Why Creativity and Data Work Better Together

Congratulations on being recognized by B&T as one of Australia’s “Best of the Best AI, Innovation & Technology Leaders!” Beyond the recognition itself, what does this achievement mean to you personally?

In this ever-changing world with increasing uncertainty about the future, this is clear validation that we are on the right track. Even though my name made the list, it's important to note that, like everything, those achievements are years in the making, and this should be seen as a recognition for Monks and our data team.

Of everything B&T could have highlighted, they chose to focus on Creative Intelligence. Why do you think that stood out, and what part of building it are you most proud of that didn’t make it into the article?

Years ago, a friend of mine introduced me to Neri Oxman's Krebs Cycle of Creativity. That somewhat became a mantra which defined my vision of the world. To Neri’s point, breakthroughs will appear in spaces where multiple knowledge domains converge. Building toward that vision was a major bet that paid off. Now, we are seeing this new space gain momentum, offering a unique perspective where data and creativity converge in ways that were technically impossible just a few years ago.

Tell us about your career journey, from Poland, through Datalicious and MightyHive, to leading Data & Platforms at Monks across APAC. Was that a deliberate path, or did one opportunity lead to the next? What drew you to Monks specifically? 

There was a leap of faith, a moment when an opportunity to abandon comfortable life in a home country presented itself. Long story short, it all came to a toss of a coin—and here we are leaving in a complete part of the world. I cannot say it was a deliberate plan, but my partner and I seriously looked into all opportunities presented.

Where it comes to how I landed at Monks, I joined MightyHive just a few months before its merger. I was really inspired by the leadership and energy displayed there. The merger resulted in a tsunami of opportunities. I got involved in merger & acquisition strategy and then post-merger integration, and it was a great roller coaster, as a lot of the wildest moments occurred over the pandemic. As acquisitions slowed down and we moved into integration, our Head of Creative introduced me to Krebs Cycle of Creativity, and it has been the cornerstone of what draws me to Monks ever since.

Looking back, was there a defining moment that made you realize AI and data would fundamentally change marketing?

What if this the other way around? Has marketing fundamentally changed AI? It may be a bit stretched, but in the ‘70s, mathematical papers were released about a modeling technique which today is known as Hidden Markov Models (HMM). In the ‘80s, progress was made around speech recognition through the application of HMM, some big breakthroughs which carried through the ‘90s. In the late 2000s, HMM began to be applied to marketing and attribution, peaking in mid 2010.

Coincidentally, Datalicious was one of the leaders in that space. That popularized the technique in the corporate world and commercially validated “latent state modeling.” Evolutions and variations of HMM become important in the development of LLMs, creating the world we live in today. I wouldn’t dare to say that we contributed or delivered breakthroughs in that space, but I find it exciting how adjacent and intertwined things are.

Personally, I am following statistics and analytics from very early iterations—who remembers web counters in mid ‘90s?—although it took me years to understand how much money is involved in marketing and how much money can be made by applying data in that domain. On the AI side, the pivotal moment was when I saw the first Dalle model from OpenAI which could generate pictures based on the prompt. Seeing in the past how Moore’s law was applied to growing computer power, we all could see what level of growth we can expect in image generation capabilities.

What's unique about leading AI and data transformation across APAC compared to other regions? 

Because APAC is such a diverse region, AI adoption is incredibly fascinating—it is driven by completely different localized needs and is moving in entirely unique directions. Aussies are obsessed with risk aversion, governance, and compliance. Parts of Asia (Japan, Korea and Thailand) are facing (or about to face) major challenges with an aging population. India copes with its massive scale and internal diversity.

AI is often discussed in terms of productivity and automation. How do you distinguish between using AI to improve efficiency versus using AI to create better business outcomes?

The last few years were dominated in many aspects by the need to do more with less, which translated to a push for business efficiency. Those who did not protect margins got punished on the stock market, heavily. 

Now I can see increasing focus on effectiveness. As confidence in AI grows and proven use cases become firmly established, we are entering a new era. For example, Creative Intelligence is a new capability that wouldn’t be possible without AI. It gives an edge, it shifts the way we think about creative production, and it sets a direction for self-improving assets. 

This represents a shift toward true effectiveness. Creative Intelligence allows us to produce visually stunning, high-fidelity video assets that meet the highest standards of craft, giving our creative teams the freedom to focus on high-level storytelling. Blending technological tools with deep strategic insights gives us a significant advantage in driving better business outcomes.

Monks has been investing heavily in AI through solutions like Monks iQ, Monks.Flow and Creative Intelligence. From your perspective, what makes our approach to AI different from what you’re seeing across the industry?

In my opinion, our free flow of information between disciplines is superior, and we still have some work to do in that area. Our investments in tech pillar yields superior results in applying AI to creative production. Our M&A bets, in light of AI developments, worked well in our favor. There is something to be said about the extraordinary commitment of people who work here to make things outstanding; I still cannot get my head around the fact that we have over 300 Cannes Lions. Combine that with deep technical knowledge and a portfolio of work demonstrating how our strategic integration of AI helps clients scale complex, ambitious ideas. The way we see the world is truly progressive, and being able to deliver on that I think makes us truly stand out.

What problem were you trying to solve with Creative Intelligence, and how does it complement our broader vision for AI-powered marketing? 

The north star here is to enable a self-improving asset production workflow. Media platforms with AI algorithms demand more assets, and increased diversity of them. In order to prevent AI slop (or general ad slop), we need a decent technique to monitor quality at scale—that's the role of Creative Intelligence. By identifying exactly what drives performance, it guides how we optimize creatives for the next iteration. This tighter integration between creative production and media platforms ultimately unlocks entirely new workflows and strategic thinking. As platforms start to report on fatigue, and algorithmic campaigns require new assets more often, Creative Intelligence helps to respond to those pressures and manage them.

In the B&T article, you said we're moving from “knowing an ad performed well” to “understanding which specific moments drove conversion.” Can you explain what that shift looks like in practice?

If we think about the collection of ads in a campaign, many of them will have similar components, but at the same time they will need to have differences. A self-benchmarking system with rich metadata, which can interpret those differences and understand which made an impact on the performance, gives inputs to the next iteration of ads.

Can you share an example where Creative Intelligence uncovered an insight that would have been difficult, or even impossible, to identify using traditional measurement? How do those insights translate into faster or smarter creative decisions across the wider Monks AI ecosystem?

Without diving into client use cases, I am most comfortable talking about the scale. Manual asset tagging frameworks have existed for a while. One of our legacy labels, Decoded, made massive progress in that area.  We increased the level of detail of that metadata and linked it with performance. So older, more manual methodologies were more expensive and did not scale that well. Now, we can analyze thousands of assets in a matter of minutes, considering many more elements. 

AI can optimize for short-term performance, but brands also need to build long-term brand equity. How do you balance those two objectives?

This is a massive topic, and another by-product of Creative Intelligence as it evolves around Market Mix Modeling (MMM) and overall econometrics. We are working on ways to cluster ads and map them to “big ideas,” so we can then leverage MMM and work on incremental testing to establish the impact of those ideas. 

This space is in its infancy, but it sits in the center of our attention. The assumption here is that the big idea will live longer, while the shelf time of a particular asset will drop dramatically. At the same time, we will see an explosion of ad variants with increasing diversity. This means rapidly dropping cost per asset.

We want to help brands measure the long-term equity of their core creative concepts. Our goal is to connect immediate performance data with deep brand strategy, proving to financial leaders that big, brave ideas drive sustained growth. We give our teams the tools to refine execution details in real time, keeping the central creative concept strong and relevant across every channel.