A lot of folks in the industry have an adverse reaction to the phrase “AI can democratize research and insights.” It’s heard as: “Anyone can now do a researcher’s job. Your craft is about to be automated away.”

That reaction is understandable. Most “democratization” narratives are either shallow marketing or, worse, explicitly dismissive of expertise.

At Dawnify, we interpret democratization very differently. For us, democratization isn’t about erasing researchers. It’s about changing who gets access to good research, how flexibly that research can be delivered, and which languages, budgets, and team shapes the tools are built around. It’s less “anyone can be a researcher now” and more “researchers can show up in many more places, because the tools finally fit reality.”

To see why this matters, it helps to look at how “democratization” actually shows up in research tech today.

How research is “democratized” today

Spend enough time around research platforms, and you start to see a familiar pattern. Most “democratizing” tools fall into a few predictable buckets, based on who they really serve.

The enterprise-first platforms were built for large, global organizations with annual research calendars, deep pockets, and dedicated insight teams. They democratize access inside those organizations, but they still assume you have procurement, IT, and someone whose job is “owning the platform”.

The English-first platforms proudly support dozens of languages on the back end, but the buying process, onboarding, documentation, and UX all assume English. Research can technically happen in Bahasa, Thai, Hindi, Japanese, or Spanish, but the mental model of the product is rooted in a single language of business.

The “lite” self-serve tools target smaller buyers, but they quietly flatten nuance. You can run quick surveys, simple concept tests, or basic feedback polls, but anything more complex quickly hits the ceiling. The tools democratize activity (“everyone can run research!”) more than they democratize quality.

Most of the industry stops there. A bit more self-serve, a bit more dashboard access, a bit more language support. It feels like progress. But if you zoom out, you see a much bigger missed opportunity: the chance to reshape who research tech is for.

Democratization as flexibility, not one-size-fits-all

The first shift we need is brutally simple: democratization has to mean flexibility.

Real projects are messy. One month you’re doing exploratory qual on a shoestring budget for a start-up. Next you’re helping a global client validate a multi-market positioning. Even inside the same company, needs swing wildly from “we just need to sanity-check three ideas” to “we’re rebuilding the innovation pipeline.”

Most research tech doesn’t like that mess. It nudges you towards repeating the same workflows, using the same templates, with the same volume of work, to justify the same pricing tier. The platform becomes the shape you squeeze everything into.

A more honest form of democratization would start from the opposite place. It would accept that needs vary from project to project, that what worked beautifully once may be entirely wrong for a different market, category, or client, and that smaller organizations shouldn’t have to commit to enterprise-style scope just to get a decent answer.

That means solutions that are modular rather than monolithic. It means scope and cost that can stretch up or down without forcing a complete change of vendor. It means being able to right-size research for a two-person team without turning it into “research lite”.

In that world, democratization is not “making everyone a researcher.” It’s making it viable for researchers to serve many more types of teams and briefs, without being trapped in the straightjacket of enterprise-only platforms.

Democratization as truly multilingual, not “English with subtitles”

The second shift is about language.

So much expertise in our industry is locked behind language boundaries. Fieldwork happens where people live, think, and joke, often in Bahasa, Thai, Vietnamese, Hindi, Japanese, Spanish, or any number of local languages. But many deliverables still need to be in the language of business, which is very often English.

If you’ve ever run a project where the research happens in one language and the PowerPoint is in another, you know the cost: hours lost translating discussion guides, transcripts, quotes, and topline narratives; nuance squeezed out in the process; local teams feeling unheard because their words don’t quite survive the journey.

Most platforms treat multilingual support as a checkbox. Yes, the models can process multiple languages. Yes, you can upload transcripts in different scripts. But everything around that core , from sales conversations to training materials to UX labels , assumes an English-speaking buyer.

That is not democratization. That’s English with subtitles.

A genuinely democratized future would treat multilingual reality as a core design constraint. It would assume from day one that the language of research and the language of reporting may not be the same, and that researchers need to move between them without losing their sanity or their nuance.

It would let teams conduct, tag, and interpret research in their working language, and then selectively “lift” insights into English (or any other business language) when needed , for a stakeholder presentation, an executive summary, or a global roll-up. Multilingual wouldn’t just mean “the AI model understands your words.” It would mean “the tool understands your world.”

Democratization as growing the pie, not slicing it thinner

The third shift is about who research tech is even built for.

We talk a lot about “democratizing insights”, but most of our tools are still aimed at a very specific buyer: a large organization with an annual budget, a procurement process, and a core research team. That’s where the category grew up, and it still shapes most roadmaps.

Meanwhile, there’s a huge group of potential users at the edge of that core who rarely show up in roadmaps at all: independent researchers and small boutiques, digital marketing and PR agencies that need rigorous insight occasionally but can’t justify a dedicated insights team, start-ups and SMEs who know they should talk to users but live month to month, not year to year.

For these teams, traditional research tech is either too heavy, too expensive, or too rigid. They stitch together free tools, do heroic manual work, or skip research entirely and rely on gut feeling. When they hear “AI will democratize research,” it usually means “another dashboard they don’t have time to set up.”

A more serious version of democratization would try to grow the pie instead of slicing the existing enterprise budget thinner. It would ask: what would it take for a three-person agency, a solo consultant, or a small SaaS team to reasonably commission and use good research three or four times a year? How would we package, price, and deliver value for them, without assuming an annual contract or a platform owner?

This isn’t about undercutting traditional agencies. It’s about making it possible for more people to ask better questions more often , and often still choose to work with expert researchers to interpret and act on the answers.

If we don’t serve those teams, someone else will. And that “someone else” may not care as much about rigor as the insights industry does.

How to actually democratize, without dumbing down

So what does real democratization look like in practice? Not as a slogan, but as a set of design choices.

The companies that seem to be getting this right don’t start with “how do we make everyone a researcher?” They start with one painful, recurring job that today is either horribly manual or rarely done at all. Turning 90-minute focus groups into usable highlights for a mixed stakeholder group. Creating multilingual topline summaries that local teams recognise and global teams can act on.

They design as if their only buyer is a lean team: a two-person shop, a single in-house researcher, a start-up that might only be able to pay for a few projects a quarter. That forces simplicity. Onboarding has to be lightweight. Workflows have to make sense without a full-time admin. Value has to show up quickly, or the experiment won’t survive the next budget cut.

They treat multilingual workflows as a core constraint, not a feature request. That shows up everywhere: in how transcripts are handled, in how prompts and tags are defined, in how quotes are surfaced and stitched into the story. Language is not an afterthought left to machine translation; it’s a first-class part of the user journey.

And crucially, they don’t try to replace expertise; they try to unburden it. AI handles transcription, first-pass coding, pattern surfacing, cross-project retrieval. Researchers still frame the questions, interpret the outliers, and carry the responsibility of telling the story well. The machine makes their time more valuable; it doesn’t try to impersonate them.

That’s the version of democratization we care about at Dawnify.

Why this matters right now

Budgets are under pressure. Teams are leaner. Clients still expect depth, speed, and global coverage. Researchers are already being asked to do more with less , more markets, more stakeholders, more data sources , while spending a big chunk of their time inside tools that weren’t built for the way they actually work.

In that environment, the gap between the marketing version of “democratization” and the reality of who can access good research is only going to get more painful. Enterprise-only, English-first, annual-license tools will keep serving a shrinking centre of gravity. The edge , independents, agencies, start-ups, SMEs, multilingual teams , will keep growing.

We can either let generic AI tools define “democratized research” as a world where anyone can push a button and get a shallow answer. Or we can define it ourselves as a world where serious research is available to more people, in more languages, at more price points, without burning out the experts who make it meaningful.

The bottom line

To genuinely democratize research, AI isn’t the enemy and it isn’t the hero. It’s just the enabling layer that makes a different set of choices possible.

The real work is in those choices: building solutions and pricing that fit lean operations and varying needs; designing for truly multilingual workflows instead of treating English as the default; and creating tools that are not just for big enterprises, but for the rest of us.

That’s what we’re trying to do with Dawnify: use AI to free researchers from the parts of the job that machines can handle, so they can bring their expertise to people and teams who’ve never had access to it before.