Every consumer insights team has been told that AI will change how they work. Far fewer can point to what it has actually changed. At i-Genie.ai, the person closest to that question is Linda Hoeberigs, who leads data science and AI, and whose team builds the models that turn consumer conversations into decisions brands can act on.
“My job is really making sure that the science is solid and that it actually works for the clients in the real world,” she says. That is a deceptively simple description of the work, and a useful test for the whole category.
Prefer to hear it first-hand? Watch the full conversation below. Linda explains how i-Genie.ai turns millions of consumer conversations into decisions brands can act on, why a brand’s direction shows up in the data long before it shows up in sales, and what separates serious AI from AI theatre.
Consumer insights at a scale surveys cannot reach
Traditional research asks a few hundred people what they think. i-Genie.ai reads what millions of people have already said, in reviews, posts, searches and video, and reads it in the language each conversation was written in. The platform runs native NLP in over 20 languages across more than 30 markets, which matters more than it first sounds. Translate everything into English at the start and you lose the slang, the emotion and the local product truth that made the comment worth reading.
Linda’s team narrows, summarises and enriches those conversations into something a brand team can act on. “We read every single conversation online about a brand,” she says, “and that’s tens of millions, sometimes hundreds of millions of opinions.” The difficult part is not the reading. It is the judgement about what deserves to surface, and the engineering that makes that judgement repeatable.
Brand measurement that moves before the sales figures
Scale only counts if it tells brands something they could not otherwise know. The clearest example is Brand Pulse, i-Genie.ai’s brand measurement product, which gives clients an early read on where their brand equity is heading before the movement shows up in sales figures.
“What really excites me is that we’re able to do things that simply weren’t possible before,” Linda says. That distinction, between compressing an existing process and creating a new capability, is the one worth holding onto as the market fills with AI claims.
At the frontier of AI market research
i-Genie.ai has been working with large language models since GPT-2, which in AI terms is several eras ago. Linda points to that history as evidence of something structural rather than lucky: the company was built to absorb new capability quickly.
“When something gets released, in a couple of weeks we can see that live in our products already,” she says. No waiting for approvals, no waiting for budget cycles. It was one of the reasons she left Unilever, where she had spent eight years and led data science, innovation and AI across social media and business analytics, to join i-Genie.ai as a founding employee.
Her trajectory was visible to others well before it was obvious. “I recognised what Linda could do long before most people did,” says Stan Sthanunathan, Executive Chairman and Founder of i-Genie.ai. “I brought her into a Unilever leadership meeting to demonstrate the future of insights when she was still early in her career, and she reframed the conversation for everyone in the room. That instinct for where the industry is heading is rare, and it is exactly what we built i-Genie.ai to act on.”
Science before shipping
The counterweight to that speed is rigour, and this is where Linda is most direct. “We put serious science behind all of these algorithms before we ship them to make sure that our clients can rely on them.”
It is a discipline she has been recognised for repeatedly, including Dataiku Frontrunner awards for responsible AI and for her team’s idea-spotting work, an I-COM Data Creativity award, and a LinkedIn Top Voice in AI listing. Her position on what AI is for is equally clear. The point is not to insert AI into things, and not to replace researchers. “The point is really to amplify their curiosity, and to allow them to spend time turning insights into action.”
It also explains why 80% of the i-Genie.ai team are data scientists and data engineers with CPG experience. In AI market research, that combination is rare: the models are built by people who have sat on the commercial side of the decision they are informing.
The person behind the modules
Linda is Dutch and lives in The Hague, where the long sandy coastline is her favourite part of the city. Her favourite place to eat is not a restaurant but a bakery that reinvents its pastries monthly, most recently with a goat’s cheese and kimchi croissant that works far better than it sounds.
She is also a former national basketball champion, and someone who reaches for data in her own life as readily as she does at work. “Apparently I can’t have any life experiences without just turning them into one big data problem and trying to solve that.”
It is a throwaway line, but it captures the mindset. The instinct to measure a thing rather than assume it is the same one that insists an insight holds up before a global brand acts on it.
As Linda puts it: i-Genie.ai isn’t an AI trick. The answers brands need already exist in reviews, posts and searches. The work is listening at a scale no human team could manage, then proving the answer is right.
That kind of work rarely gets a spotlight, which is part of what the 2027 Greenbook Future List is for. It recognises the people shaping where market research and consumer insights go next, and nominations are open until 25 October 2026. Anyone in the industry can put a name forward. If rigorous, useful AI is the kind of contribution worth celebrating, Linda is a good place to start.
