals — it eliminated the need for human price-tape readers and spot traders — AI will eliminate the need for human data grinders.

But just like in fintech, new roles will emerge, e.g. AI Strategists who are professionals who can define AI prompts, workflows, and objectives that align with strategic business needs, Data Engineers & Infrastructure Builders who design and curate pipelines, governance layers, and ethical frameworks that keep AI outputs meaningful. Or AI Supervisors / Risk Controllers, who enforce data quality, check for algorithmic bias, and ensure outputs meet real-world validity, or Business Integrators who translate AI-generated insights into strategy, product decisions, and customer journeys.

This isn’t science fiction — leading organizations (from consultancy to analytics firms) are already redefining traditional roles. Analysts are now expected to spend more time interpreting context than crunching numbers. McKinsey’s most recent research emphasizes that real transformation requires workflow redesign, not incremental efficiency gains.

The Deep Change: Market Research as an Engineered System

Here’s the radical re-framing I want you to carry forward, if I have to call out one:

Market research will not disappear — but it will become a system, not a craft.

Research becomes realtime pipelines, not static reports
Insight becomes prediction and simulation, not description
Audience understanding becomes behavioural intelligence, not opinions and clicks
Strategy becomes continuous adaptation, not occasional projects

That’s a paradigm shift of the first order.

It’s the difference between dial-a-survey and real-time human behaviour synthesis.

It’s the difference between post-hoc explanation and forward-looking decision support.

And it’s the difference between reporting what happened and guiding what will happen.

In this new world, what we call “market research” will look more like intelligence engineering.

Where Behavix Fits In — Not As an Artifact of the Past, But an Architect of the Future

At Behavix, we’re not building tools that mimic the old MR workflows. We’re building foundations for new forms of insights value — where proprietary behavioural data, engineered pipelines, and AI-driven synthesis converge in a more programmatic and “liquid” data market place.

We believe:

AI models are amplifiers, not originators.
Strategic advantage belongs to those with unique access to deeply structured, consented data.
The real economic moat is data ownership + continuous real-time intelligence + human orchestration.

Other companies chase incremental KPIs: faster surveys, prettier dashboards, automation add-ons. We are building platforms that ingest raw behavioural signals, refine them into actionable intelligence, and enable businesses to act with speed and authority others cannot emulate. That’s fundamentally a different proposition.

And it’s why we aren’t afraid of AI — we embrace it. But we also build around it in a way that cannot be commoditized easily. AI powers us — but it does not define us.