Dragonfly AI raises €5.7m for creative testing

Dragonfly AI’s €5.7 million raise signals rising confidence in predictive creative intelligence

Dragonfly AI, a London-based company focused on AI-powered creative testing, has announced a €5.7 million (about £5 million) investment round to push its technology forward and broaden its market footprint. The funding is led by 24Haymarket, with Guinness Ventures and Foresight also participating, and the company is adding Fiona Dent to its board.

At a time when marketing leaders are being asked to justify spend with the same rigor as product, operations, and finance, the demand for reliable creative measurement is growing quickly. Dragonfly AI’s positioning is clear: it aims to predict how people will actually respond to creative assets before campaigns go live, helping brands reduce waste, iterate faster, and bring stronger work to market with fewer rounds of guesswork.

From a marketing ecosystem lens, this is also part of a bigger shift: “creative” is no longer treated as an isolated art form that lives only inside studios and agencies. It’s increasingly connected to data, systems, and scalable workflows—especially across omnichannel environments where the same idea must work on a shelf, inside an eCommerce listing, in a six-second video, and as a social post. Dragonfly AI explicitly frames its platform as an omnichannel solution that can predict performance across contexts, whether online or in-store.

As the ai landscape matures, brand teams are learning a hard truth: adopting AI tools is easy, but adopting AI in a way that improves outcomes is the real challenge. That’s one reason the ai world organisation keeps spotlighting applied AI—AI that changes day-to-day execution, not just strategy decks—across the ai world summit and other ai world organisation events. In that context, tools that help marketers improve creative effectiveness are becoming a core part of how modern teams plan, produce, and optimize campaigns.

Who backed the round, and what changes at the top

The round’s leadership and participation profile matters because it signals what investors believe is defensible in martech right now. 24Haymarket led the investment, and Guinness Ventures and Foresight participated, bringing total funding in the round to more than £5 million. Alongside the capital, Dragonfly AI has appointed Fiona Dent to its board, adding experience in scaling and transformation from senior leadership roles, including work tied to digital transformation at Time Inc.

Leadership commentary around the raise centers on “science-led” differentiation and the idea that the platform has already achieved meaningful enterprise trust. Dragonfly AI states that its technology is already used by more than 70 global brands to help ensure ads are noticed and remembered, and that the funding will help deepen customer impact while integrating its algorithms into everyday tools and workflows. The investor perspective highlighted the platform’s usability and the strength of the underlying algorithm developed at Queen Mary University of London, alongside evidence of continued business growth during the investment process.

Those points—ease of use, proven adoption, and workflow integration—are important because creative teams will not adopt tools that slow them down. In practice, “AI for marketing” only wins when it fits into existing production pipelines, creative review cycles, and performance reporting rhythms. This is also why platform integrations keep showing up as a strategic priority: it’s often easier for enterprises to adopt AI as a layer that plugs into what they already use, rather than as a brand-new standalone environment.

For the ai world organisation community, this is a familiar theme. The strongest applied AI stories are not just about novel models; they are about frictionless adoption, clear metrics, and faster execution at scale—exactly the kind of case studies that perform well at ai conferences by ai world and on stage at the ai world summit.

What makes Dragonfly AI “neuroscience-led,” and why that positioning stands out

Dragonfly AI consistently emphasizes that its predictive approach is grounded in a patented “biological algorithm” and more than a decade of research, developed in partnership with Queen Mary University of London. The platform’s value proposition is that it can help teams understand and improve creative by assessing factors that drive effectiveness, including attention and other dimensions tied to how people process visual information.

On its own site, Dragonfly AI describes its work as inspired by human biology and visual neuroscience, aiming to mimic how the human brain responds to visuals so teams can make data-driven decisions and reduce the time and resources spent on trial-and-error. In plain terms, the promise is not “we generate creative for you,” but “we help you validate and improve creative before it launches,” which is a different category from generative tools that produce images, copy, or video drafts.

Another notable claim in the funding announcement is the company’s stance on training data. Dragonfly AI says its patented technology operates without reliance on training data, which it argues helps avoid biases common in many generative AI models and enables a breakdown of how creative assets will perform in real-life conditions. Whether a marketer agrees with every part of that framing or not, it reflects a growing market demand: teams want predictive systems that are auditable, repeatable, and consistent enough to be trusted for high-budget decisions.

The adoption list reinforces that this kind of work is already mainstream in certain enterprise circles. Dragonfly AI reports that its platform has been adopted by global brands including Nestlé, PepsiCo, Unilever, Coca-Cola, and L’Oréal, and that it integrates into platforms such as CreativeX. Separately, Dragonfly AI’s own website also shows it is trusted by large CPG brands and agencies, listing major brand logos including Nestlé and L’Oréal.

From the lens of the ai world organisation, this is exactly the kind of applied AI narrative that resonates with business decision-makers: a clear use case (creative effectiveness), a measurable benefit (better performance and reduced waste), and an adoption story that proves it works beyond pilot experiments. It’s also a reminder that, as we head from ai world summit 2025 into ai world summit 2026, the competitive advantage is shifting from “who can access AI” to “who can operationalize AI across marketing and growth teams.”

European funding context: steady capital for practical AI tools

This round also sits inside a broader European funding storyline, especially across 2025 and 2026 where investors continued to back applied AI tools—often with measured check sizes rather than hype-driven extremes. In the same conversation, other UK-based funding included a different London startup named Dragonfly (not Dragonfly AI), which raised €3 million at pre-seed, and Wonder, which raised €2.6 million to build a generative AI-focused creative studio. Outside the UK, Covision Media in Italy raised €5 million for AI-enabled 3D content and product visualization tech, showing capital flow into adjacent content-technology categories.

Put together, those disclosed rounds total about €10.6 million, which suggests continued interest in tools that sit near creative production and decision-making, but not an unlimited funding environment. In that landscape, Dragonfly AI’s raise stands out for both its size and its specialized focus on neuroscience-led creative effectiveness. It also supports a broader point: Europe continues to produce AI companies that compete through research depth and applied outcomes, not only through scale narratives.

For founders and growth leaders, the message is practical. Markets reward AI businesses that can translate technical sophistication into workflow adoption, and that can speak to value in terms enterprises understand—time saved, waste reduced, conversions improved, and brand consistency strengthened across channels. For marketers, it’s a signal that “creative intelligence” is becoming an expected layer of the stack, similar to analytics, attribution, or experimentation.

This is also why the ai world organisation keeps building meeting points for operators—not only researchers—through the ai world summit and other ai world organisation events, because implementation is where most value is won or lost. In many industries, the teams that move fastest are not those with the most tools, but those with the cleanest systems and clearest execution playbooks.

What the new funding is expected to accelerate—and why marketers should care

Dragonfly AI says the investment will speed up several strategic priorities, including new Emotion & Memory metrics, deeper video analysis across platforms, growth of “Dragonfly Connect” for enterprise integrations, expansion of US sales and customer success teams, and continued R&D and product innovation. These priorities are highly aligned with what modern marketing teams need: more video, more channels, more speed, and more accountability—without sacrificing creative quality.

Video analysis matters because many brands now build creative as a modular system: hooks, scenes, supers, product moments, and calls-to-action must work in different sequences depending on the platform. When you layer in regional variation, language localization, and creator partnerships, the testing and iteration burden becomes enormous. Any predictive system that can shorten feedback loops—while keeping insight understandable to both creatives and performance marketers—has a clear path to adoption.

Enterprise integrations matter for a different reason: scale. Even the best insights don’t change outcomes if they live outside the tools teams use daily. That’s why Dragonfly AI highlights bringing algorithms directly into workflows, and why it points to integration with CreativeX as an example of how it can embed into existing ecosystems. In large organizations, integration is often the difference between an innovation project and an operational standard.

For the ai world organisation audience, there’s a direct connection to how we talk about AI maturity. At the ai world summit, the most actionable sessions are usually the ones that explain how to turn “promising AI” into “repeatable operations”—how to train teams, set standards, choose metrics, and avoid chasing shiny tools that don’t stick. Tools like predictive creative testing sit right in the middle of that operationalization story because they link creative choices to business outcomes, and they can be adopted by marketing teams without requiring every user to become a machine learning expert.

If you’re tracking this trend and want to learn how applied AI is reshaping marketing, creative performance, and growth systems, the ai world organisation is running multiple global gatherings in 2026, including AI World Summit 2026 Asia in Singapore on May 28, 2026. The organisation’s upcoming schedule also includes events in India such as the Talent, Tech & GCC Summit in Delhi on April 17, 2026, and a GCC Conclave in Hyderabad on March 14, 2026—relevant for leaders building AI adoption programs across business functions. For those planning beyond 2026, the same calendar references AI World Summit 2027 San Francisco (Agentic AI summit) in January 2027.

From an editorial standpoint, the Dragonfly AI raise is not just a funding headline; it’s a snapshot of where the market is moving. Brands want science-backed signals about what will work, investors want applied AI with measurable enterprise pull, and marketing leaders want tools that make creative development faster and more accountable without flattening originality. This is the future-facing intersection of performance and creativity—and it’s exactly the type of transformation we continue to track and feature through ai conferences by ai world, the ai world summit, and the wider the ai world organisation ecosystem.