As artificial intelligence continues to evolve from hype to real-world application, the true challenge for businesses lies in execution. Sjef Kerkhofs, Managing Director of Franke Media, offers a refreshingly pragmatic view shaped by hands-on experience implementing AI at scale. With a clear focus on aligning technology with business vision, Kerkhofs has successfully leveraged AI to fuel Franke Media’s international expansion, combining automation, generative AI, and data integration to overcome the traditional barriers of global growth. In this Executive Insight, he shares how AI is transforming problem-solving, the importance of human intuition alongside machine learning, and why decisive leadership remains critical to achieving long-term success with AI.
How has AI/ML reshaped your approach to solving business problems?
The beauty of AI is that it provides you with an entire extra workforce on top of your own team, a workforce that is fast, intelligent, relatively low-cost, and almost endlessly scalable. For leaders, that’s an incredible asset, as it shortens the time span between decision-making and implementation and increases the growth capacity of the organisation. Once a decision is made, implementation can begin immediately by combining human capacity with technology.
It’s also no longer (only) about automating analytical work or operations. AI allows us to significantly increase the speed and agility of problem-solving within the business. With the thinking power of AI, we now have far more intelligence available, which can be processed much faster and in greater detail than ever before. While decision-making remains in the hands of (human) managers, AI provides far better insights and knowledge to base those decisions on.
What key challenges do you face when implementing AI-driven solutions?
The key challenge is no longer the technology itself, for the first time in human history, it has essentially become a commodity. Anyone can access it, anyone can use it, whenever and wherever they want. You no longer need to be a developer to take advantage of what tech has to offer. AI is turning technology into a product that’s easy to use.
What truly matters now is how we implement AI, how we actually use everything that’s available to us. So, if technology is no longer the issue, what is? The main challenge lies in implementation and adoption. There’s a significant gap between the capabilities of AI and the knowledge and experience within the labour force to apply it effectively in day-to-day work and strategic organisational contexts.
Deloitte describes this as ‘the speed of technology’ versus ‘the speed of organisations’. Technology moves much faster than organisations are able to process. To bridge this gap, we need pragmatic, decisive project leaders who can successfully implement AI across both business and government.
When I implemented a large-scale AI project within my own organisation, I learned that the technology itself accounted for only about 20% of the work, the remaining 80% was about proper implementation and integration. This is something we, as organisations, need to adapt to in the coming years. We need to dramatically improve our tech implementation capabilities.
How do you balance human intuition with machine learning insights in decision-making?
The interesting thing is that human experience, intuition, and decision-making skills are becoming even more important than before. Without them, you can’t properly feed AI or fully leverage its capabilities. When I speak to specialists who fear their jobs might be at risk, I always tell them that their years of experience in the industry are actually becoming more valuable.
Experienced specialists and leaders know how to make decisions. They understand the industry and what it takes to be successful—and that experience becomes excellent ‘fuel’ for AI. Human insight, combined with AI, becomes a very powerful tool for scaling your business.
The balance between human intuition and ML/AI is about creating a feedback loop between data and decision-makers. That means using ML/AI to uncover patterns and trends, while relying on human judgment to interpret those insights within the full business context.
You can also flip the process through reverse engineering using human intuition and experience to form hypotheses, which ML/AI can then test and validate.
Can you share a successful use case where AI significantly improved outcomes?
Absolutely. There’s a lot of talk about AI, but many so-called gurus who speak on the topic haven’t actually implemented it at scale with real impact. Just scroll through LinkedIn, it’s full of AI experts showing off “Look at this cool visual I created with ChatGPT.” In my view, that’s not what AI is really about. It’s about structurally embedding AI into organisational processes. While there still aren’t a huge number of mature case studies out there, I’d like to share an example from our own business.
At Franke Media, we’ve set our sights on becoming a much more internationally focused platform. Historically, we’ve concentrated on our core markets, The Netherlands and Belgium, but these markets are relatively small, so international growth is essential. Scaling with a purely human workforce would require hiring teams in nearly every country we enter, which would be time-consuming and expensive.
Instead, we’ve used automation and AI to generate localised content by connecting multiple data sources and applying generative AI algorithms to transform raw data into high-quality content. This approach has had a huge impact on our ability to expand internationally and significantly accelerated our growth.
We also use AI to generate shopping-related video content, which has greatly improved the user experience. Traditionally, platforms like ours (and those of our competitors) focused mainly on text-based content. But content marketing has evolved—short-form video is now the standard. Based on content created by our editorial team, we can now automatically generate localised videos with shopping tips, tailored to individual stores or brands.
How do you ensure AI adoption aligns with your company’s long-term goals?
I’d actually turn that phrase around. It’s not about aligning AI adoption with our long-term goals—it’s about making AI a structural part of those long-term goals. For me, AI isn’t just a tool; it’s a game changer for our business. It has become part of the strategy.
By fully utilising AI, we’re able to redefine our long-term goals and make them far more ambitious. That said, it ultimately comes down to leadership. Goals often driven by human experience, intuition, and vision (as discussed earlier) remain the guiding force behind all business processes. When you truly embrace the power of AI, those goals not only become more ambitious, but also more achievable at speed and scale.
