In a marketing landscape where audience attention is growing scarcer by the minute, Dragonfly AI offers attention analytics that provide instant feedback on assets, like product packaging and advertisements, based on where viewers are most likely to look.
FutureWeek sat down with Steve King, CEO of Dragonfly AI, to find out how he sees AI shaping the future of marketing and its wider impact on society.
How does the Dragonfly AI platform work?
Dragonfly is a SaaS platform designed to help users optimise visual content for maximum impact. When users log in, they can upload an image – whether it’s a marketing packet, digital ad, or other creative asset. Because Dragonfly operates deep in the tech stack, it’s compatible across all channels. At its core, the platform predicts human attention, similar to predictive eye-tracking technology.
What sets us apart is speed and accuracy. Users simply upload their creative, and in moments, receive a detailed heat map with attention scores, pinpointing exactly where viewers’ eyes will go first.
Our technology enables customers to strategically place key visual elements where they’re most likely to be noticed – and then we track how those visuals are processed by audiences in real-world conditions. On average, creatives optimised through Dragonfly see a 20 percent increase in attention compared to unoptimised versions. Why? Because when you can anticipate where people will look, you dramatically improve click-through rates and engagement.
What kind of customers do you have?
Our customer base is roughly divided into three segments: about a third are focused on in-store visibility, wanting to ensure their packaging stands out on the shelf; another third are engaged in out-of-home advertising and live events, aiming to boost brand presence in physical spaces; and the final third are digital-first, leveraging Dragonfly for social media, e-commerce, and online advertising campaigns.
What are some of your use cases?
We have a wide range of use cases across industries. One of the most compelling examples comes from the e-commerce space where a client of ours recreated their Amazon product page and ran A/B tests across two different layouts. They tested performance across different countries, times of day, and user segments. As a result, they saw a 24 percent increase in sales driven entirely by the changes informed through Dragonfly. It was one of the most data-driven validations of our technology to date.
Beyond commercial applications, we also use Dragonfly for good. We regularly partner with charities to help optimise their creative and increase donation conversions, completely free of charge. It’s important to us that the platform delivers value not just to brands, but also to causes that matter.
Our core customer base is largely in consumer packaged goods (CPG) and marketing, but we also support outdoor advertising and in-store optimisation. For example, we have a contact sheet system that helps brands evaluate how effective an out-of-home ad will be before it goes live. In retail, we support planogram testing and layout decisions. One client, for instance, reduced the physical footprint of their ad but increased sales simply by getting the attention dynamics right. So it’s not always about bigger or louder – it’s about being smarter.
In terms of technology, the AI itself is the easy part. The challenge lies in integration – making it easy to embed into existing workflows and getting creative and marketing teams aligned around using it. That’s why we focus so much on helping customers understand not just the technical benefits, but the real financial impact.
We’ve designed the platform to be as simple as possible – ideally, a three-click process. But even then, internal buy-in is key. No matter how intuitive a tool is, it has to fit naturally within the creative process to drive real adoption and value.
How is Dragonfly’s approach to AI different from traditional models, and what implications does that have for bias and fairness in AI systems?
AI, like any other computational system, follows the principle of “garbage in, garbage out.” If it’s trained on biased data, it will naturally produce biased outcomes – meaning certain perspectives may be excluded or misrepresented.
What makes Dragonfly different is that our algorithm isn’t trained in the traditional sense. Instead of relying on large external datasets, our model is built around the core principles of human visual attention – what we sometimes refer to as the “deep core of consciousness.” Because it doesn’t rely on environmental data inputs, it operates extremely quickly and avoids the kinds of systemic bias that often arise in more data-driven models.
That didn’t happen by chance – it took extensive work. We combined large-scale eye-tracking studies with traditional coding and deep research into how the human brain processes visual information. Our focus was on teaching the algorithm to recognize true patterns of attention, rather than simply feeding it data and hoping it would find the right answers.
Looking ahead, I think we’re starting to see a shift. Tools like DeepSeek are proving that AI can be made more efficient and less resource-intensive, while also reducing dependence on biased datasets. That’s a big step forward in terms of fairness and accessibility.
Personally, I think the most important question isn’t whether AI is powerful – it clearly is. The real challenge now is understanding how it can be useful in a way that’s intentional, fair, and genuinely solves the problems we’re asking it to address.
Which part of the media and marketing supply chain do you think is most at risk of disruption?
It really comes down to timing. Since we are a digital marketing tool, we’re able to create impact almost instantly – we can get in front of people fast and drive results quickly. Because of that immediacy, I believe marketing will be one of the first sectors to experience the full wave of AI transformation.
That said, with the rise of increasingly sophisticated human-like bots, it’s only a matter of time before more traditional areas – like supply chain operations and manual, human-driven tasks – are also fully automated.
So, I think we’ll see AI adoption happen in phases. The pace will vary depending on how complex the problem is. Marketing is ripe for disruption right now, while other areas will follow as the technology matures. Eventually, the entire system will be restructured – piece by piece – and then reassembled under new frameworks that can operate across all those disrupted domains.
In short: marketing will likely go first, and then we’ll see AI move deeper into operational and physical tasks as those challenges are solved.
Who do you think benefits most in media and marketing from the AI revolution?
Digital marketing, because it’s so fast and can be changed quickly, and that’s where AI can work. You also get a feedback loop, so you can see if someone clicked on something or bought something, and that’s gold dust for AI improvement. Otherwise, it will take longer to work out if a packet was effective or not. So, I think the fast-top digital things are where the biggest game-change comes straight away.
What is it about AI that keeps you awake at night?
One of the areas I’m most concerned about, while also deeply fascinated by, is medicine. I’m both excited and a little uneasy. The idea that AI can help us extend human life, or solve in days what’s taken researchers decades, is nothing short of revolutionary. I believe we’re on the brink of a major transformation in both medicine and the human condition.
But the challenge is: we’re not ready for it. Our societal systems – whether environmental, economic, or social – aren’t built for people living 200 or 300 years. Yet AI is accelerating us toward that possibility, and fast.
In many ways, AI is advancing in medicine almost as rapidly as it is in marketing. But the stakes are far higher. We’re not just talking about optimising ads – we’re talking about mental health, longevity, quality of life, and the sustainability of our environment. These are areas where the consequences are profound, and the ethical questions are complex.
How are you using AI in your own workflows?
We’re incredibly proud of the AI that powers our product – but interestingly, we don’t rely heavily on AI internally for building the tool itself. However, we do leverage AI extensively across the broader business, especially in marketing and customer success. From content generation and quality checks to automated responses and customer support, AI plays a big role in how we operate.
It’s also proving to be a valuable asset for our sales team and helping them identify what’s working, what’s not, and feeding those insights back into our operations for continuous improvement.
Ultimately, I think using AI effectively comes down to mindset. If you’ve got a team of smart, driven people who are focused on working smarter and being more efficient, AI becomes a natural fit. It’s less about the technology itself and more about the culture that embraces it.
What will the impact of AI be on employment?
I genuinely believe the impact of AI will ultimately balance out – it will be net zero. That doesn’t mean there won’t be disruption, and I completely understand the anxiety people feel. Sometimes I even catch myself feeling conflicted – excited about the potential, yet aware that real people could lose their jobs to automation, at least in the short term.
But history gives us some perspective. In the 1970s, before Microsoft, there were early tools similar to Excel that sparked fears of job loss in finance. And yet, finance departments haven’t disappeared – they’ve grown. The roles evolved, and the value people brought to their companies shifted.
That’s the pattern I see repeating. These technologies are excellent at taking over repetitive, predictable tasks. But as humans, we adapt. We find new ways to apply ourselves, and the economy adjusts. The money doesn’t vanish – it just moves to new areas. Efficiency in one space creates opportunities in another.
So no, I don’t think we’re heading toward a future where 80% of us are out of work. I think we’ll just be doing different work, and hopefully, more meaningful work.
